Reviewerly/exHarmony huggingface dataset:
Two trajectories of the development of pottery forming methods in central Europe in the Iron Age: The contribution of analysis of the orientation of components of a ceramic body

In this article, the development of pottery-forming techniques employing rotational movement during the La Tène period in two different regions of central Europe is compared. One region represents a small, rather peripheral, region in Eastern Bohemia (Czech Republic). Results of the technological analysis in this region have already been published. The other region is a newly-analysed region situated in South Moravia (Czech Republic) with evidence of intensive use of the local landscape throughout the La Tène period. The comparison is based on a quantitative analytic technique measuring the orientation of a ceramic body’s components. Results of the analysis indicate different conditions for the transmission of the technology. In one case, there is evidence for disruptive changes in the technology, while in the other case, continuous development with an increasing proportion of wheel-thrown pottery is observed.
Online motion generation using accumulated swept volumes

This study describes a method for generating the online motion of multi-degree of freedom robot. To quickly generate motion, we adopt an approach that accumulates a robot's past motion experiences and subsequently selects and modifies the appropriate motions from this experience. The key aspect of our method involves using swept volume. As a pre-processing step, the robot's movements for reaching various target postures in a range of environments are derived using a conventional motion planning method. At this time, the volume occupied by the robot, i.e. the swept volume, is recorded using the destination posture and generated motion. Then, using a new target hand pose, the system searches past experience data closely linked to the target posture and selects the motion with a swept volume that does not interfere with the current environment. With our implementation of swept volume, collision detection is achieved only one bitwise operation. Moreover, it is possible to generate a larger range of behaviors outside the collected behavior data by connecting multiple motions and swept volumes. Our method's effectiveness was then confirmed by motion generation experiments using a mobile manipulator.
An Algorithm for Multi-Fame Image Super-Resolution Under Applicative Noise Based on a Convolutional Neural Network

The paper considers the problem of multi-frame super-resolution under applicative noise which generates distributed regions of outlying observations (damaged regions) in initial low resolution images. It analyses the existing solutions to the problem based on using recurrent algorithms of optimal filtering of a sequence of low resolution images. The paper also considers the existing super-resolution algorithms based on convolutional neural networks and deep learning models. A new approach and a corresponding image processing scheme is suggested for multi-frame image super-resolution under applicative noise based on using a convolutional neural network. The paper presents the results of the experiment conducted in order to compare the considered approaches to multi-frame image super-resolution by means of a sequence of low resolution frames under applicative noise.
Reinforcement Learning Based Framework for Real Time Fault Tolerance

Smart autonomous systems are system that take decisions independently and on run time without the need for human interaction. One of the most important components that plays a big roll in system autonomy is fault tolerance and avoidance which is a basic capability that autonomous systems should have in order to be able to survive surrounding environment state change without the need for supervision. In this paper, a framework is proposed where fault tolerance and avoidance is achieved through a reinforcement learning based framework. The framework adapts to changes and learns new processes for fault tolerance and avoidance whenever environment states change. The framework has a set of predefined actions and an observable environment. Reinforcement learning is being applied in order to learn the sequence of actions that needs to be taken to avoid or tolerate failure. The outcome of the learning process is a sequence of actions to help the system reach a desired state while avoiding fault states. These are used for later execution when the same situation occurs when the agent is in similar environment state while having the similar readings. Two Theorems and a Lemma are proposed to define the validity and correctness of the framework. The proposed framework is then simulated and tested.
Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)

We explore path planning followed by kinodynamic smoothing while ensuring the vehicle dynamics feasibility for MAVs. We have chosen a geometrically based motion planning technique "RRT*" for this purpose. In the proposed technique, we modified original RRT* introducing an adaptive search space and a steering function which help to increase the consistency of the planner. Moreover, we propose multiple RRT* which generates a set of desired paths, provided that the optimal path is selected among them. Then, apply kinodynamic smoothing, which will result in dynamically feasible as well as obstacle-free path. Thereafter, a b spline-based trajectory is generated to maneuver vehicle autonomously in unknown environments. Finally, we have tested the proposed technique in various simulated environments.
Secure Mobile Sensor Data Transfer using Asymmetric Cryptography Algorithms

