Reviewerly/exHarmony huggingface dataset:
Supervised global path planning for mobile robots with obstacle avoidance

The presence of mobile agents in the industrial environment is growing, introducing specific safety issues in their path planning. This paper proposes the implementation of a three-level path planning procedure, which allows: (i) the imposition of a set of waypoints, tending to a safe path, generated by a supervisory planner on the basis of a static map of the environment (not necessarily fully updated), (ii) the generation of a global path including such waypoints exploiting a cost-based algorithm, taking into account also the obstacles not included in the static map, but detected at the beginning of the global planning phase, and (iii) the avoidance of dynamic obstacles appearing during the robot motion, thanks to the action of a local planner. The procedure has been experimentally tested to plan the motion of a differential mobile robot.
Data Sharing System Integrating Access Control Based on Smart Contracts for IoT

Development of Internet of Things (IoT) network brings new concept of Internet. The dramatic growth of IoT increased its usage. IoT network facilitates in several manners, more specifically, in access control and data sharing among IoT devices. However, it has many challenges, such as: security risks, data protection and privacy, single point of failure through centralization, trust and data integrity issues, etc. This work presented a blockchain based access control and sharing system. The main aim of this work is to overcome the issues in access control and sharing system in IoT network and to achieve authentication and trustworthiness. Blockchain technology is integrated with IoT, which simplifies the access control and sharing. Multiple smart contracts: Access Control Contract (ACC), Register Contract (RC), Judge Contract (JC), are used that provide efficient access management. Furthermore, misbehaviour judging method utilizes with penalty mechanism. Additionally, permission levels are set for sharing resources between users. Simulation results show the cost consumption. Bar graphs illustrate the transaction and execution cost of smart contracts and functions of main contract.
Cutting the Unnecessary Long Tail: Cost-Effective Big Data Clustering in the Cloud

Clustering big data often requires tremendous computational resources where cloud computing is undoubtedly one of the promising solutions. However, the computation cost in the cloud can be unexpectedly high if it cannot be managed properly. The long tail phenomenon has been observed widely in the big data clustering area, which indicates that the majority of time is often consumed in the middle to late stages in the clustering process. In this research, we try to cut the unnecessary long tail in the clustering process to achieve a sufficiently satisfactory accuracy at the lowest possible computation cost. A novel approach is proposed to achieve cost-effective big data clustering in the cloud. By training the regression model with the sampling data, we can make widely used k-means and EM (Expectation-Maximization) algorithms stop automatically at an early point when the desired accuracy is obtained. Experiments are conducted on four popular data sets and the results demonstrate that both k-means and EM algorithms can achieve high cost-effectiveness in the cloud with our proposed approach. For example, in the case studies with the much more efficient k-means algorithm, we find that achieving a 99 percent accuracy needs only 47.71-71.14 percent of the computation cost required for achieving a 100 percent accuracy while the less efficient EM algorithm needs 16.69-32.04 percent of the computation cost. To put that into perspective, in the United States land use classification example, our approach can save up to $94,687.49 for the government in each use.
3D Modelling Intestine Anatomy with Augmented Reality for Interactive Medical Learning

Abstract Humans, one of the heterotrophic living things must meet energy needs by consuming food. The food is then described in the digestive system to become an energy source. The digestive system is composed of the digestive tract and digestive gland glands. In the excerpt of the book “Prometheus” human anatomy, the digestive tract acts as a tool for receiving, chewing, delivering, storing, digesting, absorbing (absorbing) and eliminating food. The small intestine, large intestine, and rectum are organs included in the digestive process. This organ is one of the materials taught to science students to the level of students, especially in medicine. Submission of material about the digestive system in humans themselves is still through conventional media such as blackboards, and images contained in books. Several innovations have been carried out, but still, the development of learning innovations must continue to be developed, so that less interesting subject matter makes it easier for students to know a subject matter with the help of technology and multimedia. The conventional method can only be told without being visualized. If using Augmented Reality (AR) has advantages, namely virtual storage (memory), can be learned at any time, can only be installed on a smartphone, each of them can be studied without thematic tools, and can be visualized by scenarios or stories about problems. The marker was used from the Prometheus book [10] then the AR as the visualization of the organs. The results by developing a 3D model using blender and unity showed that the optimal models could be created by combining both their applications and the testing result of marker detection stable from 15-45 cm and tilt marker detection between 45° - 150°.
VARiable HMD

