Reviewerly/exHarmony huggingface dataset:
Forensic Analysis of 2GIS Navigation App Installed on an Android- Based Smartphone

The popularity of Android-based smartphones propelled their wide-spreading among the users. The functionality of these products allows recording and storing various types of information about their owners, including their location. Because of the value of such information for computer analysis, the issue of obtaining and using geodata by smartphone applications is of academic and practical interest. This paper presents the results of examining the content of files generated by the 2GIS application on Android-based smartphones. 2GIS is the map and infrastructure handbook that may be used both online and offline. By accessing these files, one can obtain data on the user, restore the history of the user's work with the application, e.g. routing, searching for the mapped objects, receiving information on previewed objects. It was also found that, discerning whether the user had actually traveled to a destination from the mapping application data was possible. The analysis of the obtained results is of interest to computer forensics.
Wind Energy Forecast Conditioned on Gro&amp;#223;wetterlage (large scale weather situation)

&amp;lt;p&amp;gt;Obtaining a quantitative measure for the uncertainty of forecasts for renewable energy has proven to be a challenging problem in the past. We present results on predicting uncertainty of a forecast conditioned on the large weather situation (Gro&amp;amp;#223;wetterlage). As a first attempt, we use the objective weather classification by the German Meteorological Service (DWD), which sorts the weather into 40 situations based on wind direction, cyclonality and moisture in the atmosphere.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The considered forecasts concern the day-ahead production of solar power for two exemplary solar parks. To quantify the uncertainty, we define five different metrics (based on normalized absolute error and probability distribution), where each one is trained individually using machine learning. As a result, we obtain measures for over- and underprediction conditioned on the said Gro&amp;amp;#223;wetterlage.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We consider this to be a very promising yet accessible approach to derive a quantitative measure for uncertainties based on the current, day-to-day weather situation. Future work may concern an improvement of the Gro&amp;amp;#223;wetterlagencharacterization and a general, probabilistic formulation of the problem, e.g. using Bayesian inference.&amp;lt;/p&amp;gt;
From the Philosophies of Geographies to the Applied Ontology of Geography

This chapter pursues two main goals. The first one is to explore the domain of investigation of applied ontologyOntologyof geography of geographyApplied ontology of geography, by providing an overview of the mutual interactions among the disciplines encompassed in the domain, namely philosophy, geography, and computer science. The second goal is to reveal the disconnections, by delineating some possible strategies designed to increase the interdisciplinary dialogue. In accordance with such goals, Sects. 1.1–1.5 respectively examine the connections between philosophy and geography, philosophy and geographies, philosophicalOntologyphilosophical ontologies and geographies, computer science and geographies, and philosophical ontologies and IT/computerOntologyIT/computer ontologies. Section 1.6 acknowledges that the domain of research of applied ontology of geographyApplied ontology of geography should include, at least, two different kindsKinds of geography: empiricalGeographyempirical geography and academicGeographyacademic geography. Then, Sects. 1.7 and 1.8 point out that philosophical and IT/computer ontologies are more connected to empirical geography than to academic geography. Finally, Sect. 1.9 explores the reasons why academic geography is currently disconnected to the (philosophical) ontological debate, and then Sects. 1.10 and 1.11 outline some possible strategies to provide a way out of the disconnections.
GCN-ICF: Graph Convolution Networks for Item-based Recommendation

Item-based Collaborative Filtering (ICF) is wildly used recommendation method. Under the assumption that a user tends to choose an item similar to what she has interacted before, ICF uses items that a user has interacted to learn a user's profile, and then makes recommendations by calculating the similarity between a target item and the user's profile. Therefore, the method of learning item representations from data is crucial in ICF. However, most existing ICF methods only consider the low-order connectivity between a target item and a target user's interacted items. The consideration limits the improvement of recommendation qualities. In this paper, by using the message-passing idea of Graph Convolution Networks (GCN), we propose GCN-ICF to capture the high-order connectivity between users and items in the user-item interaction bipartite graph structure. The method enriches the representation of each item through its neighbors in the bipartite graph structure. The experimental results on three public datasets verify the highly positive effect of the performance of the GCN-ICF method.
Analysis of Aviation Industry for Aggressive Decision Making

Big Data Analytics is very useful for the business users and data scientists. It is very useful to take better, faster and right decision for the organization. Organizations and individuals should exhibit the circumspection while utilizing Big Data. In this work we intend to develop a methodology for getting ethical access of big data and ethically scrutinize it to attain the business objectives. We consider the case study of aviation sector, formulate some questions to upraise the system. We attain the ethical permission from twitter for this purpose. We consider the tweets of general public as they were posted in public areas and falls under informed consent category.
Paragraph Boundary Recognition in Novels for Story Understanding

