Reviewerly/exHarmony huggingface dataset:
«Digitalization Destroys School Walls»: the Experience of School Digitalization in Finland

The article discusses the history of implementation of digital technology in the educational process of the Finnish school over the past 20 years and impact of first digital textbooks. Digitality is viewed in a broad and narrow sense in Finland: in a broad sense as the most important part of the modern human ecosystem where a human is a responsible member of society, in the narrow sense as the use of digital devices for educational and pedagogical purposes for instance for both assessing processes (teacher assessment and pupil self - assessment). Project-oriented learning and interdisciplinary communication are methods witch widely used in Finnish educational system. Digitalization significantly helps in their successful implementation and allow to combine in lessons those elements: 1) pupil’s digital skills and their desire to use digital devices; 2) performance of a specific learning tasks which have for students the goal (finding relevant information), the benefit (new knowledge) and interest (multimodality); 3) a trusting relationships between students and their teachers when the students make their tasks independently, creatively. The research methods are the method of critical interpretation, case study, the method of conceptualization, the observational method. The article also provides specific examples of the use of digital technology in different lessons in both ungraded and urban schools, in lower and upper secondary schools.
A Multitarget Tracking Method for Estimating Carotid Artery Wall Motion from Ultrasound Sequences

Analyzing the motion of the wall of the common carotid artery (CCA) yields effective indicators for atherosclerosis. In this work, we explore the use of multitarget tracking techniques for estimating the time-varying CCA radius from an ultrasound video sequence. We employ the joint integrated probabilistic data association (JIPDA) filter to track a set of “feature points” (FPs) located around the CCA wall cross section. Subsequently, we estimate the time-varying CCA radius via a non-linear least-squares method and a Kalman filter. The application of the JIPDA filter is enabled by a linearized state-space model describing the quasi-periodic movement of the FPs and the measurement extraction process. Simulation results using the Field II ultrasound simulation program show that the proposed multitarget tracking method can outperform a state-of-the-art method.
Development of integrated library automation system: a case study of Universitas Negeri Semarang

Based on the evaluation of Technology Acceptance Model (TAM) that has done on the library automation system of Universitas Negeri Semarang, it is necessary to improve the existing system to meet the needs of the users. System improvements are compiled using Rapid Application Development (RAD). The output of this research is an integrated library information system. The system is applied in all majors in Semarang State University so that users can borrow libraries across the majors.
Articulating heterogeneous data streams with the attribute-relation file format

The processing strategy based on measurement metadata is a data stream engine running on Apache Storm, who is able to process measures in real-time. In the data stream context, the data have no an associated limit, they are al-ways arriving. The Attribute-Relation File Format (ARFF) is used by popular software like Weka, allowing offline analysis in the machine learning and data mining area. However, the ARFF file has a finite size. The CincamimisConversor library allows exporting from the data streams organized under a measurement interchange schema to a columnar-data organization in real-time. Here, an extension to the library is introduced for supporting the real-time translating and storing from the heterogeneous data streams to the ARFF file format. This is very useful, because through the library now is possible to collect data from heterogeneous data sources (e.g. Internet-of-Thing-IoT-devices) and export them in real-time for offline analysis in Weka. Even, this could foster a lot of educational applications among IoT, the measurement process with heterogeneous sources, data stream processing strategy, and Weka. A discrete simulation was carried out, obtaining promising results. It is just required at most 0.2387 ms for translating 5000 measures, while the storing operation for them consumed less than 0.2028 ms on a Solid-State disk.
Hybrid Cloud Computing Architecture Based on Open Source Technology

The advance of technologies such as distributed computing, Internet and grid computing, have enabled Cloud Computing to become part of a new model of computing and business. Cloud Computing is transforming the traditional ways in which companies use and acquire Information Technology (IT) resources. After an initial boom in Public Cloud, companies begun to mount hybrid Clouds that offer the advantages of Cloud Computing in addition to the privacy of data they consider strategic. A hybrid Cloud solution allows the integration of both systems. Leading companies in cloud solutions have understood this evolution and begun to offer hybrid solutions. Moreover, many of these companies are taking the next step by offering solutions based on open source standards that allow a high degree of interoperability and portability.
Adaptive Joint Attention with Reinforcement Training for Convolutional Image Caption

