Reviewerly/exHarmony huggingface dataset:
TU Delft Strategic Plan Open Science 2020-2024: Research and Education in the Open Era

Open Science is creating new forms of scientific interaction that were impossible or undreamed of in an earlier age. This has a strong impact on core academic processes like research, education and innovation. It is, for instance, easier to replicate an experiment if the relevant data sets are digitally available to any scientist who wishes to corroborate her colleague’s findings. TU Delft has a long history of engagement with Open Science. Yet, with its Open Science Programme 2020-2024, Research and Education in the Open Era, TU Delft wishes to take Open Science to the next level: a situation in which Open Science has become the default way of practising research and education, and the “information era” has become the “open era”. It is TU Delft’s ambition to be frontrunner in this revolutionary process. This is reflected in the TU Delft Strategic Framework 2018-2024, with “openness” as one of its major principles. The TU Delft Open Science Programme 2020-2024 tackles all areas of scholarly engagement where restrictions limit the flow of academic knowledge. It proposes new approaches to the process of research, education and innovation, with a strong focus on transparency, integrity and efficiency. The programme consists of five interrelated projects: Open Education, Open Access, Open Publishing Platform, FAIR Data, and FAIR Software. The projects are aimed at creating and disseminating various types of resources for the benefit of TU Delft researchers, teachers and students, as well as the general public. They will range from educational materials and software to a publishing platform. All outputs of the programme will be as ‘FAIR’ as possible: findable, accessible, interoperable and reusable.
Cryptanalysis and Improvement of a Remote Anonymous Authentication Protocol for Mobile Multi-server Environments

Recently, Feng et al. proposed an anonymous remote biometric authentication protocol for mobile multi-server environments. The protocol is vulnerable to session key attack, anonymity attack, replay attack, offline password guessing attack, and impersonation attack. To withstand these flaws, we propose a new secure and efficient anonymous remote biometric authentication protocol for mobile multi-server environments based on cryptographic hash function and elliptic curve cryptography(ECC). Security analysis shows that our protocol can achieve the authenticated key exchange in the random oracle model, with strong anonymity and perfect forward security guarantees, and can resist all known Internet attacks. Performance evaluation shows that our protocol is more secure than the previous protocols, and the computational efficiency and communication efficiency are more suitable for the application requirements of the mobile multi-server environments.
Kecerdasan Emosi Siswa Ditinjau dari Jenis Kelamin dan Jenjang Kelas

Emotional intelligence refers to the capability of a person to manage and control his or her emotions and possess the ability to control the emotions of others as well. Emotional intelligence is a very important skill for students. It is said to have five main elements such as self-awareness, self-regulation, motivation, empathy, and social skills. The purpose of this study First, to describe the level of emotional intelligence of junior high school students of Malang. Second, describe the differences of emotional intelligence based on gender and grade level. This research method is quantitative descriptive. The research sample was 313 students of State Junior High Schools in Malang selected by cluster random sampling technique. The research instrument used a scale of emotional intelligence consisting of 48 items, data analysis using descriptive analysis and T-test. The results showed 1) 25.9% students of State Junior High Schools in Malang were at a high level of emotional intelligence, 61.3% students of State Junior High Schools in Malang were at the level moderate emotional intelligence, 12.8% of students are at a low level of emotional intelligence. (2) there is a significant difference between the level of emotional intelligence of students based on gender, (3) there is a significant difference in the level of emotional intelligence of students from the grade level
SAAONT: Ontological knowledge-based development to support intelligent decision-making systems for Saudi Arabian agriculture

Ontologies have become an essential tool for domain knowledge representation and a core element of many intelligent systems.It considered an appropriate solution to represent complex concepts and relationships within the agricultural domain.Over the last years, there has been an increasing number of undertaken efforts to develop ontology-based agricultural systems.These existing agricultural ontologies may not be sufficient to provide the desired level of information to individual farmers in Arabic regions, i.e.Saudi Arabia.Additional work is therefore needed to focus on building Arabic ontologies to provide the relevant, contextual and scientifically correct information in Arabia.Furthermore, the current practices within the Saudi Arabian agriculture sector are traditional and lack the technological foundations necessary to build and support intelligent, and sustainable technical solutions.Besides the contribution to the body of knowledge, this paper outlines a state-of-art ontological knowledge-based development for the agriculture sector in Saudi Arabia.It proposes an ontology-driven information retrieval system for agriculture in Saudi Arabia (SAAONT).It aims to firstly, structure and standardize agricultural terminology in Arabic and secondly, provide accurate information to decision-makers, to establish a smarter agriculture environment.
Generating Users’ Desired Face Image Using the Conditional Generative Adversarial Network and Relevance Feedback

