Reviewerly/exHarmony huggingface dataset:
Control Strategies Based on Interval Type-2 Fuzzy Logic for Autonomous Mobile and Humanoid Robots

The main purpose considered in this chapter is to maintain a specific location and behavior for a robot that uses type-2 fuzzy logic for controlling its behavior. In this chapter, we propose a combination of behaviors by following a trajectory without leaving or losing it and avoiding obstacles in an omnidirectional mobile platform. The results of the simulation show the advantages of the proposed approach. We describe the previous knowledge concerning type-2 fuzzy logic, the virtualization of the mobile robot and its modeling according to real situations. The proposed control system is developed in Matlab-Simulink, the system can model and guide a mobile robot, successfully in simulated and real environments. We also discuss how the proposed techniques could be extended to humanoid robotsHumanoid robots.
The grammar of Indonesian language: Case study of RBS students

Program Bahasa Indonesia bagi Penutur Asing (BIPA) at Bina Nusantara University (Binus) is organized on the basis of Indonesian language's growth in the global era as Indonesia has a strategic location among ASEAN countries. This research objective is to describe to deviation forms done by six RBS students when they learnt Indonesian language. Qualitative research and purposive sampling technique are used. The results show that the diction and sentence deviations become major problem faced by the students. The deviations themselves occur because of language transfer from students' first language to target language. The results of this study have implications in the materials development of BIPA program to assist the foreign students in learning Indonesian language.
Feature Selection through Clustering to Classify High-Dimensional Data

High-dimensional data generally degrades the performance of data mining algorithms. To improve the performance, we can reduce dimensionality by eliminating irrelevant, redundant, and uninformative features, as such features only exert undue burden on algorithms and increase the time and resources needed to build the model. The exclusion of such features is proposed by the clustering-based feature selection method. The main purpose of this method is to select the most influencing relevant feature space by discarding irrelevant features that lead to more accurate results. The efficacy of the proposed model is compared with the standard FS approach (relief and info-gain approach).
Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks

In recent decades, various solutions had been sought for reducing operating costs while increasing the production of minerals in mining operations. Equipment health monitoring technologies had been used for monitoring and increasing the availability of machines. However, the data obtained from these technologies had only been used for monitoring the equipment health, and not for the prediction of failures. In this paper, it was relied on alarms and signals collected through real-time health monitoring technologies for predicting crucial mining truck failures. Sequential Pattern Mining (SPM) Method for Predictive Maintenance had been developed and implemented as a methodology to discover which group of alarms and signals might be related to specific truck failures. The results indicate that the SPM method is able to detect machine failures of trucks with high accuracy with an average 96%. The proposed methodology may reduce the maintenance time, and the expenditures caused by truck breakdowns in the mining industry.
Multi-task Neural Shared Structure Search: A Study Based on Text Mining

Multi-task techniques are effective for handling the problem of small size of the datasets. They can leverage additional rich information from other tasks for improving the performance of the target task. One of the problems in the multi-task based methods is which resources are proper to be utilized as the auxiliary tasks and how to select the shared structures with an effective search mechanism. We propose a novel neural-based multi-task Shared Structure Encoding (SSE) to define the exploration space by which we can easily formulate the multi-task architecture search. For the search approaches, because these existing Network Architecture Search (NAS) techniques are not specially designed for the multi-task scenario, we propose two original search approaches, i.e., m-Sparse Search approach by Shared Structure encoding for neural-based Multi-Task models (m-S4MT) and Task-wise Greedy Generation Search approach by Shared Structure encoding for neural-based Multi-Task models (TGG-S3MT). The experiments based on the real text datasets with multiple text mining tasks show that SSE is effective for formulating the multi-task architecture search. Moreover, both m-S4MT and TGG-S3MT have better performance on the target aspects than the single-task method, multi-label method, naïve multi-task methods and the variant of the NAS approach from the existing works. Especially, 1-S4MT with a sparse assumption on the auxiliary tasks has good performance with very low computation cost.
Assessing usability and user experience of immersive web VR platforms for tourism destinations

