Reviewerly/exHarmony huggingface dataset:
A Dynamic Knowledge Generation System for Cognitive Agents

In this work we describe a knowledge based system employed for the dynamic, goal-directed and creative generation of novel knowledge in cognitive agents. This system exploits a recently introduced extension of a Description Logic of typicality (TCL) able to combine prototypical (i.e. commonsense) descriptions of concepts. In particular: given a goal expressed as a set of properties, in case an intelligent agent cannot find a concept in its knowledge base able to fulfill these properties, our system exploits the Description Logic TCL in order to find at least two concepts whose creative combination allows to satisfy the goal. The knowledge base of the agent is then extended via a mechanism of commonsense concept combination where the resulting combined concept represents the solution for the initial goal. The proposed approach has been tested in the task of object composition and compared with the responses provided by the OROC system.
Examinations on the Performance of Classification Models for Thai News Articles

This research aims to examine automatic models to classify Thai online news articles. The data set is six thousands of news articles from three mainstream websites. The news articles are classified into four categories—crime news, politic news, sport news, and entertainment news. Examinations on the classification algorithms of Decision Tree, Support Vector Machine (SVM), and Deep Learning are conducted. The performance is measured by the accuracy, the recall, the precision, and the F-Measure. The results show that the accuracies of Decision Tree, SVM, and Deep Learning models are 86%, 94%, and 95%, respectively.
Unimodal optimization using a genetic-programming-based method with periodic boundary conditions

This article describes a new genetic-programming-based optimization method using a multi-gene approach along with a niching strategy and periodic domain constraints. The method is referred to as Niching MG-PMA, where MG refers to multi-gene and PMA to parameter mapping approach. Although it was designed to be a multimodal optimization method, recent tests have revealed its suitability for unimodal optimization. The definition of Niching MG-PMA is provided in a detailed fashion, along with an in-depth explanation of two novelties in our implementation: the feedback of initial parameters and the domain constraints using periodic boundary conditions. These ideas can be potentially useful for other optimization techniques. The method is tested on the basis of the CEC’2015 benchmark functions. Statistical analysis shows that Niching MG-PMA performs similarly to the winners of the competition even without any parametrization towards the benchmark, indicating that the method is robust and applicable to a wide range of problems.
An EM Algorithm based Method for Constructing Dynamic Saliency Maps considering Characteristics while Driving

In this paper, we propose a novel construction method for dynamic saliency maps to predict the gaze of human drivers. In the proposed method, multiple feature maps are calculated from input images recorded by a vehicle-mounted camera. The dynamic saliency map consists of these multiple feature maps after center-biasing and normalization processes. The mixing ratios for these processed feature maps are determined with the Expectation--Maximization algorithm by considering the dynamic saliency map as a mixture distribution consisting of the processed feature maps as components. In addition, this paper introduces two models for constructing dynamic saliency maps. While the mixing weights are static in the first model, the mixing ratios are dynamically computed based on scene features calculated from input images in the second model. The proposed method is validated with these two models using fixation point data extracted from the DR(eye)VE dataset, which consists of videos recorded by both eye tracking glasses and a roof-mounted camera.
Designing website for online business in the agricultural sector

Abstract The purpose of this research is to discuss designing a website for agriculture so that it can help with every process in marketing in the agricultural sector. The method used in this study was a descriptive method to help describe the entire picture to be easier to understand, supported by several references related to websites and online businesses from several journals and books. The results of this study are a website to improve and win the business competition as a field of promotion and sales of production. In the process of using e-commerce buying and selling activities or more efficient marketing where the use of e-commerce will show the ease of promotion, transactions, cost reduction, and speed up the transaction process.
Arabic text diacritization

As recurrent neural networks and statistic models continue to give better results in different fields in science and because Arabic text diacritization is paramount important for many Arabic language processing tasks, we are going in this paper to present an overview of systems handling that problem. Besides, we are going to propose an automatic diacritic restoration system for Arabic texts. We propose here an approach using Long Short Term Memory LSTM network and Alkhalil Morpho Sys2.
A Novel Video Face Verification Algorithm Based on TPLBP and the 3D Siamese-CNN

