Reviewerly/exHarmony huggingface dataset:
Work-in-Progress: Towards detection and syntactical analysis in UML class diagrams for software engineering education

Learning how to create UML class diagrams from requirements specifications in textual format is one of the fundamental competences of students in Information Technologies. However, students seem to struggle creating those with all the requested elements. This process is not only challenging for students, but for teachers as well. Teachers, who correct the students solutions, have to take a deeper look at each created diagram to verify whether it is correct or not. Created diagrams can differ from each other or a given solution, but can nevertheless be correct. Also we observed, that the manner of creating class diagrams differ from student to student. Some create them using tools like the Enterprise Architect, others draw them by hand. To support students in the progress of learning how to create UML class diagrams and support teachers, we began realizing a prototype which can visually detect UML class diagram elements and compare them to a given solution. Visual recognition enables universal support no matter how a student created the diagram. Deep learning technologies have lead to major achievements in visual image processing in the recent years. Therefore this paper investigates that they are of use in UML element detection and syntactical analysis.
Sequential importance sampling for multiresolution Kingman–Tajima coalescent counting

Statistical inference of evolutionary parameters from molecular sequence data relies on coalescent models to account for the shared genealogical ancestry of the samples.However, inferential algorithms do not scale to available data sets.A strategy to improve computational efficiency is to rely on simpler coalescent and mutation models, resulting in smaller hidden state spaces.An estimate of the cardinality of the state space of genealogical trees at different resolutions is essential to decide the best modeling strategy for a given dataset.To our knowledge, there is neither an exact nor approximate method to determine these cardinalities.We propose a sequential importance sampling algorithm to estimate the cardinality of the sample space of genealogical trees under different coalescent resolutions.Our sampling scheme proceeds sequentially across the set of combinatorial constraints imposed by the data which, in this work, are completely linked sequences of DNA at a nonrecombining segment.We analyze the cardinality of different genealogical tree spaces on simulations to study the settings that favor coarser resolutions.We apply our method to estimate the cardinality of genealogical tree spaces from mtDNA data from the 1000 genomes and a sample from a Melanesian population at the β-globin locus.
An Improved Augmented-Reality Framework for Differential Rendering Beyond the Lambertian-World Assumption

In augmented reality, it is important to achieve visual consistency between inserted virtual objects and the real scene. As specular and transparent objects can produce caustics, which affect the appearance of inserted virtual objects, we herein propose a framework for differential rendering beyond the Lambertian-world assumption. Our key idea is to jointly optimize illumination and parameters of specular and transparent objects. To estimate the parameters of transparent objects efficiently, the psychophysical scaling method is introduced while considering visual characteristics of the human eye to obtain the step size for estimating the refractive index. We verify our technique on multiple real scenes, and the experimental results show that the fusion effects are visually consistent.
Estimation of Whole Blood (WB) using Unscented Kalman Filter in Surabaya

Abstract As the need for blood transfusion increases, an improved blood stock management for blood transfusion is required. In terms of quality and quantity, blood transfusion is needed by patients with various health problems. Because of that need, it is urgent that the stability of blood stock be maintained to avoid possible excessive blood stock that leads to unnecessary blood disposal. For that purpose, prediction of blood demand is required. One of the blood transfusion units is PMI, a national organization dealing with social concern and humanity field. The objective of this paper is to make estimation of blood demand for PRC and WB at PMI Surabaya using UKF method. The simulation results show that the UKF method is effective with a high accuracy and an error of less than 4%.
Enhanced Image Restoration by GANs using Game Theory

We propose a deep learning Generative Adversarial Network (GAN) which has two major components Generator Model in which our network is fed with input data and it attempts to figure out the joint probability distribution of the input data in order to generate more data points using the same distribution. We are using variational auto encoders, class-based generators and other generator models. Second model being Discriminator Model in which the network is a simple classifier model which classifies images produced by generator as either same as target image or different from target image. Throughout the process our aim is to get images from generator closest to target image's probability distribution. This entails a competitive environment between the generator and discriminator. Game theory consists of two important concepts regarding this competition known as Minmax Algorithm and Nash Equilibrium Using the above two concepts in conjugation with the negative-f divergence loss, we restore images using GANs eliminating the prevalent issue of overfitting among contemporary networks and thereby enhancing the image restoration performance of our GAN network.
User Experience in Excavator Simulator using Leap Motion Controller in Virtual Reality Environment

