Reviewerly/exHarmony huggingface dataset:
Feature points-based image registration between satellite imagery and aerial images of agricultural land

Rapid advancement in remote sensing sensors has resulted in an enormous increase in the use of satellite imagery (SI) and images taken from unmanned aerial vehicles (UAVs) in a wide range of remote sensing applications. These applications include urban planning, environment monitoring, map updating, change detection, and precision agriculture. This paper focuses on an agricultural application of SI and UAV images. SI-UAV images possess high temporal, textural, and intensity differences due to rapid changes in agricultural crops with the passage of time. Feature points such as scale invariant feature transform (SIFT), oriented FAST and rotated BRIEF (ORB), and speeded-up robust features (SURF) are not invariant to such differences and underperform in SI-UAV image registration. To deal with this problem, we propose a new method that combines the strength of nearest neighbor (NN) and brute force (BF) descriptor matching strategies to register SI?UAV images. The proposed method is named NN-BF. For NN-BF first corresponding feature point descriptor matches are identified between SI-UAV images of the training set with overlap error. Then the corresponding descriptors are matched with the descriptors of SI images of the test set with NN strategy. The resulting descriptor matches are then further matched with the descriptors of UAV images of the test set using BF strategy. Finally, the descriptor matches obtained are processed with RANSAC to remove outliers and estimate a homography for image registration. Experiments are performed on an agricultural land image dataset. The experimental results show that the NN-BF method improves SIFT, SURF, and ORB feature point performances and also outperforms recently proposed feature matching strategies for remote sensing images. SIFT on average obtains 6.1% and 18.9% better precision scores than SURF and ORB with NN-BF, respectively. SIFT also obtains lower root mean square error than SURF and ORB with NN-BF.
Exploring a Mixed Reality Framework for the Internet-of-Things: Toward Visualization and Interaction with Hybrid Objects and Avatars

Smart hyper-connected environments are becoming a central part of daily life in modern society. Such environments apply the internet-of-things (IoT) paradigm [1], which refers to the growing field of interconnected devices and the networking that supports smart, embedded applications. The IoT has many human-computer interaction (HCI) challenges [2], however, and central to these challenges is the need to provide more human-friendly approaches to communicating sensor information and meaningful visualizations of contextual states to users of IoT systems. Highly expressive, and engaging smart environment interfaces are uncommon, and this work applies mixed reality as a tool to better visualize and express the underlying behaviors and states within IoT smart devices. This extends the authors' previous research [3], providing a new head-mounted display framework and interconnection architecture for an augmented reality representation of a physical IoT device, an IoT Avatar. The video submission demonstrates contributions for: i) an exploration of how mixed reality can be used to enrich smart spaces and hybrid objects, and ii) an early use case and functionality evaluation of a simple avatar hybrid smart object that expresses emotion through immersive media. It is expected that this research will help foster immersive and engaging human-centered interaction in future smart environments.
Minimax formula for the replica symmetric free energy of deep restricted Boltzmann machines

We study the free energy of a most used deep architecture for restricted Boltzmann machines, where the layers are disposed in series. Assuming independent Gaussian distributed random weights, we show that the error term in the so-called replica symmetric sum rule can be optimised as a saddle point. This leads us to conjecture that in the replica symmetric approximation the free energy is given by a minmax formula, which parallels the one achieved for two-layer case.
Grasshopper optimization algorithm combining Gaussian and chaos theory for optimization design

Grasshopper optimization algorithm is one of the latest swarm intelligence algorithms, however it is easy to fall into the local optimum and has the defect of poor search ability. Therefore, Gaussian and chaos theory are combined to improve the original grasshopper optimization algorithm. Firstly, the Sinusoidal chaotic map is used to initialize the solution space of the population; then, Gaussian and Logistic chaotic map are used to improve the position update formula of grasshopper optimization algorithm; finally, eight benchmark numerical test functions are used to verify and test the search performance of this algorithm with ALO, PSO, GOA. It can be seen from the experimental data that the improved grasshopper optimization algorithm has better convergence accuracy and stability than the other three swarm intelligent algorithms. Besides, GC-GOA has good performance in the design of three-bar truss problem.
Analysis of Rogue Access Points Using SDR

