Reviewerly/exHarmony huggingface dataset:
Moving object detection via TV-L1 optical flow in fall-down videos

There is a growing demand for surveillance systems that can detect fall-down events because of the increased number of surveillance cameras being installed in many public indoor and outdoor locations. Fall-down event detection has been vigorously and extensively researched for safety purposes, particularly to monitor elderly peoples, patients, and toddlers. This computer vision detector has become more affordable with the development of high-speed computer networks and low-cost video cameras. This paper proposes moving object detection method based on human motion analysis for human fall-down events. The method comprises of three parts, which are preprocessing part to reduce image noises, motion detection part by using TV-L1 optical flow algorithm, and performance measure part. The last part will analyze the results of the object detection part in term of the bounding boxes, which are compared with the given ground truth. The proposed method is tested on Fall Down Detection (FDD) dataset and compared with Gunnar-Farneback optical flow by measuring intersection over union (IoU) of the output with respect to the ground truth bounding box. The experimental results show that the proposed method achieves an average IoU of 0.92524.
Too Big to Cheat: Mining Pools' Incentives to Double Spend in Blockchain Based Cryptocurrencies

Conventional wisdom in blockchain-based cryptocurrencies suggests that centralization of mining power undermines the credibility of cryptocurrencies because it enhances the risk of double spending attacks, where the same funds are used multiple times. However, centralization of mining power is commonly observed in cryptocurrencies. Contrary to a firmly held premise, our main findings indicate that under meager economic profits incentives to act honestly are increasing in pool size. Our results illustrate how to design dynamic incentives that generate self-enforcing mechanisms in cryptocurrencies. In this setting the prospect of losing future income provides powerful incentives for large pools (which have centralized power in mining) to voluntarily act honestly and preserve integrity of the cryptocurrency. In our model we compare the double spending costs generated by these vested interests to the cost advantage of being a large pool. Our model conclusion that centralization is harmless is consistent with practices in 13 major proof-of-work cryptocurrencies.
STORYTELLING: ONE PACKAGE LEARNING IN IMPROVING LANGUAGE SKILL AND IMPLANTING CHARACTER EDUCATION ON CHILDREN

The development of technology makes the world become narrow and various kinds of information and shows become easy to access, including by children. Various kinds of acts of violence, crime, even videos or content that are not yet suitable for children to watch are more easily accessible through devices. This has a negative impact on the lives of the nation's children, for example theft, sexual violence, and brawl committed by children and adolescents. Thus, the inculcation of moral values by both parents and teachers becomes a must thing to do so that the next generation of the nation is maintained. If we talk about implanting moral values on children, we surely need the right media to use. One way is by storytelling. By listening to stories, directly or not, children will absorb the moral values of what they hear. In addition, knowledge of the vocabulary of a language will be absorbed by children. We know that there are golden ages in children where children have periods where their brains are like sponges that are able to absorb many things. This paper was a literature study that discussed the correlation between storytelling in the process of improving language skills and implanting moral values on children. Based on several available sources, it can be concluded that the use of storytelling as a medium was an effective thing to use on children.
Receding-Horizon Vision Guidance with Smooth Trajectory Blending in the Field of View of Mobile Robots

Applying computer vision to mobile robot navigation has been studied for over two decades. One of the most challenging problems for a vision-based mobile robot involves accurately and stably tracking a guide path in the robot limited field of view under high-speed manoeuvres. Pure pursuit controllers are a prevalent class of path tracking algorithms for mobile robots, while their performance is rather limited to relatively low speeds. In order to cope with the demands of high-speed manoeuvres, a multi-loop receding-horizon control framework, including path tracking, robot control, and drive control, is proposed in this paper. This is done within the vision guidance of differential-driving wheeled mobile robots (DWMRs). Lamé curves are used to synthesize a trajectory with G 2 -continuity in the field of view of the mobile robot for path tracking, from its current posture towards the guide path. The platform twist—point velocity and angular velocity—is calculated according to the curvature of the Lamé-curve trajectory, then transformed into actuated joint rates by means of the inverse-kinematics model; finally, the motor torques needed by the driving wheels are obtained based on the inverse-dynamics model. The whole multi-loop control process, initiated from Lamé-curve blending to computational torque control, is conducted iteratively by means of receding-horizon guidance to robustly drive the mobile robot manoeuvring close to the guide path. The results of numerical simulation show the effectiveness of our approach.
A Structured Light Encoding Method for M-array Technique

