Reviewerly/exHarmony huggingface dataset:
Looking up substance and procedure via alternative DNS providers: the tale of injunctions to enforce copyright

The request to contribute to an issue to celebrate the work of Marianne Levin brought back plenty of nice memories. I had the pleasure of knowing Marianne for many years and I remember with pleasure those sessions I taught in Stockholm with Marianne and her dog in the back of the room. And when they took place on a Saturday her father would come along too and on a couple of occasions we ended up lunching on pizza at Pic-Nic on campus. Later on, we did our EU funded project on human embryonic stem cells and on one occasion Marianne chaired a meeting from campus whilst I was stuck on a plane that had diverted to Gothenburg because the radar at Arlanda Airport had gone down. With the doors of the plane open I could join the meeting via mobile and when I finally hurried in by taxi from Bromma Airport the draft we had been discussing was all ready and merely needed proofreading.
Polynomial Based Nonlinear Analysis of CCCS Thin Isotropic Rectangular Plate

This work is aimed at formulating a polynomial function for the nonlinear analysis of CCCS isotropic rectangular thin plate. The previous researchers used trigonometry function as their shape function on the decoupled Von Karman’s equations to obtain particular stress and displacement function respectively. Trigonometry function can only be used effectively for SSSS and CCCC plates; apart from these boundaries conditions its efficiency reduces. This present work hence used a polynomial function to formulate the approximate shape function for the CCCS plate. Direct variational calculus was used applied on Von Karman’s equations to obtain the general form of minimized total potential energy which serves as a platform for the determination of coefficient factor( Amplitude or coefficient of deflection). The numerical values of CCCS plate under unit load were obtained using Amplitude equation formulated. These values were obtained for various aspect ratio (ranging from 1 to 1.5 with an increment of 0.1). This work was compared with the previous work [1] and the percentage difference in the results are within the acceptable limit. This results indicate that the approach adopted by the present work is adequate, reliable and satisfactory for the analysis of CCCS rectangular plate.
Sparse Optical Flow Implementation Using a Neural Network for Low-Resolution Thermal Aerial Imaging

This study is inspired by the widely used algorithm for real-time optical flow, the sparse Lucas–Kanade, by applying a feature extractor to decrease the computational requirement of optical flow based neural networks from real-world thermal aerial imagery. Although deep-learning-based algorithms have achieved state-of-the-art accuracy and have outperformed most traditional techniques, most of them cannot be implemented on a small multi-rotor UAV due to size and weight constraints on the platform. This challenge comes from the high computational cost of these techniques, with implementations requiring an integrated graphics processing unit with a powerful on-board computer to run in real time, resulting in a larger payload and consequently shorter flight time. For navigation applications that only require a 2D optical flow vector, a dense flow field computed from a deep learning neural network contains redundant information. A feature extractor based on the Shi–Tomasi technique was used to extract only appropriate features from thermal images to compute optical flow. The state-of-the-art RAFT-s model was trained with a full image and with our proposed alternative input, showing a substantial increase in speed while maintain its accuracy in the presence of high thermal contrast where features could be detected.
Recognition of Facial Expressions in Infrared Images for Lie Detection with the Use of Support Vector Machines

Lie detection is needed in various areas like police investigations, terrorism, security, interpersonal relationships. Traditional methods of detecting lies are inaccurate and time-consuming. Detecting and analyzing the facial expression of fear and lie is a challenging task. This study focuses on the application of support vector machines (SVMs) for automatic recognition and classification facial expression detection for deception detection. The objective of this article is to study different techniques for recognition of facial expression and to detect human faces using thermal imaging for feature extraction. Support Vector Machine (SVM) is used to solve classification or regression problems. It can tackle linear and non-linear problems and work well for some useful issues. The algorithm creates a line or a hyperplane which helps in isolating the data into classes. It helps to find an optimal boundary between the possible outputs. The main aim of SVM is to discover the closest points that maximize the separation of the data points to their affiliated classes. The proposed technique divides a face area into rectangular regions. These regions find prominent points of the face, like the head, neck, leftmost point, rightmost point to get the boundaries, etc. The image is represented in a two-dimensional space. Further, this black and white image is processed and given to the supervised machine learning model Support Vector Machine to extract its features. Experimentation shows that SVMs give higher search accuracy and better image segmentation systems than traditional query refinement methods and neural networks.
Study and Investigation of PKI-Based Blockchain Infrastructure

