Reviewerly/exHarmony huggingface dataset:
Fall Detection in Video Sequences Based on a Three-Stream Convolutional Neural Network

Human falls are more susceptible in advanced ages and the second most common cause of accidental death to elders, because of the later detecting falls became a crucial research topic to an aging population. To this effect, we propose and evaluate the employment of a multi-stream approach to detect fall events. Three features (optical flow, saliency map, and RGB data) fed to each stream of a VGG-16 and classified by an SVM of whether there was or not a fall event. Experiments are conducted on two datasets, URFD and FDD, achieving accuracy rates of 98.84% and 99.51%, respectively, which outperforms the majority of the reviewed solutions.
The Graph and its Role for Speeding up the Elliptic Scalar Multiplication Algorithms

Elliptic curves play a major role in cryptography. It used increasingly to form the public key cryptosystems and digital signature schemes. The smaller key sizes and the efficient implementations on the elliptic curve cryptography (ECC) gave the same level of security in comparison with the RSA (Rivest-Shamir- Adleman) and other cryptosystems. The security on the ECC depending on the hardness of the elliptic curve discrete logarithm problem (ECDLP) to solve. The efficiency of the ECC is determined based on the efficient computation of the scalar multiplication on elliptic curves defined over the finite fields. A scalar multiplication is considered as a core operation in the ECC. It is not only the main computational operation but it also forms a central time-consuming process. The efficient performances of scalar multiplication directly determine the ECC performance.
3bij3 – Developing a framework for researching recommender systems and their effects

Today's online news environment is increasingly characterized by personalized news selections, relying on algorithmic solutions for extracting relevant articles and composing an individual's news diet.Yet, the impact of such recommendation algorithms on how we consume and perceive news is still understudied.We therefore developed one of the first software solutions to conduct studies on effects of news recommender systems in a realistic setting.The web app of our framework (called 3bij3) displays real-time news articles selected by different mechanisms.3bij3 can be used to conduct largescale field experiments, in which participants' use of the site can be tracked over extended periods of time.Compared to previous work, 3bij3 gives researchers control over the recommendation system under study and creates a realistic environment for the participants.It integrates web scraping, different methods to compare and classify news articles, different recommender systems, a web interface for participants, gamification elements, and a user survey to enrich the behavioural measures obtained.
Automatic 3D color shape measurement system based on a stereo camera

This study proposes an automatic three-dimensional (3D) color shape measurement system based on images recorded by a stereo camera. The system, comprising several off-the-shelf components, is cost-effective yet capable of obtaining quality color 3D objects. In the proposed system, a turntable carrying a checkerboard is used to assist the simultaneous calibration of the stereo camera and the turntable. A slit laser is driven to swing forward and backward for generating stripes on test objects. The stereo images are collected and analyzed for obtaining matching pixels, and, consequently, the 3D point coordinates based on epipolar geometry are obtained. Screened Poisson reconstruction is utilized to integrate and smooth the scanned surfaces. With additional color images from the same camera, several multi-view texturing methods are benchmarked. We concluded that our proposed system can successfully and automatically reconstruct quality 3D color shapes of various objects.
The Design of a Learning Process for Promoting Teamwork using Project-Based Learning and the Concept of Agile Software Development

The design of a learning process for promoting teamwork using project-based learning and the concept of Agile software development had the following objectives: a) analyze and synthesize the conceptual framework for a learning process for computer students aimed at promoting teamwork using project-based learning and the concept of Agile software development; and b) a evaluate the suitability of a learning management model design for computer students aimed at promoting teamwork via project-based learning and the concept of Agile software development by 10 experts in which the model consists of six parts: 1) preparation; 2) group division; 3) project-based learning; 4) an Agile working process; 5) teamwork; and 6) an assessment process derived from the result of the assessment of the suitability of the learning style, which indicated the highest level. According to the assessment result, the model can be applied to learning management.
Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square problem, which can be done by finding the eigenvector corresponding to the smallest, or zero, eigenvalue of a matrix representing a linear system. Incorporating this in deep learning frameworks would allow us to explicitly encode known notions of geometry, instead of having the network implicitly learn them from data. However, performing eigendecomposition within a network requires the ability to differentiate this operation. While theoretically doable, this introduces numerical instability in the optimization process in practice. In this paper, we introduce an eigendecomposition-free approach to training a deep network whose loss depends on the eigenvector corresponding to a zero eigenvalue of a matrix predicted by the network. We demonstrate that our approach is much more robust than explicit differentiation of the eigendecomposition using two general tasks, outlier rejection and denoising, with several practical examples including wide-baseline stereo, the perspective-n-point problem, and ellipse fitting. Empirically, our method has better convergence properties and yields state-of-the-art results.
VNS-BASED ALGORITHMS FOR THE CENTROID-BASED CLUSTERING PROBLEM