In this paper, we have designed a structured light encoding algorithm based on the fill-and-test-as-you-go-along method. The algorithm achieves significant improvement for the structured light encoding technology, which could encode a lager resolution pattern with a certain number of symbols for encoding. It is significant to improve the accuracy of 3D recognition using the structured light (SL) technology, and reduce the complexity of the structured light algorithm, which is wildly used in 3D reconstruction, tactile sensor based on vision, grasping and so on. In the comparative experiment, we obtain nearly twice the size of the pattern encoded by the proposed encoding algorithm.
Non-functional Requirement Patterns for Agile Software Development

Following the agile principles, agile software development is popular among software developing organizations. The attractive characteristics of agile development are that it embraces frequent changes and that it gives high priority to users. Due to such characteristics, non-functional requirements are usually not identified in the requirements elicitation process. They are often neglected in an early phase, and even overlooked in the later stages of software development. This results in poor quality software. To enhance non-functional requirements identification in agile development, this paper proposes a development of a set of non-functional requirement patterns in the form of requirement templates for agile development. The set is composed of 10 security requirement patterns and 13 fault tolerance requirement patterns, which are derived from an analysis of security and fault tolerance design patterns. The proposed non-functional requirement patterns can facilitate non-functional requirements gathering and help agile team members in writing the requirements. In an experiment on a Scrum team, the team members took less time to write security and fault tolerance requirements for a number of given problems when using the proposed patterns, compared with the case of not using the patterns. In addition, the non-functional requirement patterns helped the team members to write more complete security and fault tolerance requirements. Despite the effort they had to spend in learning the proposed requirement patterns, the team also agreed that the patterns are useful in practice.
Public Auditing Scheme for Cloud Data Integrity Verification

Cloud computing is one that provides an on-demand accessibility for computer resources, particularly data storage and computing power by the user. It is generally used to describe data hubs that remain accessible to users over the internet. The users place their data in cloud to save their local memory. The cloud service providers will control the data that is placed in the cloud. As users upload their data in cloud that is controlled by service providers, they lose control over their data. Hence, we must have few safety measures to retain the user’s data securely. Here, we described an auditing system that makes use of external auditor. We utilized Advanced Encryption Standard (AES) and Merkle Hash Tree (MHT) for ensuring integrity of cloud data. This work is accomplished in Amazon EC2 ubuntu16.04 server instance. From the results gained, we determine that our scheme is intact and considers a firm time to process files.
Interpretable Feature Learning of Graphs using Tensor Decomposition

In recent years, node embedding algorithms, which learn low dimensional vector representations for nodes in a graph, have been one of the key research interests of the graph mining community. The existing algorithms either rely on computationally expensive eigendecomposition of the large matrices, or require tuning of the word embedding-based hyperparameters as a result of representing the graph as a node sequence similar to the sentences in a document. Moreover, the latent features produced by these algorithms are hard to interpret. In this paper, we present two novel tensor decomposition-based node embedding algorithms, that can learn node features from arbitrary types of graphs: undirected, directed, and/or weighted, without relying on eigendecomposition or word embedding-based hyperparameters. Both algorithms preserve the local and global structural properties of the graph by using k-step transition probability matrices to construct third-order multidimensional arrays or tensors and perform CANDECOMP/PARAFAC (CP) decomposition in order to produce an interpretable and low dimensional vector space for the nodes. Our experiments encompass different types of graphs (undirected/directed, unweighted/weighted, sparse/dense) of different domains such as social networking and neuroscience. Our experimental evaluation proves our models to be interpretable with respect to the understandability of the feature space, precise with respect to the network reconstruction and link prediction, and accurate with respect to node classification and graph classification.
DeepFake Video Analysis using SIFT Features

Recent advantages in changing faces using DeepFake algorithms, which replace a face of one person with a face of another, truly represent what artificial intelligence and deep learning are capable of. Deepfakes in still images or video clips represent forgeries and tampered visual information. They are becoming increasingly successful and even difficult to notice in some cases. In this paper we analyze deepfakes using SIFT (Scale-Invariant Feature Transform) features. The experimental results show that in deepfake analysis using SIFT keypoints can be considered valuable.
Novel Ranking Approach using Pattern Recognition for Ontology in Semantic Search Engine

