Reviewerly/exHarmony huggingface dataset:
$ \ell_{1} $-norm based safe semi-supervised learning

In the past few years, Safe Semi-Supervised Learning (S3L) has received considerable attentions in machine learning field. Different researchers have proposed many S3L methods for safe exploitation of risky unlabeled samples which result in performance degradation of Semi-Supervised Learning (SSL). Nevertheless, there exist some shortcomings: (1) Risk degrees of the unlabeled samples are in advance defined by analyzing prediction differences between Supervised Learning (SL) and SSL; (2) Negative impacts of labeled samples on learning performance are not investigated. Therefore, it is essential to design a novel method to adaptively estimate importance and risk of both unlabeled and labeled samples. For this purpose, we present ℓ1-norm based S3L which can simultaneously reach the safe exploitation of the labeled and unlabeled samples in this paper. In order to solve the proposed ptimization problem, we utilize an effective iterative approach. In each iteration, one can adaptively estimate the weights of both labeled and unlabeled samples. The weights can reflect the importance or risk of the labeled and unlabeled samples. Hence, the negative effects of the labeled and unlabeled samples are expected to be reduced. Experimental performance on different datasets verifies that the proposed S3L method can obtain comparable performance with the existing SL, SSL and S3L methods and achieve the expected goal.
Outlier data Mining of large Data Sets relying on fast decomposition simulated annealing algorithm

Simulated annealing method is put forward based on fast decomposition of large data sets from the group of data mining algorithm, using clustering techniques for data preprocessing, and then put the data in the appropriate space fast decomposition of non-empty fast simulated annealing algorithm and decomposition of simulated annealing algorithm using three-dimensional fast decomposition tree of simulated annealing algorithm (indexes, most of the data set is located in high density area and has nothing to do with the data from the group of data will be filtered, to avoid a large amount of unnecessary computation. Experiments show that this algorithm can quickly and accurately mine outlier data from the big data set, and improve the detection speed of outlier data.
Online Optimized Product Quantization for Dynamic Database Using SVD-Updating

Approximate nearest neighbor (ANN) search allows us to perform similarity search over massive vectors with less memory and computation. Optimized Product Quantization (OPQ) is one of the state-of-the-art methods for ANN where data vectors are represented as combinations of codewords by taking into account the data distribution. However, it suffers from degradation in accuracy when the database is frequently updated with incoming data whose distribution is different. An existing work, Online OPQ, addressed this problem, but the computational cost is high because it requires to perform of costly singular value decompositions for updating the codewords. To this problem, we propose a method for updating the rotation matrix using SVD-Updating, which can dynamically update the singular matrix using low-rank approximation. Using SVD-Updating, instead of performing multiple singular value decompositions on a high-rank matrix, we can update the rotation matrix by performing only one singular value decomposition on a low-rank matrix. In the experiments, we prove that the proposed method shows a better trade-off between update time and retrieval accuracy than the comparative methods.
MergeNet: Single High Dynamic Range Image Reconstruction Method

The high dynamic range (HDR) environment mapping could provide great dynamic range and irradiance contrast for improving the fidelity of Computer Graphics Rendering, however, the acquisition of HDR images is much harder for most scenarios. We propose the improved deep merger network (MergeNet) to reconstruct an HDR image from a single filtered low dynamic range (FLDR) image with the feature extraction ability of deep learning methods and the band transmission characteristics of optical filters. Qualitative and quantitative comparisons have been executed for our method and other similar ones with multiple evaluation indicators. Experimental results show that our MergeNet performs favorably against state-of-the-art HDR image reconstruction methods.
Enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments

Big moving objects arise as a novel class of big data objects in emerging environments. Here, the main problems are the following: (i) tracking, which represents the baseline operation for a plethora of higher-level functionalities, such as detection, classification, and so forth; (ii) analysis, which meaningfully marries with big data analytics scenarios. In line with these goals, in this paper we propose a novel family of scan matching algorithms based on registration, which are enhanced by using a genetic pre-alignment phase based on a novel metrics, fist, and, second, performing a finer alignment using a deterministic approach. Our experimental assessment and analysis confirms the benefits deriving from the proposed novel family of such algorithms.
A Lightweight Defect Detection Algorithm of Insulators for Power Inspection

