Reviewerly/exHarmony huggingface dataset:
Digitisation and technological challenges of the banking industry

The strong competition and digital revolution impose to the banks their profound transformation demanding a high cost on investments. The banking system should continue, at the same time, its efforts to disengage from non-productive assets as well as to reduce the running costs. Moreover, banks should consider becoming more flexible and productive, by taking advantage of the opportunities of the new digital economy and by ameliorating accordingly the offered services. The current paper discusses and analyses the challenges for the banks, regarding the technological evolution, the digitization of banking services and the technologies of blockchain and big data.
Applicability Assessment of an Emergency Management Support System “KADAN”

In a crisis event, in order for multiple organizations to cooperate with each other, there are various problems such as sharing goals and facilitation of information sharing. Particularly, in cooperation between organizations, in order to form an agreement based on the role of each organization, the scope of responsibility, etc., decision making is required according to the purpose as the cooperative organization as a whole. In this way, it is necessary to unify situation recognition across the organization that responds to the crisis and devise strategies coordinated to solve the problem. The authors have been researching and developing the system called “KADAN” which means quick and decisive and supports emergency management at an EOC (Emergency Operations Center). KADAN provides a framework to support registering the governance and operation process that has been planned in advance, facilitating communication at the time of crisis response, and visualizing the whole situation of crisis response in style of tables and maps. The effectiveness of KADAN has been showed in exercises against natural disasters or cyber-attacks so far. In this paper, we examined usefulness of main function on KADAN at a large international sports event, “2017 Sapporo Asian Winter Games”.
Fast and Full-Resolution Light Field Deblurring Using a Deep Neural Network

Restoring a sharp light field image from its blurry input has become essential due to the increasing popularity of parallax-based image processing. State-of-the-art blind light field deblurring methods suffer from several issues such as slow processing, reduced spatial size, and a limited motion blur model. In this work, we address these challenging problems by generating a complex blurry light field dataset and proposing a learning-based deblurring approach. In particular, we model the full 6-degree of freedom (6-DOF) light field camera motion, which is used to create the blurry dataset using a combination of real light fields captured with a Lytro Illum camera, and synthetic light field renderings of 3D scenes. Furthermore, we propose a light field deblurring network that is built with the capability of large receptive fields. We also introduce a simple strategy of angular sampling to train on the large-scale blurry light field effectively. We evaluate our method through both quantitative and qualitative measurements and demonstrate superior performance compared to the state-of-the-art method with a massive speedup in execution time. Our method is about 16K times faster than Srinivasan et. al. [22] and can deblur a full-resolution light field in less than 2 seconds.
An Optimal Iterative Placement Algorithm for PIR from Heterogeneous Storage-Constrained Databases

We study private information retrieval (PIR) where a user privately downloads one of $K$ messages from $N$ databases (DBs) such that no DB can infer which message is being downloaded. Moreover, we consider the general case where DBs are storage constrained such that DB$_n$ can only store a $\mu[n]KL$ symbols where $0\leq \mu[n] \leq 1$ and $L$ is the number of symbols per message. Let $t= \sum_{n=1}^{N}\mu[n]$ be an integer, a recent work by Banawan et al. showed that the capacity of heterogeneous Storage Constrained PIR (SC-PIR) is $\left( 1+ \frac{1}{t} + \frac{1}{t^2} + \cdots + \frac{1}{t^{K-1}} \right)^{-1}$. However, an achievable, capacity achieving scheme was only developed for a network of $N=3$ DBs. In this paper, we propose an iterative placement algorithm for arbitrary $N$ which achieves heterogeneous SC-PIR capacity when $t$ is an integer. The algorithm defines storage contents of the DBs by assigning sets of sub-messages to $t$ DBs in each iteration. We show that the proposed placement algorithm converges within $N$ iterations and the storage placement requires at most $N$ sub-messages per message without considering the sub-message requirement for the delivery. Finally, we show that the proposed solution can be applied to the case of non-integer $t$ while still achieving capacity.
Real-Time Multiple Faces Tracking with Moving Camera for Support Service Robot

This paper proposes a real-time robot vision system to track multiple faces. This system supports service robots to communicate with consumers simultaneously. The Viola-Jones algorithm to detect faces early in the process, while the Kanade-Lucas-Tomasi algorithm is used to track detected facial features. The system follows the multiple human faces and synchronizes software and hardware to move the webcam in the middle position of the frame. Extraction of the center point from a set of faces as core information for controlling webcam movement. The challenge that can be overcome is that it can maintain multiple faces remains in the middle of the frame with various poses and the use of accessories. This system uses the PID controller which makes the webcam move in a fast to follow the face and maintain the stability and accuracy of actuator movements when speed increases. The experiments were done in the 42.052 frames per second as the maximum speed of system performance.
Proposing a Localized Relevance Vector Machine for Pattern Classification