In this study, we propose a novel method for generating an image of the target face by using the generative adversarial network (GAN) and relevance feedback.Combining GAN with relevance feedback compensates for the lack of user intervention in GAN and the low image quality in traditional methods.The feature points of face images, namely, the landmarks, are used as the conditional information for GAN to control the detailed features of the generated face image.An optimum-path forest classifier is applied to categorize the relevance of training images based on the user's feedback so that the user can quickly retrieve the training images that are most similar to the target face.The retrieved training images are then used to create a new landmark for synthesizing the target face image.The experimental results showed that users can generate images of their desired faces, including the faces in their memory, with a reasonable number of iterations, therefore demonstrating the potential of applying the proposed method for forensic purposes, such as creating the face images of criminals based on the memories of witnesses or victims.
Statistical game-theoretic model of the optimal labor resources distribution

Abstract In this paper, a competitive model of the static assignment problem is constructed and investigated. The paper considers the static theoretical and game model of the optimal purpose problem. The algorithm of finding the equilibrium situation is constructed. This algorithm based on the given utility matrixes for workers and enterprises shows what purpose will be equilibrium. Finding compromise gains and equilibrium situations are analyzed by examples
Computational Syntax-Semantics Interface with Type-Theory of Acyclic Recursion for Underspecified Semantics

The paper provides a technique for algorithmic syntax-semantics interface in computational grammar with underspecified semantic representations of human language.The technique is introduced for expressions that contain NP quantifiers, by using computational, generalised Constraint-Based Lexicalised Grammar (GCBLG) that represents major, common syntactic characteristics of a variety of approaches to formal grammar and natural language processing (NLP).Our solution can be realised by any of the grammar formalisms in the CBLG class, e.g., Head-Driven Phrase Structure Grammar (HPSG), Lexical Functional Grammar (LFG), Categorial Grammar (CG).The type-theory of acyclic recursion L λ ar , provides facility for representing major semantic ambiguities, as underspecification, at the object level of the formal language of L λ ar , without recourse of meta-language variables.Specific semantic representations can be obtained by instantiations of underspecified L λ arterms, in context.These are subject to constraints provided by a newly introduced feature-structure description of syntax-semantics interface in GCBLG.
Temporal multispectral and 3D analysis of Cerro de Pasco, Peru

Mining operations across the world often lead to contamination of land, water resources, ecosystems and in some cases, entire communities.Results of recent health and ground sampling studies revealed extensive lead contamination within the populace and around the City of Cerro de Pasco, Peru. Tailings excavated from a large open pit zinc mine in the center of the city have been aggregated in four large stockpiles within close proximity to neighborhoods, schools, and hospitals. Visual comparison of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery from 2001 and Sentinel-2 imagery from 2018 suggests a size increase in one tailing stockpile in particular near the neighborhood of Paragsha. Due to ongoing mining efforts, the hypothesis motivating the work presented here is that Pb-bearing minerals would be detectable through multispectral analysis, an increase in Pb mineral percent abundance would be observed and tailing stockpile volume would be detectable between 2001 and 2016. This hypothesis is tested using Spectral Angle Mapper (SAM), Adaptive Coherence Estimator (ACE), and Jeffries-Matusita distance calculation on ASTER (2001) and Sentinel-2 (2018) VNIR and SWIR bands. Volume and area estimate of tailing stockpiles were calculated using a photogrammetrically derived point cloud. SAM detected the presence of five Pb-bearing minerals around Cerro de Pasco and Paragsha. The results of the temporal SAM analysis displayed an increase of approximately 17% of Pb-bearing minerals around the greater Cerro de Pasco city area and approximately 11% for the neighborhood of Paragsha. Jeffries-Matusita distance results suggest clear correlation between contamination sources and affected locations. Total tailing stockpile volume was measured to be approximately 200,300,000 m3. Volume for Pile 4 was estimated to have increased by approximately 46,000,000 m3 between 2001 and 2018. These presented results will hopefully inspire and guide future remote sensing campaigns, perhaps involving a UAV or aircraft-based hyperspectral instrument.
An Improved Sauvola Approach on QR Code Image Binarization

The paper presents a novel QR code image binarization method to deal with uneven illumination. Our method is based on an adaptive local binarization approach proposed by J. Sauvola. For different illumination condition, it chooses different rule to select the threshold used to distinguish foreground and background. It can use adaptive window size based on lighting condition while calculating mean and standard deviation. The proposed method uses a specific dataset for evaluation and is compared across multiple methods. The experimental results show that the new binarization method can be done with high accuracy and stability, and it is obviously superior to the preceding ways.
Investigating single image super-resolution algorithm with deep learning using convolutional neural network for chest digital tomosynthesis