The rapid development of smart grid causes a large increase of power equipments. Power insulator is one of the most important infrastructures in the power transmission lines which is vital to ensure the safe operation of power system. As a common defect, the missing-cap issues will affect the structural strength of power insulators and the safe operation of power lines. Consequently, the monitoring and assessment of abnormal power insulators are of extreme importance for the safe power transmission lines. Due to the good feature expression ability, machine learning algorithms have got much applications on power line inspection, and they could be divided into two categories: shallow learning and deep learning algorithms. Nevertheless, the defect recognition issues on power insulators are always against complex power inspection environment. It will bring a certain effect to the handcrafted feature design of shallow learning algorithms. Meanwhile, the small-scale defect data set will affect the model training of the deep network model. To address the above issues, aimed at the feature of the limited processing power of airborne processor, a novel lightweight defect detection algorithm is proposed for the defects of power insulators which fuses the advantages of shallow learning and deep learning models. Firstly, an insulator location algorithm based on lightweight deep convolutional neural network (DCNN) model is proposed to remove the disturbance of complex backgrounds and serve the high-precision defect detection. On the basis, the high-level image feature based on the improved transfer learning is acquired to effectively distinguish the normal and abnormal power insulators. Finally, a defect recognition method based on Support Vector Machine (SVM) is proposed to solve the small-scale defect detection issue. Experiments show that the proposed method could well meet the precision and speed demands of power system compared with other inspection methods.
Improving Long-tail Relation Extraction with Knowledge-aware Hierarchical Attention

Relation Extraction (RE) is a crucial step to complete Knowledge Graph (KG) by recognizing relations between entity pairs. However, it usually suffers from the long-tail issue, especially when using distantly supervision algorithm. In this paper, inspired by the rich semantic correlations between head relations and tail relations, we proposed a knowledge-aware hierarchical attention (KA-HATT) relation extraction model. According to relational hierarchy, the multiple layers of attention were established, which take advantage of the knowledge from data-rich classes to boost the performance of data-poor classes at the tail. We have conducted extensive experiments on available dataset New York Times (NYT). Experimental results show that, compared with baseline models, our model achieves significant improvements on relation extraction, especially for long-tail relations.
Improvement in Task Scheduling Capabilities for SaaS Cloud Deployments Using Intelligent Schedulers

Task scheduling on the cloud involves processing a large set of variables from both the task side and the scheduling machine side. This processing often results in a computational model that produces efficient task to machine maps. The efficiency of such models is decided based on various parameters like computational complexity, mean waiting time for the task, effectiveness to utilize the machines, etc. In this paper, a novel Q-Dynamic and Integrated Resource Scheduling (DAIRS-Q) algorithm is proposed which combines the effectiveness of DAIRS with Q-Learning in order to reduce the task waiting time, and improve the machine utilization efficiency. The DAIRS algorithm produces an initial task to machine mapping, which is optimized with the help of a reward &amp; penalty model using Q-Learning, and a final task-machine map is obtained. The performance of the proposed algorithm showcases a 15% reduction in task waiting time, and a 20% improvement in machine utilization when compared to DAIRS and other standard task scheduling algorithms.
An Application to Support end-users of wi-fi Hotspots in the Township of Makhanda

Having access to the Internet is a powerful resource that can change one's life, but connectivity inequality continues to be a significant issue, especially for disadvantaged communities. The usage of Wi-Fi hotspots is becoming very useful for accommodating many people living in underprivileged areas to connect to the Internet. Primarily, these hotspots get deployed in public places like hotels, restaurants, etc. This paper discusses how such a system can be made to work as a solution for disadvantaged communities considering the user base in such communities and adhering to government regulations. The research is developed in collaboration with Khula Tech Solutions ISP that provides wireless Internet services in Makhanda (formally known as Grahamstown). Khula Tech Solutions is starting an initiative of installing Wi-Fi hotspots around the township of Makhanda and needs an application that will support users of the Wi-Fi hotspots. The research's result is a proof-of-concept mobile application developed through iterative and incremental software development techniques and mobile application technologies. With this research, the hope is that it is the beginning of further work for the users to become connected citizens.
Elephant herding optimization based on Orthogonal opposition-based learning and Sugeno inertia weights

In the engineering field, many global optimization problems are nonlinear, multimodal, and complex. Elephant herding optimization performs well in solving these problems. The original elephant herding optimization suffers from several shortcomings, such as low convergence accuracy and eases to fall into the local optimum. In this paper, an elephant herding optimization based on orthogonal opposition-based learning and Sugeno inertia weights, called EHOOBS, is proposed. First, orthogonal-opposition learning is embedded into the proposed algorithm to improve the optimization seeking efficiency. By designing orthogonal tests to construct candidate solutions in some dimensions, the information of forward and reverse agents in different dimensions is fully utilized. Furthermore, the optimal balance between exploration and exploitation is tried to provide by introducing Sugeno inertia weight into the proposed method. Based on the modifications, better solutions are presented for the algorithm. To verify the effectiveness of the algorithm, experiments are conducted on the benchmark function. The results show that the proposed algorithm has stronger convergence performance and superior capabilities of exploration and exploitation.
Evolutionary Parameter-Free Clustering Algorithm