Traditional Public Key Infrastructure has many security issues. With centralized trusted certificate authorities, the secure operation of SSL/TLS is handled by a set of trusted Certificate Authorities (CAs) and failure of trusted CAs will collapse the whole system. The drawbacks of PKI are overcome with blockchain infrastructure. Blockchain is designed to satisfy today’s market requirements. Blockchain is considered more secure than PKI due to its transparency and decentralized nature within the network. A decentralized network doesn’t allow any small change without confirmation of other nodes in the network. The metadata of the system is read-only so no modification is possible. Blockchain-based PKI uses public ledgers to identify public keys. Transaction records are made reliable with smart contracts and IoT device interactions. Studies show that blockchain-based PKI performs better than previous systems like CRL and OCSP etc. Hence, PKI-based blockchain infrastructure is ideal for securing digital information, to reduce the risk and cost within a business network.
Methods for the Classification of Radar Objects

Currently, many different algorithms have been developed that implement the classification problem. Of particular interest is the use of automatic classification algorithms in radar systems. In each such system, one has to solve the classification and recognition problem for detected targets. The work of an operator analyzing the information can take place in conditions that hinder assess of the control object’s state. This is due to the absence or insufficient amount of reliable information about some radar target properties. A radar target is understood as any material object that can be detected, and its location and movement parameters can be measured using radar methods. We consider air, ground and surface objects as a radar object. Object classification algorithms are used in various fields when analyzing the properties of an object. This paper discusses the main methods for the classification of radar objects.
AN UPDATED CRYPTANALYSIS ON THE BFHP-DLP SIGNING SCHEME

The concept of public-key cryptography introduced the notion of a digital signature scheme. In the era of online and digital communications, a signature scheme that works perfectly to achieve the goals of cryptography- confidentiality, authentication, data integrity, and non-repudiation, is urgently needed. However, every cryptosystem, including a digital signature scheme requires a well-defined difficult mathematical problem as its fundamental security strength, as demonstrated by the Diffie-Hellman key exchange with its discrete logarithm problem (DLP). Another problem called BFHP used by the AAβ-encryption scheme, has also withstood any destructive cryptanalysis since the scheme was introduced in 2013. Later, a digital signature scheme was introduced that combines both BFHP and DLP as difficult mathematical problems. Mathematical cryptanalysis was also performed against this scheme to test its security strength. This paper presents new cryptanalysis of the signing scheme. While the previous cryptanalysis focused only on BFHP, the obtained new results showed some improvement by scrutinizing the other difficult mathematical problem, DLP. In addition, several potential attacks on the future implementation by introducing side-channel and man-in-the-middle attacks against the scheme also will be discussed in this work. The countermeasures for each attack to enable the best-practice implementation of the scheme are also presented.
Neural Machine Translation for Native Language Aymara to English

In Latin America, there is a culture called Aymara, which has its own language also named Aymara. It is a native language in danger of extension declared by UNESCO and a heritage of the Peruvian nation. The work of Neural Machine Translator since its appearance has been able to translate many languages of the world, however it is not very well researched with native languages, in this work we experience for the first time the automatic translation from Aymara to English with the seq2seq model. First interesting results were obtained that could open up new research projects.
Need Analysis of Mathematics Edutainment Applications to Improve Numeracy Ability of Students with Mathematic Learning Difficulties in Inclusive Schools

A key component of many learning difficulties is a significant discrepancy factor between students' ability and academic achievement. The objective of this research is to determine the students' need for mathematics edutainment applications to improve the numeracy ability of students with mathematic learning difficulties in inclusive schools. This research used a descriptive qualitative method. The research subject consisted of 17 students of four-grade with mathematic learning difficulties in inclusive schools. The research data was collected by questionnaire, interview, and material analysis. The research results revealed that learning in schools has never used technology-based media, many learning materials have not been achieved, and learning is still adopted by teacher-centered learning. The learning was monotonous, less attractive, and less motivating for students to build mathematical concepts. Based on the results, it is concluded that it is necessary to develop mathematical edutainment applications that allow the realization of effective, adaptive, and fun learning media for involving students and helping students' achievement actively.
Smart Gnathometer for Bite Force Measurement

Bite force measurement on teeth is one of the crucial factors contributing to the development of advanced technology and longevity in the fabrication of prosthesis. Throughout the years, many researchers have developed devices to measure and analyse the maximum bite force. However, the innovations of the devices have some drawbacks, such as precision, measurement accuracy and cost-effectiveness. Moreover, some of the devices may affect the health of patients due to the usage of hazardous material. Therefore, a non-hazardous gnathometer has been innovated in the study to precisely measure and analyse the maximum bite force of dentures. A 3D model of the new gnathometer was designed using CATIA V5 and fabricated using a Fused Deposition Modelling (FDM) machine. The gnathomether also consisted of electronic parts, such as an ESP32 and Arduino Nano as a microcontroller, and a flexible force sensor to accurately measure the bite force measurement. A calibration test was carried out on this innovation to measure the accuracy, drift presence and consistency of bite force. This study demonstrated that the force mappings were directly proportional to the voltage and has shown that the resistance gradually decreases until it reaches a minimum or steady state as the force increases. This research on the calibration of force was valid as the accuracy calculated was a minimum of 94.80% with a maximum accuracy error of 5.20%, resulting in high accuracy and reliable results. This study suggested that the gnathometer can analyse the maximum bite force of dentures and is one of the best approaches to obtain precise and accurate bite force measurements. This research can improve dentures studies in the dental industry using the Internet of Things (IoT) technology embedded in the system and real-time bite force monitoring.
Development of solar isodose lines: Mercatorian and spatial guides for mapping solar installation areas