Relevance vector machine (RVM) can be seen as a probabilistic version of support vector machines which is able to produce sparse solutions by linearly weighting a small number of basis functions instead using all of them. Regardless of a few merits of RVM such as giving probabilistic predictions and relax of parameter tuning, it has poor prediction for test instances that are far away from the relevance vectors. As a solution, we propose a new combination of RVM and k-nearest neighbor (k-NN) rule which resolves this issue with regionally dealing with every test instance. In our settings, we obtain the relevance vectors for each test instance in the local area given by k-NN rule. In this way, relevance vectors are closer and more relevant to the test instance which results in a more accurate model. This can be seen as a piece-wise learner which locally classifies test instances. The model is hence called localized relevance vector machine (LRVM). The LRVM is examined on several datasets of the University of California, Irvine (UCI) repository. Results supported by statistical tests indicate that the performance of LRVM is competitive as compared with a few state-of-the-art classifiers.
Learning Anatomy with Augmented Reality

An Augmented Reality (AR) application was developed to help students at SDU to learn the anatomy of the human body (mediastinum). This research project intended to evaluate whether AR strengthened the students’ self-efficacy and motivation, improved learning, and provided a good learning experience.&#x0D; This study focusses on how AR can help students to translate two-dimensional into three-dimensional understanding and evaluates formats of the app (the use of quizzes) together with the didactic design of the teaching sessions with AR. The objectives were to examine A. the effectiveness of using AR on student’s short, long term, and transfer learning outcome compared with traditional teaching, B. the effect of quizzes, and C. app design in relation to didactic design.&#x0D; In the AR world students saw a standardized hologram body combined with selected images from a computed tomography (CT) scan. The CT images were presented at the proper spatial positions in a hologram body. The user could select which structures to be shown at the body (e.g. skin, the vascular system, etc.). They could approach the body, circumvent it, study structures in details and compare the concurrent presentation with the different CT images.&#x0D; During class one group received traditional teaching and two groups studied CT scans in Augmented Reality. One AR-group complemented the AR app with quiz questions and received corrective feedback. The other AR group did not use quiz questions but studied content on their own hand. A teacher was present and ready to help students in all groups.&#x0D; The aim of this paper is to share lessons learned from this intervention and suggest solutions for app design and learning design to facilitate deeper learning processes and scaffold learners’ needs with AR as a learning resource.
Assessment of Employees Level of Awareness in the Aspect of Information Security

Abstract The strategic importance of information for the functioning of each economic entity forces entrepreneurs to properly protect them against loss, unauthorized disclosure or unauthorized modification. Hence, organizations build complex security systems taking into account state-of-the-art technical solutions, while belittling often the most important element, which is the human factor. It should be emphasized that it is the intentional or accidental actions of the human that can lead to the loss of information security. In addition, it is also the potential of human capabilities and skills can provide an effective defense against the failure or technical security. The article presents the basic stages of human resource management in the aspect of information security. Complementing these considerations will be the presentation and discussion of the results of surveys aimed at assessing the level of employee awareness in the area of information security.
A Fuzzy Enabled Genetic Algorithm for Task Scheduling Problem in Cloud Computing

Background &amp; Objective: Cloud computing emerges out as a new way of computing which enables the users to fulfill their computation need using the underlying computing resources like software, memory, computing nodes or machines without owning them purely on the basis of pay-per-use that too round the clock and from anywhere. People defined this as the extension of the existing technologies like parallel computing, distributed computing or grid computing. Lots of research have been conducted in the field of cloud computing but the task scheduling is considered to be the most fundamental problem which is still in infancy and requires a lot of attention and a proper mechanism for the optimal utilization of the underlying computing resources. Task scheduling in cloud computing environment lies into the category of NP-hard problem and many heuristics and Meta heuristics strategies have been applied to solve the problem. Methods: In this work, Fuzzy Enabled Genetic Algorithm (FEGA) is proposed to solve the problem of task scheduling in cloud computing environment as classical roulette wheel selection method has certain limitations to solve complex optimization problem. Results &amp; Discussion: In this work, an efficient fuzzy enabled genetic algorithm based task scheduling mechanism has been designed, implemented and investigated. The efficiency of the proposed FEGA algorithm is tested using various randomly generated data sets in different situations and compared with the other meta-heuristics. Conclusion: The authors suggest that the proposed Fuzzy Enabled Genetic Algorithm (FEGA) to solve the task scheduling problem helps in minimizing the total execution time or makespan and on comparing with other Meta-heuristic like genetic algorithm and greedy based strategy found that FEGA outperforms the both in different set of experiments.
A secure regenerating code‐based cloud storage with efficient integrity verification