When people connect to the Internet with their mobile devices, they do not often think about the security of their data; however, the prevalence of rogue access points has taken advantage of a false sense of safety in unsuspecting victims. This paper analyzes the methods an attacker would use to create rogue WiFi access points using software-defined radio (SDR). To construct a rogue access point, a few essential layers of WiFi need simulation: the physical layer, link layer, network layer, and transport layer. Radio waves carrying WiFi packets, transmitted between two Universal Software Radio Peripherals (USRPs), emulate the physical layer. The link layer consists of the connection between those same USRPs communicating directly to each other, and the network layer expands on this communication by using the network tunneling/network tapping (TUN/TAP) interfaces to tunnel IP packets between the host and the access point. Finally, the establishment of the transport layer constitutes transceiving the packets that pass through the USRPs. In the end, we found that creating a rogue access point and capturing the stream of data from a fabricated "victim" on the Internet was effective and cheap with SDRs as inexpensive as $20 USD. Our work aims to expose how a cybercriminal could carry out an attack like this in order to prevent and defend against them in the future.
A Tourism and Travel Recommendation System Based on User-Location Vector

E-Tourism Recommendation system aim is to help the tourist for selecting a destination. Social Networking sites allows tourist to understand user’s interest in various Tourism and Travel services. Most recommendation systems provide valuable tools for users in which user can find out information related to users point of interest. Challenge faced by the tourism and travel recommendation system is to mainly exploit the existing relationship between a user and their Point of Interest and hence recommend appropriate places of attraction. In this paper, we present a technique known as User Location Vector for identifying the implicit relationship between Users and their Point of Interest.
Image Compression Using Convolutional Autoencoder

Image compression is one of the advantageous techniques in several types of multimedia services. And recently deep learning has been so developed that it is being used for image compression. In image compression, consider we have images of various dimensions. One such dimension is 28 by 28. Images are formed by combining red, green and blue (RGB) in various proportions to obtain any color in the visible spectrum. The image is made up of pixels and have some noise in them. We propose a Convolutional Auto encoder neural network for image compression by taking MNIST (Modern National Institute of Standards and Technology) dataset where we up sample and downs sample an image. We take an image 28 by 28 images with noise, which is an RGB image. By developing deep learning image should be compressed to 28 by 1 dimensional dense vector. After the compression final resulting image should have the original dimension of 28 by 28. The main objective is to compress an image without affecting the quality of image radically.
Making Inside the Augment: Augmented Reality and Art/Design Education

This chapter deals with the developing genre of Augmented Reality (AR) design from architecture to fine arts. Although a great deal of literature exists in this area within the architectural field, this chapter will seek to delve into more holistic approaches to the use of AR in art and design. This will engage the HCI-based issues of handheld device compared to headset (HoloLens, Magic Leap) technologies in terms of Lichty’s theory of immersive gesture. Differences between these platforms, as well as AR’s distinctions from other immersive media offer insights into the development of pedagogical models in the creative disciplines. Of particular interest is the use of AR in Design Education, allowing future possibilities for hybrid models of creative expression, like augmented overlays on physical models/artworks, to the use of AR as a tool in material work. This chapter aims to look at the current literature, draw inferences through analyzing case studies how educational models are being constructed through the theory of gesture and consider models for the use of these technologies in improving the creative disciplines.
Research on Terminal Path Selection Based on Hybrid Genetic-Ant Colony Algorithm

Airport information weak current system is the core of airport terminal, which is responsible for information interaction, power resource and energy consumption management among subsystems of airport terminal. Meanwhile, it is also responsible for main business dispatch management of entire airport terminal. Therefore, starting with basic ant colony algorithm and considering respective advantages of genetic and ant colony algorithms, optimization cycle of the two algorithms is introduced multiple times in this paper. Moreover, after each loop iteration of ant colony algorithm, optimal solution generated by ant colony algorithm will be added to genetic algorithm so that iterative speed of genetic algorithm can be accelerated. Meanwhile, solution calculated by genetic algorithm will be set to better path to update pheromone allocation in ant colony algorithm and achieve parameter adjustment. Experimental test proves that geneticant colony hybrid algorithm proposed in this paper has good convergence and solution efficiency, which can find the shortest path with suitable conditions in complex road networks.
BERT-BiLSTM-CRF for Chinese Sensitive Vocabulary Recognition

There are tens of thousands of unstructured textual data generated from social media platforms every day, as well as vast sensitive words that may spread harmful information and even insult with each other. Therefore, effectively identifying sensitive words on the network, can not only filter out these inappropriate remarks, but also build a healthy and clean network environment. Recently, there are many researchers devote themselves into the study of emotional vocabulary, just fewer studies draw attention to the identification of sensitive vocabulary on the network. Furthermore, traditional methods of vocabulary recognition need a lot of manpower to make rules and extract features. In this paper, we firstly construct the dataset of uncivilized language (ULN dataset), which is acquired through web crawler on the website. Secondly, we propose a method to identify Chinese sensitive words on the network, which combine the Bidirectional Encoder Representations from Transformers with Bidirectional Long Short Term Memory Network and Conditional Random Field (BERT-BiLSTM-CRF). We use three models to identify sensitive words in ULN dataset. The experimental results show that, the model proposed in this paper has excellent performance, compared with the classical Bidirectional Long Short Term Memory Network (BiLSTM-CRF) and Convolutional Neural Network (CNN).
Correction to: Aggregating neighborhood information for negative sampling for knowledge graph embedding