In order to reduce the computational consumption of the training and the testing phases of video face recognition methods based on a global statistical method and a deep learning network, a novel video face verification algorithm based on a three-patch local binary pattern (TPLBP) and the 3D Siamese convolutional neural network is proposed in this paper. The proposed method takes the TPLBP texture feature which has excellent performance in face analysis as the input of the network. In order to extract the inter-frame information of the video, the texture feature maps of the multi-frames are stacked, and then a shallow Siamese 3D convolutional neural network is used to realize dimension reduction. The similarity of high-level features of the video pair is solved by the shallow Siamese 3D convolutional neural network, and then mapped to the interval of 0 to 1 by linear transformation. The classification result can be obtained with the threshold of 0.5. Through an experiment on the YouTube Face database, the proposed algorithm got higher accuracy with less computational consumption than baseline methods and deep learning methods.
Real-Time Aerial Suspicious Analysis (ASANA) System for the Identification and Re-Identification of Suspicious Individuals using the Bayesian ScatterNet Hybrid (BSH) Network

Video monitoring and safety systems have been used to keep track of hostiles, conduct border control operations as well as to monitor the suspicious entities in public spaces. However, these systems are inadequate for the monitoring of large crowds due to the limited field of view of cameras. This paper introduces the Aerial Suspicious Analysis (ASANA) System for the Identification and Re-Identification of suspicious Individuals in large public areas using the Bayesian ScatterNet Hybrid (BSH) Network. The BSH network first estimates the human pose in each frame. Next, a batch of frames is used by the Bayesian 3D ResNext to identify individuals with suspicious postures. The system can also re-identify the identified suspicious individuals as they tend to move after committing the suspicious event. The proposed architecture is advantageous as it can learn meaningful representations quickly using the ScatterNet with fewer labelled examples. This is of great importance as real-life annotated training samples are hard to collect, especially for these applications. The pose estimation, suspicious individual identification, and re-identification performance of the proposed framework is compared with the state-of-the-art techniques. The proposed dataset is also made public which may encourage other researchers who are interested in using the deep learning technique for aerial visual crowd monitoring.
Work in Progress – Establishing a Master Program in Cyber Physical Systems: Basic Findings and Future Perspectives

This paper reports on the basic findings and future perspectives of a capacity building project funded by the European Union. The International Master of Science on Cyber Physical Systems (MS@CPS) is a collaborative project that aims to establish a master program in cyber physical systems (CPS). A consortium composed of nine partners proposed the project. Three partners are European and from Germany, UK and Sweden; while the other six partners are from the South Mediterranean region and include: Palestine, Jordan and Tunisia. The consortium is led by the University of Siegen in Germany who also manages the implementation of the work packages. CPS is an emerging engineering subject with significant economic and societal implications, which motivated the consortium to propose the establishment of a master program to offer educational and training opportunities at graduate level in the fields of CPS. In this paper, CPS as a field of study is presented with an emphasis on its importance, especially with regard to meeting local needs. A brief description of the project is presented in conjunction with the methodology for developing the courses and their learning outcomes.
Puddle Detection for Avoidance Path Planning of Wheeled Mobile Robot Using Laser Reflection Intensity

Avoiding obstacles is one of the essential functions of outdoor mobile robots. Detection of three-dimensional obstacles and avoidance methods have been studied actively. However, planar obstacles, such as puddles, have been ignored generally. Avoiding puddles is important to prevent electrical problems, odometry error, and feeling displeasure. This paper proposes a method to detect puddles by using laser reflection intensity obtained from 2D LRF and a method for path planning to avoid the puddles. Laser reflection intensities of several road surfaces are investigated, and the effectiveness of the proposed methods are confirmed through experiments with a real-robot.
Nibble-Based Face Recognition Using Convolution of Hybrid Features

Face Recognition technique is used to identify human beings effectively without their intervention. In this paper, we propose a novel nibble-based face recognition using convolution of hybrid features. Face images are converted from color to grayscale anda decimal values of each pixel is represented using 8-bit binary. The novel technique of converting 8-bit binary pixel into Left-Side Nibble (LSN) and Right-Side Nibble (RSN) is introduced to increase computational speed. The 4-bit LSN is converted to a decimal value varying between 0 and 240. DWT is applied on LSN matrix and only LL band coefficients are considered as first set of transform domain features. The 4-bit RSN is considered and converted into decimal values ranging between 0 and 15. HOG is applied on RSN matrix to generate second set of spatial domain features. The final hybrid features are generated by performing linear convolution on spatial- and transform-domain features. The Artificial Neural Network (ANN) is used to compare and classify the database image features and test image features to compute performance parameters of proposed algorithm. It is observed that the performance of the proposed method is better than the existing methods.
A Flexible Secure Key Delegation Mechanism for CP-ABE with Hidden Access Structure

