Reviewerly/exHarmony huggingface dataset:
The Repaschine: A Robot to Analyze and Repair Roads Using Cutting-Edge Technologies

From past decades, transportation is playing a crucial role in establishing strong civilizations. The roads were laid mainly to make the transportation facile for humans. Later these roads became mandatory once the vehicles reached the common man. The roads we use for traveling became the primary culprits for many accidents caused. The reasons for the accidents are the potholes formed on the roads, which are to be repaired manually by humans. The manual repair of these potholes takes around a couple of weeks leading to disturbance of traffic, work labor and time taking. The accidents caused due to these potholes on roads can only be cleared if fast repair is possible. The governments, as well as the traffic department, are in search of mechanism which repairs the road and can avoid the accidents from happening. Thus we propose a system that works on the roads in place of humans and fill the potholes using the carbon substance. The machines we propose have a design of a small-sized vehicle that traverses a particular road and identifies any potholes and perform a fast repairing of it. The proposed machine utilizes precise technologies such as image processing for evaluating metrics of the potholes under the cutting-edge technological concepts. The proposed method benefits the people as they can travel safely, and also the government can make use of it for a reliable, fast, and facile rebuilding of roads at a way cheaper cost than the present-day system.
An Application of Machine Learning and Image Processing to Automatically Detect Teachers’ Gestures

Providing teachers with detailed feedback about their gesticulation in class requires either one-on-one expert coaching, or highly trained observers to hand code classroom recordings. These methods are time consuming, expensive and require considerable human expertise, making them very difficult to scale to large numbers of teachers. Applying Machine Learning and Image processing we develop a non-invasive detector of teachers' gestures. We use a multi-stage approach for the spotting task. Lessons recorded with a standard camera are processed offline with the OpenPose software. Next, using a gesture classifier trained on a previous training set with Machine Learning, we found that on new lessons the precision rate is between 54 and 78%. The accuracy depends on the training and testing datasets that are used. Thus, we found that using an accessible, non-invasive and inexpensive automatic gesture recognition methodology, an automatic lesson observation tool can be implemented that will detect possible teachers' gestures. Combined with other technologies, like speech recognition and text mining of the teacher discourse, a powerful and practical tool can be offered to provide private and timely feedback to teachers about communication features of their teaching practices.
Security Characteristic Evaluation of New Students Admission Information System Based on ISO/IEC 25010 Quality Standard

A lot of activities or businesses has become easier by the information system. One of the activities that has been improved by the use of information system is admission of new students. The new students admission information system was built to handle everything which related to the admission process, especially the selection process. This research measured one of mandatory software quality that has to be fulfilled in the new students admission information system, which is security. The purpose of this research is to give recommendations regarding system improvement in security aspect for system development in subsequent years. This research implemented one of quality standards which is ISO/IEC 25010. Several stages were conducted in this research to measure the quality, such as defining the security characteristics, defining attributes for measurement, implementation of measurement, and proposing the recommendation. The result of the measurement proposed a recommendation in integrity sub characteristic, especially in the aspects of validity of array accesses. Overall, from the research that has been conducted, PPDB Surabaya 2019 Information System has average value above the threshold, so it can be concluded that the system has been met ISO/IEC 25010 quality standard.
Evaluation and formulation of the sense of social telepresence in video‐mediated communication systems: Contribution of eye contact to enhancing social telepresence

Abstract Video‐mediated communication systems are generally used to enable face‐to‐face‐like communication between people in distant locations. However, in terms of social telepresence, video communication systems are generally inferior to actual face‐to‐face communication, which is a long‐standing challenge for online communication systems. Multiple studies have reported that several specific components, such as the resolution of the transmitted video and the width of the visual field, affect social telepresence separately. However, methods have not been established to comprehensively evaluate the sense of social telepresence provided by video communication systems, that is, integrated multiple component systems with various specifications. In this study, employing a regression analysis based on subjective evaluation results, we establish a new method to predict the sense of social telepresence based on the specifications of some system components. Using this formulation, it is clear that realizing eye contact and life‐size scaling make relatively large contributions to enhancing social telepresence. Finally, we develop a prototype system realizing eye contact and life‐size scaling and perform additional subjective evaluations to reconfirm the effectiveness of these components to enhance the sense of social telepresence in real‐time communication.
Door Access Control based on Illumination Invariant Face Recognition in Embedded System