Numerous efforts have been continuously made toward the realization of high spatial resolution images for medical imaging devices. Specifically, the image super-resolution technique with deep learning using convolutional neural network (CNN) has been making excellent advancements recently. Accordingly, this study is focused on developing a single image super-resolution (SISR) algorithm using deep CNN (DCNN) with supervised learning that can drastically improve the spatial resolution of chest digital tomosynthesis (CDT) images. In addition, we attempt to demonstrate the superiority of the SISR algorithm by a quantitative analysis. The proposed SISR algorithm uses a total of 5000 training CDT images (low-resolution and high-resolution) and a fully CNN based on residual structure. The image performance was analyzed using various parameters, such as intensity profile (full width at half maximum), contrast to noise ratio, coefficient of variation, and normalized noise power spectrum parameters, and the results demonstrated that the proposed SISR algorithm significantly improves the spatial resolution of the images. Further, the noise properties of the images obtained with the SISR algorithm were similar to those of the low-resolution images with up-sampling. Thus, we successfully developed the deep learning architectures in this study to improve the spatial resolution of the CDT reconstructed images.
OLAP parallel query processing in clouds with C‐ParGRES

Summary The advent of big data technologies has changed the way many companies manage their data. Several companies moved their data to the cloud using the concept of database‐as‐a‐service (DBaaS). Moving databases to the cloud presents several challenges related to flexible and scalable management of data. Although some of these companies migrated to NoSQL databases, most still rely on relational databases in the cloud to manage data, especially data that is critical to the decision making process. Online analytical processing (OLAP) queries take a long time to be processed, thus demanding high‐performance capabilities from their associated database systems to get results in a feasible time. In this article, we propose a middleware solution that can be deployed in any cloud provider, named C‐ParGRES , which explores database replication and interquery and intraquery parallelism to efficiently support OLAP queries in the cloud. C‐ParGRES is an extension of ParGRES, an open‐source database cluster middleware for high‐performance OLAP query processing in clusters. C‐ParGRES exploits cloud capabilities such as on‐demand resource provisioning and elasticity. In addition, C‐ParGRES can create multiple and independent virtual clusters for different database and users. We evaluate C‐ParGRES with two real‐world OLAP applications, both from the Brazilian Institute of Geography and Statistics. Results show that C‐ParGRES is a cost‐effective solution for OLAP query processing in the cloud.
Analysis of the Computational Aspects of the Applied Optimization Algorithms

When solving applied optimization problems, questions arise in analyzing the computational aspects of the methods used. The use of test problem generators is the most promising in this direction. We consider the implementation of the test problem generator that meets these requirements and allows us to generate the following classes of test problems: degenerate, ill-conditioned, indefinite, convex, and general- purpose tasks.
A Contact-Based Data Communication Technique Using Capacitive Touch Screen Panel and Support Vector Machine Classifier

This paper demonstrates a data communication technology based on the capacitive touch screen scheme that can discriminate stylus and finger using a support vector machine (SVM) classifier. The transmitter in a stylus sends the binary data with no-touch and stylus-touch and the receiver in a touch screen panel (TSP) recovers the received data contents. In addition, the proposed transmission protocol implements an artificial finger-touch on the stylus side for initial and terminal codes. Furthermore, to compensate for common-mode drifts caused by HUM noises, upward and downward drift compensation circuits are proposed for the Tx pulse sensing circuit of the transmitter. The proposed method is evaluated by means of 8-inch capacitive TSP, programmable system-on-chip board, and host processor board. It is verified that the proposed method can support the high data rate of 100 bit per second (bps) or more with less bit error rate than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-7</sup> , compared to several bps of previous touch-based communication techniques.
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget

In many applications, it is desirable to extract only the relevant information from complex input data, which involves making a decision about which input features are relevant. The information bottleneck method formalizes this as an information-theoretic optimization problem by maintaining an optimal tradeoff between compression (throwing away irrelevant input information), and predicting the target. In many problem settings, including the reinforcement learning problems we consider in this work, we might prefer to compress only part of the input. This is typically the case when we have a standard conditioning input, such as a state observation, and a ``privileged'' input, which might correspond to the goal of a task, the output of a costly planning algorithm, or communication with another agent. In such cases, we might prefer to compress the privileged input, either to achieve better generalization (e.g., with respect to goals) or to minimize access to costly information (e.g., in the case of communication). Practical implementations of the information bottleneck based on variational inference require access to the privileged input in order to compute the bottleneck variable, so although they perform compression, this compression operation itself needs unrestricted, lossless access. In this work, we propose the variational bandwidth bottleneck, which decides for each example on the estimated value of the privileged information before seeing it, i.e., only based on the standard input, and then accordingly chooses stochastically, whether to access the privileged input or not. We formulate a tractable approximation to this framework and demonstrate in a series of reinforcement learning experiments that it can improve generalization and reduce access to computationally costly information.
Development of Augmented Reality (AR) Based Gamelan Simulation with Leap Motion Control