The k-means algorithm with the corresponding problem formulation is one of the ﬁrst methods that researchers use when solving a new automatic grouping (clus-tering) problem. Its improvement, modiﬁcation and combination with other algorithms are described in the works of many researchers. In this research, we propose new al-gorithms of the Greedy Heuristic Method, which use an idea of the search in variable neighborhoods for solving the classical cluster analysis problem, and allows us to obtain a more accurate and stable result of solving in comparison with the known algorithms. Our computational experiments show that the new algorithms allow us to obtain re-sults with better values of the objective function value (sum of squared distances) in comparison with classical algorithms such as k-means, j-means and genetic algorithms on various practically important datasets. In addition, we present the ﬁrst results for the GPU realization of the Greedy Heuristic Method.
Blockchain-based Cross-Organizational Execution Framework for Dynamic Integration of Process Collaborations

Cross-organizational business processes involving multiple participants are choreographed, thus rely on mutual trust of collaborators or need to be coordinated by a central instance. Using Smart Contracts, business processes can be executed without a mutually trusted and centralized orchestrating authority. Former Blockchain-based execution framework proposals focus on orchestration diagrams as a basis for execution. Contrary, this work focuses on BPMN process collaboration diagrams as implementation basis and makes additional transformation steps obsolete. With the herein proposed framework for execution of cross-organizational process collaborations, another approach for the implementation and execution of interorganizational processes on a Blockchain is presented, including a voting mechanism for process deployment as well as a subscription service to facilitate process handovers between participants more efficiently. The framework is exemplified and evaluated with a use case from a large German industrial manufacturing company.
Secure and efficient outsourcing computation on large-scale linear regressions

Solving large-scale linear regression (LR) becomes more and more prevalent in theoretical research and engineering application, which however is too expensive to solve for limited-resource clients. To meet this challenge, more and more clients shift their expensive computations to powerful clouds in practice. The previous works on secure outsourcing LR usually possess heavy workloads, some of which even have potential limitations. In this paper, we first reveal several limitations in the state-of-the-arts. To overcome these shortcomings, we then propose a novel protocol for securely outsourcing large-scale LR. In our design, a client only utilizes two random vectors to encrypt his/her LR problem and decrypt the solution returned from the cloud, which is quite simple. In addition, the proposed protocol only involves with several vector-vector operations, two matrix-vector multiplications and one matrix-matrix subtraction, which thus achieves high efficiency. We also demonstrate that the proposed protocol can accomplish correctness guarantee, privacy protection and cheating resistance under the assumption of a malicious cloud. At last, extensive theoretical analysis and experimental results are provided to further validate the superiority of the proposed protocol.
A systematic comparison of deep learning strategies for weakly supervised Gleason grading

Prostate cancer (PCa) is one of the most frequent cancers in men. Its grading is required before initiating its treatment. The Gleason Score (GS) aims at describing and measuring the regularity in gland patterns observed by a pathologist on the microscopic or digital images of prostate biopsies and prostatectomies. Deep Learning based (DL) models are the state-of-the-art computer vision techniques for Gleason grading, learning high-level features with high classification power. However, for obtaining robust models with clinical-grade performance, a large number of local annotations are needed. Previous research showed that it is feasible to detect low and high-grade PCa from digitized tissue slides relying only on the less expensive report{level (weakly) supervised labels, thus global rather than local labels. Despite this, few articles focus on classifying the finer-grained GS classes with weakly supervised models. The objective of this paper is to compare weakly supervised strategies for classification of the five classes of the GS from the whole slide image, using the global diagnostic label from the pathology reports as the only source of supervision. We compare different models trained on handcrafted features, shallow and deep learning representations. The training and evaluation are done on the publicly available TCGA-PRAD dataset, comprising of 341 whole slide images of radical prostatectomies, where small patches are extracted within tissue areas and assigned the global report label as ground truth. Our results show that DL networks and class-wise data augmentation outperform other strategies and their combinations, reaching a kappa score of <i>&kappa;</i> = 0:44, which could be further improved with a larger dataset or combining both strong and weakly supervised models.
DF-SSD: a deep convolutional neural network-based embedded lightweight object detection framework for remote sensing imagery