The performance of the clustering algorithms depends mainly on the setting of artificial parameter values which is usually difficult in practical application. In addition, the dataset is usually incremental, and the clustering algorithm applied to the static dataset cannot develop with the change of the dataset. If new sample points are added, algorithm parameters need to be readjusted to cluster again, leading to a great time cost. This paper proposed an evolutionary parameter-free clustering algorithm (EPFC) for the above problems, which imitates the human clustering mechanism of objective things. EPFC algorithm takes the average distance between each sample and its nearest neighbour sample as the threshold value to judge whether the sample can be grouped into one cluster. The threshold value is adaptively updated without setting an artificially parameter value as the samples increase. A large number of experiments on benchmark datasets show that EPFC is effective on datasets with different characteristics, and the algorithm has strong robustness.
Hybrid Multi-Tasking Scheduling Scheme based on Dynamic Time Quantum using Slice Bit for improving CPU Throughput

Process Scheduling is an essential task that deals with deciding the order of execution for processes. Existing approaches of process scheduling such as Round Robin (RR) fully depend on the Time Slice of Time Quantum. However, it is not optimal in its traditional form as it suffers from the bottlenecks of high waiting time and too many context switches. The computation of an optimal time quantum is essential for improving the performance of the RR approach. In this paper, a Multi-Tasking Scheduling Scheme is proposed that optimizes the system’s throughput and reduces the overheads caused by context switches. The proposed scheme is a hybrid of Round Robin and Shortest Remaining Time First approaches as it encompasses the advantages of both methods. In this scheme, Slice Bit and remaining Burst Time decide the update of time quantum. The proposed scheme may not be as fair as traditional Round Robin; however, it improves the utilization of CPU and reduces the overheads that occurred mainly due to too many context switches. Thorough experiments on 12 different cases confirm the effectiveness of the proposed scheme versus other well-established scheduling methods.
Video Summarization Using SIFT Features and Niblack's Thresholding

Video Summarization is a technique that aims to generate a short video summary of the given video and helps to understand the context of the video. A lot of research has been done on reducing the redundant images in the video summary and obtaining optimized frames through various techniques. Researchers have also worked on eliminating the redundancy of images and the complexity of the process for achieving these optimized frames. In this paper, we propose an efficient system to perform video summarization using the SIFT features along with feature differencing. The feature differencing applied along with Niblack's threshold on feature vectors helps to eliminate the redundant frames. The proposed technique is tested on the open vide database videos viz, v24 and v37. The precision and recall values calculated for open video library V24 and V37 videos are comparable to the classical video summarization techniques proposed in the literature. Performance analysis done for the proposed technique based on subjective and comparative show comparable results.
Fuzzy Controller for Simplified Model of Mobile Vehicle Steering Booster

Abstract The article is aimed on the possibility of fuzzy control of an experimental device that represents the simplified model of automobile steering booster. The main part of model consists of a graphical unit illustrating the control of speed and supporting torque. Based on the structure of fuzzy controller, various counts and configurations of fuzzy sets were defined, whereby the relevant results were achieved by their application on controlled system.
An IoT Ontology Class Recommendation Method Based on Knowledge Graph

Ontology is a formal representation of a domain using a set of concepts of the domain and how these concepts are related. Class is one of the components of an ontology for describing the concepts of the system. It is used to create, update, search or delete instances which are digital representations of physical things. With the development of the IoT (Internet of Things) technology, developers create and manage the corresponding IoT instances on IoT platform. With the user’s query of a few key words, how to find the ontology classes accurately is a hard problem. IoT Ontology classes recommender system can help developers find the ontology classes that they want to use efficiently. In a general recommender system, user’s historical usage records, background features and input keywords are used for making personalized recommendations. However, the newly established IoT platforms do not have a large number of user usage records to optimize recommendation results. And recommendation based on input words’ semantics lacks relevance between the IoT ontology classes. This paper proposed a method for recommendation of IoT ontology classes based on knowledge graph building and semantics to introduce more auxiliary information and relationships for the recommendation. And the result shows that our proposed recommendation method can recommend more related IoT ontology classes and have better performance in results’ accuracy.
Embedding-Based Network Alignment Using Neural Tensor Networks