Mobile sensors are playing a vital role in various applications of a normal day life. Key size in securing data is an important issue to highlight in mobile sensor data transfer between a smart device and a data storage component. Such key size may affect memory storage and processing power of a mobile device. Therefore, we proposed a secure mobile sensor data transfer protocol called secure sensor protocol (SSP). SSP is based on Elliptic Curve Cryptography (ECC), which generates small size key in contrast to conventional asymmetric algorithms like RSA and Diffie Hellman. SSP receive values from light sensor and magnetic flux meter of a smart device. SSP encrypts mobile sensor data using ECC and afterwards it stores cipher information in MySQL database to receive remote data access. We compared the performance of the ECC with other existing asymmetric cryptography algorithms in terms of secure mobile sensor data transfer based on data encryption and decryption time, key size and encoded data size. In-addition, SSP shows better results than other cryptography algorithms in terms of secure mobile sensor data transfer.
Triangle Counting with Cyclic Distributions

Triangles are the simplest non-trivial subgraphs and triangle counting is used in a number of different applications. The order in which vertices are processed in triangle counting strongly effects the amount of work that needs to be done (and thus the overall performance). Ordering vertices by degree has been shown to be one particularly effective ordering approach. However, for graphs with skewed degree distributions (such as power-law graphs), ordering by degree effects the distribution of work; parallelization must account for this distribution in order to balance work among workers. In this paper we provide an in-depth analysis of the ramifications of degree-based ordering on parallel triangle counting. We present approach for partitioning work in triangle counting, based on cyclic distribution and some surprisingly simple C++ implementations. Experimental results demonstrate the effectiveness of our approach, particularly for power-law (and social network) graphs.
Defining Embedding Distortion for Sample Adaptive Offset-Based HEVC Video Steganography

As a newly added in-loop filtering technique in High Efficiency Video Coding (HEVC), sample adaptive offset (SAO) can be utilized to embed messages for video steganography. This paper presents a novel SAO-based HEVC video steganographic scheme. The main principle is to design a suitable distortion function which expresses the embedding impacts on offsets based on minimizing embedding distortion. Two factors including the sample rate-distortion cost fluctuation and the sample statistical characteristic are considered in embedding distortion definition. Adaptive message embedding is implemented using syndrome-trellis codes (STC). Experimental results demonstrate the merits of the proposed scheme in terms of undetectability and video coding performance.
Comparison of deep CNN and ResNet for Handwritten Devanagari Character Recognition

Handwritten Optical Character Recognition is a lush area of research and is used in various real time applications. This research is based on comparative analysis of handwritten OCR by using Deep CNN and ResNet for Devanagari script, a regional language. Devanagari character contains two elements, diacritics and the main grapheme. Key challenge associated with Devanagari script is many a time different characters look similar. Secondly some characters are written differently by different individuals. Proposed ResNet manages vanishing gradient issue and improves capability of traditional Deep CNN. It uses dynamic flow of activation. ResNet identity blocks help in to overcome vanishing gradient issues. Proposed architecture scored close to 99% accuracy for the DHCD, which is better than other state-of-art results. Training phase of proposed model is reasonably less than many other variants of deep CNN.
Unsupervised Ensemble Classification With Sequential and Networked Data

Ensemble learning, the machine learning paradigm where multiple models are combined, has exhibited promising perfomance in a variety of tasks. The present work focuses on unsupervised ensemble classification. The term unsupervised refers to the ensemble combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. While most prior works on unsupervised ensemble classification are designed for independent and identically distributed (i.i.d.) data, the present work introduces an unsupervised scheme for learning from ensembles of classifiers in the presence of data dependencies. Two types of data dependencies are considered: sequential data and networked data whose dependencies are captured by a graph. For both, novel moment matching and Expectation-Maximization algorithms are developed. Performance of these algorithms is evaluated on synthetic and real datasets, which indicate that knowledge of data dependencies in the meta-learner is beneficial for the unsupervised ensemble classification task.
Subgraph feature extraction based on multi-view dictionary learning for graph classification

Subgraph feature extraction of graph data has an efficiency problem that has become increasingly significant. A new architecture of subgraph feature extraction named GMADL is proposed in this paper. Dictionary learning approaches are put forward to extract the features of graph data to enhance the discrimination of model. To improve the efficiency of extraction, the analysis dictionary is designed as a bridge to generate the sparse code directly. Each sparse code represents the feature matrix of a graph. Through constructing the multi-view support vector machine (SVM) classifiers, the problem can be transferred into the multi-view problem so that the information of the whole view is utilized to predict the classification model. The comparison of the proposed architecture with the state-of-the-art approaches manifests the feasibility and the competitive performance in graph classification.
An entropy based salient edge enhancement using fusion process