The understanding of narrative stories by computer is an important task for their automatic generation. To date, high-performance neural-network technologies such as BERT have been applied to tasks such as the Story Cloze Test and Story Completion. In this study, we focus on the text segmentation of novels into paragraphs, which is an important writing technique for readers to deepen their understanding of the texts. This type of segmentation, which we call “paragraph boundary recognition”, can be considered to be a binary classification problem in terms of the presence or absence of a boundary, such as a paragraph between target sentences. However, in this case, the data imbalance becomes a bottleneck because the number of paragraphs is generally smaller than the number of sentences. To deal with this problem, we introduced several cost-sensitive loss functions, namely. focal loss, dice loss, and anchor loss, which were robust for imbalanced classification in BERT. In addition, introducing the threshold-moving technique into the model was effective in estimating paragraph boundaries. As a result of the experiment on three newly created datasets, BERT with dice loss and threshold moving obtained a higher F1 than the original BERT had using cross-entropy loss as its loss function (76% to 80%, 50% to 54%, 59% to 63%).
Path-following cooperative control strategies for driverless vehicles

In order to improve the tracking accuracy of unmanned vehicle paths, while taking into account steering frequency and vehicle stability, and to be able to identify nonlinear road realities, a comprehensive collaborative governance strategy for unmanned vehicle driving is proposed, taking into account real-time influencing factors on the road as far as possible. On the basis of lateral trajectory following control, a driver operation discrimination module is introduced, a vehicle dynamics model and a tyre model are established, and an upper trajectory following lateral controller is designed using strong robust structural control theory based on the assumption of steady-state circular motion, optimal pre-sighting theory. The fuzzy controller with gain adjustment and the underlying actuator controller are used to reduce the sliding mode controller jitter in the automatic steering mode and to reduce the error of the theoretical analysis. For the ESP system, a more robust non-linearised fuzzy PID control strategy is used to improve it.
Development of an Integrated Complex of Knowledge Base and Tools of Expert Systems for Assessing Knowledge of Students in Mathematics within the Framework of a Hybrid Intelligent Learning Environment

The paper is devoted to the development of instrumental and methodological support for the assessment of students' knowledge in mathematics in the framework of a hybrid intellectual learning environment. According to the structural scheme of the model of intellectual assessment of knowledge and creative potential of students in mathematics, the structure of the corresponding knowledge base is formalized. Knowledge base within the framework of data mining methods to create a module for monitoring and evaluating knowledge in mathematics has been developed. To model the educational process the authors propose methods for solving clustering, dimensionality reduction, and classification problems based on machine learning and artificial neural networks. The basics for the development of integrated knowledge base complexes and artificial intelligence tools for the educational process are presented. The obtained results are aimed at creating methods which ensure the processes of operational training, control and evaluation of knowledge.
Occam: Optimal Data Reuse for Convolutional Neural Networks

Convolutional neural networks (CNNs) are emerging as powerful tools for image processing in important commercial applications. We focus on the important problem of improving the latency of image recognition. While CNNs are highly amenable to prefetching and multithreading to avoid memory latency issues, CNNs’ large data – each layer’s input, filters, and output – poses a memory bandwidth problem. While previous work captures only some of the enormous data reuse, full reuse implies that the initial input image and filters are read once from off-chip and the final output is written once off-chip without spilling the intermediate layers’ data to off-chip. We propose Occam to capture full reuse via four contributions. First, we identify the necessary conditions for full reuse. Second, we identify the dependence closure as the sufficient condition to capture full reuse using the least on-chip memory. Third, because the dependence closure is often too large to fit in on-chip memory, we propose a dynamic programming algorithm that optimally partitions a given CNN to guarantee the least off-chip traffic at the partition boundaries for a given on-chip capacity. While tiling is well-known, our contribution determines the optimal cross-layer tiles. Occam’s partitions reside on different chips, forming a pipeline so that a partition’s filters and dependence closure remain on-chip as different images pass through (i.e., each partition incurs off-chip traffic only for its inputs and outputs). Finally, because the optimal partitions may result in an unbalanced pipeline, we propose staggered asynchronous pipelines (STAPs) that replicate bottleneck stages to improve throughput by staggering mini-batches across replicas. Importantly, STAPs achieve balanced pipelines without changing Occam’s optimal partitioning. Our simulations show that, on average, Occam cuts off-chip transfers by 21× and achieves 2.04× and 1.21× better performance, and 33% better energy than the base case, respectively. Using a field-programmable gate array (FPGA) implementation, Occam performs 6.1× and 1.5× better, on average, than the base case and Layer Fusion, respectively.
Research libraries as digital publishers: The foreign experience