The use of conventional keylock is sometimes insecure due to it can still be hijacked by a thief. In this paper, we address the problem by offering a biometric-based keylock system based on automatic face recognition. We proposed an innovation into our face recognition based electronic keylock system by an ability to recognizing person even in a harsh illumination condition. An illumination invariant face recognition is adopted to robustly recognize a person even there is a change in illumination. This method is simple, and it can be embedded into a low-cost embedded system, like Raspberry Pi or Lattepanda. Based on our experimental results on Lattepanda, our system shows good performance and is very promising to improve the ability of conventional electronic keylock systems.
Blockchain-based scalable authentication for IoT

Device identity management and authentication are one of the critical and primary security challenges in IoT. In order to decrease the IoT attack surface and provide protection from security threats such as introduction of fake IoT nodes and identity theft, IoT requires scalable device identity management systems and resilient device authentication mechanisms. Existing mechanisms for device identity management and device authentication were not designed for huge number of devices and therefore are not suitable for IoT environments. This work presents results of a blockchain-based identity management approach with consensus authentication, as a scalable solution for IoT device authentication management. Our identity management approach relies on having a blockchain secure tamper proof registry and lightweight consensus-based identity authentication.
Recognition of distorted QR codes with one missing position detection pattern

Quick response (QR) codes are widely used in many fields. Various QR code recognition approaches have been proposed to improve the accuracy of decoding QR code. However, the recognition of distorted QR codes with one missing position detection pattern (PDP) remains a problem. In this study, based on the vector relationship and the structural features, the authors introduce a new method for decoding distorted QR code with one missing PDP. Three methods, Zxing, Halcon, and the newly proposed method, are used to test the decoding capability. For QR codes with one missing PDP, the experimental results show that the proposed method could meet the recognition angle range as much as 110°, while Zxing fails to recognise, and the angle of decoding for Halcon is 90°. Especially, the proposed method is available at an extremely harsh luminance and contrast environment, e.g. both phases as a 60% discount, when the decoding angle of Halcon is only 35°, while the proposed one better than 2.7 times of it. Besides, the proposed method is more robust to decode the QR codes with a missing PDP under different backgrounds and noisy images.
Research on the application of face recognition system

ABSTRACT For the unrestricted investment of face recognition technology, whether it is to protect the safety of people’s life and property or to infringe on people’s basic rights. Face recognition is the most typical one of pattern recognition. It extracts the feature information of face in different ways, compares or tests the collected information with the original information, to realise the record verification of identity or the tracking and positioning of personnel. Nowadays, face recognition technology has been widely used in everyone’s daily life. On the one hand, it brings great convenience to life, on the other hand, whether personal information security and privacy can be guaranteed. Everything has two sides, and face recognition technology is no exception. How to balance the advantages and disadvantages of privacy and security still needs to be further explored.
Gradually typing strategies

The Stratego language supports program transformation by means of term rewriting with programmable rewriting strategies. Stratego's traversal primitives support concise definition of generic tree traversals. Stratego is a dynamically typed language because its features cannot be captured fully by a static type system. While dynamic typing makes for a flexible programming model, it also leads to unintended type errors, code that is harder to maintain, and missed opportunities for optimization.
Zones of participation – a framework to analyse design roles in early childhood education and care (ECEC)

Current research has addressed children’s involvement in codesign from several different perspectives such as empowerment, design thinking, participation, and learning. The literature presents many roles that children can have in participatory design (PD) e.g. as testers, informants, users, co-designers, and co-researchers. Both critical views and solutions to support children’s genuine participation exist. The investigation was motivated by examining whether and how children’s participation can be enhanced through PD practices. The study conducted a participatory design (PD) process in a kindergarten in Finland together with adults and children.The Grounded Theory analysis elicited different roles of the participants as prominent indicators of the interaction in and quality of participatory process. These roles were further elaborated as Zones of participation (ZoP) that constituted of the Zone of audience, Zone of whisperer, Zone of actor and Zone of director. The analysis shows that there are significant co-occurrences between participant groups accessing different zones.We suggest that the quality of participation in the design process can be investigated by identifying participants’ access to different ZoP. Movement between the zones indicates that there is no one-sided control over actions and dialogue between the participants is prominent.
An Approximation of Theta Functions with Applications to Communications

