Reviewerly/exHarmony huggingface dataset:
Values as Defining Elements of Culture

The article considers values as one of the defining elements of culture. It is noted that culture in the value aspect is a kind of social mechanism that allows a person to navigate in the surrounding space. Value ensures the integration of society, helps a person make the right (socially approved) decision. In the course of the historical development of a society, several parallel processes take place in its culture: development, modernization and preservation, stability, and continuity. On the one hand, culture absorbs or creates new values, and on the other hand, it preserves its identity. The integrity of culture is guaranteed by social selection. Any culture retains at its core only what corresponds to its logic.
On a videoing control system based on object detection and tracking

In this paper, we propose a camera control system towards occasionally videoing preassigned objects. Based on the technique of real-time visual detection and tracking, using the Kalman filter and re-identification (ReID), we propose continuous composition of lens, based on the atomic rules of shots, and give the trajectory planning of the camera, to generate the PID controller to the pan-tilt. By both simulation and emulation by frame-wise cropping of video clips, we illustrate the efficiency of this method. Based on this model, we design and produce an AI automatic camera for lively photography and clip videoing.
Distribution System Anomaly Detection Based on AnoGAN Embedded with Cross-Stitch Units

We consider the anomaly detection problem in distribution systems. Many distribution systems are not observable due to their limited numbers of real-time meters, thus state estimation based approaches are not applicable. Moreover, the number of abnormal samples are usually small comparing to the diversity of anomalies, imposing challenges to machine learning based methods as well. To this end, AnoGAN, an unsupervised learning method, is applied to the distribution system anomaly detection in this paper. Only normal samples are needed during its training process. In the testing process, it generates corresponding samples based on its knowledge trained from normal ones, which are compared against real measurements to detect if there are possible anomalies. To achieve better granularity, we propose to partition a large distribution system into sub-networks, establish parallel AnoGANs, and employ Cross-stitch units to incorporate their correlations. Simulations have been done to show the satisfactory accuracy and efficiency of the proposed approach in detecting anomalies in distribution system.
Study of Fault Diagnosis Distribution Network Based on Rough Set and Artificial Intelligence

Abstract The current real-time data collected by the power grid includes remote measurement, remote signaling, and other fault factors. The research in this paper is based on the fault diagnosis technology of rough set theory and self-learning theory, taking fault influencing factors as conditional attributes, fault type as decision-making attribute, and generating rough set rule table through self-learning reduction through a large number of fault history records, the influence of meteorological factors, environmental factors, equipment factors and other factors on the probability of equipment failure, which can realize the route Risk level prediction and fault location. Case analysis shows that the distribution network diagnosis technology based on rough set plays an extremely important role in predicting the risk level of each section of the line and positioning after a fault occurs.
Pedestrian Detection in Crowded Crowd Scene Based on Deep Learning

Abstract Detection-based methods typically detect and locate each person on a crowd image by using a designed pedestrian target detector and obtain counting results by accumulating each detected person. However, these methods require a large amount of computational resources and are often limited by human occlusion and complex background in real scenes, resulting in inaccurate detection. Based on the characteristics of computer depth learning and population density distribution map, a more optimized pedestrian detection approach is proposed.
Research on the Application of Classical Literature Based on Computer Retrieval

Abstract In recent years, computer information technology has brought great impact on the traditional life model and has also brought great influence to the traditional academic research model. At present, in the field of “text”, the most important thing is the data mining based on big data, that is, the direction of computer retrieval. In the face of data mining, there are two ideas: either to manually make enough rules to break unstructured text into semi-structured or structured text that machines can understand, or to make machines more and more intelligent through deep learning. This is of course the trend of scientific and technological development. In this general trend, as researchers, we should also study different styles with a positive attitude. For example, the analysis platform of Yuefu poetry can be developed according to the key elements of Yuefu poem, such as title, usage, skill, tune and style. In view of the unique writing style of epitaphs and letters, data mining can be carried out on such styles, and the generated knowledge pedigree is also of great benefit to the related research of other styles.
Protection method of continuous location uploading based on local differential privacy

Research on the protection of location privacy of mobile users is now receiving widespread attention. Many scholars have proposed many methods to protect user's location. However, it is still difficult to ensure that the user's location information is not leaked when the user's location is continuously uploaded. In response to these problems, a location privacy protection method based on LDP is proposed. The two-dimensional map is projected into one-dimensional space through the Hilbert curve, and the sub-areas are dynamically divided according to the number of users in the area; the RAPPOR is used to make sure the user's location information is disturbed and then uploaded to the server. In the case of continuous upload, the server can obtain the distribution of users in real time, but cannot know the precise location data of each user. Through experiments on real data sets and simulation experiments, the results prove that the LDP-based location privacy protection method can effectively protect the continuously uploaded user location information from being leaked.
PaintPath: Defining Path Directionality in Maps for Autonomous Ground Vehicles

