Reviewerly/exHarmony huggingface dataset:
Comparison between Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) on sunlight intensity prediction based on air temperature and humidity

Abstract Weather prediction expecially in predicting sunlight intensityhas important role in energy usage. As effort for controlling petrol-based fuel usage, government manage energy usage by converting solar energy from sunlight intensity to electric through solar cell.Sunlight intensity depends on air temperature and humidity. Two methods on prediction process will be applied : Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Type of NN used in prediction process is Backpropagation. Backpropagation consists of forward propagation, backward propagation, and update weight matrices. ANFIS uses hybrid method to train consequent parameters and premise parameters. In this research, NN will be compared with ANFIS. From five trials of NN simulations, the number of maximum epoch for making the root of mean square error(RMSE) in training data is very large so that the computation time is very long. From the comparison result of two methods, we can see that ANFIS can make faster prediction than NN with the number of maximum epoches is smaller than NN so that computation time is faster.
Economic feasibility study for stealthy socialbot network establishment

Socialbots are intelligent software that controls all behaviour of fake accounts in an online social network. Since they are armed with detection evasion techniques, it is valuable to be able to determine the effectiveness of these techniques. In this study, an analytical model is developed to estimate a lower bound for the cost of automatic establishment of a socialbot network. Moreover, by considering fake accounts purchasing as an establishment strategy, an upper bound is suggested for acceptable costs. These two boundaries are compared to decide on the economic feasibility of a socialbot network design strategy. To demonstrate the practicality and effectiveness of the model, two case studies are investigated. They show that although designing a fully stealthy socialbot network is economically feasible, the infiltration time would be unacceptable. Thus, this ideal situation in which the establishment is fully stealthy, performs in a tolerable time, and satisfactory infiltration scale, is impractical. A possible solution could be achieved by reducing the time and cost in exchange for less stealthy behaviour while the infiltration scale kept unchanged. Since the model presents a trade-off between stealthiness, time, and cost, it is a useful tool facilitating the design of a possible strategy.
Academic performance and mathematical processes under a causal model. Correlations found in three public institutions

Abstract This research implemented a model of structural equations aimed at determining the possible causal relationship between the mathematical processes mentioned in the national teachers council mathematics and academic performance in mathematics. The level of development of the mathematical processes promoted by the teacher was analyzed, in students of the fourth to seventh grades of basic education in three public educational institutions of the San José de Cúcuta, Colombia, characterized by obtaining levels of performance (respectively) in the Saber 11 tests of 2018. The sample size was 400 students enrolled in 2019. A questionnaire was proposed and validated that included sociodemographic information of students and then determined the presence and development of the various mathematical processes in classroom work. The results highlight that, of the five mathematical processes, reasoning, representation and problem solving are presented as determining variables of academic performance in students, while the processes of communication and connections are little work in the classroom by teachers. From the results obtained arises as future research, determine what teachers understand by mathematical problems? How do you promote problem solving in the classroom?
Application Research of Fast UAV Aerial Photography Object Detection and Recognition Based on Improved YOLOv3

Abstract UAV aerial photography is affected by weather, altitude, illumination, occlusion and other factors resulting in the objective fact that the object has many variations in scale and perspective. This paper proposes an improved YOLOv3 algorithm which achieve object detection and recognition in view of the above complex working conditions quickly and accurately. Firstly, the network framework of YOLOv3 is altered and the BN layer is integrated into the convolution layer. The network structure is simplified and the speed of model detection is greatly accelerated but the detection accuracy is almost the same as original. Secondly, GIOU is adopted as the loss function for bounding box regression to prevent the existence of zero gradient and improve mean average precision (mAP) of the detection model. Finally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes independently. The results of a large number of comparative experiments show that the improved YOLOv3 algorithm can achieve object detection for the UAV aerial photography faster and more accurately. The detection speed is increased by 27%, the mAP is increased by 2.32% and the detection performance is also improved for small object significantly compared with YOLOv3.
Design patterns for modeling first-order expressive Bayesian networks

Abstract First-order expressive capabilities allow Bayesian networks (BNs) to model problem domains where the number of entities, their attributes, and their relationships can vary significantly between model instantiations. First-order BNs are well-suited for capturing knowledge representation dependencies, but literature on design patterns specific to first-order BNs is few and scattered. To identify useful patterns, we investigated the range of dependency models between combinations of random variables (RVs) that represent unary attributes, functional relationships, and binary predicate relationships. We found eight major patterns, grouped into three categories, that cover a significant number of first-order BN situations. Selection behavior occurs in six patterns, where a relationship/attribute identifies which entities in a second relationship/attribute are applicable. In other cases, certain kinds of embedded dependencies based on semantic meaning are exploited. A significant contribution of our patterns is that they describe various behaviors used to establish the RV’s local probability distribution. Taken together, the patterns form a modeling framework that provides significant insight into first-order expressive BNs and can reduce efforts in developing such models. To the best of our knowledge, there are no comprehensive published accounts of such patterns.
Low Cost Lightweight Obstacle Detection System for Micro Aerial Vehicles