A convolutional decoder for image caption has proven to be easier to train than the Long Short Term Memory (LSTM) decoder [2]. However, previous convolutional image captioning methods are not good at capture the relationship between generated words, which could lead to failure in predicting visually unrelated words. To address this issue, we propose an Adaptive Joint Attention (AJA) module for the convolutional decoder, by incorporating self-attention to explore the correlation between generated words automatically. Specifically, we develop a word gate with multi-head self-attention to directly capture words’ relationship. An adaptive combination strategy is proposed to learn which word is less related to image information. Besides, reinforcement learning is applied for directly optimizing the non-differentiable metrics while avoiding the exposure bias during inference. Extensive evaluations on two public data sets demonstrate that our method outperforms several state-of-the-art approaches in image captioning task.
Automatic Segmentation of Heart Cavity in Echocardiography Images: Two &amp; Four-Chamber View Using Iterative Process Method

The heart is a vital part of the human organ which is responsible for receiving and pumping blood throughout the body. According to statistical data held by the World Health Organization noted that heart disease accounts for 31% of human deaths worldwide. Examination of heart health conditions usually uses an echocardiography device with sound waves. In echocardiography, images have speckle noise and need a repair process. Noise speckle is the fine-grained texture-like pattern seen in echocardiography, and indeed in all modalities of clinical ultrasound. This study proposed the two and four-chamber cavity segmentation method in the heart wall cavity. Segmentation process to obtain contour the of heart cavity, required several stages. Improve the image using a median filter to reduce speckle noise, high boost filters and morphology is used to clarify information on the part of the heart cavity. Segmentation cavity using adaptive thresholding and canny edge detection filter to the image. The results of edge detection need to be improved using the region area and collinear. Contour results obtained will be reviewed for the open and closed condition of heart cavity. An iterative process is performed, with increasing high boost filter parameter values that may affect the results of the closed contour. Based on these combinations, the accuracy contour area is 89,409%, and the results of calculation the contour similarity level approach the value of 0. The results of the research have conducted contour searches on the heart wall cavity, and future work focused on the tracking process and measurement of heart contour movements using optical flow.
Analysis of DES Plaintext Recovery Based on BP Neural Network

Backpropagation neural network algorithms are one of the most widely used algorithms in the current neural network algorithm. It uses the output error rate to estimate the error rate of the direct front layer of the output layer, so that we can get the error rate of each layer through the layer-by-layer backpropagation. The purpose of this paper is to simulate the decryption process of DES with backpropagation algorithm. By inputting a large number of plaintext and ciphertext pairs, a neural network simulator for the decryption of the target cipher is constructed, and the ciphertext given is decrypted. In this paper, how to modify the backpropagation neural network classifier and apply it to the process of building the regression analysis model is introduced in detail. The experimental results show that the final result of restoring plaintext of the neural network model built in this paper is ideal, and the fitting rate is higher than 90% compared with the true plaintext.
The accuracy comparison of vector support machine and decision tree methods in sentiment analysis

Abstract Analysis of customer satisfaction with company service is one of the tools for the company to be able to find out the lack of services and be able to know the expectations of users of the company. This is very possible in real-time, especially companies that are engaged in buying and selling online because one of the features they provide is a comment column from the user. In addition to using websites or known as e-commerce sites that can be accessed by consumers through media websites, currently online buying and selling providers can also be accessed through Google Play. Consumers download e-commerce applications and install them on their smartphones. This study aims to process user comments using Support Vector Machine (SVM) and Decision Tree algorithms to see the accuracy of the algorithm used and see positive and negative reviews. In addition to producing sentiment analysis, SVM classification algorithms and Decision Tree methods will be compared to their accuracy values in calculating datasets.
Linear methods for classification

Linear classification methods are highly prevalent in clinical neuroimaging and have been used to predict diagnosis and outcome in many brain disorders. Here, we provide a concise introduction to these methods aimed at the beginning practitioner. We introduce the two main variants: penalized linear models and probabilistic classification models, highlighting their relative strengths and weaknesses. We describe discriminative mapping, which is the ability to visualize the model coefficients in the input space and is a crucial benefit of linear models because it helps to understand which features of the data drive the predictions. We also introduce the notion of sparsity, which further assists interpretation in that it can be used to restrict the discriminative pattern to a small number of brain regions. Finally, we provide an overview of studies using linear models along with two illustrative applications using linear models to discriminate patients with autism and schizophrenia from healthy participants.
Brain Storm Optimized Swarm Collaboration for Bus Scheduling

Smart traffic control is a promising technology to exploit the available transportation resource and relieve traffic congestion in smart cities. This paper considers the bus scheduling problem, where a swarm of unmanned buses is supposed to intelligently accomplish tasks such as on-demand dynamic passenger pick-up or drop-off. Inspired by the dynamic window approach (DWA), To analyze this problem, the dynamic window approach (DWA) is adapted for the bus swarm scenario, and then an artificial sparrow model of neighborhood collaboration strategy is employed. Particularly, swarm coordination is realized by using a normalized Finsler geometric measured area instead of traditional Euclidean norm to coordinate the service area coverage among buses, which can help improve the local path planning of the bus swarm. The brain storm optimization (BSO) is further applied to enable the dynamic optimization and adjustment of bus swarm through analyzing the swarm motion, distribution, and the speed at which the individual reaches the target. Numerical results corroborate the effectiveness of the proposed detection and decision mechanism.
Facial action units recognition by de-expression residue learning