Summary Cloud storage is gaining popularity as it relieves the data owners from the burden of data storage and maintenance cost. However, outsourcing data to third‐party cloud servers raise several concerns such as data availability, confidentiality, and integrity. Recently, regenerating codes have gained popularity because of their low repair bandwidth while ensuring data availability. In this paper, we propose a secure regenerating code‐based cloud storage (SRCCS) scheme, which utilizes the verifiable computation property of homomorphic encryption scheme to check the integrity of outsourced data. In this work, an error‐correcting code (ECC)–based homomorphic encryption scheme (HES) is employed to simultaneously provide data privacy as well as error correction while supporting efficient integrity verification. In SRCCS, server regeneration process is initiated on detection of data corruption events in order to ensure data availability. The ECC‐based HES significantly reduces the probability of server regeneration and minimizes the repair cost. Extensive theoretical analysis and simulation results validate the security, efficiency, and practicability of the proposed scheme.
Improvement in Classification Algorithms through Model Stacking with the Consideration of their Correlation

In this research we analyzed the performance of some well-known classification algorithms in terms of their accuracy and proposed a methodology for model stacking on the basis of their correlation which improves the accuracy of these algorithms.We selected; Support Vector Machines (svm), Naïve Bayes (nb), k-Nearest Neighbors (knn), Generalized Linear Model (glm), Latent Discriminant Analysis (lda), gbm, Recursive Partitioning and Regression Trees (rpart), rda, Neural Networks (nnet) and Conditional Inference Trees (ctree) in our research and preformed analyses on three textual datasets of different sizes; Scopus 50,000 instances, IMDB Movie Reviews having 10,000 instances, Amazon Products Reviews having 1000 instances and Yelp dataset having 1000 instances.We used R-Studio for performing experiments.Results show that the performance of all algorithms increased at Meta level.Neural Networks achieved the best results with more than 25% improvement at Meta-Level and outperformed the other evaluated methods with an accuracy of 95.66%, and altogether our model gives far better results than individual algorithms' performance.
E-learning Program Effectiveness on Students’ Learning Satisfaction at Telkom University Bandung

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Self-critical n-step Training for Image Captioning

Existing methods for image captioning are usually trained by cross entropy loss, which leads to exposure bias and the inconsistency between the optimizing function and evaluation metrics. Recently it has been shown that these two issues can be addressed by incorporating techniques from reinforcement learning, where one of the popular techniques is the advantage actor-critic algorithm that calculates per-token advantage by estimating state value with a parametrized estimator at the cost of introducing estimation bias. In this paper, we estimate state value without using a parametrized value estimator. With the properties of image captioning, namely, the deterministic state transition function and the sparse reward, state value is equivalent to its preceding state-action value, and we reformulate advantage function by simply replacing the former with the latter. Moreover, the reformulated advantage is extended to n-step, which can generally increase the absolute value of the mean of reformulated advantage while lowering variance. Then two kinds of rollout are adopted to estimate state-action value, which we call self-critical n-step training. Empirically we find that our method can obtain better performance compared to the state-of-the-art methods that use the sequence level advantage and parametrized estimator respectively on the widely used MSCOCO benchmark.
A Calibration Method for System Parameters in Direct Phase Measuring Deflectometry

Phase measuring deflectometry has been widely studied as a way of obtaining the three-dimensional shape of specular objects. Recently, a new direct phase measuring deflectometry technique has been developed to measure the three-dimensional shape of specular objects that have discontinuous and/or isolated surfaces. However, accurate calibration of the system parameters is an important step in direct phase measuring deflectometry. This paper proposes a new calibration method that uses phase information to obtain the system parameters. Phase data are used to accurately calibrate the relative orientation of two liquid crystal display screens in a camera coordinate system, by generating and displaying horizontal and vertical sinusoidal fringe patterns on the two screens. The results of the experiments with an artificial specular step and a concave mirror showed that the proposed calibration method can build a highly accurate relationship between the absolute phase map and the depth data.
Image Demosaicking via Chrominance Images with Parallel Convolutional Neural Networks