Virtual reality (VR) and augmented reality (AR) with head-mounted displays (HMDs) are rapidly becoming popular. Unfortunately, optical see-through HMDs for AR are not compatible with VR and users must prepare the other type of HMD to experience VR. We present a new method for optical see-through HMDs that enables switching between VR and AR by changing the degree of transparency. The proposed method controls liquid crystals to change the transparency in an enclosure constructed of polarizing plates to achieve the switch. The proposed method does not require the use of expensive modules and is applicable to various HMDs including those that are smartphone based and retinal projection based. We construct a prototype implementing the proposed method and evaluate its performance and unique characteristics.
Prediction of RSSI by Scikit-Learn for Improving Position Detecting System of Omnidirectional Wheelchair Tennis

The wheelchair with good performance for the aged and disabled people is attracting attention from the society. Also, the wheelchair can provide the user with many benefits, such as maintaining mobility, continuing or broadening community social activities, conserving energy and enhancing quality of life. The wheelchair body must be compact enough and should be able to make different movements in order to have many applications. In our previous work, we presented the design and implementation of an omnidirectional wheelchair. In this paper, we propose a position detecting system for improving the performance of omnidirectional wheelchair tennis. This is achieved by predicting RSSI value using Scikit-learn. The proposed system can find correctly the wheelchair position for avoiding the collision.
Optimizing the Performance of Timed-Constrained Business Processes in Cloud-Fog Environment

Fog computing has emerged as a promising paradigm which aims to solve several problems of Cloud based systems. It aims to reduce the financial cost as well as the transmission latency compared to Cloud resources. One of the key issues in a Cloud-Fog environment is how to find the assignment of business process tasks to the most suitable resources while seeking the trade-off between cost and execution time. Business processes are often constrained by hard timing constraints which are specified by the designer. To address such a problem, we propose in this paper two resource allocation algorithms. The first one is based on an exact solution that aims to provide an optimal assignment. However, the second represents a meta-heuristic solution which uses the particle swarm optimization (PSO) technique. Our algorithms aim to optimize the financial cost of Cloud-Fog resources while satisfying the time constraint of the business process. A set of simulation experiments are presented to illustrate the performance of the approach.
Four-State Modulation in Middle of a Quantum Channel for Continuous-Variable Quantum Key Distribution Protocol with Noiseless Linear Amplifier<sup>*</sup>

We characterize a modified continuous-variable quantum key distribution (CV-QKD) protocol with four states in the middle of a quantum channel. In this protocol, two noiseless linear amplifiers (NLAs) are inserted before each detector of the two parts, Alice and Bob, with the purpose of increasing the secret key rate and the maximum transmission distance. We present the performance analysis of the new four-state CV-QKD protocol over a Gaussian lossy and noisy channel. The simulation results show that the NLAs with a reasonable gain g can effectively enhance the secret key rate as well as the maximum transmission distance, which is generally satisfied in practice.
Indoor Scene Recognition based on Weighted Voting Schemes

Scene understanding represents one of the most primary problems in computer vision. It implies the full knowledge of all the elements of the environment and the comprehension of the relationships between them. One of the major tasks in this process is the scene recognition, on which we focus in this work. Scene recognition is a relevant and helpful task in many robotic fields such as navigation, localization, manipulation, among others. The knowledge of the place (e.g. “office”, “classroom” or “kitchen”) can improve the performance of robots in indoor environments. This task can be difficult because of the variability, ambiguity, illumination changes, occlusions and scale variability present in this type of spaces. Commonly, this problem has been approached through the development of models based on local and global characteristics, incorporating context information and, more recently, using deep learning techniques. In this paper, we propose a multi-classifier model for scene recognition considering as priors the outcomes of independent base classifiers. We implement a weighted voting scheme based on genetic algorithms for the combination of different classifiers in order to improve the recognition performance. The results have proved the validity of our approach and how the proper combination of independent classifier models makes it possible to find a better and more efficient solution for the scene recognition problem.
Text Summarization Technique by Sentiment Analysis and Cuckoo Search Algorithm

To manage the huge information, summarization is one of the most essential tasks. There are many techniques available for this purpose, yet this is a challenge to produce the optimum solution. This paper proposes an approach for text summarization based on sentiment analysis and cuckoo search algorithm. For solving the optimization problem in several areas, the cuckoo search algorithm is used. The cuckoo search basically is a type of nature-inspired algorithms. It is efficient for solving the global optimization problem as it is capable to proceed by maintaining balance between local and global random walks. Here we use cuckoo search algorithm with sentiment score for summarizing the text document. The experimental analysis uses benchmark database. The outcome of the proposed model has been compared in terms of ROUGE score with some existing and some human-generated output.
Validation Modeles and Simulation of Global Horizontal Solar Flux as a Function of Sunshine Duality in Southern Algeria (Adrar)