Network alignment plays a significant role in network analysis and benefits a wide range of applications. However, existing methods are sensitive to structural noises and do not consider the one-to-one constraint on the anchor links. In this paper, to tackle this problem, we propose a novel framework called embedding-based network Alignment using Neural Tensor Networks (AlignNTN). It considers the one-to-one constraint and uses the neural tensor networks to learn the relationship between nodes in two networks. The proposed method provides a powerful way to learn the relationship between nodes and does not necessarily rely on demographic features. What is more, our model can easily incorporate with node attributes to improve the effectiveness of network alignment. Extensive experiments have been conducted on four real-world datasets, and the results demonstrate that the proposed method outperforms state-of-the-art methods by a significant margin.
User Interface Model for Visualization of Learning Materials in Comic Strip Form Using Goal-Directed Design Method

In a learning process to help students remember and understand the material, one effort can be made to provide essential points. Students are less interested in delivering essential points based on text because they are considered monotonous. Nowadays, many students tend to read comic books rather than books. Comics can wash away the emotions of readers in the storyline. Therefore, we need supporting media to help teachers visualize essential points in the book in comic form. So that to help teachers in this regard, a user interface model is needed. This research was conducted with a Goal-Directed Design approach to gain user perspective in developing supporting tools to visualize the material's crucial points in the form of comic strips. Testing the resulting model was carried out twice and each test involved 15 respondents to get maximum results. Based on the evaluation results using the USE Questionnaire evaluation method for the prototype made and the results were in the excellent category. Thus, the resulting user interface model supports teachers in delivering learning material in comic form following the expected objectives.
Research on Autonomous Movement of Nursing Wheelchair Based on Multi-sensor Fusion

Aiming at the mobility needs of semi-disabled patients, an intelligent nursing wheelchair was designed to realize the autonomous movement of the wheelchair. First, the Cartographer algorithm based on graph optimization was introduced to wheelchairs to locate them in unknown environments. Secondly, multiple sensor information is fused to improve the positioning accuracy. Finally, machine vision, SLAM, path planning and other technologies are applied to the wheelchair to improve its intelligence and interactivity. Finally, an intelligent nursing wheelchair prototype is built, and the autonomous mobility system of the wheelchair is proved to have good robustness through experiments.
Guessing a Tuple

A single-letter expression is provided for the exponential growth rate of the least expected number of guesses required to recover all the sequences produced by correlated memoryless sources when each guess is of a single source sequence, with the source at the guesser's discretion.
TEACHING RECOUNT TEXT THROUGH QUANTUM TEACHING STRATEGY TO IMPROVE STUDEBTS' WRITING SKILL

The aim of this study is to find out whether or not Quantum Teaching Strategy are effective to help students enhance their writing skills. This study was carried out at SMA Negeri 1 Manganitu. The research used quantitative research. Hopefully, this research can be useful to provide information to teachers in teaching and learning activities so that teachers are more creative and know how to improve students' curiosity, to gain students’ interest in learning English in class. The population of this research was the students of SMA Negeri 1 Manganitu and class X that consisting of 20 students was taken as sample. This study used a pre-experimental, pre-test, and post-test design with only one group. The researcher gathered data by asking students to complete a written test firstly also known as pre-test the apllied Quantum Teaching Strategy, secondly delivered with written test also known as post-test. After these activities, the researcher analysed the data by comparing the mean score of pre-test and post-test. The result of post-test score was 64.2 compared to the total mean = 17.85 as the result of pre-test. This meant that Quantum Teaching Strategy was an effective way to improves student’s writing skill in recount text especially in personal recount text. It was recommended to teachers to use Quantum Teaching Strategy in order to maximize students’ capability in learning English.
Algorithm based on normal coordinate vectors with 16 segments for the data fusion from hand-written Arabic text implemented with MATLAB

Hand-written text recognition is useful for interpreting records in different fields such as healthcare, surgery and police in which professionals may avoid technical equipment and prefer writing notes on paper. In order to perform data fusion from different data sources, handwriting automatic recognition involves barriers such as different ways of writing letters and deformation due to many reasons. This work presents a novel handwriting recognition approach based on the application of coordinate vectors to find similarities in different kinds of deformations. In particular, it has been implemented using 16 segments in order to distinguish all the particularities in matching the new text considering a dataset with a machine-learning approach. The implementation of this approach with MATLAB shows promising results with accuracy of 92.8% for with ensemble and bagged trees, after analyzing 22 possible combinations of machine learning and processing techniques.