In this paper, a novel sub band replacement and fusion process with adaptive weights is proposed for image denoising and representation. These weights are required to reduce aliasing effects. In this regard, we have designed an explicit convergence criterion based on entropy measurement associated with edge enhancement strategy. The proposed method is a fully automatic version of the nonlocal means and can easily be adapted for non-stationary Gaussian noise, where the noise variance is unknown. The proposed method improves the images not only in edge enhancement but also in preservation of details while removing the noise without over-smoothing or over-sharpening the salient features of images. The edge enhanced and de-noised image validation are performed visually and quantitatively using well-established metrics.
Segmentation of Aquaculture Underwater Scene Images based on SLIC Superpixels Merging-Fast Marching Method Hybrid

Segmentation is a challenging task for the complex and low-quality underwater images, as this is prerequisite to advanced tasks in fish monitoring such as fish detection and classification. A demand exists for underwater image segmentation algorithms that can robustly segment fish from its background. A competitive approach is the integration of states-of-the-art image segmentation algorithms: SLIC superpixels merging by KAZE Keypoints clustering and Fast Marching Method (FMM) to a single framework. The combination of these established methods offers robustness towards underwater images of different visual qualities. First, a locally acquired underwater image is represented as superpixels. Then, the KAZE features of an underwater image is extracted. Such features are utilized by the k-means clustering to group superpixels which contains fish pixels into a region. Lastly, the merged region is further segmented with Fast Marching Method and corresponding morphological processes. The study presents the viability of the integration of different image segmentation techniques for localized application. The number of superpixels, KAZE Keypoint score threshold and FMM threshold are identified to affect the performance of the proposed algorithm. Qualitative observations and quantitative measures validate the robustness of this generated algorithm to address this difficult and persistent task.
NO to violence against any woman!

COVID-19's social distance increased domestic violence (DV). In Brazil, in spite of the existence of services to denounce DV, women who do not communicate through the Brazilian-Portuguese language (Pt-Br) are, in fact, excluded as users. As a previous stage to development of a mobile application to denounce DV by women who are illiterate in Pt-Br, being them Brazilian or immigrant and including Brazilians with disabilities, a study on Brazilian mobile services to denounce DV by women who are Deaf was performed. This paper presents the study's results, requirements and shows that applications are not inclusive. The requirements triggered the need for future Participatory Design activities together with each one and all women involved, in order to guarantee that their other cultural and social specificities are addressed. These activities are part of the feminist interaction design agenda, within which the application design process will be seen.
Laboratory for Electrical Engineering Using Mixed Reality

The article discusses an electrical laboratory using mixed reality technologies. The authors have developed a working version of the educational application for a computer or smartphone using mixed reality technology. The capabilities of the application developed by the authors are shown on the example of the discipline "Theoretical Foundations of Electrical Engineering". A fragment of the application structure in the form of mind maps is presented. The main stages of the application are demonstrated. Using the example of electrical engineering, it is shown that the learning process can quickly meet the requirements of the era of industrial revolution 4.0. This is possible with the help of modern technologies, one of which is mixed reality technology. The application developed by the authors allows you to increase the practical orientation of laboratory work. It gives the student the opportunity to explore the actual appearance of electrical components. Device models are demonstrated by pointing the student's smartphone camera at the corresponding markers in a real or virtual laboratory. The application was developed in the software environment of the augmented reality object constructor EV Toolbox, the basic educational version of Standard Edu. 3D models are created in Autodesk Inventor.
A Face Recognition Algorithm Based on Dual-Channel Images and VGG-cut Model

Abstract In the embedded system environment, both large amount of face image data and the slow recognition process speed are the main problem facing face recognition of end devices. This paper proposes a face recognition algorithm based on dual-channel images and adopts a cropped VGG-like model referred as VGG-cut model for predicting. The training set uses the same single-layer images of the same person combined into dual channels as a positive example, and single-layer images of different people are combined into dual channels as a negative example. After model training, the trained face verification model is used as the basis of face recognition, and the final recognition result can be obtained through loop matching and similarity ranking. The experimental results on a RISC-V embedded FPGA platform show that compared with the ResNet model called in Dlib, the VGG-like model and MobileFaceNet trained by Keras, our algorithm is increased by 240×, 88×, and 19× in recognition speed, respectively without significant accuracy reduction.
A FPGA based intra-parallel architecture for PageRank graph processing