This work is a study and modeling of global horizontal solar radiation according to duration of sunshine applied on south of Algeria Adrar area), using data of monthly sunshine duration and data extracted from the satellite images captured by the European MSG2 satellite. By this information's we have establish regression models giving the horizontal total as a function of insolation duration. The performance of these models was evaluated and validated against real radiometric measurements by calculating the most used statistical scores. The results of this modeling of monthly global horizontal irradiations values are very encouraging and promising compared to the existing literature and practical values.
An Automatic Nuclei Cells Counting Approach Using Effective Image Processing Methods

Manual counting of nuclei cells from histological images is considered tedious process, time-consuming and subjected to human errors. Therefore, automated the process of nuclei cells counting is become important and necessary for effective analyzing of histological images. Current systems and approaches of nuclei cells counting are based on color or grayscale images leading to inaccurate results and have several limitations. In this paper, we propose a novel accurate approach for automatic nuclei cells counting using effective image processing methods. The new techniques are designed based on image thresholding method, morphological image processing operations, and connected component algorithm. The new approach was evaluated experimentally on 37 images of a public data set of 100 histological images. The experimental results demonstrated that the approach achieved a high accuracy up to 89.5% compared with previous works. We concluded the effectiveness of the proposed approach for automatic counting of nuclei cells from histological images.
Filtering Inconsistent Failure in Robot Collective Decision with Blockchain

Robotic swarm is one of typical and significant distributed systems. Fault tolerance is an important and essential ability for such systems because individual robot may malfunction and even maliciously attack, thereby interfering with reaching consensus on a decision at the swarm level. One kind of fault which is difficult to deal with is called inconsistent failure (ie. Byzantine failure). It is shown as that a robot exhibits different behavior at different times to other members of the swarm. Some previous work have proved that its feasible to exclude the interference of consistent failures via blockchain technology. We expanded the work to solve the problem in situation with inconsistent failures. In this paper, we proposed a method to filter inconsistent failures with blockchain in a collective decision task of robot swarm. The basic idea is to use the blockchain to record the behaviors of the robots, and then automatically audit these behaviors through the rules in the smart contract to achieve the purpose of detecting the robot with inconsistent failures. The feasibility and effectiveness of our approach is validated by a series of experiments in a voting scenario.
Block-VN: A Distributed Blockchain-Based Efficient Communication and Storage System

Internet of vehicles (IoVs) are connected with each other through Internet. In recent years, IoV provides security mechanisms and quick information sharing schemes, etc. The rapid growth of IoV causes various challenges including data storage, intelligent transport system, selfishness of nodes, distrusted nodes and sensor’s data leakage of information. To overcome data storage and delay in services, a decentralized, distributed, secure, transparent and scalable management system is proposed using blockchain technology. Provable data possession (PDP) scheme is used to validate the new data blocks. Message transfer process argon2 (MTP-Argon2) technique is used for data filtration. Using this technique, the raw data are filtered to remove the duplicate and unnecessary data which are obtained from different nodes. Data storage, less delay in service request or response, more sensor’s data sharing and secure communication channel are achieved using proposed system.
3D Quantitative Colocalisation Analysis

In this module we will first build a 3D object based colocalisation macro step by step. Then we will practice to adapt and extend the current macro such that it can also work with intensity-based colocalisation methods.
The Application of DQN in Thermal Process Control

In recent years, deep reinforcement learning has been widely used in games, robots, autonomous driving and other fields, which proves that deep reinforcement learning is powerful in decision making and problem solving. In this paper, according to deep reinforcement learning and the characteristic of thermal process control, a thermal process control method based on DQN (Deep Q-learning Network) is proposed. Through matlab and python mixed programming, the process of DQN controller training and the simulation of control system are completed. The simulation experiment of water level control system of water tank based on DQN controller shows that deep reinforcement learning can be well applied to thermal process.
A Small Traffic Sign Detection Algorithm Based on Modified SSD

Abstract Traffic sign detection is an important part of many systems such as autonomous driving, driver safety and assistance. In this paper, the detection capability of SSD for small targets is analyzed and improved based on ssd_300 model. CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB) dataset is used to train the model for Chinese road traffic conditions. The improved model was compared with ssd_300 model. The experimental results show that the mAP of the improved model on the test dataset achieves 0.85, which is 0.13 higher than ssd_300, and the algorithm can reach real-time detection. The improved model can effectively detect three categories of Chinese traffic signs and has strong robustness against various disturbances.
Bangla Speech Recognition Using 1D-CNN and LSTM with Different Dimension Reduction Techniques