Unfortunately, this article has been published with incorrect title.
EFL LEARNERS' DIFFICULTIES IN THE STRUCTURE AND WRITTEN EXPRESSION SECTION OF TOEFL TEST IN AN INDONESIAN UNIVERSITY

This study examined the English for Foreign Language (EFL) learners' difficult topics in the structure and written expression section of the TOEFL Prediction Test, and reasons why they consider that these topics were difficult. The mixed-method research design was used in this study. Fifteen participants were selected through purposive sampling mechanism from the seventh-semester students of Department of English Language Education, Universitas Islam Negeri Ar-Raniry who have participated in the TOEFL Prediction Test to identify the difficult topics they encountered. Then, the semi-structured interviews were conducted to six underachieving student's participants with the most recorded errors made in the test to know the reasons behind their difficulties. Findings indicated that students encountered difficulties mostly when dealing with determiners, conjunctions, adjective clauses, apposition phrases, and reduced clauses in the structure section. Meanwhile, adverb connectors, subject-verb agreement, and clause of concession, relative clause, and quantifier are the difficulties they encountered in the written expression section of the test. Thus, the findings also revealed several factors identified as the reasons behind those difficulties, namely lack of practice, grammar incompetence, vocabulary shortage, time management, and low self-confidence. Given the significant impact of this study, we suggested that the lectures and English departments should address these difficulties. It is crucial that the focus of the courses related to grammar and EFL proficiency tests be incorporated into the syllabus.
Numerical Procedures and their Practical Application in PV Module Analyses. Part IV: Atmospheric Transparency Parameters - Application

Abstract The presented article relates to aspects of PV module testing using natural sunlight in outdoor conditions. It is a continuation of the article Part III: parameters of atmospheric transparency - determining and correlations. This article discusses the practical application of the indexes: atmosphere purity - k Tm , diffused component content - k s/o , beam clear sky index - K b - in testing various modules in outdoor conditions. Their influence on the conversion of modules made from various absorbers and various technologies is demonstrated. Their practical application in module testing in outdoor conditions is described and it - has been demonstrated that the results of the analyses carried out using the indexes conform to the results obtained using spectral parameters of solar radiation (i.e. APE and UF ). These are the measurements that require the use of very expensive equipment.
Learning Compact Models for Planning with Exogenous Processes.

We address the problem of approximate model minimization for MDPs in which the state is partitioned into endogenous and (much larger) exogenous components. An exogenous state variable is one whose dynamics are independent of the agent's actions. We formalize the mask-learning problem, in which the agent must choose a subset of exogenous state variables to reason about when planning; doing planning in such a reduced state space can often be significantly more efficient than planning in the full model. We then explore the various value functions at play within this setting, and describe conditions under which a policy for a reduced model will be optimal for the full MDP. The analysis leads us to a tractable approximate algorithm that draws upon the notion of mutual information among exogenous state variables. We validate our approach in simulated robotic manipulation domains where a robot is placed in a busy environment, in which there are many other agents also interacting with the objects. Visit this http URL for a supplementary video.
Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH

This work proposes an efficient Content-Based (CB) product recommendation methodology that promotes diversity. A heuristic CB approach incorporating feature weighting and Locality-Sensitive Hashing (LSH) is used, along with the TF-IDF method and functionality of tuning the importance of product features to adjust its logic to the needs of various e-commerce sites. The problem of efficiently producing recommendations, without compromising similarity, is addressed by approximating product similarities via the LSH technique. The methodology is evaluated on two sets with real e-commerce data. The evaluation of the proposed methodology shows that the produced recommendations can help customers to continue browsing a site by providing them with the necessary “next step”. Finally, it is demonstrated that the methodology incorporates recommendation diversity which can be adjusted by tuning the appropriate feature weights.
INNOVATION FOR INCLUSIVE DEVELOPMENT: MAPPING AND AUDITING THE USE OF ICTS IN THE SOUTH AFRICAN PRIMARY EDUCATION SYSTEM

Inclusive education aims to identify and minimise barriers to learning and maximise learner participation in the education system. With a focus on the use of information and communication technologies in the South African primary education system, this study aims 1) to create a framework with which to evaluate the current state of ICT uptake across core functions for a school; 2) to audit the current state of innovation uptake; 3) to use the audit outcomes to identify challenges to the uptake of ICTs; and 4) to identify measures for facilitating an improved uptake of these innovations. The researchers conclude that a need exists for government to ensure consistent ICT uptake and use throughout the education system through a better implementation of standards, policies, and processes that facilitate technology uptake.
Bug or Feature? Covert Impairments to Human Computer Interaction