In recent years, there has been an increasing interest in object detection in remote sensing imagery with onboard sensors and embedded platform based on deep convolutional neural networks. However, the limited cost, power consumption, compute complexity, and parameter size make the task challenging. The current object detection frameworks are mainly designed on the basis of graphics processing units (GPUs) and require further optimization in power consumption, and calculating quantity and parameter size. To address these issues, we propose an effective single-shot multibox detector (DF-SSD) framework, using the DepthFire module we designed to reform SqueezeNet as the backbone network to reduce the calculating quantity and improve the processing efficiency. To evaluate the effectiveness and superiority of DF-SSD, compared with the other state-of-the-art methods, extensive experiments are conducted on various hardware platforms, including GPU 1080ti, central processing unit i7-7700k, NVidia Jetson TX2, and Cambricon-1H8. Experiment results show that the designed algorithm can achieve a mean average precision of 75.2% on NWPU VHR-10 dataset, with 181, 5.2, 26.3, and 15 frames per second on the above four typical hardware platforms, respectively, which finally demonstrate the effectiveness and high accuracy of the proposed algorithm.
Probabilistic Semi-Supervised Learning via Sparse Graph Structure Learning

We present a probabilistic semi-supervised learning (SSL) framework based on sparse graph structure learning. Different from existing SSL methods with either a predefined weighted graph heuristically constructed from the input data or a learned graph based on the locally linear embedding assumption, the proposed SSL model is capable of learning a sparse weighted graph from the unlabeled high-dimensional data and a small amount of labeled data, as well as dealing with the noise of the input data. Our representation of the weighted graph is indirectly derived from a unified model of density estimation and pairwise distance preservation in terms of various distance measurements, where latent embeddings are assumed to be random variables following an unknown density function to be learned, and pairwise distances are then calculated as the expectations over the density for the model robustness to the data noise. Moreover, the labeled data based on the same distance representations are leveraged to guide the estimated density for better class separation and sparse graph structure learning. A simple inference approach for the embeddings of unlabeled data based on point estimation and kernel representation is presented. Extensive experiments on various data sets show promising results in the setting of SSL compared with many existing methods and significant improvements on small amounts of labeled data.
APB2FACE: Audio-Guided Face Reenactment with Auxiliary Pose and Blink Signals

Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person. However, existing methods can not generate vivid face images or only reenact low-resolution faces, which limits the application value. To solve those problems, we propose a novel deep neural network named APB2Face, which consists of GeometryPredictor and FaceReenactor modules. GeometryPredictor uses extra head pose and blink state signals as well as audio to predict the latent landmark geometry information, while FaceReenactor inputs the face landmark image to reenact the photorealistic face. A new dataset AnnV I collected from YouTube is presented to support the approach, and experimental results indicate the superiority of our method than state-of-the-arts, whether in authenticity or controllability.
The Digital Divide in Inclusive Classrooms

The digital divide affects equity in inclusive classrooms.It can create a gap in accessing information-communication-technology (ICT) resources or inequalities in skills and effective use.The present study is a qualitative SWOT analysis; it explores teachers' views on access and use issues that widen the digital divide in inclusive classrooms in the UAE.Six teachers from intermediate and secondary-level schools were interviewed to identify the strengths, weaknesses, opportunities, and threats associated with ICT use and access in their inclusive classrooms.The semi-structured interviews were analyzed, using thematic analysis.The study found that, although all of the students, irrespective of disabilities, have equal access to ICT resources, various factorsincluding a lack of training, insufficient resources, heavy workloads, and a shortage of human resources-are expanding the digital divide in inclusive classrooms.This study discusses ways to overcome the challenges and implications; it recommends research avenues for future studies.
Research Supervisor Recommendation System Based on Topic Conformity