Understanding human facial expressions is one of the key steps to achieving human-computer interaction. However, the facial expression is a combination of an expressive component called facial behavior and a neutral component of a person. The most commonly used taxonomy to describe facial behaviors is the Facial Action Coding System (FACS). FACS segments the visible effects of facial muscle activation into 30+ action units (AUs). So, we introduce a method to recognize AUs by extracting information of the expressive component through a de-expression learning procedure, called De-expression Residue Learning (DeRL). Firstly, we train a Generative Adversarial Network named cGAN to filter out the expressive information and generate the corresponding neutral face image. Then, we use the intermediate layers, which contains the action unit information, to recognition AUs. Our work alleviates problems of AUs recognition based on the pixel level difference, which is unreliable due to the variation between images i.e., rotation, translation and lighting condition changes, or the feature level difference, which is also unstable as the expression information may vary according to the identity information. As for experiments, we use the data augmentation method to avoid overfitting and trained deep network to recognition AUs on CK+ datasets. The results reveal that our work achieves more competitive performance than several other popular approaches.
Handling Modifiers in Question Answering over Knowledge Graphs

Question Answering (QA) over Knowledge Graphs (KGs) has gained its momentum thanks to the spread of the Semantic Web. However, despite the abundance of methods proposed in this field, there are still many aspects that need to be fully covered. One of them is the generation of SPARQL queries with modifiers, i.e. queries that are made up not only by triple patterns but also other terms belonging to the SPARQL syntax, such as FILTER, LIMIT, COUNT, ORDER BY. This task results difficult to accomplish in a generic way since the matching with natural language is not straightforward. Few works try to address this complex issue. In this paper, we propose a new approach to handle and to generate queries containing modifiers. Our method is able to generate queries with multiple modifiers, it is easily extendable to cover new modifiers and new languages, and it is independent of the KG structure. Our approach represents an extension of an existing work called QAnswer.
Deep Knowledge Tracing Based on Bayesian Neural Network

Knowledge Tracing (KT) is the de-facto standard for inferring student knowledge from performance data, modeling students’ changing knowledge state during skill acquisition. The existing knowledge tracing models have problems such as incomplete features consideration, poor effect and easy over-fitting in the process of simulating students’ knowledge acquisition. In order to better model students’ learning and track the process of students’ knowledge acquisition, a Bayesian neural network Deep Knowledge Tracing (BDKT) is proposed. The model combines Bayesian neural network with Deep Knowledge Tracing (DKT), it can not only model feature-rich students’ behavior data, but also effectively prevent over-fitting, enhance the generalization ability of the model, and accelerate the convergence speed of the model. The above performances of the model are tested by the experiments on the data sets of KDD Cup and Assistments.
Improving the Performance of Lightweight CNN models using Minimum Enclosing Ball Regularization

The aim of this paper is two-fold. First, we propose lightweight CNN models, capable of effectively operating on-drone for various classification problems, emerging in the context of media coverage of specific sport events by drones, i.e. crowd, football player, and bicycle detection. Subsequently, we propose a regularization method, namely Minimum Enclosing Ball regularization, in order to improve the generalization ability of the proposed models. The experimental evaluation on three datasets indicates the effectiveness of the proposed regularizer.
Towards a Scalable Semantic-Based Distributed Approach for SPARQL Query Evaluation

Over the last two decades, the amount of data which has been created, published and managed using Semantic Web standards and especially via Resource Description Framework (RDF) has been increasing. As a result, efficient processing of such big RDF datasets has become challenging. Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. In this study, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets using a semantic-based partition and is implemented inside the state-of-the-art RDF processing framework: SANSA. An evaluation of the performance of our approach in processing large-scale RDF datasets is also presented. The preliminary results of the conducted experiments show that our approach can scale horizontally and perform well as compared with the previous Hadoop-based system. It is also comparable with the in-memory SPARQL query evaluators when there is less shuffling involved.
The Digitalization of Musical Instruments and Musical Practice

