Reviewerly/exHarmony huggingface dataset:
Joint Autoregressive and Graph Models for Software and Developer Social Networks

Social network research has focused on hyperlink graphs, bibliographic citations, friend/follow patterns, influence spread, etc. Large software repositories also form a highly valuable networked artifact, usually in the form of a collection of packages, their developers, dependencies among them, and bug reports. This "social network of code" is rarely studied by social network researchers. We introduce two new problems in this setting. These problems are well-motivated in the software engineering community but not closely studied by social network scientists. The first is to identify packages that are most likely to be troubled by bugs in the immediate future, thereby demanding the greatest attention. The second is to recommend developers to packages for the next development cycle. Simple autoregression can be applied to historical data for both problems, but we propose a novel method to integrate network-derived features and demonstrate that our method brings additional benefits. Apart from formalizing these problems and proposing new baseline approaches, we prepare and contribute a substantial dataset connecting multiple attributes built from the long-term history of 20 releases of Ubuntu, growing to over 25,000 packages with their dependency links, maintained by over 3,800 developers, with over 280k bug reports.
Recognition of Online Handwritten Bangla and Devanagari Basic Characters: A Transfer Learning Approach

The transfer learning approach has eradicated the need for running the Convolutional Neural Network (CNN) models from scratch by using a pre-trained model with pre-set weights and biases for recognition of different complex patterns. Going by the recent trend, in this work, we have explored the transfer learning approach to recognize online handwritten Bangla and Devanagari basic characters. The transfer learning models considered here are VGG-16, ResNet50, and Inception-V3. To impose some external challenges to the models, we have augmented the training datasets by adding different complexities to the input data. We have also trained these three transfer learning models from scratch (i.e., not using pre-set weights of the pre-trained models) for the same recognition tasks. Besides, we have compared the outcomes of both the procedures (i.e., running from scratch and by using pre-trained models). Results obtained by the models are promising, thereby establishing its effectiveness in developing a comprehensive online handwriting recognition system.
A Note on the Epistemological Value of Pretense Imagination

Abstract Pretense imagination is imagination understood as the ability to recreate rational belief revision. This kind of imagination is used in pretend-play, risk-assessment, etc. Some even claim that this kind of hypothetical belief revision can be grounds to justify new beliefs in conditionals, in particular conditionals that play a foundational role in the epistemology of modality. In this paper, I will argue that it cannot. I will first provide a very general theory of pretense imagination, which I formalise using tools from dynamic epistemic logic. As a result, we can clearly see that pretense imagination episodes are build up out of two kinds of imaginative stages, so I will present an argument by cases. This argument shows that pretense imagination might indeed provide us with justification for believing certain conditionals. Despite this, I will argue that these are not the kind of conditionals that allow pretense imagination to play a foundational role in the epistemology of modality.
Blockchain Based E-Voting System

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Statistical Investigation of the Robustness for the Optimization Algorithms

Optimization algorithms are widely used in engineering applications to solve complex real-world design problems. The success of an algorithm with a vast scope should be a convergence in a short time with less iteration to keep pace with developing technology. Generally, optimization algorithms are performed without examining the effect of the algorithm parameters and characteristic of the optimization problem, statistically. Therefore, the appropriate algorithm parameter values of the scatter search optimization algorithms in this chapter have been aimed to investigate with the Taguchi method, which is a statistics-based and the robust way. By manipulating fewer trials, it is possible to gain the proper value, which changes according to the minimum or the maximum global solution design among all combinations of the algorithm parameters. Utilizing an orthogonal array proposed by Taguchi is an effective and successful tool. Thus, approaches are intended to achieve the results with less repetition and to increase the accomplishment of the algorithm, taking into account the effect of the algorithm parameters on the result with statistical analysis.
Mobile augmented reality for furniture visualization using Simultaneous Localization and Mapping (SLAM)