Ciphertext Policy Attribute Based Encryption techniques provide fine grained access control to securely share the data in the organizations where access rights of users vary according to their roles. We have noticed that various key delegation mechanisms are provided for CP-ABE schemes but no key delegation mechanism exists for CP-ABE with hidden access policy. In practical, users' identity may be revealed from access policy in the organizations and unlimited further delegations may results in unauthorized data access. For maintaining the users' anonymity, the access structure should be hidden and every user must be restricted for specified further delegations. In this work, we have presented a flexible secure key delegation mechanism for CP-ABE with hidden access structure. The proposed scheme enhances the capability of existing CP-ABE schemes by supporting flexible delegation, attribute revocation and user revocation with negligible enhancement in computational cost.
Annotation Rules for XML Schemas with Grouped Semantic Annotations

To enable the Industrial Internet of Things (IIoT), it is required to ensure Machine-to-Machine communications. Systems/devices often use different communication protocols, standards, and data representation languages, which create interoperability challenges. This paper proposes a set of annotation rules for systems meta-data, to support the translation of data exchanged between heterogeneous systems. These rules must be followed to ensure the validity of systems meta-data (XML Schemas annotated with semantic annotations and group identifiers). The meta-data can then be used as input in tools to analyze data and semantic compatibility and generate translators.
Gesture Recognition and Effective Interaction Based Dining Table Cleaning Robot

We present a framework for dining table cleaning robot, which enables the robot to detect the cleaning target and perform cleaning task correspondingly to the given instruction, without needing prior information of the cleaning target. A cleaning robot should be able to detect the object efficiently. In order to enable object detection without prior information, the background subtraction method is employed, which is based on the 3D point group data taken by a RGB-D camera. In addition to object detection, a cleaning robot should be able to modify its movement in accordance with the user's instructions. Therefore, we propose an interaction system which allows the user to use gesture to provide instructions to the robot. A pointing gesture is used to specify the cleaning target. When the information needed for the cleaning task is insufficient, the robot will ask for further information from the user. If multiple objects are detected, the robot will rank all the objects according to their distance from the pointed coordinate. The user can re-designate the cleaning target with preregistered gesture commands. Once the robot has collected enough information for its duty, it will execute the cleaning task specified by the user.
Sociotechnical Imaginaries of Digital Touch

This Chapter explores the potential of the concept of sociotechnical imaginaries for digital touch communication research and design. It defines the social imaginary and discusses how it works to produce and animate shared systems of meaning and belonging that guide and organize the world, in its histories as well as performed visions of desirable futures through advances in science and technology and imagined technological possibilities. The chapter explores the ways in which this concept can be employed as both a design resource, and as a methodological resource. We argue that as new digital touch technologies enter the communicational landscape the setting for interpersonal sociability is/will be reworked. We explore and make legible emerging sociotechnical imaginaries of digital touch, asking how might touch practices be changed through the uses of technology, and how might this shape communication. In particular, the chapter explores the core themes of the body, time, and place in relation to participants' sociotechnical imaginations of digital touch. Turning our attention to the sociotechnical imaginary as a methodological resource, we describe our use of a range of creative, making and bodily touch-based methods to access participants' sociotechnical imaginaries of digital touch and to both explore and re-orientate to the past, present and futures of digital touch communication.
Waiting Time Estimation of Hydrogen-Fuel Vehicles with YOLO Real-Time Object Detection

Widespread use of fossil fuels as a source of energy leads to automobile emissions of CO2 and other greenhouse gases (GHG) that plays a significant role in environmental pollution. A transition from fossil fuel to cleaner and more energy-efficient alternative fuel vehicles such as hydrogen fuel cell vehicles (FCVs), is a vital step in reducing the automobile emission. However, the insufficient charging stations and the long charging time compared to the conventional vehicles have to be overcome before hydrogen fuel-based vehicles can be fully adopted on a mass scale. This paper proposes a method that can count the approximate waiting time by using YOLO real-time object detection to detect how many hydrogen fuel-based vehicles are charging or queueing in the charging station. Therefore, the driver can choose the charging station that has minimal waiting time so that the insufficient charging stations and the long charging time can be managed.
Bangla Handwritten Character Recognition: an overview of the state of the art classification algorithm with new dataset

