Reviewerly/exHarmony huggingface dataset:
Tiredness Detection for a Robotic Mobility Aid

The proportion of elderly people in society is predicted to continue to rise in the coming decades. Mobility is a key aspect of many daily activities, but falls become an increasingly significant health risk with age. With the COVID-19 pandemic, many elderly users prefer or require assistive devices, rather than human support, in walking and carrying out daily tasks. However, prior work has shown that when using passive assistive mobility devices, fall risks can actually increase. This presents an opportunity for assistive robots to help maintain and improve the mobility of elderly users, with an additional emphasis on safety, made possible through sensing capabilities. In this paper, we present a computer vision system that detects the eye blink and face angle patterns for exhibiting signs of tiredness. In addition to the frame-based detection, we also introduce a time-window collation with a machine learning classifier. The system proposed here is critical in monitoring the user, performing real-time detection, and recommending they take a break if tiredness is detected. The overall system architecture and algorithmic details are presented, then a series of experiments are conducted to validate the performance of the approach.
Explaining Cyber-Physical Systems Using Decision Trees

Cyber-Physical Systems (CPS) are systems that contain digital embedded devices while depending on environmental influences or external configurations. Identifying relevant influences of a CPS as well as modeling dependencies on external influences is difficult. We propose to learn these dependencies with decision trees in combination with clustering. The approach allows to automatically identify relevant influences and receive a data-related explanation of system behavior involving the system's use-case. Our paper presents a case study of our method for a Real-Time Localization System (RTLS) proving the usefulness of our approach, and discusses further applications of a learned decision tree.
The Quality Analysis of Smart Zoning Application Web Using Webqual and Importance Performance Analysis Method, Case Study Election of Senior High School in Depok

The Smart Zoning Application is a web-based application that is used to provide information about the mapping and distribution of Public Senior High Schools in Depok, through this application users/students can calculate the nearest distance between school and student's house. To measure the service quality of this Smart Zoning Application website, it is necessary to evaluate using the Web Qual method and Importance Performance Analysis (IPA). The Web Qual method is used to define four variables, that is usability quality, information quality, interaction quality, and overall. The research data that is used to conduct the analysis is the result of filling out a questionnaire in Google Form collected from Junior High School students and teachers who have used the smart zoning application with random sampling technique. Results of the analysis illustrate that dimensions of the indicators used in Web Qual are 95,8% located in quadrant B, which defines that the indicators used have given importance and performance values according to user wishes. The results of this study can be used as a reference for the development of the Smart Zoning website to make it better and more useful for its users.
Semantically Rich Access Control in Cloud EHR Systems Based on MA-ABE

With the rapid implementation of Cloud-based Electronic Health Record (EHR) systems, health providers are specifically concerned about handling data privacy on the cloud. Existing methods have either scalability issues by requiring that patients grant access to their medical data or a trust issue by having a single authority, thereby creating the problem of a single point of attack. Hence there is a need to develop an EHR system that addresses these bottlenecks for safe, secure, and easy cloud-based EHR management. To address these bottlenecks, we have developed a novel framework that allows policy-based multi-authority access permission to Electronic Health Record systems used by multiple care providers from various places or organizations. This framework, residing on the Edge, has been built using the Multi-Authority Attribute Based Encryption (MA-ABE) and Semantic Web technologies to provide a safe, semantically rich approach to facilitate secure data sharing among organizations who manage different attributes of end-users using a shared dataset. This paper describes our novel approach and the proof of concept prototype that we created to evaluate our framework.
On the Development of Autonomous Assistive Robotic System for Drinking Task for People with Disability

Activities of Daily Living (ADLs), especially activities around the head such as drinking, feeding, shaving and oral care, are of great importance for an independent quality of life. Assistive robots for ADLs are developed to perform tasks that the disabled person would require frequent need for a caregiver. These assistive robotic systems are either manually controlled or controlled via a shared control scheme, thus are not fully autonomous. This paper proposes an autonomous assistive robotic system to perform drinking task for disabled people. The system employs a UR-10 6-DOF manipulator to convey a cup of drink to the user’s mouth and perform autonomously the drinking task in a nature manner. A Kinect sensor is used for online face and mouth detection and head pose tracking, recognition of the cup region of interest and the drink level. The robot trajectory planning and control are done such that the cup is kept upright and is conveyed at a constant speed to the user’s detected mouth point. This makes use of the 3D data acquired from image processing and is online updated for trajectory replanning when the head and mouth pose unintentionally change. Also, during drinking, the cup is continuously reoriented to keep the horizontal drink level at the mouth point and to ensure that the cup remains in contact with the user’s mouth. Simulation results prove that the proposed system can perform the drinking assistance autonomously.
Localization and Identification of UAVs System Using Image Processing