Many conventional demosaicking methods are based on hand-crafted filters. However, the filters yield false colors in salient regions like edges and textures. For acquisition of high quality images, we focus on neural networks. Neural networks lead to high accuracy in many fields. However, there are few methods in demosaicking field. For adaptation to demosaicking, we consider not only network's architecture but also the input. In this research, we utilize a Bayer image as input of our networks. However, different filter is needed in estimation at different color pixels, for example, missing red value at green pixel and that at blue pixel. Therefore, we prepare four networks with downsampling operators classified by color patterns in Bayer images. This downsampling operator not only identifies the color pattern but also reduces the calculation cost in each network due to reduction of the size of feature maps. Besides, preparation of multi-networks instead of a deep single-network is suitable for today's parallel computing. Moreover, we utilize not missing color images but chrominance images as output. Compared to results with missing color images as output, the results with chrominance images obtains higher accuracy. Experimental results show our CNN-based approach produces high quality restored images.
Technology, Socioeconomics and Education

&lt;p&gt;One of the characteristics of the late XX century and early XXI century is the constant technological, economic, and social transformations that are clearly interrelated where any technological innovation directly influences the socio-labour sphere. This fact can be seen, for example, in the growth of business models based on the economy of platforms (Gig economy) and in the robotization of productive systems that are accelerating a new labour scenario by creating, replacing and transforming many of the Occupations known so far. Some of the most representative facts that characterize these transformations would be.&lt;/p&gt;
TEACHERS AND STUDENTS ONLINE BUT DISCONNECTED

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 420-427ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0186Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images

In daily life, graphic symbols, such as traffic signs and brand logos, are ubiquitously utilized around us due to its intuitive expression beyond language boundary. We tackle an open-set graphic symbol recognition problem by one-shot classification with prototypical images as a single training example for each novel class. We take an approach to learn a generalizable embedding space for novel tasks. We propose a new approach called variational prototyping-encoder (VPE) that learns the image translation task from real-world input images to their corresponding prototypical images as a meta-task. As a result, VPE learns image similarity as well as prototypical concepts which differs from widely used metric learning based approaches. Our experiments with diverse datasets demonstrate that the proposed VPE performs favorably against competing metric learning based one-shot methods. Also, our qualitative analyses show that our meta-task induces an effective embedding space suitable for unseen data representation.
Sistem Pakar Diagnosa Gangguan Hiperaktif Pada Anak Dengan Metode Naive Bayes Berbasis Web

Disorder concentration attention or better known as ADHD (Attention Deficit Hyperactivity Disorder) is one of the main psychiatric problems that are often found in children. Hyperactive disorders are usually seen in children and as we get older the more difficult to deal with. There were 3 hyperactive disorders taken in this study namely hyperactivity, Impulsivity, Inattetion (Personality Problems). To help diagnose these hyperactive disorders, an expert system application is made to diagnose hyperactive disorders in children that can facilitate the public in diagnosing hyperactive disorders in children. Making this web-based expert system application uses the Naive Bayes method. The Naive Bayes method is a simple opportunity classification based on the application of the Bayes theorem with the assumption that between variables (independent) the presence or absence of a particular event from a group is not related to the presence or absence of other events. The final results are given in the form of a percentage of the diagnosis of hyperactivity in children.
A Framework for Anomaly Detection in Activities of Daily Living using an Assistive Robot

This paper presents an overview of an ongoing research to incorporate an assistive robotic platform towards improved detection of anomalies in daily living activities of older adults. This involves learning human daily behavioural routine and detecting deviation from the known routine which can constitute an abnormality. Current approaches suffer from high rate of false alarms, therefore, lead to dissatisfaction by clients and carers. This may be connected to behavioural changes of human activities due to seasonal or other physical factors. To address this, a framework for anomaly detection is proposed which incorporates an assistive robotic platform as an intermediary. Instances classified as anomalous will first be confirmed from the monitored individual through the intermediary. The proposed framework has the potential of mitigating the false alarm rate generated by current approaches.
INTELLIGENT ALGORITHM OF IDENTIFYING DESTRUCTIVE INFORMATION IN THE TEXT

In the context of ensuring the information and psychological security of a society in the Russian Federation, the problem of uncontrolled growth of the Internet destructive content is being considered. The problem of search and identification of destructive information demands the minimal subjectivity and the maximum automation, because it is still solved generally by expert methods with drawing up registers of the forbidden sources. Existing search methods (expert processing, thematic search, intelligent data processing methods) used to find specific words in the text have been considered, the advantages and disadvantages of these methods have been stated. For solving the task set the intellectual algorithm of the modified search of destructive content has been suggested using foul language in the textual information as an example. The suggested approach has a different system of self-replenishment of the dictionary on the basis of identification of the unknown words as foul ones. The correctness of the dictionary replenishment is controlled by an expert. A detailed description of the developed algorithm has been presented. The example of the algorithm operation and initial results of its approbation are given.