Reviewerly/exHarmony huggingface dataset:
The Enmap German Spaceborne Imaging Spectroscopy Mission: Update and Highlights of Recent Preparatory Activities

The Environmental Mapping and Analysis Program (EnMAP) is a spaceborne German hyperspectral satellite mission that aims at monitoring and characterizing the Earth's environment on a global scale. After several years delay due to a major design issue to meet the mission requirements, the mission is now back on track and planned for launch in 2021. This paper presents an update of the mission status with recent activities and developments from the space and the ground segment. Furthermore, a draft plan for the independent validation of EnMAP radiance and reflectance products was developed and will be introduced, along with highlights of the science preparatory activities in 2019 including airborne campaigns, algorithm consolidations, and HYPERedu education initiative.
Secure Lightweight Stream Data Outsourcing for Internet of Things

The epoch of the Internet of Things (IoT) has come by enabling almost everything to gather and share electronic information. Considering the unreliable factors of public IoT, how to outsource huge amounts of indispensable stream data generated by the nodes to the remote storage (RS) efficiently and securely is one of the most challenging issues. In this article, we propose a secure lightweight stream data outsourcing framework for IoT based on identity and blockchain. Taking advantage of identity-based cryptography and blockchain, for public IoT containing untrusted communication channels, nodes, RS, and even verifiers, we introduce a private mobile network and multiple verifiers to ensure that the stream data are stored intact and updated correctly, without the costs and risks brought by the public-key infrastructures (PKI). Meanwhile, the framework can also achieve privacy-preserving checking, by revealing no data to the other entities besides the RS, even in the blockchains. Our comprehensive analysis and experiments demonstrate that the proposed framework is suitable for lightweight devices and practical for IoT.
Market basket analysis using apriori algorithm to find consumer patterns in buying goods through transaction data (case study of Mizan computer retail stores)

Abstract Mizan Computer Shop is a shop that is engaged in the trading sector, especially in the field of selling computers and supporting accessories. Growing and increasing number of business actors in the computer sector, can makes the players challenged to be able to create unique differentiation and clear positioning. So, that consumers can differentiate from their competitors. Competitive and dynamic market conditions make every company should always observe competition in their business environment. Retail stores need to use all of available resources including data. Data processing is expected to be able to provide information that can be used to support marketing strategies. One of the data processing methods that are often used in marketing strategies is the use of data mining techniques i.e Market Basket Analysis using a priori algorithm. The application is designed using the waterfall method which starts from analyzing user needs, designing a process using UML which consists of: Use Case Diagrams, Activity Diagrams and Sequence Diagrams . This Market Basket Analysis application was built using the PHP programming language. From the results of the analysis in this research, it can be concluded that for the combination of 2 items with the highest confidence value 100% and the lift ratio value 3.39 i.e if a consumer buys a Laptop Charger, he will also buy a keyboard and for a combination of 3 items with the highest confidence value 100% and the lift ratio value 2.17 i.e if a consumer buys a Joystick and Laptop, he will also buy a mouse.
Product Recommendation Systems Based on Customer Reviews Using Machine Learning Techniques

Product recommendation in e-commerce is essential to deliver the competitive advantage in the sales and growth of products. Collecting product reviews based on customer opinion, feedback, etc., will be useful to improve the sales and attract more number of customers. Analyzing the product data collected from online shopping sites is helpful for the shopping portals to improve their sales. In this paper, advanced data-analysis techniques have been applied to the product data sets that provided good results. By extracting the product features and product quality and various other aspects of the user review, the product quality has been accelerated. The product recommendation has been suggested based on the customer reviews that helps the customer to choose the best product during the search process. NLP techniques have been used to enhance the review text and machine learning algorithms to classify the product based on the outcomes of NLP.
QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database

Query optimization is considered as the most significant part in a model of distributed database.The optimizer tries to find an optimal join order, which reduces the query execution cost.Several factors may affect the cost of query execution, including number of relations, communication costs, resources, and access to large distributed data sets.The success of a processed query depends heavily on the search methodology that is implemented by the query optimizer.Query processing is considered as NP-hard problem and many researchers are focusing on this problem.Researches are trying to find an appropriate algorithm to seek an ideal solution especially when the size of the database increases.In case of large queries, classical heuristic methods such as ant colony and genetic algorithm can't cover all search space and may lead to falling in a local minimum.In this paper, quantum inspired ant colony algorithm (QIACO), as one of the hybrid strategy of probabilistic algorithms, is utilized to improve the query join cost in the distributed database model.The ability of quantum computing to diversify leads to cover query large search space, which helps in selecting the best trail and thus improves the slow convergence speed and avoid falling into a local optimum.Using this strategy, the algorithm aims to find an optimal join order which minimizes the total execution time.Experimental results show that the proposed model convergence faster with better goodness than the classic ant colony model for same number of ants used.
Privacy in the Cloud: A Survey of Existing Solutions and Research Challenges