Recognition of handwritten characters from Bangla handwritten texts is of immense importance considering the complexity of the task. Researchers have explored the task of recognizing Bangla handwritten digits, but a few numbers of published works are available for Bangla Handwritten Character Recognition (BHCR). In our paper, we present a comparative overview of classification algorithms for BHCR, which may help the researcher to decide an appropriate classification algorithm for their work. We have created a new dataset of Bangla handwritten characters from 150 volunteers at different levels. We extracted around 2500 samples of Bangla characters, which consist of Bangla Vowels only. Histogram adjustment and other image preprocessing techniques are applied in handwritten characters before their classification. We compare the performance of seven commonly used classification algorithms for BHCR in terms of Sensitivity, Miss Rate, Specificity, Precision, Fall-out, F-score, and Overall Accuracy. This result shows that among the seven algorithms, ANN (Artificial Neural Network) performed best. LR (Logistic Regression) performed well compared to others in terms of the standard measures like sensitivity, specificity and error rate. This comparative overview will help scientists, especially the new researchers to give a quick start with Bangla handwritten character recognition.
Denoising Sequence-to-Sequence Modeling for Removing Spelling Mistakes

Rule-based spelling correction system focused on finding the most matched word with the misspelled word. But this approach does not work well inside a sentence with multiple errors that has a combination of possible correct words to replace but only one current sentence. Replacing each word individually will result in errors. So, the spelling corrector system must understand the context of the sentence including the tense and gender of the subject and so on. The most popular example of typing mistake correction is the one Google provides in their search engine. It was introduced quite a while ago but no such good performing system is developed by anyone else. In this work, we have proposed a spelling correction system using deep learning. The basic intuition of our approach is taken from denoising autoencoder. Here we have trained the model with noisy input generated by changing, removing or adding extra character at random position inside the sequence. The job of the model is to model this noisy input to output the original errorless sequence. We have experimented with large English dataset and reported the performance in terms of character level accuracy. The proposed model has shown impressive results in correcting the spelling mistakes.
Collaborative filtering via learning characteristics of neighborhood based on convolutional neural networks

Collaborative filtering (CF) is an extensively studied topic in Recommender System. Recent approaches use the statistical framework based on local Taylor approximations to unify both user based and item based CF algorithms and improve the performance of estimating unknown ratings. In this paper, we propose a new Machine Learning approach based on Convolutional Neural Networks to exploit complex latent user-item relations, using features extracted from the neighborhood of unknown rating via local approximations. Experimental results on two benchmark data sets demonstrate the effectiveness of the proposed approach via comparing to state-of-the-art methods.
An energy efficient time-mode digit classification neural network implementation

This paper presents the design of an ultra-low energy neural network that uses time-mode signal processing). Handwritten digit classification using a single-layer artificial neural network (ANN) with a Softmin-based activation function is described as an implementation example. To realize time-mode operation, the presented design makes use of monostable multivibrator-based multiplying analogue-to-time converters, fixed-width pulse generators and basic digital gates. The time-mode digit classification ANN was designed in a standard CMOS 0.18 μm IC process and operates from a supply voltage of 0.6 V. The system operates on the MNIST database of handwritten digits with quantized neuron weights and has a classification accuracy of 88%, which is typical for single-layer ANNs, while dissipating 65.74 pJ per classification with a speed of 2.37 k classifications per second. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
Hybrid Database Design Combination of Blockchain And Central Database

Blockchain is a decentralized distributed database. It is among the new generation technologies and its popularity is increasing day by day due to its attractive advantages. Although it was introduced in 1992 it was first used in 2008 with the Bitcoin virtual currency, which was laid experimentally by a developer codenamed Satoshi Nakamoto. After Bitcoin's success, Blockchain technology has been used in different fields. Not only in the financial sector; In many sectors such as health, energy, agriculture and supply chain management, applications have been developed in line with the needs. Security, first-hand operation, transparency and privacy advantages are the main reasons for the spread of Blockchain technology. By evaluating the advantages it provides, many companies have started to invest in Blokchains technology and the number of Blokchains projects has started to increase. HAVELSAN is a large-scale Defense Industry Software company, researching new generation technologies and conducting studies to create value at both the firm and national level. Blockchain is one of these technologies. Most of the projects and products made before the Blockchain were produced in the traditional structure using the central database. Blockchain still does not provide a central database performance. Although the value added will be added to the projects to be carried out with the block chain, the problem will arise where high transaction performance is important. Currently, Blockchain; ensures the accuracy, security and integrity of the data. However, as the Blockchain network grows, productivity decreases and is difficult to manage. The proposed solution to improve performance is to establish a hybrid model and to ensure that a centralized database works together with the Blockchain network. In this study, how to design a hybrid structure using Blockchain and central database, installation of the system and the problems encountered will be explained.