Unmanned aerial vehicles (UAVs) are widely used nowadays for photography, surveillance, mapping and research purposes. These aircrafts control relies on monitoring their location by constantly combining accelerometers inertial navigation system (INS) with satellite communication (GPS). The latter is usually referred to as localization. Alas, a UAV navigation system that relies on GPS is vulnerable to signal failure or initiated interruptions. In this study we will propose a new approach to use the camera installed on the device for localization purposes, such that it could be used as an alternative when the satellite navigation system malfunctions, and/or be easily incorporated into a sensor fusion framework. We will present a vision-based localization method that finds the location of a camera by first comparing the observed image to an orthophoto map and then, using simple geometric approach to localize the camera. The method includes projecting the outer shape of the observed frame onto the map image plane using the fundamental matrix. Then, the camera geolocation is obtained from a simple geometric relation with the frame shape. This research includes a background overview in image processing, and a mathematical analysis of the problem that the project intends to solve. Furthermore, the proposed solution is presented followed by a preliminary simulation which compares our first results to other well-known methods. Then, a description of the algorithm that was used to implement our method is presented, including flowcharts and explanation on its various phases.
Machine Learning based Human Face Recognition for Attendance System

Person identification and authentication are crucial in business. Many methods are utilized for this identification process. Especially biometric technologies are popular one because of its hardness about deception. Human Face recognition is one of the most preferred tools that helps authenticating the humans. This method allows us to detect changes in a person's face patterns. This technology can be used to identify perpetrators in crime detection and also for getting an accurate attendance systems both in university and also in companies. The part of a person's head from the forehead to the chin, or the corresponding part of an animal, is defined as a face by the Oxford Dictionary. In human. The face is the most crucial aspect in human interactions, since it contains important information about an individual. All humans will acknowledge people from their faces. The proposed solution is to create a working prototype of a system that can help lecturers monitor class attendance in lecture rooms by identifying students' faces from an image taken in the classroom. The database can hold the faces of students once the individual's face matches one of the faces stored in the database, attendance is recorded. Face recognition and detection technologies have been developed in recent years. A number of these are used on social media platforms like Facebook, banking apps, government offices, etc.
Structured Case Base Knowledge using Unsupervised Learning

In medicine, medical knowledge representation and reasoning processes are the main issues of Medical Decision Support Systems, MDSS. This paper proposes an approach allowing to design a new representation of medical knowledge model using the Case-Based Reasoning approach and the Unsupervised Learning (K-Means clustering) method. The proposed approach is evaluated using the Stroke Knowledge Base. Obtained results indicate that applying the clustering approach on the case base and, then, using the K-Nearest Neighbor for each cluster is more efficient than the only use of the K-Nearest Neighbor without applying the clustering on the case base. The provided representation and the achieved results constitute an appropriate approach in order to reach several purposes of MDSS in terms of clustering, similarity estimation and quality assessment.
Fast Convolutional Neural Network with iterative and non-iterative learning

Convolutional Neural Networks (CNNs) have achieved potentially good results for image classification. Due to their learning capabilities such networks are explored and implemented in real world applications. However, when it comes to training a CNN on large datasets without transfer learning, it takes long time and resources, due to the iterative process of weight update in fully connected layer. The backpropagation algorithm used to train the entire network suffers from slow convergence, getting trapped in a local minimum, not guaranteed to find global minimum and being hypersensitive to the learning rate set-up. Therefore, in this paper we propose a novel architecture of convolutional neural network with a non-iterative radial basis function-based classification layer which makes it more efficient for image classification tasks. This is a partially parameter free direct learning approach, which is highly beneficial in real-world applications as well as learning associated with large datasets. The proposed approach has been evaluated on four benchmark datasets such as CIFAR-10, MNIST, Digit and CE-MRI. It has achieved the accuracy scores of 99.8%, 99.8%, 98.4% and 98.3% on MNIST, Digit, CE-MRI and CIFAR-10 datasets respectively, when ResNet18 was used as backbone. Experimental results have illustrated that the proposed architecture is much better as it requires less parameter tuning, does not require handcrafted features and achieves same or higher accuracy in relatively lesser time than the standard CNN. Statistical significance tests were carried out to prove the efficiency of the proposed approach.
Decentralized Application Infrastructures as Smart Contract Codes