Private data is transmitted and stored online every second. Therefore, security and privacy assurances should be provided at all times. However, that is not always the case. Private information is often unwillingly collected, sold, or exposed, depriving data owners of their rightful privacy. In this article, various privacy threats, concepts, regulations, and personal data types are analyzed. An overview of Privacy Enhancing Technologies (PETs) and a survey of anonymization mechanisms, privacy tools, models, and metrics are presented together with an analysis of respective characteristics and capabilities. Moreover, this article analyses the applicability of the reviewed privacy mechanisms on today's Cloud Services and identifies the current research challenges to achieve higher privacy levels in the Cloud.
Serious Game Enhanced Learning for Agricultural Engineering Education: Two Games Development Based on IoT Technology

Promoting learners' experiences in agricultural disciplines is an ultimate goal of this research. Using both technology enhanced learning (TEL) and serious game, called serious game enhanced learning, plays a crucial role in motivating learners to participate with the agricultural learning lessons until they can earn more knowledge for cultivating real plant and can realize natural caring and environmental awareness and protection. In this paper, the two developed serious games using the game enhanced learning approach will be demonstrated and discussed. The former game aims to educate learners to monitor the plant growth by innovating an intelligent miniature plant pot using Internet of Thing (IoT) technology. The later game aims to innovate the game enhanced learning system by allowing learners to taking care of their real plant in intelligent plant pot. The innovation of these two games is to propose the novel approach of how to harmonize the serious game enhanced learning with the environmental factors surrounded the intelligent plant pot.
Action Unit Detection by Learning the Deformation Coefficients of a 3D Morphable Model

Facial Action Units (AUs) correspond to the deformation/contraction of individual facial muscles or their combinations. As such, each AU affects just a small portion of the face, with deformations that are asymmetric in many cases. Generating and analyzing AUs in 3D is particularly relevant for the potential applications it can enable. In this paper, we propose a solution for 3D AU detection and synthesis by developing on a newly defined 3D Morphable Model (3DMM) of the face. Differently from most of the 3DMMs existing in the literature, which mainly model global variations of the face and show limitations in adapting to local and asymmetric deformations, the proposed solution is specifically devised to cope with such difficult morphings. During a training phase, the deformation coefficients are learned that enable the 3DMM to deform to 3D target scans showing neutral and facial expression of the same individual, thus decoupling expression from identity deformations. Then, such deformation coefficients are used, on the one hand, to train an AU classifier, on the other, they can be applied to a 3D neutral scan to generate AU deformations in a subject-independent manner. The proposed approach for AU detection is validated on the Bosphorus dataset, reporting competitive results with respect to the state-of-the-art, even in a challenging cross-dataset setting. We further show the learned coefficients are general enough to synthesize realistic 3D face instances with AUs activation.
Sampling-based Kinodynamic Motion Planning Using a Neural Network Controller

This paper proposes a sampling-based kinodynamic motion planning method with several learning components. Kinodynamic RRT* for general nonlinear systems is computational inhibitive because many two-point boundary value problems (TPBVPs) are required to be solved repeatedly. This paper exploits supervised learning and offline optimal trajectories to build a state feedback neural network controller. The neural network controller aims to steers the system from points in an initial state set to points in a goal state set, thus avoiding the need for online solving TPBVPs. We develop a learning-based kinodynamic RRT* algorithm that is explicitly designed to incorporate the learned Neural Network controller and a cost-to-go Neural Network. Finally, Multiple simulation cases are studied to evaluate the performance of the proposed method.
The More, the Merrier? A Study on In-Car IR-based Head Pose Estimation

Deep learning methods have proven useful for head pose estimation, but the effect of their depth, type and input resolution based on infrared (IR) images still need to be explored. In this paper, we present a study on in-car head pose estimation on the IR images of the AutoPOSE dataset, where we extract 64 x 64 and 128 x 128 pixel cropped head images. We propose the novel networks Head Orientation Network (HON) and ResNetHG and compare them with state-of-the-art methods like the HPN model from DriveAHead on different input resolutions. In addition, we evaluate multiple depths within our HON and ResNetHG networks and their effect on the accuracy. Our experiments show that higher resolution images lead to lower estimation errors. Furthermore, we show that deep learning methods with fewer layers perform better on head orientation regression based on IR images. Our HON and ResNetHG18 architectures outperform the state-of-the-art on IR images on four different metrics, where we achieve a reduction of the residual error of up to 74%.
A Low-Cost Portable Smart Card Based Attendance System

