Reviewerly/exHarmony huggingface dataset:
Deepfaking it: experiments in generative, adversarial multispectral remote sensing

In this work we utilize generative adversarial networks (GANs) to synthesize realistic transformations for remote sensing imagery in the multispectral domain. Despite the apparent perceptual realism of the transformed images at a first glance, we show that a deep learning classifier can very easily be trained to differentiate between real and GAN-generated images, likely due to subtle but pervasive artifacts introduced by the GAN during the synthesis process. We also show that a very low-amplitude adversarial attack can easily fool the aforementioned deep learning classifier, although these types of attacks can be partially mitigated via adversarial training. Finally, we explore the features utilized by the classifier to differentiate real images from GAN-generated ones, and how adversarial training causes the classifier to focus on different, lower-frequency features.
Facial Composite Sketch Matching to Images Applying Convolutional Neural Network

This research paper aimed to test the performance of developed facial matching composite sketch by focusing on image processing using convolutional neural network (CNN) and compared its accuracy with the Forensic Sketch Matching to Mug shots using Scale-Invariant Feature Transform. This study will help law enforcement officials in finding suspected criminals with forensic sketches through the available mug shots. Previous studies shown that using CNN models for visible spectrum face recognition improved the heterogeneous face recognition. The researchers used experimental approach as its methodology to test the performance of the developed facial recognition application in terms of its precision, recall and f-measure. Data sets of 75 frontal view images and its corresponding facial composite sketch was feed and processed in the developed facial matching application. T-test was used to test the significant difference of the two application. The results of performance are as follows: precision - 86%; recall - 89%; and f1-score - 87% and its accuracy is 89%. The computed t value 4.9206 which is higher than the t critical value of 1.9925 means it failed to accept the null hypothesis. Therefore, there is a significant difference between the accuracy of the two applications. This study proved that CNN has a better performance in matching sketch images to its mugs shots.
Effects of Inter-Frame Coding on Video Sequences Containing Temporal Activity

In this era, technology has played a vital role to link two people from a large distances via video conferencing through wireless channels. Bandwidth of the wireless channel is limited for communication, so in this regard compression becomes necessary. Compression is an integral part of video coding as it reduces the size of the video at a cost of some quality lost. Every video contains temporal activity, it is vital to study the temporal analysis before compression. Two different frame within the same video sequence contain a lot of redundant information. Due to the process of compression, video sequence is distorted. This distortion tends to increase if the video contain more temporal activity and reduces if it contain less. There are certain types of distortion that corrupts the signal such as ringing, ghosting, etc. It is important to keep an eye on the Frame rate (FPS) because a system needs to process each frames at a rate of 1/(number of frames) in a second. Compression algorithm requires extensive hardware to encode the motion vector information along with the residual frame. The difference mean opinion score (DMOS) that were obtained from the masses obtained resembles the results obtained after monitoring the motion activity in the sequence before compression and decompression.
Standards and Interoperability

Standards are documented consensus agreements containing safety, technical specifications, or other precise criteria to be used consistently as rules, guidelines, or definitions of characteristics for materials, products, processes, and services. Standards are powerful tools that can help drive innovation and increase productivity. Standards on the modeling, classification, description, discovery and dissemination of the agriculture science data plays an important role to ensure the quality of, interoperability between, and easy access to the agriculture data that are independently collected, processed, and shared in each nation, state, county or even city.This chapter briefly introduces International Organization for Standardization (ISO), the Open Geospatial Consortium (OGC), European Committee for Standardization (CEN), and The American National Standards Institute (ANSI) as representative international, regional and national standard organizations. With a focus on information technology related standards that closely apply to agriculture activities, it further presents standards on data content or encoding, metadata, and data services.
DDSnet: A Deep Document Segmentation with Hybrid Blocks Architecture Network