Computer users commonly experience interaction anomalies, such as the text cursor jumping to another location in a document, perturbed mouse pointer motion, or a disagreement between tactile input and touch screen location. These anomalies impair interaction and require the user to take corrective measures, such as resetting the text cursor or correcting the trajectory of the pointer to reach a desired target. Impairments can result from software bugs, physical hardware defects, and extraneous input. However, some designs alter the course of interaction through covert impairments, anomalies introduced intentionally and without the user's knowledge. There are various motivations for doing so rooted in disparate fields including biometrics, electronic voting, and entertainment. We examine this kind of deception by systematizing four different ways computer interaction may become impaired and three different goals of the designer, providing insight to the design of systems that implement covert impairments.
LMU Bilingual Dictionary Induction System with Word Surface Similarity Scores for BUCC 2020

The task of Bilingual Dictionary Induction (BDI) consists of generating translations for source language words which is important in the framework of machine translation (MT). The aim of the BUCC 2020 shared task is to perform BDI on various language pairs using comparable corpora. In this paper, we present our approach to the task of English-German and English-Russian language pairs. Our system relies on Bilingual Word Embeddings (BWEs) which are often used for BDI when only a small seed lexicon is available making them particularly effective in a low-resource setting. On the other hand, they perform well on high frequency words only. In order to improve the performance on rare words as well, we combine BWE based word similarity with word surface similarity methods, such as orthography In addition to the often used top-n translation method, we experiment with a margin based approach aiming for dynamic number of translations for each source word. We participate in both the open and closed tracks of the shared task and we show improved results of our method compared to simple vector similarity based approaches. Our system was ranked in the top-3 teams and achieved the best results for English-Russian.
Envisioning Future Productivity for Immersive Analytics

Immersive technologies have opened many opportunities for the visual analytics and visualisation community. However, despite an emerging focus on immersive analytics research, there is currently a lack of coherent and broadly applicable visualisation and interaction design in order to make immersive analytics systems productive and usable in real world scenarios. In this workshop, we propose to identify a road-map towards productive user interfaces for immersive analytics. In particular we aim to understand how we can effectively support specific visualisation and analytics tasks in VR/AR, and identify the interactions needed to support these. Our goal is to catalyse the research community and lead toward a reference paper to inform future research.
Scalable and robust cryptography approach using cloud computing

With the development of technology and cloud computing, many users use cloud computing to manage civil or military services and projects in a cloud network, saving both their common data as well as sensitive or personal information remotely. Being multi- accessible and relatively low in cost, it is regarded as a flexible process wherein the user can increase or reduce these factors according to their needs, such as the memory, communication speed, expansion, and others. Unfortunately, the security of the cloud and the preservation of confidential information still remain a major concern for users of this service, especially for enterprises that need confidentiality in the use of technology. Therefore, this work comes to play an integral role in the increase of the security of the cloud in a technical, practical and modern way, using mixed encryption, information hiding, and information processing remotely. In this work, the RSA and RGBA images are used to encrypt and hide information in Amazon Web Services (AWS), enabling the act of cloud computing remotely between the main site (admin) and the clients. The results show a good performance in terms of time, entropy, MSE and PSNR for the evaluation process.
An accurate and fast method for eyelid detection

A novel method called refine-connect-extend-smooth (R-C-E-S) for detecting eyelids is presented. It consists of four algorithms, Canny edge detector with Prewitt operator, modified refine edge map (MREM), connect edges-extend (CEE) and smooth curve (SC). The method is not based on pre-assumptions that consider eyelids as parabola or lines and it does not use curve fitting algorithm, therefore sever deviation of the detected eyelid curve from the actual eyelid path is avoided. The method is applied to three types of database, CASIA-V1.0, CASIA-V4.0-Lamp and SDUMLA-HMT. The accuracies for detecting the lower eyelid, upper eyelid and free iris are (93.2%, 99.1%, 96.7%) for CASIA-V1.0, while for CASIA-V4.0-Lamp are (97.6%, 98.3%, 97.8%) and for SDUMLA-HMT are (95.1%, 95.3%, 96.92%). The processing times for detecting single eyelid, both eyelids and free iris are (42 ms, 49 ms, 35 ms) for CASIA-V1.0, while for CASIA-V4.0-Lamp are (23 ms, 26 ms, 21 ms) and for SDUMLA-HMT are (35 ms, 40 ms, 31 ms).