Facing the possibilities of digital music production and the Internet, the “digital revolution” promised to bring about new aesthetic possibilities as well as a democratization of music culture. How did the promises play out in the experience of musicians? This article traces the main developments in the musical instruments industry in the course of digitalization and discusses its effects on artistic strategies and musical practice. It argues for a differentiated view of the effects of digitalization for musicians. On the one hand, the computer expanded the artistic possibilities of musicians while it facilitated the precarization of working conditions on the other.
Study of Gesture Input Interface for Smart Device by Wrist Movement

In this research, we proposed a new input method which improved the problem of conventional character input method in smart devices. In this system only the acceleration sensor of smart devices has been utilized. Also, it has a simple and distinctive wrist movement input method. We proposed four types of input operations which are divided depending on whether the operation speed is fast or slow. The average discrimination rate was 90% in case of fast and 70% in case of slow.
Performance Analysis of Messages Queue in the Different Actor System Implementation

The level of design and implement models and platform of Internet of things products have many options when choosing the computational model. One of the fittest models for the Internet of things is the actor model due to excellent performance on internal concurrency controls, scalability, and fault-tolerance that make it a promising approach for developing concurrent systems. In this paper, we focusing on some existing different actor system implementation like Lift, Akka, and Scalaz using comparative analysis to provide an overview of the success rate of the designed actor model and overcome the challenges for quickly develop concurrent and distributed applications. The results of benchmarks will guide developers in selecting a suitable actor implementation for their application and facilitate in the development of other actor model implementation. The used benchmarks show that Scalaz has the best performance between actor system implementations as related to throughput and latency on another hand Akka Actor implementation best in Actor life cycle management. The future work will be focusing on improving a messages queue to increase the performance of the actor model implementation.
Nova-Bollinger Ni-Cu sulfide ore deposits, Fraser Zone, Western Australia: Petrology of the host intrusions sulfide-silicate textures and emplacement mechanisms of the ores

SummaryThe Nova-Bollinger Ni-Cu sulfide ore deposits are hosted within a suite of mafic-ultramafic intrusive cumulate bodies. The Lower Intrusion is a thin semi-conformable chonolith comprising unlayered mafic to ultramafic orthocumulates, hosting the bulk of the mineralisation. The much thicker overlying Upper Intrusion is bowl-shaped and modally layered with alternating peridotite and norite mesocumulate layers. A distinctive rock-type containing both orthopyroxene and olivine as cumulus phases is a characteristic of the Lower Intrusion. The intrusions differ in olivine and spinel chemistry, and in the volume of sulfides. Sector zoning in Cr content of pyroxenes is observed in the Lower Intrusion, and in the lower marginal zone of the Upper, and is attributed to crystallisation under supercooled conditions. Symplectite orthopyroxene-spinel-amphibole coronas at olivine-plagioclase contacts are attributed to near-solidus peritectic reaction between olivine, plagioclase and liquid during high pressure emplacement (>6 Kb), consistent with high Al contents in igneous pyroxenes and estimates of the peak regional metamorphism. The Upper and Lower Intrusion rocks represent cumulates from a similar parental magma, derived via multiple magma pulses, variably fractionated and undergoing sulfide saturation prior to emplacement into the deep crust at pressures of 6-10 Kb during the peak of regional metamorphism under extremely low cooling rates. The ores show a remarkable assemblage of textures indicative of emplacement into hot, soft country rocks at a large-scale melting-infiltration front. Sulfide infiltration was accompanied by partial melting of the country rock producing felsic leucosomes, some of them strongly enriched in garnet, showing close spatial association with sulfide infiltrations and veins. Coarse grained pentlandite – chalcopyrite – pyrrhotite “loop textures” are characteristic of all ore types, down to the scale of the infiltrating sulfides within the gneisses, and are regarded to be diagnostically magmatic textures generated by sulfide liquid fractionation and growth of high-T pentlandite by peritectic reaction between fractionated sulfide melt and early crystallised MSS.
On Location Privacy in Fingerprinting-based Indoor Positioning System

Due to the inadequacy of GPS signals in indoor spaces, Indoor Positioning Services (IPSs) have drawn great attention. The popular smartphone localization technique relies on a centralized server to achieve localization, allowing the server to acquire a user's location in fine granularity. To ensure the privacy of IPS users, we propose an Encrypted Indoor Positioning Service (EIPS) model that protects users' privacy from the centralized server and maintains localization accuracy simultaneously. Our EIPS model enables users to encrypt and decrypt their query through an Encryption and Decryption Server (EDS) bi-directionally in a commutative way, so the users' locations remain private to both EIPS and EDS. We also propose Query Split, Artificial Dimensions and Columns to prevent Known Plaintext Attack (KPA). Our analytical and experimental evaluations show that our model is resilient to a variety of privacy attacks without loss of efficiency and accuracy.