Until recently, Virtual Reality (VR) was considered as a niche technology due to slow advancement and high costs. However, thanks to new industry improvements, VR has started to approach the mainstream audience especially through the use of web VR, therefore becoming a new tool for communication. In this sense, due to its content richness and emotional density, the tourism industry reveals as a proper one to exploit the new opportunities coming from web VR. As a result, it becomes imperative to research usability and user experience of web VR platforms for tourism destinations. Despite this importance, standards and guidelines on the topic are still missing. This paper focuses on exploring usability and user experience issues of two web VR experiences devoted to tourism destinations, namely Petra, the ancient city in Jordan, and Bilbao in Spain. The methodology used for this research is based on two sets of VR heuristics applied to the MiLE+ usability evaluation method. Merging these two methodologies has allowed to explore both technical, application independent issues, as well as user experience-related, application dependent issues. Finally, guidelines on usability and user experience factors is proposed, as a first step towards the preparation of a complete list of guiding principles on this topic.
ABOUT THE SYSTEMS WITH FULL SPARK

Frames of a finite-dimensional Euclidean and unitary spaces composed of discrete Fourier transform matrices are considered. The relationship of phaseless reconstruction systems with the alternative completeness property is presented. In the complex case, alternative completeness is only a necessary condition for phaseless reconstruction. A system of vectors is constructed such that each of its subsystems with a volume equal to the dimension of space is linearly independent. These systems are called systems with full spark. In particular, such systems are optimal for phase retrieval.
Research on Multi-UAV Dynamic Mission Assignment Method Based on Clustering Algorithm

In recent years, Unmanned Aerial Vehicle (UAV) technology has become more and more mature, and the demand for UAV s in all walks of life is also increasing. UAV formations or swarms have become an emerging organizational form. As one of the cores of multi-UAV cooperative missions, mission planning has attracted more and more researchers' attention. The multi-UAV dynamic mission assignment method based on the clustering algorithm proposed in this paper can realize the reasonable planning of the multi-UAV reconnaissance mission. First of all, this paper establishes a problem model of the reconnaissance mission. Then based on the trajectory prediction of targets, the improved k-means clustering algorithm is proposed to classify the mission targets. For the targets assigned to each UAV, we propose the dynamic Dijkstra algorithm to optimize the targets reconnaissance sequence. Finally, we designed a simulation experiment and verified the effectiveness of the algorithm proposed in this paper.
CombinoChord: A Guitar Chord Generator App

A dynamic approach to the problem of generating playable guitar chord fingerings is considered. In this problem, the user is allowed to modify parameters relating to the guitar, tuning, and his or her hands at run-time. A core library along with an Android application are produced which solve this problem. The solution employs state space search, multithreading, and other techniques to achieve responsive run-times for any reasonable guitar configuration.
An Application of p-Fibonacci Error-Correcting Codes to Cryptography

In addition to their usefulness in proving one’s identity electronically, identification protocols based on zero-knowledge proofs allow designing secure cryptographic signature schemes by means of the Fiat–Shamir transform or other similar constructs. This approach has been followed by many cryptographers during the NIST (National Institute of Standards and Technology) standardization process for quantum-resistant signature schemes. NIST candidates include solutions in different settings, such as lattices and multivariate and multiparty computation. While error-correcting codes may also be used, they do not provide very practical parameters, with a few exceptions. In this manuscript, we explored the possibility of using the error-correcting codes proposed by Stakhov in 2006 to design an identification protocol based on zero-knowledge proofs. We showed that this type of code offers a valid alternative in the error-correcting code setting to build such protocols and, consequently, quantum-resistant signature schemes.
AntiPhiMBS: A New Anti-phishing Model to Mitigate Phishing Attacks in Mobile Banking System at Application Level

A main challenge in the mobile banking system is to mitigate security risks such as phishing attacks, man in the middle attacks, replay attacks, etc. Verizon's 2019 Data Breach Investigations Report (DBIR) reveals that nearly one-third of all data breaches involved phishing attacks in many kinds of ways. The phishing attack is a type of social engineering attack to steal secret information from users. This paper proposes a new anti-phishing model for Mobile Banking System (AntiPhiMBS) that prevents mobile users from phishing attacks in the mobile banking system at the application level. The model prevents mobile users from phishing app installation by using application id, token number, and a unique id for application received from the bank to operate the mobile banking system. The phisher does not know the application id, token number, and unique id and the relationship among them and is unable to install phishing apps on the user's mobile. This paper develops the new anti-phishing model AntiPhiMBS with system properties specified using Process meta language (PROMELA) that are successfully verified using Simple PROMELA Interpreter (SPIN). The SPIN verification results show that the proposed anti-phishing model is error-free, and the financial institutions can implement the verified model for mitigating phishing attacks in the mobile banking system at the application installation level.
Extended Model Space Specification for Mobile Agent-Based Systems to Support Automated Discovery of Simulation Models