This paper presents a model of Bangla speech recognition using machine learning algorithms. Mel-frequency Cepstral Coefficient (MFCC) and Mel Spectrogram are extracted from a Bangla dataset. Commonly used dimension reduction techniques, Principal Component Analysis (PCA), Kernel-PCA (k-PCA), and T-distributed Stochastic Neighbor Embedding (t-SNE) are applied to the extracted features as a dimension reduction technique. At the end, as a classification tool, 1-dimensional Convolutional Neural Network (1D-CNN) and Long-Short Term Memory (LSTM) are utilized. Experimental results demonstrate that among the dimension reduction techniques, PCA demonstrates comparatively higher accuracy than the other state-of-art models by exhibiting 94.58% and 83.12% accuracy for 1D CNN and LSTM, respectively. In addition, it has been observed that dimension reduction techniques have no positive impact on 1D-CNN and LSTM. Without any dimension reduction technique, MFCC with 1D-CNN has demonstrated better accuracy compared to MFCC with LSTM by showing 97.26% and 93.83% of accuracy, respectively.
Face Detection Technology Based on Combining Skin Color Model with Improved Adaboost Algorithm

In order to improve the accuracy of face detection, this paper proposes a method based on combination of skin color model and improved Adaboost algorithm. Using skin color model with YCbCr color space to detect the skin color, obtain the area to be detected, and then we can locate accurately the face position with the Adaboost face detection algorithm. Because the traditional Adaboost face detection algorithm needs a long time training and can not effectively distinguish feature values which are aggregated distribution, we have improved Adaboost algorithm based on two-threshold and a new method about suppression of weights updating which which prevents excessive weight gain and avoids the phenomenon of degradation during training in Adaboost.
Evaluating a Mobile Tablet Project in Rural South Africa against Criteria to Comply with Being an Innovative Educational Ecosystem

The purpose of this paper is to evaluate a specific mobile technology rural schools project known as ICT4E in South Africa to determine if it complies to be regarded as an innovative educational ecosystem. The criteria that were used were sourced from the literature. The project was explained and why it is regarded as a rural school mobile tablet project in education. Some high-level results and lessons learnt from this project are also provided that was used to evaluate whether it can be regarded as an instantiation of an innovative educational ecosystem. The evaluation methodology was applied to do the evaluation. The main results were that based on the criteria and the findings from the ICT4E project it could be regarded as an instantiation of an innovative educational ecosystem as it complied to most of the criteria except for two where it only partially complied. The project was also seen as a good example of how a country's system of innovation can be supported from a socio-cultural context-specific perspective.
Adaptive Sample-Size Unscented Particle Filter with Partitioned Sampling for Three-Dimensional High-Maneuvering Target Tracking

High-maneuvering target tracking is a focused application area in radar positioning and military defense systems, especially in three-dimensional space. However, using a traditional motion model and techniques expanded from general two-dimensional maneuvering target tracking may be inaccurate and impractical in some mission-critical systems. This paper proposes an adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF), which is used to track a three-dimensional, high-maneuvering target, combined with the CS-jerk model. In PS-AUPF, the partitioned sampling is introduced to improve the resampling and predicting process by decomposing motion space. At the same time, the adaptive sample size strategy is used to adjust the sample size adaptively in the tracking process, according to the initial parameters and the estimated state variance of each time step. Finally, the effectiveness of this method is validated by simulations, in which the sample size of each algorithm is set to the minimum required for the optimal accuracy, thus ensuring the reliability of the tracking results. The results have shown that the proposed PS-AUPF, with higher accuracy and lower computational complexity, performs better than other existing tracking methods in three-dimensional high-maneuvering target tracking scenarios.
REAL QUADRATIC FIELDS, CONTINUED FRACTIONS, AND A CONSTRUCTION OF PRIMARY SYMMETRIC PARTS OF ELE TYPE

For a non-square positive integer d with 4 ∤ d, put ω(d) :=(1 + √d)/2 if d is congruent to 1 modulo 4 and ω(d) := √d otherwise. Let a1, a2, . . . , aℓ-1 be the symmetric part of the simple continued fraction expansion of ω(d). We say that the sequence a1, a2, . . . , a[ℓ/2] is the primary symmetric part of the simple continued fraction expansion of ω(d). A notion of ‘ELE type' for a finite sequence was introduced in Kawamoto et al (Comment. Math. Univ. St. Pauli 64(2) (2015), 131-155). The aims of this paper are to introduce a notion of ‘pre-ELE type' for a finite sequence and to give a way of constructing primary symmetric parts of ELE type. As a byproduct, we show that there exist infinitely many real quadratic fields with period ℓ of minimal type for each even ℓ ≥ 6.