The author discusses the current experience of building the service of digital science publishing obtained by the libraries in the Western countries. She emphasizes that this process is incorporated into further informatization of libraries and their increasing role in managing science data. The digital publications integrate a number of interrelated programs comprising the whole cycle of scientific data management accomplished within the wider context of innovations. Digital publishing is a new type of library activities; it requires integrating competences of modern librarians (i.e. content selection, data supervising, metadata management, building digital collections, their preservation and archiving) and publishers (monitoring new trends in science and technology, selecting materials for publication, abstracting, scientific editing, developing marketing strategies). For the first time in the domestic library studies, the dynamics of this service in foreign countries is examined based on Library Publishing Directory for 2013– 2018. The author compares digital publishing services in four university libraries in different world regions and offers the findings of preliminary analysis of online publication services in foreign research libraries.
The Concept of Fusion for Clear Vision of Hazy Roads in ADAS

Automatic Driver Assistance System (ADAS) is a concept to have an innovative automatic driver which is growing rapidly now-a-days. Even though an ADAS system is functioning automatically without the necessity of driver in presence, it is not considered as completely autonomous car. In some of the practical situations, it is more typical to handle few complications in driving. So, the latest technologies have not grown to the level of creating fully autonomous driving system. The ADAS is developed to avoid the road accidents due to human errors. There are various ADAS technologies developed so-far to provide driver safety and comfort. Different control units are embedded in to the entire system for full control of the driving system. One of the control unit is Pedestrian Crash Avoidance Mitigation (PCAM). And one of the most difficult parts to assist the PCAM control unit is during the winter season when there is huge fog or haziness on the roads. So, an additional processing unit should be developed to get proper assistance to the ADAS system to avoid the crash during pedestrian crossing the road. For this purpose, a special imaging system should be introduced for better visualization of roads, nearby vehicles and people crossing the roads. Infrared imaging camera is additionally used with the normal camera system. So that the two images were processed with a concept of fusion, which combines the best properties of two images, and produce a clear vision of scene. This may improve the effective improvement in the PCAM control unit and enabling the autonomous cars to be driven successfully even in the heavy hazed areas without any crashes or accidents.
Build Italian-Chinese Parallel Sentence Corpus to Implement Neural Machine Translation

The cooperation in infrastructure, economics between China and Italy is deepen and cultural exchanges are more closely related, thus the demand for Italian-Chinese translation will increase greatly. As neural machine translation made great progress in solving cross-language obstacles, we would like to design and implement a neural machine translation system from Italian to Chinese. The main work of this paper includes the construction of an Italian-Chinese parallel sentence corpus, the training of the Italian-Chinese neural machine translation model, and the implementation of a web-based machine translation service. Our parallel corpus and the web service of machine translation can be further used to assist bilingual teaching, reducing the burden of artificial translation, and to provide reference for further research in related technologies.
An Optimized Possibilistic C-means Clustering Image Segmentation Algorithm Based on Spatial Neighborhood Information

The spatial neighborhood information is very important for rejecting the influence of noise. But for most clustering algorithms, there is no neighborhood information contained in the membership functions. They are prone to produce bad segmentation results when the gray scale of each pixel is directly utilized as the input. In this paper, an optimized possibilistic c-means clustering image segmentation algorithm (CPCM_S) is proposed. In the process of optimization, the proposed CPCM_S algorithm optimizes the typicality values of pixels in the iterative process by utilizing the neighborhood information of each pixel in the image to correct some typicality values. The algorithm first calculates the typicality value using the CPCM algorithm, and then introduces neighborhood information to the calculation of the typicality value to further modify the typicality value, thereby facilitating the separation of the background and the target object, thus improving the ability of typicality values to characterize pixel correlation and improve performance of image segmentation. Experimental results and analysis show that the proposed algorithm can separate the target and background clearly for noisy images, especially for small target images compared with FCM algorithm and CPCM algorithm.
Aesthetic Evaluation of Text Superposition Based on Balance and Symmetry

Based on the image aesthetics quality evaluation algorithm, combined with the short video applications that have been widely circulated in recent years, this paper uses the algorithm to filter the key frames corresponding to the shot switching in the short video, and superimposes the text in the dimensions of balance and symmetry to make the video cover. After studying the influence of symmetry and balance on the aesthetic attraction of text superimposed images, the consistency between the algorithm and the subjective evaluation of the subjects was discussed. The trial invited 26 participants and used the Likert scale for subjective evaluation. The research results show that the results generated by the algorithm are consistent with some conclusions of the subjective evaluation.
Research on VSLAM of UAV in Coal Mine Based on ROS