Directionality in path planning is essential for efficient autonomous navigation in a number of real-world environments. In many map-based navigation scenarios, the viable path from a given point A to point B is not the same as the viable path from B to A. We present a method that automatically incorporates preferred navigation directionality into a path planning costmap. This `preference' is represented by coloured paths in the costmap. The colourisation is obtained based on an analysis of the driving trajectory generated by the robot as it navigates through the environment. Hence, our method augments this driving trajectory by intelligently colouring it according to the orientation of the robot during the run. Creating an analogy between the vehicle orientation angle and the hue angle in the Hue-Saturation-Value colour space, the method uses the hue, saturation and value components to encode the direction, directionality and scalar cost, respectively, into a costmap image. We describe a costing function to be used by the A* algorithm to incorporate this information to plan direction-aware vehicle paths. Our experiments with LiDAR-based localisation and autonomous driving in real environments illustrate the applicability of the method.
ISO Setting Estimation Based on Convolutional Neural Network and its Application in Image Forensics

The ISO setting, which is also known as film speed, influences the noise characteristics of output images. As a consequence, it plays an important role in noise based forensics. Whenever the ISO setting information cannot be retrieved from the image metadata, estimating the ISO setting of a probe image from its content is of forensic significance. In this work, we propose a convolutional neural network, called ISONet, for ISO setting estimation. The proposed ISONet can successfully infer the ISO setting both globally (image-level) and locally (patch-level). It not only works on uncompressed images, but also is effective on JPEG compressed images. We apply the ISONet on two typical forensic scenarios, one is the image splicing localization and the other is the Photo Response Non-Uniformity (PRNU) correlation prediction. A series of experiments show that the ISONet can yield a remarkable improvement in both forensic scenarios.
Benefits of Small Size Classes in Graduate Software Engineering Education

Graduate education presents unique challenges for both instructors and students. One challenge is that of the graduate course enrollment numbers; wherein small size classes can be as low as one student. In such course, the use of traditional classroom lectures for the conveyance of theoretical knowledge has been known to be less effective than in large size classes. A pedagogical approach used in low enrollment graduate software engineering courses is that of a project-based approach. Project- based teaching is implemented in multiple forms. In the spring semester of 2019, a graduate-level course in software engineering formal specification methods at an institution in the US had an enrollment of one student. The prior offerings of the course facilitated: classroom style delivery of theory, team sessions for project guidance. This report documents the research depth achievable in this approach, towards the production of industrial- quality work product.
Enabling Secret Key Distribution Over Screen-to-Camera Channel Leveraging Color Shift Property

Recent years witnessed the emergence of visible light communication (VLC) over screen-to-camera channel, such as barcode and unobtrusive optical pattern, due to the widely adoption of screen and camera in plenty of electronic devices. The prevalence of wide viewing angle screen and high standard cameras also imposes great threat for visible light communication, and the information leakage over screen-to-camera channel has been rarely explored. In this paper, we propose a secret key distribution system leveraging the unique color shift property over screen-to-camera channel. To facilitate such design, two practical secret key distribution methods, key matching-based and nearest next hop-based, are developed to map the secret key into a unique optical pattern on screen, which can only be correctly decoded by the legitimate user situated at an accessible region. We also provide theoretical analysis on the security of both methods. The performance of the proposed system is implemented with off-the-shelf devices and validated under various experimental scenarios. The results demonstrate that our system can achieve high bit-decoding accuracy for the legitimate users while maintaining low recovery accuracy for the attackers.
Exploiting Radon Features for Image Retrieval

Radon transform is one of the features used in image reconstruction from the early days of image processing, later it has been used in many applications of astronomy and other fields, such feature is explored for the process of matching and retrieval of images. The proposed system has 4 phases, pre-processing, feature extraction, knowledgebase construction and image retrieval. The local features such as sum, mean, standard deviation, & the eigen values are extracted for each radon transformed image and stored in a knowledgebase. Cityblock distance measure and query-by-image method are employed for matching and retrieval of images respectively and the accuracy of 87% is achieved.
Using Cultural Algorithms with Common Value Auctions to Provide Sustainability in Complex Dynamic Environments