In recent years, the development of document segmentation technology is gaining more and more attention in the area of semantic segmentation, which plays an important role in the task of understanding the structure of documents. Although this demand applying deep learning approaches has undergone continuing advancement, the document segmentation systems still suffer from low accuracy rate. This paper presents a new high-performance document segmentation algorithm, namely Deep Document Segmentation Network (DDSnet), which incorporates advanced end-to-end deep learning methods for segmenting four different types of document features, including background, texts, tables, and figures. To overcome the small receptive fields, the atrous residual block is proposed, which is efficiently boosted by the adoption of multi-branches structure. For the better fine-grained output, the proposed atrous convolution residual block is conducted to achieve high accuracy. Moreover, this paper also releases the brand-new large-scale database, namely PPSD2019, for document segmentation that provides a pixel-level database for another benchmark. As documented in the experimental results, the proposed document segmentation method achieves a superior segmentation rate than that of the former competitive schemes. As a result, the proposed method and database can be considered as a very competitive candidate for the document segmentation applications.
Improving Smart Healthcare Safety and Security Using Kinect

Modern healthcare operating rooms are cumbersome, especially in the current pandemic of Coronavirus (COVID-19) to manage the safety and security of the patients and the staff. Conditions in a modern operating room are like, a patient’s medical diagnosis or x-ray or the MRI images are the vital resources to be considered. In general, the doctors are impelled to scrub out their hands every time they scroll through or examine the images in mid-operation. To avoid departing the operating table, several doctors put their faith in assistants or nurses and ask to examine the system that can be clumsy and disappointing and can rise the gathering in the room itself. To overcome this problem and emphasizing the use of the touchless smart healthcare system in medical diagnosis we have developed an application for the same. We propose and demonstrate a gesture and speech-based system to help staff to deal with such critical conditions. The proposed system is composed of two main parts: the healthcare operating software and sensor Microsoft Kinect. Test results conclude the efficiency of the viewing system in terms of time complexity, and the value and the safety of the recording system have increased.
Color Face Recognition Based on Quaternion Moment Vectors Using Polynomial Kernel Extreme Learning Machine

The quaternion moments (QMs) have been successfully utilized for numerous image processing and pattern recognition applications due to their ability to capture and represent the color information holistically. In this paper, we propose a method based on the robust quaternion pseudo-Zernike moments (QPZMs) and polynomial kernel extreme learning machine (P-KELM) for color face recognition. A new hybrid color space-XnSCr is presented that outperforms the accuracies achieved in commonly used color spaces. The comparative performance analysis of the variants of extreme learning machine (ELM) is performed and the experiments exhibit that the proposed method achieves higher recognition accuracies with minimal training time on the benchmark datasets: Faces95 and Georgia Tech.
Machine Learning Agent Communication Language Based on ACL and PMML

Agent communication is the key component for agent social interaction in the multiagent systems. It acts as the medium for the agents to exchange high level knowledge and understand these messages in order to achieve the common goal or tasks. The article describes the work of communication between multi-agent systems using data mining is discussed. Presents an overview of the languages that provide communication between agents in distributed environments. It also describes the extension of the agent communication language by including PMML standard in it. The main messages for performing data analysis are provided.
Secured Storage and Verification of Documents Using Blockchain Technology

In today’s world, producing fake documents is becoming more common. As the fake ones accurately look like the originals, it is impractical for a common man to identify the real and the duplicate one. To verify the documents, it is required to seek vast data. Personal documents are mandatory to be shown in every organization and handling it manually may damage the documents as they exist only once. They may also get lost in the mail. Fake documentation has been a current issue in both public and private sectors where all the verification processes have to be done manually. The verification method is complicated. This downside may be solved by storing the digital certificates in a blockchain. This technology is used for a selected task of storing a digital signature of assets that prove their validity. In our work, we tend to solve the problem of verifying the validity of digital assets such as a birth certificate, academic certificates, housing documents and soon by creating a digital fingerprint that would convert the uploaded file into a hash code by using the hash function. With the help of a digital fingerprint, a digital signature is created using JSON web tokens. Both the finger print and signature are stored in the blockchain and the document shall be shared through any file sharing platform. For the verification process, the digital fingerprint is checked and the request reaches the blockchain. The signature for that particular fingerprint is checked against the public and private keys stored in the block. For authorization of the institution that generates the document, an SSL certificate shall be issued and can be checked for legitimation. The existing system does not guarantee the records and depends on third-party agencies.
Video Static Foreground Detection Method for Desktop Robotic Arm