Given the severe problem of autonomous navigation when UAV can not get external assistance in the complex environment of the coal mine, this paper proposes an autonomous UAV scheme which runs VSLAM algorithm under the framework of ROS, and can independently complete the positioning and navigation tasks. By studying the principle of VSLAM algorithm and the frontier algorithm orb-slam, this paper designs and builds an autonomous navigation UAV platform. According to the scheme and characteristics of the platform, the visual positioning part is improved.
High-Low Spatial Frequency Feature for Siamese Network Visual Tracking

To alleviate the ResNet extractors issue, we propose a new ResNet framework with the high-frequency sampling convolution and the low-frequency sampling convolution for Siamese visual tracking, which is to replace AlexNet extractors of Siamese trackers. First, we introduce low frequency sampling convolution to extract global rough features. Second, exchanging the information of output features enables each layer to synchronize multi-scale information, which ensures that the subsequent features can retain the detail information of high-frequency features and the smooth appearance information of low-frequency features. That avoids the imbalance of deep features extraction information. Compared with the original version of the Siamese network trackers, our version Siamese trackers: SiamFC+ and SiamRPN+ get an improved performance in OTB and VOT.
Apples and Oranges: Comparing Crafty Sonic Circuits for Electronics Education

The Apple Amp and Orange Synth are solderless craft-based sonic teaching circuits developed to support a remote STEAM summer camp for 8 - 11 year old girls. In this paper, we use autoethnographic reflection and thematic analysis to compare and contrast the functionality and pedagogical applications of these two circuits. We explore how the circuits could facilitate complementary approaches to electronics education by pairing more and less predictable sonic outcomes. We further consider how the circuits and scaffolding activities might be redesigned to better align with goals of constructivist and constructionist learning environments.
Towards Overlapped Objects Recognition Using Capsule Network

Many approaches have proposed successful systems to recognize objects from an image. However, Overlapped objects recognition is still a main challenge for all kinds of objects. It is difficult for a machine to recognize two or more objects that partly hide each other. On the other hand, the new artificial neural network capsule network (CapsNet) has shown promising results in recognizing overlapped digits from multi-MNIST dataset. However, CapsNet has achieved low performance when it recognizes an object from an image with complex background. In this paper, we propose a contribution containing GrabCut to extract foreground from background, and then use CapsNet to recognize the overlapped foreground. As a result, we will address the overlapped objects recognition challenge and the complex background limit of CapsNet.
Object detection using improved YOLOv3-tiny based on pyramid pooling

Research on object detection algorithms with higher accuracy and faster detection speed is currently the main concern. In order to improve detection performance, an improved object detection algorithm using YOLOv3-tiny based on pyramid pooling is proposed. First, an improved pyramid pooling module using adaptive average pooling is designed to efficiently extract global feature information, and then combine the module with YOLOv3-tiny to explore the impact of different combinations on the detection results. The experiment used PASCAL VOC2007 trainval and all PASCAL VOC2012 for training and validation, and used PASCAL VOC2007 test for testing. Experimental results show that the proposed network improves mAP by 1.8% compared to YOLOv3-tiny while the detection speed is almost the same, which better achieves the balance of detection speed and accuracy.
DWT-Based Steganography for Image Transmission

Due to internet development, data transfer becomes faster and easier to transmit and receive different data types. The possibility of data loss or data modification by a third party is high. So, designing a model that allows stakeholders to share their data confidently over the internet is urgent. Steganography is a term used to hide information and an attempt to conceal the existence of embedded information in different types of multimedia. In this chapter, a steganography model is proposed to embed an image into a cover image based on DWT approach as the first phase. Then, the embedded secret image is extracted from the stego-image as the second phase. Model performance was evaluated based on signal noise ratio (SNR), PSNR, and MSE (mean square error). The proposed steganographic model based on DWT is implemented to hide confidential images about a nuclear reactor and military devices. The findings indicate that the proposed model provides a relatively high embedding payload with no visual distortion in the stego-image. It improves the security and maintains the hidden image correctness.
Quantity Analysis on the Chaining of Repetition Free Words Considering the VDE Composition Rule

The motivation behind this contribution was raised during the examination of the boundary value patterns of the Virtual Dictionary Extension (VDE) compression method, which is a re-weighted extension of the lossless, universal compression method, LZW. During the VDE compression method words are stored in the so called compression dictionary, therefore the base unit of the boundary value pattern is the word. The application of VDE is reasonable over patterns where the relatively long base fragments are recurring with short deviance at the end. In our earlier work we found and proved the existence of the pattern which results the best compression ratio. However, determination of the input pattern, which results the worst compression ratio (or worst case), is much more complicated, since it depends on the recursive extension of words.