In Computation intelligence algorithm performance is crucial especially when the complexity of the system increases and becomes chaotic (un-predictable). In Cultural Systems many algorithms are able to predict the system performance as the complexity is linear, or non-linear. However, when it is chaotic the prediction quality decreases dramatically. In this paper, we are show that Common Value Auctions are able to distribute sufficient information through the system in order to sustain the prediction rate even on the edge of chaos. This sustainability is expressed here in terms of increased resilience and robustness. Systems that rely on wisdom of the crowd based approaches are shown not to do as well when environmental change goes from linear to non-linear, and finally to chaotic.
From Text to Data

This article outlines our practical approach to digitizing historical text sources via Optical Character Recognition (OCR) for subsequent Natural Language Processing (NLP) and corpus analysis. For this purpose we developed two processing pipelines based on pyFlow for parallel computing using Tesseract OCR for OCR and spaCy for NLP. To ensure that the software is developed in terms of reusability and sustainability, the importance of free and open source software and the use of Linux containers via Docker during the development process and within a production environment is also described. Details regarding OCR preprocessing steps, e.g., binarization are also discussed. Besides the software development aspects the article contains some learnings and best practices for end users on how to create high quality input images (avoiding noise, skew, etc.) for the OCR pipeline. By following these steps, OCR results can be significantly improved. For both, our OCR and NLP pipelines, the accuracies and respective error rates are discussed at the ennd of the corresponding chapters.
Machine Computing Function Designing for Creative Thinking

With a case study of humanoid resolving mathematics application problems in primary school, this paper discusses the basic theoretical method of creative thinking. This paper holds that the memory and analogy “teaching and learning” mode is the main way to realize machine intelligence. Through certain granularity knowledge splitting and analogy reorganization, creative thinking can be realized by machine computing. By the design and implementation of the creative thinking exploration IDE, this paper realizes the basic creative thinking process by analogy. Firstly, natural language semantic representing and matching are carried out with scene framework; Secondly, machine analysis of problem resolving is carried out; Then, algorithm is used to simulate the running process of thinking; finally, the main realization challenges of the creative thinking are prospected.
Infinite Classes of Six-Weight Linear Codes Derived from Weakly Regular Plateaued Functions

The construction of (minimal) linear codes has received much attention, especially from cryptographic functions owing to the desirable properties of these functions. One application of minimal codes is the design of secret sharing schemes, which is a vital research topic of cryptography and has widely used in information protection. In the present paper, to propose new minimal linear codes we generalize the very recent construction method presented by Xu, Qu, and Luo at the international conference SETA-2020 for weakly regular plateaued unbalanced functions over odd characteristic finite fields. We derive six-weight minimal linear codes from the set of pre-image of these functions. We also construct two new infinite classes of six-weight minimal linear codes from weakly regular bent and plateaued unbalanced functions by proposing two different subsets of the pre-image of these functions.
Onboard Data Reduction for Multispectral and Hyperspectral Images via Cloud Screening

In this paper we propose a lossless and lossy onboard compression algorithm for multispectral and hyperspectral images, based on the recent CCSDS-123.0-B-2 standard, which takes advantage of cloud screening in order to perform data volume reduction, by avoiding to transmit pixels that are covered by clouds. In particular, we develop methods addressing two problems: i) how to signal the cloud mask in the compressed file, and ii) how to handle cloudy pixels in order to maximize the amount of compression. Experimental results on a set of LANDSAT 8 ETM+ and AVIRIS images show a significant data volume reduction with respect to the plain use of the CCSDS-123.0-B-2 standard.
Using UML class diagrams for content ontology design patterns engineering

Abstract The paper presents a method for Ontology Design Patterns (ODP) engineering based on the transformation of conceptual models serialized in the XML-like formats. In this paper, we use UML class diagrams as the source of conceptual models. The method includes the following activities: design and serialization of a source conceptual domain model; analysis and transformation of an XML structure of a source conceptual model to an ontological schema model; transformation of an ontological schema model to a content ODP code in the OWL 2 DL format; verification and modification of obtained content ODPs by domain experts. All transformations are implemented with the use of original domain-specific language, namely, Transformation Model Representation Language (TMRL). The proposed method is used for ODPs engineering in tasks of industrial safety inspection of technical systems. Obtained ODPs can be used for knowledge bases and intelligent systems engineering.
LWIR hyperspectral mapping of the Gamsberg deposit, Aggeneys, South Africa