With the recent advance in concepts like decentralized "cloud" and blockchain-enabled decentralized computing environments, the legacy modeling and orchestration tools developed to support centrally managed cloud-based ICT infrastructures are challenged by such a new paradigm built on top of decentralization. On the other hand, decentralized "cloud" and computing infrastructures need to support many Dapp use cases. As the complexity of these targeted application scenarios increases, there is an urgent need for developing automation and modeling tools for deploying and managing decentralized infrastructures. Instead of creating such tools from scratch, a natural approach is extending mature infrastructure modeling tools for Dapps and decentralized computing environments. To this end, in this work, we have developed extensions to the TOSCA domain-specific language to support smart contract specification of decentralized computing infrastructures for supporting Dapps, where smart contracts or chain codes manage a decentralized computing environment. The result is blockchain-based orchestration and automation for decentralized "cloud" and computing environments, which is a step forward for achieving full decentralization in general-purpose computing.
Smart Contract execution costs optimisation on blockchain network

Blockchain is a type of decentralized database where all data are kept in one large chain of interconnected blocks of data stored on a large number of servers around the world. Besides decentralization, an additional advantage of this technology is the absence of intermediaries in communication between participants in the process. With the advent of the Ethereum network which is based on blockchain technology, in addition to performing financial transactions, now it is possible to create programs that could be executed on this network. However, execution of these programs, known as Smart Contract, costs money that is billed on this network in the form of gas which is directly proportional to the complexity of the computer operations performed within these contracts. Optimizing the structure and content of smart contracts directly implies reducing the cost/gas for their execution. In this paper we propose the structure of smart contracts in the context of gas optimization for their execution.
Face Expression Classification in Children Using CNN

One of the turbulent emotions can be recognized from facial expressions. When compared with adults, children's facial expressions are more expressive for positive emotions and ambiguous for negative emotions so that they are much more difficult to recognize. Ambiguous in terms of negative emotions, for example, when children are angry, sometimes they show an expressionless face, making it difficult to know what emotions the child is experiencing. Therefore, it is proposed research using Convolutional Neural Network with ResNet-50 architecture. According to [1] CNN Resnet-50 is superior to other facial recognition methods, specifically in the classification of facial expressions. CNN ResNet-50 generates a model during the training process, and the model will be used during the testing process. The dataset used is Children's Spontaneous facial Expressions (LIRIS-CSE) data proposed by [2]. CNN ResNet-50 can identify children's expressions well, including expressions of anger, disgust, fear, happy, sad and surprise. The results showed a very significant increase in accuracy, namely in testing data testing reached 99.89%.
An overview of data integration principles for heterogeneous databases

Modern large-scale information systems often use multiple database management systems, not all of which are necessarily relational. In recent years, NoSQL databases have gained acceptance in certain domains while relational databases remain de facto standard in many others. Many "legacy" information systems also use relational databases. Unlike relational database systems, NoSQL databases do not have a common data model or query language, making it difficult for users to access data in a uniform manner when using a combination of relational and NoSQL databases or simply several different NoSQL database systems. Therefore, the need for uniform data access from such a variety of data sources becomes one of the central problems for data integration. In this paper we provide an overview of the main problems, methods, and solutions for data integration between relational and NoSQL databases, as well as between different NoSQL databases. We focus mainly on the problems of structural, syntactic, and semantic heterogeneity and on proposed solutions for uniform data access, emphasizing some of the more recent proposals.
WSN Application Based on Image Compression Using AHAAR Wavelet Transform

Wireless sensor networks (WSNs) require a revolutionary lossy compression picture compression approach known as Adaptive Haar wavelet transform theory due to the limited energy storage capacity of sensors. Battery life may be extended by reducing data transfer. As a consequence, a novel grayscale compression approach has been proposed by this research. Basically, this approach compresses the input picture with the least amount of data loss possible, thereby overcoming the original theory's drawbacks by enhancing compression capabilities while still maintaining the output image's aesthetic appeal. By minimizing the amount of energy needed to convey pictures in WSNs, this study intends for a longer sensor lifespan by reducing the amount of visual information that is lost while compressing images. The compression ratio was put to the test using an image library of universal grayscale pictures. An image's KB size before and after compression, its energy before and after compression, and its post-compression energy usage are all compared.
Siamese Network for Face Recognition in Degraded Conditions