In a higher education such as universities, final project are under supervision of one or more supervisors with a similar research interest or topic.The determination of the final project supervisor is an important factor in the work of the student's final project.However, the lack of information about the supervisor can hamper students in making the determination of the supervisor.Thus, a system is needed that can facilitate students in determining the final project or thesis advisors in accordance with the research topic.This problem is the basis of this research.The study is conducted by developing a web-based system and applying the TF-IDF word weighting and cosine similarity method.TF-IDF method is a way to give the weight of the relationship of a word to the document.The cosine similarity is a method for calculating the similarity between two objects expressed in two vectors by using keywords from a document as a measure.The results of the advisor recommendation system can provide recommendations to students regarding the final assignment advisor who has conducted research in accordance with the topic of the student's final assignment written in Indonesian.In 20 testing, the accuracy of the comparison of the results of the system recommendations with the actual data obtained an average of 75% by comparing system recommendation with actual assigned supervisor.
Optimised KYC Blockchain System

Know your customer or simply KYC is the process of validating and verifying the identity of its users and examining potential risks of illegal intentions for the business relationship. A few problems with the existing manual KYC process are that it is less secure, time consuming and costly. With the advent of Blockchain technology, its properties such as immutability, security, decentralization make them a good solution to such problems. While commercial solutions like “kyc-chain.com”, “KYC.legal” right to enable blockchain-based KYC verification, it provides a method for documents to be validated by a trusted participant in the network. In this work, Ethereum based Optimized KYC Blockchain system using symmetric AES encryption and LZ based compression mechanism is proposed. This system ensures transparency by distributed ledger, secured by cryptography, efficient by compression algorithm and overall optimized by blockchain features.
Predicting Media Memorability Using Ensemble Models.

Memorability, defined as the quality of being worth remembering, is a pressing issue in media as we struggle to organize and retrieve digital content and make it more useful in our daily lives. The Predicting Media Memorability task in MediaEval 2019 tackles this problem by creating a challenge to automatically predict memorability scores building on the work developed in 2018. Our team ensembled transfer learning approaches with video captions using embeddings and our own pre-computed features which outperformed Medieval 2018’s state-of-the-art architectures.
Competing Inheritance Paths in Dependent Type Theory: A Case Study in Functional Analysis

This paper discusses the design of a hierarchy of structures which combine linear algebra with concepts related to limits, like topology and norms, in dependent type theory. This hierarchy is the backbone of a new library of formalized classical analysis, for the Coq proof assistant. It extends the Mathematical Components library, geared towards algebra, with topics in analysis. Issues of a more general nature related to the inheritance of poorer structures from richer ones arise due to this combination. We present and discuss a solution, coined forgetful inheritance, based on packed classes and unification hints.
Discriminatiοn of Irοn Deposits Using Feature Οriented Principal Cοmpοnent Selectiοn and Band Ratiο Methοds: Eastern Taurus /Turkey

Kayseri and Adana region of Taurus mountain belt is considered in the second range for iron mineralization in Turkey. The region is characterized by high-grade iron οre deposits under exploitation, development and exploration stage. The objective of this research is to assess the potential of Landsat-8 OLI and Advanced Spacebοrne Thermal Emissiοn and Reflectiοn Radiοmeter (Aster) data for mapping iron oxide zones hosted by carbonate units in low vegetation cover in Tufanbeyli (Adana) and surrounding region. The methods used in this study for the detection of the iron oxides are Band Ratio (BR) and Feature-Oriented Principal Cοmpοnent Selectiοn (FOPCS). The obtained results have good correlation with 92% confidence intervals in BR method for Ferric iron (2/1), ferric oxide (4/3) and ferrous iron (5/4 +1/2) of Aster and iron oxide (4/2) and ferrous iron (6/5) οf the Landsat-8. We note that the results obtained in this study using PC4 of FOPCS depict an excellent spatial correlation by comparison with the previously mapped iron deposits. We conclude that the Landsat-8 OLI and Aster images present good precision for iron ore discrimination in low vegetation lands.
Phonological markedness effects in sentence formation

Earlier research has found that phonological markedness constraints (for example, against stress clash or sibilant sequences) statistically influence speakers’ choices between particular syntactic constructions and between synonymous words. In this study, we test phonological constraints not just in particular cases, but across the board. We employ a novel method that statistically models the distribution of word bigrams (consecutive two-word sequences) and how this distribution is influenced by phonological constraints. Our study of multiple corpora indicates that several phonological constraints do indeed play a statistically significant role in English sentence formation. We also show that by examining particular subsets of the corpora we can diagnose the mechanisms whereby phonologically marked sequences come to be underrepresented. We conclude by discussing modes of grammatical organization compatible with our findings.