As we know, World Wide Web (WWW) has been developing and it's performance growth is increasing since last few years. So, it requires the need of fast and precise method to reach the information. Ranking is playing an important role to achieve the goal of receiving relevant information. Search Engines have an important component as ranking. It helps user to retrieve the pages that relates to his/her query. Semantic Web (SW) is the extension of WWW. It is a great technology, which helps search engines to retrieve most relevant pages. So, the concept of SW approach is satisfied, only if the results of retrieval are extremely related to user query. Than only, we can consider that due to best ranking schemes user expectations are increasing. Because by accurate result as per the query the scope of knowledge searching increases and make user to view the relevant need for query on top most.
Neuroevolutionary learning in nonstationary environments

Abstract This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantum-inspired evolutionary models to automatically configure the ensemble members and combine their output. The quantum-inspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour.
Person Re-Identification Using Color Features and CNN Features

Pedestrian re-identification task is a most challenge problem because the image angle, brightness and other factors change too much due to the different installation positions or resolutions of the camera. The traditional re-identification method, such as the manually annotating features, are not particularly desirable. In this paper, based on the idea of deep learning and feature representation, a neural network pedestrian re-identification algorithm combining color features and convolutional neural network extraction feature based on ResNet-50 model is proposed. Tested on a public pedestrian re-identification data set, experiments show that our method has a great improvement compared to the traditional pedestrian re-identification algorithm.
Context-Aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model

Context-Aware Security demands a security system such as a Smart Door Lock to be flexible in determining security levels. The context can be in various forms; a person’s activity in the house is one of them and is proposed in this research. Several learning methods, such as Naïve Bayes, have been used previously to provide context-aware security systems, using related attributes. However conventional learning methods cannot be implemented directly to a Context-Aware system if the attribute of the learning process is low level. In the proposed system, attributes are in forms of movement data obtained from a PIR Sensor Network. Movement data is considered low level because it is not related directly to the desired context, which is activity. To solve the problem, the research proposes a hierarchical learning method, namely Hierarchical Hidden Markov Model (HHMM). HHMM will first transform the movement data into activity data through the first hierarchy, hence obtaining high level attributes through Activity Recognition. The second hierarchy will determine the security level through the activity pattern. To prove the success rate of the proposed method a comparison is made between HHMM, Naïve Bayes, and HMM. Through experiments created in a limited area with real sensed activity, the results show that HHMM provides a higher F1-Measure than Naïve Bayes and HMM in determining the desired context in the proposed system. Besides that, the accuracies obtained respectively are 88% compared to 75% and 82%.
Curved TVs improved watching experience when display curvature radii approached viewing distances: Effects of display curvature radius, viewing distance, and lateral viewing position on TV watching experience

Although watching TV often involves multiple viewing distances and viewers, less attention has been paid to the effects of display curvature radius, viewing distance, and lateral viewing position on TV watching experience. This study examined the effects of four display curvature radii (2300R, 4000R, 6000R, and flat), two viewing distances (2.3 m and 4 m), and five lateral viewing positions (P1-P5; 0, 35, 70, 105, and 140 cm off-center) on seven TV watching experience elements (spatial presence, engagement, ecological validity, negative effects, visual comfort, image quality, and user satisfaction). Fifty-six individuals (14 per display curvature radius) were seated in pairs to watch videos, each time at a different viewing position (2 viewing distances × 5 paired lateral viewing positions). The spatial presence and engagement increased when display curvature radius approached a viewing distance and lateral viewing position approached P1, with 4000R-4m-P1 (display curvature radius-viewing distance-lateral viewing position) providing the best results. Lateral viewing position alone significantly affected five TV watching experience elements; the spatial presence and engagement decreased at P3-P5, and ecological validity, image quality, and user satisfaction decreased at P4-P5. However, display curvature radius alone did not appreciably affect TV watching experience, and viewing distance alone significantly affected visual comfort only, with a 4-m viewing distance increasing visual comfort. This study demonstrated that effective display curvature radii for watching TV are viewing distance-dependent, and less off-center lateral viewing positions (P1-P2) are recommended for TV watching experience. Finally, among the TV watching experience elements, engagement explained user satisfaction to the greatest degree.
Knowledge internalization in e-learning management system