Computing the theta series of an arbitrary lattice, and more specifically a related quantity known as the flatness factor, has been recently shown to be important for lattice code design in various wireless communication setups.However, the theta series is in general not known in closed form, excluding a small set of very special lattices.In this article, motivated by the practical applications as well as the mathematical problem itself, a simple approximation of the theta series of a lattice is derived.A rigorous analysis of its accuracy is provided.In relation to this, maximum-likelihood decoding in the context of compute-and-forward relaying is studied.Following previous work, it is shown that the related metric can exhibit a flat behavior, which can be characterized by the flatness factor of the decoding function.Contrary to common belief, we note that the decoding metric can be rewritten as a sum over a random lattice only when at most two sources are considered.Using a particular matrix decomposition, a link between the random lattice and the code lattice employed at the transmitter is established, which leads to an explicit criterion for code design, in contrast to implicit criteria derived previously.Finally, candidate lattices are examined with respect to the proposed criterion using the derived theta series approximation.
Research and analysis on the development of intelligent toilet

Abstract Intelligent toilet, also known as “electronic toilet” or “smart toilet”, was first invented by the Americans in 1964, and then introduced to Japan by Japanese businessmen. After the product is optimized in design, it began to be popularized in Japan in the 1980s and 1990s.In the early 1990s, Japanese businessmen has introduced intelligent toilet into China, so far, opened the domestic. The development of domestic intelligent toilet can be roughly divided into three stages: the first stage of domestic intelligent toilet development is 1990-1995, also known as the birth period of Chinese intelligent toilet, domestic intelligent toilet experienced the first stage of the cradle period, began to enter its growth period 1995-2015;2015 is a special and unusual year in the history of China’s intelligent toilet. After 2015, domestic intelligent toilet enters its third stage of development.
Safe and Optimum Navigation of Wheeled Mobile Robot using Grey Wolf Optimization Algorithm

Abstract This paper discusses our research in developing a track planner for a mobile robot using a swarm intelligence technique for optimal track planning in a short computational time to achieve better results in track planning. Through this technique, we proposed grey wolf optimization (GWO) for generating fastest and optimal path planning. this paper introduces an algorithm for rapid and global motion preparation for a mobile robot in a complex environment with static obstacles. the performing analysis for GWO algorithm was evaluated in two different maps. Finally, A comparative study was evaluated between the algorithm built and the other algorithm exist, the simulation results, which carried out using Matlab 2018a showed that the GWO algorithm made results for generating optimal path planning and efficiently in terms of path distance, execution-time.
Measuring components of the memory of order

An experienced event has many distinctively different qualitative attributes. Besides the informational content, such as which word was presented on an experimental list at a specific temporal instant, there is also information about the ordinal properties of the event relative to other events. A multinomial processing tree (MPT) model is provided for measuring four different states for the memory of the order of any arbitrary set of three events. This model, which is called the Event Order (EO) model, is linked to a specific experimental protocol for testing ordinal memory. Importantly the EO model requires neither the stipulation of a specific format for the memory encoding nor the specification of a model of order learning. The EO model is proved to be identifiable. Also the statistical estimation of the parameters of the EO model is developed using either the population parameter mapping (PPM) procedure or the maximum likelihood estimation (MLE) method, and these procedures are compared by means of Monte Carlo studies. Furthermore three experiments are described that employ the experimental protocols that enable the estimates of the parameters of the EO model. The experimental evidence shows that item knowledge is quickly acquired; whereas the corresponding acquisition of ordinal information requires many more training trials. The EO model thus provides a new way to measure ordinal information.
Distributed Deep Learning With GPU-FPGA Heterogeneous Computing

In distributed deep learning (DL), collective communication algorithms, such as Allreduce, used to share training results between graphical processing units (GPUs) are an inevitable bottleneck. We hypothesize that a cache access latency occurred at every Allreduce is a significant bottleneck in the current computational systems with high-bandwidth interconnects for distributed DL. To reduce this frequency of latency, it is important to aggregate data at the network interfaces. We implement a data aggregation circuit in a field-programmable gate array (FPGA). Using this FPGA, we proposed novel Allreduce architecture and training strategy without accuracy degradation. Results of the measurement show Allreduce latency reduction to 1/4. Our system can also conceal about 90% of the communication overhead and improve scalability by 20%. The end-to-end time consumed for training in distributed DL with ResNet-50 and ImageNet is reduced to 87.3% without any degradation in validation accuracy.
Implementation of Modified Tangent Bug Navigation Algorithm for Front Wheel Steered and Differential-Drive Robots