Acceleration of large-scale graph processing by FPGA has been proved an energy efficient method, especially in edge computation scenarios. FPGA acceleration of graph calculation includes inter partition parallel and intra partition parallel. In this paper, we focus on intra partition parallel and present a novel data selection network of less complexity. Moreover, by intra sub-graph compression, proper inter subgraph scheduling, and some initialization modules, we get our graph processing architecture. We test PageRank algorithm on data set of Twitter using the novel architecture. Experiment shows that the FPGA can support a data selection network of 128 processing units and has an effective processing ability of 51 edges per cycle and a throughput of up to 7650 Mega Traversed Edges PER Second (MTEPS), which results in a 3x throughput improvement and 1.5x DRAM bandwidth utilization rate improvement comparing with state-of-the-art design.
A Comparison of Supervised Text Classification and Resampling Techniques for User Feedback in Bahasa Indonesia

User feedback is one of the most important sources of information for improving the quality of software products. Our current research focuses on a software product that is often used in many universities, the E- Learning system. To reduce the effort of manually reading all submitted user feedback, building an automatic text classification using various machine learning approaches is a popular solution. However, there is often a challenge of imbalanced data that could jeopardize the ability of the machine to find the pattern and classify feedback correctly. Several techniques ranging from random resampling of data to artificially creating more data (e.g. SMOTE) have already been proposed for handling imbalanced data and show promising results in terms of performance. This paper aims to implement several numerical representations and implementing resampling techniques (to handling imbalanced data), which then are followed by evaluating some popular supervised machine learning classification algorithms, which are the Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Decision Tree. Finally, evaluating performance with and without using resampling techniques by macro-average F1 Scores. The results show generally the implementation of oversampling techniques leads to better performance, except in a few cases where under-sampling techniques perform better.
A Method of Finding Optimal Parameters of Speckle Noise Reduction Filters

The reduction of multiplicative speckle noise in synthetic aperture radar (SAR) images is an important problem. Many speckle noise reduction filters have been proposed. Most of them have several parameters that control their operation. Finding the optimal values of these parameters is often a non-trivial task. A method of automating the search for optimal parameters is proposed. The method uses two variants of a specially designed test image, original noise free image and the same image but with speckle noise added. Then the Structural Similarity Index (SSIM) metric is used for finding the parameters that make the filtered image as close to the original noise free image as possible. The application of the method is illustrated using the Frost filter applied to various images, but the method can be used for any filter type.
Enhanced interaction

The use of mobile devices is part of the modern scenario for interaction, work, and leisure. Their usage in entertaining events, as sports and cultural ones are easily observable. Considering those new interaction possibilities, we discuss in this work elements of crowd engagement through the use of a mobile game developed in the context of entertainment events, either with the crowd in the same place or in different locations. This game is called Bumbometer and it uses movement sensors during an interactive dynamic with the crowd using concepts from Mobile Crowd Sensing and User eXperience. Bumbometer was evaluated in two experimental studies: the first one was in a real setting of a cultural event; the second one was carried out in a controlled environment simulating a large-scale event in a social isolation scenario. Results indicate that people feel immerse and engaged during interaction through the game and they consider the game an immersive creative way for interacting in entertainment events.
Context-Aware Android Applications Testing

Context-aware applications (CAAs) are those that use information from the environment based on sensors such as gyroscope, GPS, and accelerometer. Compared to desktop and web applications, there are additional challenges to test CAAs. It is necessary to take input from users and sensors into account, which can lead to the explosion of possible situations. This work aims to present an approach that makes it possible to automate the black-box testing of context-aware Android applications that follow paths using GPS. Our approach consists of selecting, through pairwise testing, combinations of sensor values with events that occur during the execution of the application under test (AUT). The approach is implemented in a tool supporting GPS-based applications. The approach and tool were analyzed through an empirical study with four real GPS-based applications. Initial results show they can be effective in detecting possible defects related to the context.
Decentralized and Privacy-Preserving Key Management Model

Data centralization and the growing rate of security breaches and identity fraud have led us to seek privacy-preserving and decentralized identity management solutions. The success of decentralized models, such as Self-Sovereign Identity, hinges on the positive-sum combination of usability and security. To support this goal, we propose a decentralized system capable of performing key management operations including key generation, key backup, and key recovery. In addition we propose our preliminary solution for a decentralized identity verification protocol. To this end, we design a digital wallet that relies on Shamir's Secret sharing scheme and blockchain technology, and we present a number of the security parameters affecting our model.