Abstract Although there are many efficient methods to detect the foreground from video frames, these methods are not completely applicable to the application scenario of desktop robotic arm. This paper presents a video static foreground detection method suitable for the desktop robotic arm application scenario. In our method, an auxiliary board is used to decrease false detection caused by camera-shake and work area movement, and to effectively eliminate non-target foreground interference such as human hands. In addition, with the auxiliary board we can also accurately calculate the 2-dimensional position and posture of the foreground. This method is general applicable to most desktop robotic arm application scenarios. Furthermore, compared with the traditional foreground detection method, this paper presents the 3-frames threshold method to solve the problem of multi-texture background environment appears many noise. What’s more, for the problem that it is difficult to classify when the foreground colors are similar to the background colors, this paper presents the difference degree to describe pixels.
Research of the ELA Algorithm for Identifying Editing Fact in Jpeg Images

The process of detecting the fact of image modification using the method of analyzing the level of errors in jpeg images is considered. It is proposed to use artificial neural networks based on an adaptive resonance theory with a hierarchical memory structure as a classifier. A number of experiments were conducted to test the system for detecting the fact of modification of the original image. The results of the experiments show the speed and high accuracy to detect a modified image.
Towards Elastic and Sustainable Data Stream Processing on Edge Infrastructure

Much of the data produced today is processed as it is generated by data stream processing systems. Although the cloud is often the target infrastructure for deploying data stream processing applications, resources located at the edges of the Internet have increasingly been used to offload some of the processing performed in the cloud and hence reduce the end-to-end latency when handling data events. In this work, I highlight some of the challenges in executing data stream processing applications on edge computing infrastructure and discuss directions for future research on making such applications more elastic and sustainable.
Artificial Intelligence: Back to the Future of Patent Law

Artificial intelligence questions patent law: is it a subject or an object of patent law? This surprising question has however been at the heart of debates in recent months, since the famous DABUS affair. It is now necessary to analyze the difficulties effectively raised by the reception of AI by patent law.
Use of digital tools for teaching in Spain during the COVID-19 pandemic

As a consequence of the pandemic produced by COVID-19, in March 2020, Spanish educational institutions are forced to suspend face-to-face teaching activities by focusing on their performance in a virtual way, which implies a redesign of the usual teaching and learning situations In this sense, educational digitization with the use of digital tools for teaching, and the digital competence of teachers acquire a fundamental role in the teaching and learning process at all levels of the Spanish educational system This study analyzes the use made by 108 active teachers of fifteen digital tools for teaching (educational platforms, blogs, wikis, gamification tools, tools to create collaborative content, interactive content, to create surveys, to record audio and video, to edit videos and to program) during the months of confinement, and examines their evaluations regarding the use of these tools, through the application of an online questionnaire designed ad hoc The reliability of the instrument is examined and a factorial analysis is developed with which nine components are obtained that explain 71 643% of the total variance Subsequently, descriptive and parametric analyzes were carried out, finding statistically significant differences in the use of digital tools when were considered as grouping variables: sex, civil service, autonomous community and type of educational center In light of these results, the educational implications of the COVID-19 pandemic and the consequent confinement, as well as, the substantial change produced in the teaching and learning process with educational digitization materialized in the use of various digital tools for teaching
Entropy Based Cluster Selection

Clustering has emerged as a method of unsupervised partitioning of a given set of data instances into a number of groups (called clusters) so that instances in the same group are more similar among each other with respect to instances in other groups. But there does not exist a universal clustering algorithm that can yield satisfactory result for any dataset. In this work we consider an ensemble (collection) of clusterings (partitions) of a dataset obtained in different ways and devise two methods that judiciously select clusters from different clusterings in the ensemble to construct a robust clustering. The superior performances of the proposed methods over well-known existing clustering algorithms on several benchmark datasets are empirically reported.
A Collaborative Ensemble System with Picture Story for Aesthetic Sensibility Learning