One of the traditional musical instrument from Indonesia is &lt;em&gt;gamelan&lt;/em&gt;, that is now less desirable because people nowadays prefer to play modern musical instruments, or because of its expensive price. This study combines the modern technology of AR and Leap Motion and also utilizes the sophisticated smartphone to develop a &lt;em&gt;gamelan&lt;/em&gt; simulation that is presentable to the user. There are two kinds of an experiment performed in this research. The first is Augmented Reality (AR) experiment which authenticates that the distance and the height parameters are the keys to get a 100% success in running the &lt;em&gt;gamelan&lt;/em&gt; simulation. The distance is ranged from 45 cm to 65 cm and the height is ranged from 25 cm to 30 cm. And the second one is the usability evaluation to five users, who resolves the completion task very well, acquires the result of 86.48%.
Isolation Forest Filter to Simplify Training Data for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is an active research area. When the historical data is limited or a new project is to develop, establishing CPDP models is very useful, which assists software testers to judge the defect-prone entities or software managers to focus on the "important" parts by allocating the manpower, budget, time. However, the dissimilarity of data distributions between the source projects and the target project decreases the performance of CPDP models. How to simplify or the cross-project training data is an important problem. To solve this issue, an isolation forest (iForest) filter is proposed. We use 15 versions of different java projects from open PROMISE Data Repository and five typical predictors (naïve bayes (NB), decision tree (DT), logistic regression(LR), k-nearest neighbor(k-NN) and random forest(RF) to build 1050 (15*14*5) software defect prediction models (SDPM). Meanwhile, we compare our models with Burak Filter models and Peter Filter models. From the results of performance measures, called AUC, balance, G-measure, G-mean, F1-measure, we can know that our iForest filter is feasible and even better than other two. Therefore, using iForest filter can make cross-project training data simple and build efficient SDPM.
DARB: A Density-Adaptive Regular-Block Pruning for Deep Neural Networks

The rapidly growing parameter volume of deep neural networks (DNNs) hinders the artificial intelligence applications on resource constrained devices, such as mobile and wearable devices. Neural network pruning, as one of the mainstream model compression techniques, is under extensive study to reduce the model size and thus the amount of computation. And thereby, the state-of-the-art DNNs are able to be deployed on those devices with high runtime energy efficiency. In contrast to irregular pruning that incurs high index storage and decoding overhead, structured pruning techniques have been proposed as the promising solutions. However, prior studies on structured pruning tackle the problem mainly from the perspective of facilitating hardware implementation, without diving into the deep to analyze the characteristics of sparse neural networks. The neglect on the study of sparse neural networks causes inefficient trade-off between regularity and pruning ratio. Consequently, the potential of structurally pruning neural networks is not sufficiently mined.In this work, we examine the structural characteristics of the irregularly pruned weight matrices, such as the diverse redundancy of different rows, the sensitivity of different rows to pruning, and the position characteristics of retained weights. By leveraging the gained insights as a guidance, we first propose the novel block-max weight masking (BMWM) method, which can effectively retain the salient weights while imposing high regularity to the weight matrix. As a further optimization, we propose a density-adaptive regular-block (DARB) pruning that can effectively take advantage of the intrinsic characteristics of neural networks, and thereby outperform prior structured pruning work with high pruning ratio and decoding efficiency. Our experimental results show that DARB can achieve 13× to 25× pruning ratio, which are 2.8× to 4.3× improvements than the state-of-the-art counterparts on multiple neural network models and tasks. Moreover, DARB can achieve 14.3× decoding efficiency than block pruning with higher pruning ratio.
Embedding Compression with Isotropic Iterative Quantization

Continuous representation of words is a standard component in deep learning-based NLP models. However, representing a large vocabulary requires significant memory, which can cause problems, particularly on resource-constrained platforms. Therefore, in this paper we propose an isotropic iterative quantization (IIQ) approach for compressing embedding vectors into binary ones, leveraging the iterative quantization technique well established for image retrieval, while satisfying the desired isotropic property of PMI based models. Experiments with pre-trained embeddings (i.e., GloVe and HDC) demonstrate a more than thirty-fold compression ratio with comparable and sometimes even improved performance over the original real-valued embedding vectors.