This paper describes a lightweight obstacle detection system for Micro Aerial Vehicles (MAVs). This system is composed of multiple infrared (IR) proximity sensors interconnected through an open source development board. Standard methods for obstacle detection for UAVs commonly rely on sensory systems, such as, cameras and range finders. These sensors are usually very expensive. Moreover, their use is limited by both the payload and the computational resources available on the vehicle. By contrast, proximity sensors are cheap as well as lightweight. They can allow to detect the presence of objects in the nearby without any physical contact. We installed the system on a Parrot AR. Drone. This drone is a MAV with low payload capacity. Experiments in both simulation and real environments have been performed to test the effectiveness of our system.
Importance of Proper Pairing in Agile Teams Reinforced with Umbrella Traversal Problem

To expedite the late running projects, addition team members are added. Additional communication overhead and the side effects of coordinating among team memers, will make the project further delay. Adding more members to the team of late-running projects can make them later [1] because of the need for increased communication overhead and side effects of coordinating among team members. Collaborative problem-solving methods in agile methodologies like Pair programming insist pairs of programmers, but of those having equal caliber for performing the tasks. Even if there are persons with exactly equal caliber in solving a particular task, they need not exhibit this equality in other project situations because of the varying nature of project situations. Also, the levels of knowledge and skills of team members can be different from person to person. Finishing the project goals within the constraints of collaborating and coordinating team members having different levels of proficiency, speed, quality, and the like demands special care and optimization techniques. Improper formulation of teams can bring big losses and adverse aftereffects. This paper reinforces these concepts with the help of a metamodel puzzle involving traversal of persons having different walking speeds on a rainy day, under the constraints imposed by the requirement of using a single available umbrella having the limited capacity of occupying at most two persons at a time.
Accuracy Tolerant Neural Networks Under Aggressive Power Optimization

With the success of deep learning, many neural network models have been proposed and applied to various applications. In several applications, the devices used to implement the complicated models have limited power resources, and thus aggressive optimization techniques are often applied for saving power. However, some optimization techniques, such as voltage scaling and multiple threshold voltages, may increase the probability of error occurrence due to slow signal propagation, which increases the path delay in a circuit and fails some input patterns. Although neural network models are considered to have some error tolerance, the prediction accuracy could be significantly affected, when there are a large number of errors. Thus, in this paper, we propose a scheme to mitigate the errors caused by slow signal propagation. Since the delay of multipliers dominates the critical path, we consider the patterns significantly altered by the slow signal propagation in a multiplier. We propose two methods, weight distribution and error-aware quantization to prevent the patterns from failure. Since we modify a neural network on the software side and it is unnecessary to re-design the hardware structure. The experimental results show that the proposed scheme is effective for several neural network models. It can improve the network accuracy by up to 27% under the consideration of slow signal propagation.
Model-Based Deep Encoding Based on USB Transmission for Modern Edge Computing Architectures

With the advance of deep neural networks (DNNs), artificial intelligence (AI) has been widely applied to various applications in our daily lives. These DNN-based models can be stored in portable storage disks or low-power Neural Compute Sticks. They can then be deployed in edge devices through the USB interface for AI-based applications, such as Automatic Diagnosis Systems or Smart Surveillance Systems, which provides solutions to incorporating AI into the Internet of Things (IoT). In this work, based on our observation and careful analysis, we propose a model-based deep encoding method built upon Huffman coding to compress a DNN model transmitted through the USB interface to edge devices. Based on the proposed lopsidedness estimation approach, we can exploit a modified Huffman coding method to increase the USB transmission efficiency for quantized DNN models while reducing the computational cost entailed by the coding process. We conducted experiments on several benchmarking DNN models compressed using three emerging quantization techniques, which indicates that our method can achieve a high compression ratio of 88.72%, with 93.76% of the stuffing bits saved on average.
3DQ-Nets: Visual Concepts Emerge in Pose Equivariant 3D Quantized Neural Scene Representations