Mercatorian and spatial studies of solar power potential (SPP) provide technical guides for mapping actual solar installation areas for the efficient performance of photovoltaic plants. Acquisition and processing of satellite and on-station data on clearness index, relative sunshine hours, latitude, longitude and SPP preceded their modeling and simulations. The mercatorian SPP model is a geometric function of latitude and longitude, whereas the spatial SPP model is a function of x and y coordinates developed from the Haversine formula. Multiple isodose lines and a single maximum isodose line characterized the distributed and concentrated SPP contours, respectively. The present geometric SPP model validated well with the measured SPP with insignificant error results for the study areas. The Concentrated SPPs: 757.5, 635.2, 557.5 and 405.9 W/m2 with their corresponding percentage concentrated areas (actual): 28.85(29084.6), 41.48(15368.6), 4.37(1179.6) and 0.75(635.7)%(m2) for Northern Region (NR), Eastern Region (ER), Central Region (CR) and Western Region (WR), respectively. These results support the efficient performance of solar facilities within the confine of the SPP concentrated areas. The effective mercatorian coordinates were useful in identifying districts within the SPP concentrated areas. Furthermore, the high magnitude of the SPP in ER and NR supports that they are favored for the installation of solar facilities.
Performance Evaluation of Simultaneous Sensor Registration and Object Tracking Algorithm

Reliable object tracking with multiple sensors requires that sensors are registered correctly with respect to each other. When an environment is Global Navigation Satellite System (GNSS) denied or limited – such as underwater, or in hostile regions – this task is more challenging. This paper performs uncertainty quantification on a simultaneous tracking and registration algorithm for sensor networks that does not require access to a GNSS. The method uses a particle filter combined with a bank of augmented state extended Kalman filters (EKFs). The particles represent hypotheses of registration errors between sensors, with associated weights. The EKFs are responsible for the tracking procedure and for contributing to particle state and weight updates. This is achieved through the evaluation of a likelihood. Registration errors in this paper are spatial, orientation, and temporal biases: seven distinct sensor errors are estimated alongside the tracking procedure. Monte Carlo trials are conducted for the uncertainty quantification. Since performance of particle filters is dependent on initialisation, a comparison is made between more and less favourable particle (hypothesis) initialisation. The results demonstrate the importance of initialisation, and the method is shown to perform well in tracking a fast (marginally sub-sonic) object following a bow-like trajectory (mimicking a representative scenario). Final results show the algorithm is capable of achieving angular bias estimation error of 0.0034 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> , temporal bias estimation error of 0.0067 s, and spatial error of 0.021m.
School learning barriers in higher education students

The main objective of this article was to compare the perception that the college student has with the scholar learning barriers. The methodology that was used was the quantitative, observational, transversal, descriptive and comparative. The compilation of this info was carried out through an instrument with 11 learning barriers questions of the academic environment, with 320 surveys in phase 3 of the Covid-19 Pandemic, applied to the faculties of Ciencia, Educación y Humanidades, Sistemas, Ingeniería, Mercadotecnia and Jurisprudencia of the Universidad Autónoma of Coahuila, the method of sampling that was utilized was not probabilistic through the strategy of sampling for convenience. The statistical techniques that were applied were the descriptive through frequencies and percentages, comparative with Kruskal-wallis method for independent samples and Mann Whitney U for two independent samples. The contribution of the study lies in that the students from faculty de Sistemas differ from the ones of the faculties of Mercadotecnia, Educación, Ingeniería and jurisprudencia in that they consider in large extent that the educational and pedagogical practices during the hybrid modality for the COVID-19 Lockdown, has been a learning barrier.
Artificial Intelligence: Extreme Reward and Risk