Automated discovery of simulation models is a different simulation modeling approach from the traditional approach, where simulation models are handcrafted by modelers. Our previous work developed an automated simulation modeling approach for mobile agent-based systems that allows automated search of candidate models based on a search space and desired simulation behaviors. This paper extends the model space specification from previous work to support an expanded search space for automated discovery of simulation models. The extended specification includes supporting user-defined properties to capture internal states and other properties of mobile agents and adding a new Activation component for behaviors so that priorities among multiple behaviors can be dynamically computed based on surrounding environment. The extended specification is demonstrated by supporting discovery of simulation models that are not in the previous search space.
Properties of Mass Consciousness in the Digital Economy

The article considers the theoretical aspects of mass consciousness and its forms, the essential characteristics of mass behavior, as a result of which the definition of this term is formulated and corrected. The authors analyze the evolution of concepts of mass consciousness in historical retrospect and at the present stage, its features in the conditions of transition to the digital economy. A fresh look at the problem of mass manipulation in a digital society through various mechanisms is given, and the transformation of mass behavior in the 21st century is analyzed by comparison.
Laser curve extraction of a train wheelset based on an encoder-decoder network

An algorithm of laser curve segmentation for a train wheelset based on an encoder- decoder network is proposed. Aiming at the rich local features and simple semantic features of the train wheelset laser curve image, a neural network with shallow depth, high resolution, and good detail performance was designed. The proposed neural network makes full use of the dense connection mechanism and the upsampling module to enhance feature reuse and feature propagation. It can extract context semantic information at multiple scales with fewer parameters. Experimental results show that the encoder-decoder network has better performance than other neural networks in laser curve extraction of train wheelset. Based on the encoder-decoder neural network, mIOU, Recall, Accuracy, and F1_score of the laser curve dataset of the train wheelset, the score index reached 86.5%, 89.2%, 99.9%, and 85.0%, which can accurately extract the laser stripe of the train wheelset. Additionally, the encoder-decoder network can diminish the influence of noise on the extraction of laser fringes of a train wheelset to a certain extent. Therefore, it has good application in railway safety.
CampusMaps: Voice Assisted Navigation Using Alexa Skills

Voice assistants have become a common occurrence that are present in our smart-phones, speakers or dedicated devices like the Google Home and Alexa Echo. Over the years, these voice assistants have become more intelligent and the number of tasks that can be performed by them has increased. Of the many voice assistants that exist, Amazon’s Alexa is one of the most compatible voice assistants, which can be programmed to suit specific use cases by the use of Amazon’s Alexa Skills Kit. Through this paper, we leverage the power of Alexa’s voice assistance by designing a navigation system for our college campus, which allows users to request directions in the most intuitive way possible. This is a cost-effective and scalable solution based on Amazon Web Services (AWS) Lambda.
Data mining and data visualization for analysing the rate of bed availability at hospitals due to COVID 19

This study started in July 2020 during the COVID 19 pandemic period to analyze & visually illustrate insights of data of biomedical facilities' information.The objective of this study is to present major issues faced, solutions found, and a roadmap for future work in developing visual analytics for interactive & data visualization for biomedical facility applications.This chapter starts with a brief introduction of data mining and data visualization, followed by a description of data mining tasks and we'll build a decision tree model, followed with the concrete examples on various data visualization charts(namely Histogram, Bar Chart, Pie Chart, Line graph, etc), this paper which is intended for visual analysis of "Bed availability rate" in hospitals and decision support for patients and is based on Data Mining & Visualization techniques.
Pretrained Equivariant Features Improve Unsupervised Landmark Discovery

Locating semantically meaningful landmark points is a crucial component of a large number of computer vision pipelines. Because of the small number of available datasets with ground truth landmark annotations, it is essential to design robust unsupervised and semi-supervised methods for landmark detection. Many of the recent unsupervised learning methods rely on the equivariance properties of landmarks to synthetic image deformations. Our work focuses on such widely used methods and sheds light on its core problem, its inability to produce equivariant intermediate convolutional features. This finding leads us to formulate a two-step unsupervised approach that overcomes this challenge by first learning powerful pixel-based features that are then exploited in a second step within standard landmark detection approaches. Our methodology produces state-of-the-art results on several benchmarks such as the BBC Pose dataset, the Cat-head dataset and performs comparably in other situations.
New results in biclique cryptanalysis of full round GIFT