In recent years, face recognition become a primordial technique in computer vision and machine learning. However, masks and many other accessories such as glasses, sunglasses and scarfs lead to face occlusion, which inhibits face recognition and degrades its performance. The automatic recognition of this kind of faces is challenging because: 1) the occluded area hides a significant part from the face, 2) there is a lack of annotated occluded face images for training, and 3) the various degraded conditions make the face recognition task more difficult and complex. To overcome these difficulties, we suggest a new approach for occluded face recognition grounded on the few-shot learning technique. Our suggested approach is based on a Siamese network based on the pre-trained Inception-v3 model for multi-class face recognition under degraded conditions. It aims to represent face images in a new embedding space by extracting the most significant features through the Inception-v3 network. Our proposed network is optimized utilizing the contrastive loss which is calculated between two input images. The suggested network overcomes the existing literature techniques and proves its performance against pre-trained models on both IST-EURECOM LFFD and EKFD datasets.
Correlation Power Analysis and Protected Implementation on Lightweight Block Cipher FESH

With the development of the Internet of Things (IoT), the demand for lightweight cipher came into being. At the same time, the security of lightweight cipher has attracted more and more attention. FESH algorithm is a lightweight cipher proposed in 2019. Relevant studies have proved that it has strong ability to resist differential attack and linear attack, but its research on resisting side-channel attack is still blank. In this paper, we first introduce a correlation power analysis for FESH algorithm and prove its effectiveness by experiments. Then we propose a mask scheme for FESH algorithm, and prove the security of the mask. According to the experimental results, protected FESH only costs 8.6%, 72.3%, 16.7% of extra time, code and RAM.
The Proposed Method of Measuring How Mixed Reality Can Affect the Enhancement of the User Experience

The rapid development of technology and the acceptance of augmented reality (AR) and mixed reality (MR) opens a new chapter in human behavior. AR & MR is changing how we walk, interact with other people, and live in physical and digital worlds. The line between the two worlds is becoming increasingly blurred. With a wide range of possibilities, the potential of AR & MR to create new value for a person lies in its ability to create an enhanced personalized user experience in different aspects of human life. This paper proposes the methodology of measuring the extent of mixed reality influence on the user experience enhancement for the museum visitors. It measures the visitor’s emotion type and level of emotional intensity during the artwork observation. Microsoft Kinect is used as a measurement device to determine emotion type and level of emotional intensity. Microsoft HoloLens will guide a visitor through the correct sequence of steps and ensure that all parts of artworks are visited in the correct order. The high precision of measurements obtained by Kinect and HoloLens make all measurements highly objective.
Automatic, configurable, and partial assessment of student SQL queries with subqueries

Universities provide database-related courses, where students demonstrate their acquired knowledge by writing SQL code as a solution to presented problems. Manual assessment of students’ solutions lasts for days and brings up additional problems: inconsistency in grading, students waiting long for their grades, and they get clarification feedback with details only on demand. Also, not a negligible number of teachers’ clarification results in correction of initially obtained points. Many authors research the possibility to create computer systems for fully automated assessments, which would minimize or completely annul listed challenges. The authors propose a novel system for automatic, configurable, and partial assessment of student SQL queries. Initially, it supported only basic SELECT statements but was upgraded later to support also complex joins and groupings. The comparison of new system output and teachers’ manual assessments concluded that the novel, fully automated assessment system is very promising. Now, the authors upgraded the assessment model to support subqueries, which are widely used, and thus, an integral part of database courses and assessments. This paper compares the output of the proposed system with the teachers’ manually provided grades, focused on subqueries. The results of this more comprehensive solution remain promising, even with the new features added.
Hardware-aware 3D Model Workload Selection and Characterization for Graphics and ML Applications

3D models are widely used in computer graphics, computer vision, and robotics applications. Multiple hardware accelerators are used for running 3D model related applications, since the computations required for models in 3D space are an order of magnitude higher than the computations in 2D space. Due to the high computation intensity of 3D model workloads, using large 3D model datasets for performance characterization is not a feasible choice during accelerator design. Representative subsets are widely used to save the execution or simulation time, e.g, ModelNet10, a subset of ModelNet40, is widely used in the machine learning (ML) domain to save training and inference time. However, this subset is picked by programmers from a software and application perspective.In this paper, we deploy statistical analysis based methodologies to guide the identification of hardware-aware representative subsets, which can maintain higher performance accuracy and achieve larger execution time savings with respect to subsets picked by software programmers. We believe that the methodology proposed in this paper can help hardware architects and engineers design efficient graphics or ML accelerators rapidly.