Abstract The risk that could happen in operating heavy machinery, such as excavator, can be avoided by doing special training to the operator. The operator of an excavator can learn the use of each joystick and lever in excavator by using a virtual reality without worrying about any damage. A virtual reality controller, such as Leap Motion Controller, can improve human computer interaction. To get a result of user experience in an excavator simulator, a survey of user agreement level and user satisfaction is required. The result of this research is none of the statements in the survey has an average score of 4.
A Data Masking Guideline for Optimizing Insights and Privacy Under GDPR Compliance

The General Data Protection Regulation (GDPR) has been enforced since May 2019 and became a disruptive issue to every organization due to its severe penalties in the data breaches or use of personal data for illegal purposes, e.g., lack of the consent of data subject. Therefore, the data Pseudonymization and Anonymization are one of the employed techniques to protect and reduce the privacy risks from the data breach. Unfortunately, they also destroy the pattern of the data, which represents the fact that it could be analyzed or monetized to gain useful insights by data analytics or data science approaches. This paper focuses on optimizing the privacy and insight method that the data could be useful for analyzing and also compliance with the GDPR. This paper proposes the guideline consists of three techniques: tokenization, suppression, and generalization to protect personal data by calculating risk scores from two methods: data classification and data uniqueness. The criteria in the guideline are experimented to achieve the optimized classification performance in protected data compared with five original open data by analyzing with three data mining algorithms with the hyperparameter tuning process. The results show that the protected data by the proposed guideline can protect adequate information and achieve insignificant classification performance when compared to the unprotected data.
Anomaly Detection in Embedded Systems Using Power and Memory Side Channels

We propose multi-modal anomaly detection in embedded systems using time-correlated measurements of power consumption and memory accesses. Time series of power consumption of the processor and memory accesses between L2 cache and memory bus under known-good conditions are used to train one-class support vector machine (SVM) and isolation forest classifiers. These side channels have complementary anomaly detection capabilities. Experiments on a high-fidelity processor emulator show that the method accurately detects anomalies.
Bigdata Clustering using X-means method with Euclidean Distance

Abstract Centroid is the central point of data in the grouping process, it is necessary to analyze the centroid in determining the initial value in the initial clustering process. So it is used as a cluster center point in the X-Means algorithm clustering process. Determine cluster center points or centroid, measure the performance of the X-Means algorithm with range cluster parameters by measuring distances between centroid for a fast and efficient way to group unstructured data, and to speed up the model construction process and divide several centroid in half to match the data as a test tool for the analysis of the X-Means method. From testing using the X-Means algorithm with the determination of the number of Centroid clusters carried out by modifying the X-Means method to do some determination of the centroid to get the results of 11 iterations. From the results of these tests produce good cluster members the level of similarity of data with other data and in determining the number of clusters, using the modification of the Euclidean distance method, get better results of the similarity level of each member compared to randomly determining the number of clusters with several iterations.
Right to an Explanation Considered Harmful

Lay and professional reasoning has it that newly introduced data protection regulation in Europe – GDPR – mandates a ‘right to an explanation’. This has been read as requiring that the machine learning (ML) community build ‘explainable machines’ to enable legal compliance. In reviewing relevant accountability requirements of GDPR and measures developed within the ML community to enable human interpretation of ML models, we argue that this reading should be considered harmful as it creates unrealistic expectations for the ML community and society at large. GDPR does not require that machines provide explanations, but that data controllers – i.e., human beings – do. We consider the implications of this requirement for the ‘explainable machines’ agenda.
Security Guidelines for the Design of ATM Interfaces

In Peru, 41% of the population over 18 is banked and 76% of them use an Automatic Teller Machine or ATM. However, in some cases the interaction between the users and the ATM (and the User Experience in general) could be frustrating and presents inconveniences in topics about trust and security, considering that clients are mainly carrying out operations that involve withdrawal and depositing of their own money in cash. In that sense, the influence of security (and the perception of security by the user) in the experience of using ATM, given their nature and form of use, has been evidenced. According to the above, we found in the literature that specific design and heuristic guidelines have been proposed for the usability of ATM interfaces, but no specific guidelines for other relevant aspect such as the security of those interfaces. The main objective of this work is to provide a proposal of security guidelines for the design of ATM interfaces, which complement existing guidelines on other facets of the User Experience, such as, Usability and Accessibility, which seeks to strengthen the User Experience of the product. To obtain the mentioned guidelines, a compilation of good practices, recommendations and guidelines found in Peruvian and international literature and regulations was made. For the validation of this proposal, these guidelines were validated through expert judgment by three ATM interface design experts and four domain experts working in 3 of the most important banks in Peru. As a result, we proposed seven ATM interface security guidelines and their respective definitions. From this work, we can conclude the importance of not just evaluating Usability but also complementing security issues when it comes to a channel as sensitive as ATMs, and in that sense, for this domain, consider security as an important part of the User Experience.
Bag-of-Visual Words based Improved Image Retrieval Algorithm for Vision Indoor Positioning