Knowledge management (KM) is gaining significance as a worthy research subject due to its contribution to the success of wide range of organizations, including higher education institutions. Knowledge internalization is mainly related to capability to see the relevance of one’s knowledge in a real situation. e-learning management system (eLMS) provides an online teaching and learning platform for students (as novice users) and lecturers (as experts in their specific domains) with the potential to improve students' knowledge acquisition. Thus, this empirical study was conducted to investigate the impact of knowledge internalization in eLMS among students in Iraq. To achieve these aims, survey research design was adopted and the sample comprised of 109 undergraduate students attending College of Information Technology in Iraq, all of whom were actively engaged in eLMS activities. The findings show that knowledge can be effectively transferred from lecturers to students via eLMS. Additionally, eLMS enable students to improve their prior knowledge through the internalization process, while also motivating them to share their knowledge with other students.
Bidirectional LSTM-CRF models for keyword extraction in Chinese sport news

State-of-the-art methods of keyword extraction from news are based on traditional machine learning and their performances rely heavily on hand-crafted feature and domain-specific knowledge. In this paper, we propose a new character-based method for keyword extraction from Chinese sport news, which based bidirectional Long Short-Term Memory with Conditional Random Field (BILSTM-CRF). The experiments result shows that BILSTM-CRF can effectively improve the performance of keyword extraction in Chinese sport news.
Improvement of Automated Essay Grading by Grouping Similar Graders

Automated essay evaluation is a widely used practical solution for replacing time-consuming manual grading of student essays. Automated systems are used in combination with human graders in different high-stake assessments, where grading models are learned on essays datasets scored by different gra ders. Despite the definition of the standardized grading rules, human graders can unintentionally introduce subjective bias into scores. Consequently, a grading model has to learn from data that represents a noisy relationship between essay attributes and its grade. We propose an approach for partitioning a set of essays into subsets that represent similar graders, which uses an explanation methodology and clustering. The results confirm our assumption that learning from the ensemble of separated models can significantly improve the average prediction accuracy on artificial and real-world datasets.
Context-Aware Security Evaluation Ontology for Cloud Services

Cloud computing is witnessing a fast development and widespread adoption from small to large organizations due to its cost-effective and resource-efficient services. Nevertheless, security risks remain major concerns. One of the main challenges is how to evaluate the security level of cloud services. Among the problems arising are the large variety of knowledge associated with the security domain and the heterogeneity of cloud services descriptions. An effective solution to such problems is ontologies. Ontologies provide a more structured and formal representation of a particular domain, which will help to reduce the conceptual ambiguity and enable automatic reasoning functionalities. In this paper, we propose an ontology for cloud services security evaluation based on industry standards and best practices. Some of the core concepts included in the ontology are the target, criteria, yardstick, data gathering techniques and data synthesis approaches. The concepts are grouped into five general categories, namely: context, assumptions, external factors, activities, and outcomes. The ontology is implemented using Protégé tool and validated through different uses case scenarios.
Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods with Adjusted Mean Rank

In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment. In the current experimental setting, multiple different scores are employed to assess different aspects of model performance. We analyze the informative value of these evaluation measures and identify several shortcomings. In particular, we demonstrate that all existing scores can hardly be used to compare results across different datasets. Moreover, this problem may also arise when comparing different train/test splits for the same dataset. We show that this leads to various problems in the interpretation of results, which may support misleading conclusions. Therefore, we propose a different evaluation and demonstrate empirically how this helps for fair, comparable and interpretable assessment of model performance.
Filter Size Optimization on a Convolutional Neural Network Using FGSA

This paper presents an approach to optimize the filter size of a convolutional neural network using the fuzzy gravitational search algorithm (FGSA). The FGSA method has been applied in others works to optimize traditional neural networks achieving good results; for this reason, is used in this paper to optimize the parameters of a convolutional neural network. The optimization of the convolutional neural network is used for the recognition and classification of human faces images. The presented model can be used in any image classification, and in this paper the optimization of convolutional neural network is applied in the CROPPED YALE database.