Abstract Managing attendance may be a vital record-keeping activity in any institution. There are different methods of maintaining the attendance from manual system where attendance is marked in sheets to automated attendance on the likes of biometrics. The combination of frequency Identification (RFID) with fingerprint biometric technology was to enhance the safety level and integrity of the records. The designed system not only makes the system design simpler but also enhances the productivity of the institution both in terms of man power and time. The system doesn’t only simplify the method of taking attendance but reduces error and allows for faster verification of student attendance, all with minimal human interaction. This technique will help the authorities manage the attendance system in a more organized, efficient and time saving manner. The planning of the system is straightforward and portable making it an honest candidate for commercial and academic purpose. A GUI based version and app-based version has been designed to make the system both PC and mobile friendly. The system is built with various levels of access permissions and security. The system was tested for a small group of 100 students and was found to provide accurate marking of attendance for all users.
A Dynamic Calibration and Compensation Method for the Asynchronous Time Between Two Inertial Navigation Systems

The carrier is usually equipped with two or more inertial navigation systems (INS) as redundant backup system configuration to improve reliability. In this case, information fusion from two or more INSs can achieve error detection and system state monitoring. However, due to the different information collection and process mechanism, the measurements from different INSs would not be synchronized, which would decrease the accuracy of the fusion algorithm and would cause wrong monitoring results. In this paper, a dynamic calibration and compensation method for the errors caused by time-gap between two asynchronous INSs is proposed. The causes of asynchronous time from different INSs are analyzed and the asynchronous time is modeled. Based on the model, an online estimation and compensation Kalman filter for asynchronous time is established. Using the difference of navigation information between two sets of INSs as measurements, the filter can estimate the asynchronous time between the two INSs. The simulation results show that the proposed method could achieve estimation accuracy of 0.027ms for the asynchronous time between different INSs, and the vehicle experimental results also demonstrate the effectiveness of the method.
Active label distribution learning

Label Distribution Learning (LDL) is a new learning paradigm to describe supervision as probability distribution and has been successfully applied in many real-world scenarios in recent years. In LDL applications, the availability of a large amount of labeled data guarantees the prediction performance. In this paper, we cogitate the active learning for LDL to reduce the annotation cost. The center element in practice any active learning strategy is building the criterion that measures the usefulness of the unlabeled data and decides the instances to be selected to label manually. We are probably the first to focus on active instance selecting for label distribution learning. We propose a strategy named Active Label Distribution Learning (ALDL) to select the most informative instances for LDL applications. The fundamental idea of the ALDL strategy is to quantify the degree of disagreement for each unlabeled instance by the committee consisted of selected LDL algorithms, and identify the instances to be labeled manually. ALDL maintains composing the committee with selected LDL algorithms and measure the value of unlabeled instances, and a weight vector is used both parts. Besides, we discuss the convergence and the parameter selecting of ALDL. Finally, compared with other active learning methods, the experimental results on the datasets show the effectiveness of our method.
A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

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Exploiting the Redundancy in Convolutional Filters for Parameter Reduction

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks over the years. However, this comes at the cost of heavy computation and memory intensive network designs, suggesting potential improvements in efficiency. Convolutional layers of CNNs partly account for such an inefficiency, as they are known to learn redundant features. In this work, we exploit this redundancy, observing it as the correlation between convolutional filters of a layer, and propose an alternative approach to reproduce it efficiently. The proposed `LinearConv' layer learns a set of orthogonal filters, and a set of coefficients that linearly combines them to introduce a controlled redundancy. We introduce a correlation-based regularization loss to achieve such flexibility over redundancy, and control the number of parameters in turn. This is designed as a plug-and-play layer to conveniently replace a conventional convolutional layer, without any additional changes required in the network architecture or the hyper-parameter settings. Our experiments verify that LinearConv models achieve a performance on-par with their counter-parts, with almost a 50% reduction in parameters on average, and the same computational requirement and speed at inference. Source is available at https://github.com/kkahatapitiya/LinearConv.
The Effect of Semi-Transparent and Interpenetrable Hands on Object Manipulation in Virtual Reality