Aiming at the problem of existing bag-of-visual words based image retrieval algorithm, such as poor stability and low retrieval accuracy, a bag-of-visual words based improved image retrieval algorithm (IBVW) is proposed, which extracts features from the images in the database. The approximate K-means algorithm is adopted to cluster the image features into visual words and store them in the database. And then, the feature extraction and the inverted index in the online stage are implemented on the query image to find the images with high similarity in the database. At last, the best matching image is acquired through the similarity calculation, voting scheme and homography based matching algorithm. Simulation results and performance analysis show that the accuracy of our retrieval algorithm is improved by about 10% compared with existing methods for vision indoor positioning.
Segmentation of Frequently Used Handwritten Gujarati Conjunctive Alphabet

The segmentation of touching symbols is one of the key factors which decrease the performance of the Optical Character Recognition (OCR) system. The existence of touching characters in the documents is a major problem of the effective character segmentation system. In this paper, we have presented an algorithm for the segmentation of frequently used handwritten Gujarati conjunctive characters into its constituent symbols and characters. A predictive algorithm is developed for selecting the possible cut column for the segmentation of conjunctive characters. This algorithm uses the structural properties of the Gujarati alphabet. The possible cut column is defined by using the information derived from the neighboring pixels. This algorithm covers 728 handwritten conjunctive characters of Gujarati Script. In this conjunctive characters are segmented into easily separable characters which can be further sent to the classifier for recognition.
Multi-Kernel Fusion for RBF Neural Networks

Abstract A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple kernels which provide significant performance benefits compared to the previous generation using only a single kernel. In existing multi-kernel RBF algorithms, multi-kernel is formed by the convex combination of the base/primary kernels. In this paper, we propose a novel multi-kernel RBFNN in which every base kernel has its own (local) weight. This novel flexibility in the network provides better performance such as faster convergence rate, better local minima and resilience against stucking in poor local minima. These performance gains are achieved at a competitive computational complexity compared to the contemporary multi-kernel RBF algorithms. The proposed algorithm is thoroughly analysed for performance gain using mathematical and graphical illustrations and also evaluated on three different types of problems namely: (i) pattern classification, (ii) system identification and (iii) function approximation. Empirical results clearly show the superiority of the proposed algorithm compared to the existing state-of-the-art multi-kernel approaches.
Blockchain Potential Contribution to Reducing Corruption Vulnerabilities in the Brazilian Context

The recent popularization of distributed ledger technologies, which is better known in the financial sector due to digital currencies, has led to the appearance of numerous applications developed for the blockchain environment. The goal of this research is to investigate how a blockchain can contribute to the reduction of the vulnerabilities to corruption in the Brazilian context. Two stages of a literature review have been performed. The first identified the vulnerabilities to corruption in the Brazilian context and the second one identified the effective uses of blockchain characteristics. Subsequently, a deductive analysis was performed, aiming to verify which of the Blockchain initiatives presented could be potentially applied in the fight against corruption. This study points out ways to mitigate fraud and other causes of corruption to help regain society's trust in state institutions in Brazil, which has been suffering over the years from corruption scandals. Additionally, a research agenda leading to anti-corruption studies has been discussed.
Blockchain Technologies Towards Data Privacy—Hyperledger Sawtooth as Unit of Analysis

For digital business models data is the most crucial asset—this calls for increased awareness of appropriate privacy protection measures. The European Union General Data Protection Regulation is a consequence that followed the discussions and now forces organizations to ensure that their information ecosystems comply with the law. There is currently an emerging trend to apply blockchain technologies to business models that rely on data exchange, because the technology promises to make a centralized data authority redundant. We have taken this as the purpose for our efforts to provide insights that will help decision-makers select a suitable blockchain configuration that complies with data privacy regulatory requirements. By applying design science, we created a morphological box along with a grid, serving as a ‘data privacy assessment tool’ for the blockchain configuration Hyperledger Sawtooth. The research results can potentially be generalized to assess any other blockchain configuration.
Improving the accuracy of SqueezeNet with negligible extra computational cost