In this paper, the Tangent Bug algorithm has been studied and implemented for navigating a robot towards the desired destination. The navigation environment consists of the convex and concave obstacles in the robot's path. The Tangent Bug algorithm is implemented for both the Front Wheel Steered (Ackermann) as well as differential drive (Pioneer P3-DX) robots. For simulation purpose, the built-in robot models in Virtual Robotic Experimentation Platform (V-REP) are used. For obstacle detection, the Ackermann and Pioneer robot models are equipped with Hokuyu Laser Scanner URG-04LX-UG01 characterized by a finite scan area of 240 degrees with an angular resolution of 0.36 degrees and a maximum detection range of 4 meters approx. To evaluate the performance of Tangent Bug algorithm using both Front Wheel Steered and Differential-Drive robots, experiments were carried out in MATLAB and V-REP Platform. A separate MATLAB script was written to interface MATLAB with V-REP software utilizing various MATLAB Remote API functions. The simulation results show that the two robots navigate towards the desired goal position successfully avoiding all the obstacles in their path.
Evaluation of Transfer Learning with CNN to classify the Jaw Tumors

Abstract Artificial Intelligence” (AI) This term refers to the idea that the machines can perform human tasks. Recently, researchers, professionals and companies around the world introduce deep learning and image processing systems that can analyze hundreds of X-Ray and Computer Tomography (CT) images rapidly to speed up the diagnosis of medical image and help to contain them. Dental diseases analysis is among the most innovative research fields, offering diagnostic and decision-making facilities for a variety of diseases, such as oral and maxillofacial diseases. Inside this paper, we present a comparison of recent architectures of the Deep Convolutional Neural Network (DCNN) for the automatic classification of two diseases depending on transfer learning with fined tuned using a pre-trained network (VGG16, VGG19). The proposed work was tested using a small scale X-Ray panoramic dataset containing 116 images (58 ameloblastoma and 58 Complex Odontoma). As a result, we can assume that the pre-trained network (VGG19) demonstrates highly satisfactory results with a rate of increase in the accuracy of training and validation. Unlike CNN, pre-trained network (VGG16) demonstrates less performance when a small image dataset is available.
Active Sampling for Learning Interpretable Surrogate Machine Learning Models

The use of machine learning methods to inform consequential decisions is increasingly expanding across many fields. As a result, the ability to interpret these models has become to a greater extent crucial to increase the related-technologies acceptance level and reliability. In this paper, we propose an active sampling approach for learning accurately interpretable surrogate machine learning model to better approximate black-box models for supervised learning problems. Hence, the surrogate model is used to learn the black-box model and reflect its properties. Active sampling is used as an informed sampling method to adaptively and iteratively build an optimized training set based on the predictions of the black-box model to enhance the accuracy of the surrogate model. Subsequently, the surrogate model is used to interpret and debug the black-box model. The developed method is flexible and can be used to approximate any family of black-box models using any type of interpretable machine learning models, as it only requires the ability to compute their outputs. It is also applicable to both regression and classification tasks. In this work, we bring focus to decision tree due to their proven high interpretability. An experimental evaluation of the method on several real-world data sets is presented to show its flexibility and its robustness compared to traditional approaches for learning surrogate models.
Online Transaction Detection Method Using Catboost Model

The blossom of online transaction has given rise to fraud, especially credit card fraud. Thus, Fraud Detection algorithm has become a critical issue, and several attempts have been made to detect transaction fraud using data mining methods. However, these methods suffer from lack of data or inadequate feature engineering. In this paper, we applied sufficient feature engineering and proposed a fraud detection algorithm based on Catboost. The experimental result indicates that our model outperforms other classical models, such as Logistic Regression, Support Vector Machine, and Random Forest. Moreover, we also illustrate the feature importance, which is valuable for feature selection and performance tuning.
A Self-tuning PID Controller Design based on Fuzzy Logic for Nonlinear Chemical Processes

A fuzzy self-tuning Proportional-Integral-Derivative (PID) controller, the f-SmartPID, was designed to promote high quality and stable control, and to save time and energy by ensuring continuity in nonlinear chemical processes. Because PID controllers were developed using linear structures, its performance is severely affected when working with nonlinearities of the process. A PID fuzzy filter was designed to deal with nonlinearities. Two fuzzy inference systems were incorporated to the conventional PID controller to modify the closed loop time constant when required, originating a fuzzy closed loop time constant. f-SmartPID updates the controller gain to avoid undesirable oscillations and maintain stability in the control loop under extreme conditions. This controller was tested in a highly nonlinear process facing disturbances and set point changes, showing optimal performance in linear and nonlinear conditions. It was compared with the Conventional PID controller and the IMC (Internal Model Controller).