Manipulating objects with the hand is a common task in Virtual Reality (VR).However, some issues can occur during these manipulations.Occlusion is one issue that happens when the virtual hand covers an object, and therefore the perception of that object is hindered.Semi-transparent hands could solve this problem.Another issue is the difficulty of delicate hand object manipulation.By using interpenetrable hands, turning off the physics of the hands, this difficulty might decrease.Still, there is a lack of research into how significant the impact is while using these methods.In this paper, with the designs of semi-transparent hands and interpenetrable hands, we present the results of our conducted user study focusing on the effect of semi-transparent and interpenetrable hands on hand object manipulation tasks in virtual reality.The user study includes a VR environment where participants are asked to perform tasks.These tasks are recorded in objective results of accuracy and speed.Afterwards, they fill out a questionnaire about their opinion on the difficulty while using the different methods in which the subjective findings are recorded.Additionally, we improved the semi-transparent hands by adding a feature that smoothly transitions the hand from opaque to semi-transparent.As an input device, we used the Leap Motion Controller (LMC) for this user study.However, any hand tracking sensor that tracks hands from the VR headset's point of view could be used.Semi-transparent and interpenetrable hands have shown significant improvement for precise manipulation, which was verified by user feedback from the questionnaire and the data from the tasks.
Catalog and Illustrative Examples of Lightweight Cryptographic Primitives

Abstract The main objective of this chapter is to offer to practitioners, researchers and all interested parties a brief categorized catalog of existing lightweight symmetric primitives with their main cryptographic features, ultimate hardware performance, and existing security analysis, so they can easily compare the ciphers or choose some of them according to their needs. Certain security evaluation issues have been addressed as well. In particular, the reason behind why modern lightweight block cipher designs have in the last decade overwhelmingly dominated stream cipher design is analyzed in terms of security against tradeoff attacks. It turns out that it is possible to design stream ciphers having much smaller internal states.
Medical Chatbot for Novel COVID-19

Conversational agents or more universally known as the chatbot were industrialized to respond to user's queries in a particular domain. Chatbot would serve as a software delegate which enables a computer to converse with human via natural language. A chatbot is a human-like conversational character (Shaikh et al. Int J Eng Sci Comput 6:3117–3119, 2016 [1]. This technology was coined in 1960s, with the intention to impersonate a human (how he would reply to a particular situation) so that the user feels that he is talking to a real person and not a machine. Conversational agent that interacts with user's turn by turn using natural language (Shawar A, Atwell E (2005) ICAME J Int Comput Arch Mod Med English J 29, 5–24, 2005 [2]). The world of chatbot has seen much of the advance since the invention, and they have progressed from conventional rule-based chatbot to unorthodox AI-based chatbot. The chat agents are expert in their fields [3]. The prime focus of this paper is to show implementation of a retrieval-based chabot with voice support, and we will investigate other standing chatbot and how it is useful in helping the patients fetching all the necessary details about COVID-19.
Reducing Modern Slavery Using AI and Blockchain

The Modern Slavery Act of the UK has failed in reducing modern slavery significantly in UK organizations and their supply chains. One of the critical reasons for the failure is that these organizations utilize human-centered modern slavery due diligence processes, which have significant weaknesses in identifying modern slavery offenses. This paper aims to propose modern slavery due diligence process which leverages the self-sovereign digital identity component, an AI-based modern slavery offense monitoring component, and a modern slavery offense blockchain component designed to help improve the identification of modern slavery offenses which would otherwise remain hidden. This process enables managers to identify and act on more modern slavery offenses due to low-cost continuous monitoring instead of high-cost sample-based monitoring. This proposed solution can significantly reduce modern slavery offenses and generate tangible business benefits.
Tourism Recommender System using Machine Learning Based on User's Public Instagram Photos

In the past decade, recommender systems have become an essential part of online services such as NetFlix, YouTube, online shopping, etc. The tourism agencies such as TripAdvisor or Expedia also apply the recommender system to their services. For Thailand, the tourism industry is one of the most important revenues of the country. The problem is that the recommender system for planning a trip to Thailand still not effective enough. Users require a lot of effort when planning a trip. Therefore, the objective of this study is to develop the prototype of a tourism recommender system that automatically understands the user's preferences of their favorite tourist attractions without asking them any question. It applied machine learning to extract the user's preferences from the user's Instagram photos. Those preferences then use to compute the similarity with the attributes from 23 example tourist attractions in Ubon Ratchathani Province. A user study was conducted with 42 participates to preliminary study the precision and the adoption of the prototype. The results suggested that the prototype has been judged as satisfactory by participants for both precision and adoption. Moreover, the findings of this study will serve as insights for the direction of our planned future research such as applying the recommender system to other provinces of Thailand.