Concept learning lies at the very heart of intelligence, providing organizing principles with which to comprehend the world (6). Most computer vision models learn concept classifiers or detectors using labelled examples of object boxes, poses and categories. Self-supervised or unsupervised approaches mostly focus on (pre)training CNNs on auxiliary pretext tasks to lessen the need for human labels for a downstream recognition task. The visual "concepts" learnt from pretext tasks are implicit and represented as distributed neural CNN activations (7). We see the following limitations with representing concepts (solely) as neural activations across multiple layers of a deep network: i) visual memory and computation are not separated (3), which means that computation increases exponentially with the number of visual concepts learnt, ii) concepts are not stored somewhere and cannot be referred to or retrieved on demand, iii) the number of concepts cannot increase automatically based on new visual experiences, rather it is determined by the processing architecture; this misaligns with the idea that animals are capable of spontaneous concept instantiation in novel scenes (2), iv) concepts cannot be mentally manipulated by imagining variations, transformations or mental simulations (8), v) concepts do not have any spatial dimension and are hard to use for spatial reasoning.
Experimental Study of Network Traffic Overhead in Cloud Environments

In this paper, we aim to analyze and build upon a framework cloud computing environments based on our previous research [1]. Cloud computing has benefited the technological scene massively over the past few years. With this massive benefit comes some interesting questions regarding the performance and reliability of cloud computing architectures. By rigorously testing a virtual cloud environment, we will discuss open research challenges regarding the proposed framework. Cloud computing has a vast array of implementations as well as architectures, therefore it is critical to test a common cloud architecture and discover possible drawbacks and solutions for cloud computing environments.
Match or No Match: Keypoint Filtering based on Matching Probability

Keypoints that do not meet the needs of a given application are a very common accuracy and efficiency bottleneck in many computer vision tasks, including keypoint matching and 3D reconstruction. Many computer vision and machine learning methods have dealt with this issue, trying to improve keypoint detection or the matching process. We introduce an algorithm that filters detected keypoints before the matching is even attempted, by predicting the probability of each point to be successfully matched. This is realised using a flexible and time efficient Random Forest classifier. Experiments on stereo and multi-view datasets of building facades show that the proposed method decreases the computational cost of a subsequent keypoint matching and 3D reconstruction, by correctly filtering 50% of the points that wouldn't be matched while preserving 73% of the matchable keypoints. This enables a subsequent processing with minimal mismatches, provides reliable matches, and point clouds. The presented filtering leads to an improved 3D reconstruction of the scene, even in the hard case of repetitive patterns and vegetation.
Participatory construction of futures for the defense of human rights

Participatory design allows for designing speculative futures through a collaborative approach. This paper explores how a human rights defense non-governmental organization (NGO) and a group of designers could explore speculative futures collaboratively. It also reflects on how prototypes of these futures help the organization face potential changes in the country's social model to make an impact on the defense of human rights during the next ten years. This case study presents how the use of participatory design and speculative design can allow NGOs to explore the futures, identify the opportunities and challenges they offer, and co-design a roadmap to act accordingly.
Feature Selection and Reduction Based on SMOTE and Information Gain for Sentiment Mining

The problem of classifying the sentiment analysis was found that there were many features for the sentiments that caused the less accuracy in classifying the sentiment, for example the negative class for those sentiments. The purpose of this study was to Figure out the amount of suitable features of each class for learning data with applied integration of information gain (IG) technique which used to reduce the factor and integrated to synthetic minority over-sampling technique (SMOTE) in order to adjust the imbalanced class. In this study, it enhanced the efficiency of accuracy in every class, and then it was evaluated by four methods consisting of J48, Naïve Bayes, k-Nearest Neighbor where k=1, 2, 3, and Support Vector Machine (SVM) to compare the efficiency of accuracy. The TP Rate was employed as the evaluation metric for the accuracy of each class including the positive and the negative whereas the efficiency of accuracy was the TP Rate of Positive and the TP Rate of Negative. As the results of this study, it revealed that the IG and the SMOTE suggested the number of suitable features for the sentiment analysis. SVM method given the higher efficiency of the accuracy, that obtained the TP Rate of Positive as 86.50 % and TP Rate of Negative as 89.10 % and the level of SMOTE suitable by 300 %.
Region Proposal Generation: A Hierarchical Merging Similarity-Based Algorithm