This paper presents a collaborative ensemble system with pictures stories and nature sounds by students' emotions and intentions, represented by body motions. The cultivating aesthetic sensibility for children is especially important for enriching their open-minded and creative personality. Moreover, it can support music and video-applications based on the eurhythmics approach. Current advanced computer-aided education systems can support emotional education efficiency and brings new experiences to both teachers and students. However, the conventional computer-aided education system cannot sufficiently interpret students' emotions, feelings, and intentions for amplifying and conveying an atmosphere shared by students during the class of aesthetic sensibility learning. The feature of our system is that our system can amplify and convey the atmosphere created by the student's physical motions by dynamically changing pictures and sounds through a collaborative ensemble with picture stories and nature sounds. By several experiments testing our prototype, we evaluate the feasibility of the proposed system.
Performance Evaluation of Weighted Entropy Based Fusion Technique for Face Recognition with Different Pre-processing Techniques

In this work, preprocessing methods are applied to both whole image and block sized image to improve the performance of face recognition. In holistic method, preprocessing methods like Locally Tuned Inverse Sine Nonlinear (LTISN), Self-Quotient Illumination (SQI), Gamma Intensity Correction (GIC), Histogram Equalization (HE), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Difference of Gaussian (DoG) filtering are applied to the complete image (holistic) and then the images are block sized. Subsequently, K- Nearest Neighbor (K-NN) classifier and weighted entropy-based fusion method are applied. In the block-based method, input image is divided into small blocks in a non-overlapping pattern. Then above-mentioned preprocessing techniques are applied to each block. Subsequently K-NN classifier and weighted entropy-based fusion method are applied to the output of each classifier. The recognition accuracy is computed for various block sizes and threshold values. This paper presents comparison of various pre-processing methods applied to both the holistic and block-based methods to mitigate the effects of illumination variation and preserve the essential details that are needed for recognition. The applied preprocessing methods enhance the image quality and increases the face recognition accuracy on FERET dataset under lighting variations. The block-based weighted entropy-based fusion method performs better than holistic method in FERET database. The maximum accuracy of the proposed method increases from 85.1% to 88.2% after the application of preprocessing methods.
Long-Tail Recommendation Framework Using Frequent Neighbors

The neighborhood-based recommendation method is a basic collaborative-filtering method, and one of the methods still used in the industry. The main basis for recommendation is the user's scoring data in the neighborhood-based recommendation method. However, due to the sparsity of user scoring data, the number of recommendations for long-tail items in the neighborhood-based method is relatively small. Although deep-learning technology has been widely applied to the neighborhood-based method, user scoring data are still needed in the methods, so the number of long-tail items in recommendation results is still very small. In order to increase the number of long-tail items recommended in the neighborhood-based method, this paper proposes a long-tail recommendation framework using frequent neighbors, and applies the framework to two real-world datasets. Experiments showed that the recommendation framework proposed in this paper effectively increases the number of long-tail items recommended in the neighborhood-based method and significantly improves the coverage metric.
Smart Meeting Room Monitoring System with Sanitizing Alerts

In the wake of recent growing fears of contracting the COVID-19 virus, people are scared to attend meetings in physical meeting rooms. A study was carried out, which was based on interviewing university students and workers in professional environments. Therefore, it is crucial to have a booking system for meeting rooms that allows the meeting attendees to get information about the sanitization status of the meeting rooms they would attend meeting in. To help prevent the fear of attending meetings, a web-interface was developed to give the users the necessary information that would enhance their sense of safety. The system was evaluated in a qualitative approach where students and professionals were asked to provide feedback. The evaluation results showed a considerable increase in people’s confidence and comfort to attend meetings in meeting rooms.
Development and Evaluation of Swahili Text Based CAPTCHA

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a test used in computing to determine whether the user is human or not and can be used in websites to offer protection against malicious programs (bots) which run automated tasks such as posting fake comments. Most of the Swahili language based websites in Tanzania still use English language based CAPTCHAs such as Google's reCAPTCHA v2 which has poor usability for native Swahili users and poor robustness. This paper proposes Swahili CAPTCHA which requires users to recognize and correctly spell the distorted and misspelled Swahili words. Evaluation findings reveal that Swahili CAPTCHA is easier to learn and use and has similar robustness compared to reCAPTCHA v2.
Implications of FCB Case

The FCB case is an unique ATM hacking incident that was successfully solved by Taiwanese law enforcement, while hundreds of similar cases occurred internationally and unsolved. These ATM hackings share very similar modus operandi, and thus the FCB case offers many generalizable implications in practice and theory. This chapter concludes with discussing development of similar cybercrime likely to occur in the COVID-19 and post-pandemic time.