Security of a recently proposed bitwise block cipher GIFT is evaluated in this paper. In order to mount full round attacks on the cipher, biclique cryptanalysis method is applied. Both variants of the block cipher are attacked using Independent biclique approach. For recovering the secret keys of GIFT-64, the proposed attack requires 2127.45 full GIFT-64 encryption and 28 chosen plain texts. For recovering the secret keys of GIFT-128, the proposed attack requires 2127.82 full GIFT-128 encryption and 218 chosen plain texts. The attack complexity is compared with that of other attacks proposed previously. The security level of GIFT is also compared with that of the parent block cipher PRESENT, based on the analysis.
Sirius: Sequential Recommendation with Feature Augmented Graph Neural Networks

Many practical recommender systems recommend personalized items for different users by mining user-item interaction sequences. The interaction sequences, as a whole, imply the manifold collaborative relations among users and items. Further, from the view of users, the item orders and time intervals between interactions could expose the evolution of user interests, and from the view of items, attributes of the items on interaction sequences may reveal the variation of item popularity. However, most of the existing recommendation models ignore those valuable information, and cannot fully explore the intrinsic implication of interaction sequences. In the paper, we propose a method named Sirius, which develops GNNs (Graph Neural Networks) to model the collaborative relations and capture the dynamics of time and attribute features in sequences. We give the workflow of the Sirius method, and describe the implementations about graph construction, item embedding generation, sequence embedding generation and next-item prediction. Finally, we give an example of Sirius recommendations, which visually shows the impact of feature information on the recommendation results. At present, Sirius has been adopted by MX Player, one of India's largest streaming platforms, recommending movies for thousands of users.
Fine-Grained Entity Typing via Label Noise Reduction and Data Augmentation

Fine-grained entity typing aims to assign one or more types for entity mentions in the corpus. Recently, distant supervision has been utilized to generate training data. However, it has two drawbacks. First, the same labels are assigned to every entity mention in a context-agnostic manner, which introduces label noise. Some approaches alleviate this issue by hand-crafted features. However, they require efforts from experts. Second, the entity mentions out of Knowledge Base (KB) are ignored and hence cannot be added to the training data, which decreases the size of the training data. Furthermore, the existing entity typing systems neglect the types of other entity mentions in the same context which provide evidence to infer the types of the target entity mentions. In this paper, we first propose graph-based and sampling-based approaches, to reduce the label noise generated by the distant supervision, and then augment the training data by finding potential entity mentions in the corpus and inferring their types. Moreover, we propose a hierarchical neural network, which involves the types of other mentions in the context and satisfies the type consistency, to predict the types. Experiments on two datasets show that our system outperforms state-of-the-art entity typing systems.
A High Performance Frequency Synthesis Method Based on PLL and DDS

A high performance frequency synthesis method based on PLL and DDS is introduced. The synthesized signal has the characteristics of high frequency tuning resolution, ultra-low phase noise and low spurious. The frequency synthesis method takes the wideband VCO as the frequency source of the PLL and the direct digital frequency synthesizer as the reference signal of the PLL. It has the characteristics of high frequency resolution and low phase noise of the direct digital frequency synthesizer, and improves the spurious level of the direct digital frequency synthesizer through the loop filtering ability of the PLL.
A context aware application to support the execution of daily tasks of people with cognitive disabilities

Technology has been little involved in the special education models for people with cognitive disabilities, and there are regulations that support their labor insertion under equal conditions, but due to the difficulties that these people present, it is not possible to guarantee their inclusion. The present work has as proposal the development of a prototype of Android application based on beacons technology called "TK-2" for the assistance of people with cognitive disabilities in the execution of tasks, which is designed with activities of ascending difficulty and automatic data collection, which shows results on the screen of the device, thanks to this the data are visible, accurate and reliable. In the execution of tests, users were considered based on the experience (average and none) in the proposed activity. These results were contrasted to determine that the application contributes to gradually reduce the evaluation parameters (time, errors and aids) until the user begins to perform the activity independently, also understood as TK-2 acts in activities that are rare and those that are repetitive.