This paper presents a hierarchical algorithm using region merging with the aim of achieving a powerful pool of regions for solving computer vision problems. An image is first represented by a graph where each node in the graph is a superpixel. A variety of features is extracted of each region, which is next merged to neighbor regions according to the new algorithm. The proposed algorithm combines adjacent regions based on a similarity metric and a threshold parameter. By applying different amounts for the threshold, a wide range of regions is acquired. The algorithm successfully provides accurate regions while can be represented through the bounding box and segmented candidates. To extensively evaluate, the effectiveness of features and the combination of them are analyzed on MSRC and VOC2012 Segmentation dataset. The achieved results are shown a great improvement at overlapping in comparison to segmentation algorithms. Also, it outperforms previous region proposal algorithms, especially it leads to a relatively great recall at higher overlaps (≥ 0.6).
Design, modeling and control of an owl-inspired robot in a standing posture

This paper deals with an original study based on anatomical observation of owl's motions in a standing posture. Owls are specific birds typified by upright stance, sharp talons and adapted feathers for silent flight. Owls can rotate their heads and necks as much as a complete turn without any difficulty. A new kind of robot is proposed here inspired from owls in two phases: standing posture and flight. The first complicated case presents the goal of this work. To model different possible motions of owl's head and neck, a large observation of the natural behavior can allow us to propose a close design to reality. A dynamic model is then generated to verify miscellaneous rotations and movements. Finally, a fuzzy control system is developed to reach desired values of head and neck's angles. The paper is ended with a conclusion and future work suggestions.
Research on Visual Control System of Inverted Pendulum Based on Pixel Displacement

Abstract In inverted pendulum visual servo control system (IPVSCSs), how to achieve the fast and effective measurement of the state information of the inverted pendulum is an important task. In this paper, the characteristics of the inverted pendulum are considered and a series of image processing and positioning algorithm of the pendulum are proposed. Furthermore, the inverted pendulum control system model based on the pixel displacement is established and the LQR controller is designed. Finally, the feasibility and superiority of the proposed method are verified by real-time control experiments.
Deterministic Factors Influencing Learners' Online Learning Behaviors by Applying IT-assisted Music Curriculum

Because of the learning behaviors of learners include multiple characteristics and tendencies and the behaviors with complex and tangled associations, in this study, ISM with Fuzzy MICMAC and Fuzzy DEMATEL methods (approach) were applied. Analyze and discuss the dimensions and criteria of applying information technology to assist music teaching to influence learners' learning behaviors and to clarify the relationship between learning behaviors and to provide educational institutions and schools to introduce or improve the integration of information technology into music teaching of the teaching strategies and curriculum design and planning to trigger learners' learning behavior. Studies show that in terms of dimension, the integration of information technology into music teaching will motivate learners in areas such as online learning attitudes, music learning motivation, and learning engagement, and self-directed learning. The self-directed learning factor is an important key to the dimension of learning behavior; among these criterions, online teaching of musical performance, musical instrument teaching, and music theory will influence about 23 criteria of the learners' learning behavior, and computer/smart-phone preferences, online learning, computer/smart-phone, and computer and network confidence are the three keys to learning behavior criterion, therefore, in addition to establishing a good music teaching environment, strengthening learners' self-directed learning and online learning attitudes are important learning guidelines.
A List based Redundancy Removal Approach by Mining Closed and Non-Derivable Frequent Itemsets

Mining Frequent Itemsets has become an integral component of research lately, mainly because it can be applied to a variety of areas, including clustering of documents for information retrieval, inventory management, association rule mining, and outlier detection. Existing techniques that extract "frequent items" do not take care to address redundancy. An itemset is said to be redundant if their support can be derived from other existing itemsets using standard deduction techniques. The presence of such itemsets in the collection was found to increase the size of the collection of the mined frequent items. This in turn affects the memory and run time of the mining process. To overcome these drawbacks, this paper employs a list-based approach that generates only "Closed and Non-Derivable Itemsets." This is a collection of items ‘I’ such that ‘I’ has no superset with the same support as that of itself, and I belongs to the collection of those items whose lowest upper bound and highest lower bound values are not the same. With the help of experiments, it has been proved that the proposed approach addresses redundancy better by generating a compressed set of non-redundant frequent items.
Cross-domain few-shot classification through feature confusion

Abstract Few-shot classification has made great progress in recent years which aims to solve new tasks using the prior knowledge obtained from a set of similar tasks with few labeled examples. However, the performance of current methods drops when domain shift exists between training and testing samples. Considering the situation where target domain examples are hard to collect, it is necessary to focus on the cross-domain problem. In this paper, we propose a simple method which embeds the domain adaptation method into the few-shot problem under a novel cross-domain few-shot setting, and focus on supervised setting in the target domain where few labeled data available for domain adaptation. Extensive empirical evidence shows the effectiveness of our method.