A LWIR hyperspectral airborne campaign was carried out to map the lithology and to detect the zinc mineralization of the Gamsberg at the Northern Cape in South Africa. Therefore, L WIR data was acquired with a GSD of 2.5 meter in July/ August 2015. The data preprocessing included the orthorectification, atmospheric correction as well as a temperature and emissivity separation approach. The lithological mapping and detection based on an endmember procedure extracting 11 representative endmember and a spectral classification using the spectral angle mapper. The final result shows a detailed lithological map with better differentiation of the schist into three subunits as compared to the geological map. The gossan, an indicator for the mineralization, was detected and mapped as well. This is in good agreement with existing field findings and demonstrated the potential of L WIR hyperspectral data to support exploration activities.
Q-learning in Continuous Action Space by Extending EVA

Today, in order to use reinforcement learning in the real world, we need to improve the efficiency of reinforcement learning in continuous action spaces. In this study, we propose Q-learning in continuous action space based on an agent's past series of experiences (i.e., trajectory information). We evaluate the effectiveness of our proposed method by conducting comparative experiments on several tasks.
A Novel Probabilistic Visual Cryptography Scheme Using EDBS for Grayscale Image

The reconstructed image of probabilistic visual cryptography (VC) is inevitably accompanied by the loss of secret image information and degradation of visual quality. Since halftone technique can realistically simulate grayscale continuous tone image with a binary image, in this paper, by combining VC sharing with grayscale image halftone technique, we propose a probabilistic VC scheme for grayscale image using EDBS (Efficient Direct Binary Search) to improve the quality of the reconstructed image. The probabilistic VC sharing is adopted into the EDBS halftone process, and the reconstructed image is taken as the optimization object. Through local optimizations and global iterations, the optimal reconstructed image is finally achieved under the established optimization strategy. Theoretically the proposed scheme possesses the same security strength as the traditional VC and experimentally the comparative analysis verifies the effectiveness of this scheme.
Pengembangan Media Stopmotion Berbantuan Storytelling Pada Pembelajaran IPS SD

Social studies learning problem is an urgent problem and it is necessary to immediately find alternative solutions to problems to improve and improving the quality of social studies learning at SDN 2 Pangenjurutengah. Research that has the aim of increasing the motivation for learning Social Sciences at SD Negeri 2 Pangenjurutengah is increasing. To overcome these learning problems, it is necessary to apply innovative learning models that can increase student motivation including teacher skills, student activities, and student learning outcomes that encourage active student involvement in learning. The subjects studied in this study were SD Negeri 2 Pangenjurutengah, Class IV 2018/2019 Academic Year. The type of methods use in this research is development or Research and Development (R&amp;D). The resulting product is social studies learning with stopmotion media assisted by storytelling consisting of learning media, teacher's books and students. The instrument used was tested for validity and reliability testing. Before analyzing the data, the analysis prerequisite test was carried out which consisted of a normality test. The results of the study obtained a validation score of 77.6% of material experts and a score of 78.9% of media experts. Data collection using a questionnaire in the learning model, usability, media design, material coverage of Elementary School Social Sciences obtained by the prerequisite analysis test, namely the normality test getting a score of 0.546&gt; 0.05 (Kolmogorov Smirnov table) so that the distribution is normal. Hypothesis testing or t test get sig.0.000 &lt;0.05 and t count 0.76 &lt;1.69 (t table). From the results of the research conducted, it can be concluded that the stopmotion media is to increase the motivation of elementary school students in social studies learning.
A Method of Mining Visualization Rules from Open Online Text for Situation Aware Business Chart Recommendation

Selecting business charts based on the nature of the data and the purpose of the visualization is useful in business analysis. However, current visualization tools lack the ability to help choose the right business chart for the context. Also, soliciting expert help about visualization methods for every analysis is inefficient. Therefore, the purpose of this study is to propose an accessible method to improve business chart productivity by creating rules for selecting business charts from online published documents. To this end, Korean, English, and Chinese unstructured data describing business charts were collected from the Internet, and the relationships between the contexts and the business charts were calculated using TF-IDF. We also used a Galois lattice to create rules for business chart selection. In order to evaluate the adequacy of the rules generated by the proposed method, experiments were conducted on experimental and control groups. The results confirmed that meaningful rules were extracted by the proposed method. To the best of our knowledge, this is the first study to recommend customizing business charts through open unstructured data analysis and to propose a method that enables efficient selection of business charts for office workers without expert assistance. This method should be useful for staff training by recommending business charts based on the document that he/she is working on.