Over the coming decades, artificial intelligence and robotics will continue to grow rapidly in power and scope, exerting a transformative impact on our lives. Seven trends are likely: the machines will become progressively more versatile and multifunctional in their capabilities; they will be designed to improve their own software and hardware over time, as they interact with their environment; the line between AI and robots will gradually blur, as machines come to permeate our society at all levels; these machines will be entrusted with complex practical tasks that require them to develop sophisticated commonsense knowledge about the human social world; the machines will be interconnected in functional networks that multiply their powers; such machines will need to be able to refuse to obey certain kinds of human commands, raising fundamental questions about who controls their actions; and the logic of arms races will apply to such machines, impelling nations and corporations to develop increasingly powerful machines as quickly as possible, with only a secondary concern for caution and safety.
Work in Progress: Noise Reduction Through Artificial Intelligence Techniques: An Introductory Study

Signals from the real world, resulting from various data acquisition methods, have many components which can influence the quality of the data. One of the most important factors is the noise. It can cause different kind of problems in Artificial Intelligence algorithms, as the learning model can interpret the noise as a data pattern. Because of this, reducing or eliminating the noise from a signal is a crucial process. This paper is focusing on treating the problem of signal denoising. A method to eliminate the noise from signals by using of a Convolutional denoising auto-encoder was developed. The developed autoencoder has six layers: an input layer, two convolutional layers serving as encoder, two convolutional layers as decoder and an output layer. One of the main advantages of this approach is that, compared to other methods used for denoising signals, autoencoders can be trained with the downstream classifier, improving the accuracy of the system. This introductory study will be extended and improved so that the autoencoder presented in this paper will to be able to eliminate thermal noise from a signal using a similar machine learning model.
ANALISIS ISI BUKU AJAR BAHASA INDONESIA BAGI PENUTUR ASING ‘BAHASAKU 1: AYO BICARA BAHASA INDONESIA’

Teaching material is crucial for any teaching and learning process, including the Indonesian Language for Foreign Speakers (BIPA). Thus, the textbook becomes the essential facilitator and acts as the deciding factor for the success of the BIPA program. This study aims to deeply explore the content of the textbook planned and designed by Alam Bahasa Indonesia. The findings of this study would contribute as a recommendation for other BIPA course institutions, tutors, and activists of BIPA in terms of the BIPA textbook. The research method applied was descriptive qualitative. Data gathering utilized comprehensive documentation of the textbook. The source textbook is BIPA ‘Bahasaku 1: Ayo Bicara Bahasa Indonesia’. The researcher became the critical instrument for data gathering and analysis. The results show that the textbook is printed in A4 size (21 x 29,7 cm) with a paper width of 80 gsm. The book consists of 10 units of lessons, and each has illustrated conversation, grammar focus in a text box, grammar exercises, cultural notes, and a vocabulary list. The materials included in every lesson unit are (1) target language grammatical structure; and (2) materials in written text, topic, and exercises.
Towards behavior-independent in-hand user authentication on smartphone using vibration

As the human hand makes direct physical contact with smartphones, significant efforts have recently been made to study the behavioral information of hand gripping of smartphones for user authentication purposes. Most existing methods leverage hand gripping behavior (e.g., gripping gesture, gripping position, gripping strength) of smartphones as biometrics to identify users. However, behavioral-based biometric authentication approaches may suffer from two problems: authentication performance (accuracy) degradation due to high-intra class variations arising from changes in user behavior over time, and vulnerability under spoofing attacks. To address these issues, we propose HoldPass, which is a behavior-independent in-hand user authentication method using vibration. HoldPass is able to adapt to the changes of hand gripping behavior of smartphones by extracting unique and stable physical features of human hands and eliminating the behavior-related prior information. Specifically, in HoldPass, we propose an adversarial neural network to achieve authentication based on unique physical features. Experiments with 10 users show that HoldPass can authenticate users with 97.39% accuracy while keeping False Accepted Rates (FAR) at a minimum of 2.1%.
Toward an Agent-Based Model of Community of Practice: Demonstration

Communities of Practice services enable users that share same interests and exchange knowledge in an ongoing learning process. The Stack Overflow platform supports such communities where the majority of the content are produced by a small number of highly active users. We propose an agent-based model of a Community of Practice exhibiting a social dynamics which is compliant with this insight.
A projector calibration method for fringe projection system

Accurate calibration of image acquisition and projection device in fringe projection measurement system is the basis of system work, and the calibration accuracy of the parameters directly affects the measurement accuracy of the system. An improved projector calibration method is proposed. Using a high-precision circular calibration plate, the camera is calibrated by Zhang Zhengyou's calibration method and nonlinear optimization; using the vertical and horizontal two sets of grating images projected by the projector, the corresponding relationship between them and the camera image is established, and then the center of the mark point in the projection is obtained. Then use the camera model with radial distortion to calibrate the projector; analyze the phase error when the projector is calibrated, and use the improved multifrequency fringe phase shift method to reduce the error; finally, through the calibration and the three-dimensional measurement experiment verifies the method in this paper. The experimental results show that the calibration method in this paper is simple in operation, high in precision and wide in application.