The deep neural network has a strong ability to solve problems in different fields such as computer vision and natural language processing. The development of lightweight models makes neural networks more efficient and can be widely applied to various tasks. SqueezeNet is a typical lightweight neural network composed of fire modules and other common modules in neural networks. However, the original paper achieves only 57.50% and 80.30% Top-1 and Top-5 ImageNet accuracy and uses deep compression, an additional model compression method, to reduce the model size. In this paper, we improve the network from the aspects of training method and network microarchitecture to enhance the practicality of SqueezeNet at negligible extra computational cost. These two improvements significantly improve the performance of the SqueezeNet. Experiments performed on the ImageNet datasets have shown that the proposed method has improved the Top-1 and Top-5 accuracy of SqueezeNet to 64.55% and 85.09%, which means 7.05% and 4.79% relative improvement over the original paper, without additional loss. It only increases the number of parameters by 0.48%. Additionally, the inference speed of our models is almost the same as baseline models on different platforms.
Comparative Analysis of Eigenface and Learning Vector Quantization (LVQ) to Face Recognition

Abstract Face recognition is a topic most often discussed in this era because it can be applied and developed for several needs that can be useful in daily life. Face recognition always use learning method like eigenface and learning vector quantization (LVQ). The learning process is using the face of a digital image taken from a camera in five angles for one person that will apply for dataset learning (training and learning data set), and the live image is taken from the camera for testing (testing data image). The first step image will be detected with a haar cascade and captured by a camera. It will use to processing images to get the best image and prepare to be input into the network. From the experiments with 10 testings with various parameter values, the experiment results obtained the LVQ is more accurate than eigenface to identifying faces with average accuracy 66.29% and eigenface is 56.67% with comparison 9.62%, but eigenface is faster in running time with average time 4.39 seconds and 7.38 seconds for LVQ with comparison 2.98 seconds.
Influence of Genetic Processes on Geochemistry of Fe-oxy-hydroxides in Supergene Zn Non-Sulfide Deposits

In supergene Zn non-sulfide deposits, the Fe-oxy-hydroxides (FeO/OH) are mainly concentrated in the residual zones (gossan) on top of the oxidized ore bodies, although they can also be found throughout the whole weathering profile coexisting with the primary and secondary ore assemblages. Fe-oxy-hydroxides are rarely pure as they form in systems where a wide range of metals, most of them of economic importance (e.g., Zn, Pb, Co, REE, Sc, Ga, Ge, V, etc.), freely circulate and can be “captured” under specific conditions. Although their occurrence can be widespread, and they have a potential to scavenge and accumulate critical metals, FeO/OH are considered gangue phases during the existing processing routes of Zn non-sulfide ores. Moreover, very little is known about the role of the deposit type on the geochemistry of FeO/OH formed in a specific association. Therefore, this paper provides a comprehensive assessment of the trace element footprint of FeO/OH from a number of Zn non-sulfide deposits, in order to define parameters controlling the metals’ enrichment process in the mineral phase. To achieve this, we selected FeO/OH-bearing mineralized samples from four supergene Zn non-sulfide ores in diverse settings, namely Hakkari (Turkey), Jabali (Yemen), Cristal (Peru) and Kabwe (Zambia). The petrography of FeO/OH was investigated by means of scanning electron microscope energy dispersive analysis (SEM-EDS), while the trace element composition was assessed using laser ablation-ICP-MS (LA-ICP-MS). Statistical analyses performed on LA-ICP-MS data defined several interelement associations, which can be ascribed to the different nature of the studied deposits, the dominant ore-formation process and subsequent evolution of the deposits and the environmental conditions under which FeO/OH phases were formed. Based on our results, the main new inferences are: (A) Zinc, Si, Pb, Ga and Ge enrichment in FeO/OH is favored in ores where the direct replacement of sulfides is the dominant process and/or where the pyrite is abundant (e.g., Cristal and Hakkari). (B) When the dissolution of the host-rock is a key process during the supergene ore formation (i.e., Jabali), the buffering toward basic pH of the solutions favors the uptake in FeO/OH of elements leached from the host carbonate rock (i.e., Mn), whilst restricting the uptake of elements derived from the dissolution of sulfides (i.e., Zn, Pb, Ga and Ge), as well as silica. (C) The input of exotic phases can produce significant enrichment in “unconventional” metals in FeO/OH (i.e., Cr and Co at Kabwe; Y at Cristal) depending on whether the optimal pH-Eh conditions are attained. (D) In the Kabwe deposit, FeO/OH records heterogeneous geochemical conditions within the system: where locally basic conditions prevailed during the alteration process, the V and U concentration in FeO/OH is favored; yet conversely, more acidic weathering produced Zn- and Si-bearing FeO/OH.