Abstract The purpose of this research is to visualize furniture objects from a catalogue using a markerless augmented reality tracking. Augmented reality is applied to help customers to envision an augmented object for learning the product knowledge of the furniture chosen. The markerless tracking method used in this research is a Simultaneous Localization and Mapping (SLAM), where a targeted surface of the floor for an augmented object can be determined in certain areas in the real world without using a marker. Testing was performed with parameters such as with a minimum of lighting intensity from without lighting, low lighting and bright lighting, a minimum of distance from 50 cm, 100 cm, and 150 cm, a minimum of height from 60 cm,120 cm,160 cm, and a minimum of angle from 30,50 and 70 degree of detection from the surface of the floor. The result of this research is the Simultaneous Localization and Mapping method is confirmed to be adequate for visualizing the 3D furniture model on targeted the surface of the floor without markers.
Large-Context Conversational Representation Learning: Self-Supervised Learning For Conversational Documents

This paper presents a novel self-supervised learning method for handling conversational documents consisting of transcribed text of human-to-human conversations. One of the key technologies for understanding conversational documents is utterance-level sequential labeling, where labels are estimated from the documents in an utterance-by-utterance manner. The main issue with utterance-level sequential labeling is the difficulty of collecting labeled conversational documents, as manual annotations are very costly. To deal with this issue, we propose large-context conversational representation learning (LC-CRL), a self-supervised learning method specialized for conversational documents. A self-supervised learning task in LC-CRL involves the estimation of an utterance using all the surrounding utterances based on large-context language modeling. In this way, LC-CRL enables us to effectively utilize unlabeled conversational documents and thereby enhances the utterance-level sequential labeling. The results of experiments on scene segmentation tasks using contact center conversational datasets demonstrate the effectiveness of the proposed method.
Development of automatic temperature and humidity control system in kumbung (oyster mushroom) using fuzzy logic controller

Abstract National mushroom production in 2018, 2017, 2016 and 2015 amounted to: 31052, 3702, 40915 and 33485 tons. In 2018 the per capita availability is 0.15, this figure needs to be increased as an alternative food solution, this is based on the texture of mushrooms that resembles meat and is delicious, making this plant often become a variety of processed foods such as chips, rendang, satay, shredded, meatballs and supplement. Mushrooms cultivated in Indonesia have high nutritional value and even mushrooms contain 19-35 percent higher protein than rice (7.38 percent) and wheat (13.2 percent). Cultivation of mushrooms, especially oyster mushrooms, has the potential to be developed more widely in Indonesia because it has economic value and is environmentally friendly. However, oyster mushroom cultivation has challenges, especially to produce maximum production in each kumbung. The challenge that arises in oyster mushroom cultivation is that it grows well at temperatures of 16-30 ° C and relative humidity of 80 - 95%. Environmental conditioning is carried out by the average farmer by manually spraying water in the mushroom kumbung. The use of manual methods requires high human resources, apart from that it is not effective and efficient. This study aims to develop a Temperature and Humidity Control System in Kumbung using a Fuzzy Logic Controller. This research uses Fuzzy Proportional-Integral-Derivative (PID) Controller, the sensor data (Temperature and Humidity) will be processed as a sensor array, where to determine the temperature and humidity the system will evaluate the readings of several sensors at once. The system developed is an adaptive system using the Fuzzy method which will suit the environment, the test results show an accuracy rate of 86%, so it is expected that the configuration generated by the tool can be used to increase the precision of mushroom cultivation, so that production efficiency and increased mushroom production can be achieved.
Creating a Foundation for Next-Generation Autonomous Systems

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Editor’s notes:</i> This article advocates the need for a new autonomics foundation with a focus on decision-making logic and its processes for building trustworthy autonomous systems. — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Selma Saidi, TU Dortmund</i>
VizGRank: A Context-Aware Visualization Recommendation Method Based on Inherent Relations Between Visualizations

Visualization recommendation systems measure the importance of visualizations to make suggestions. While considering each visualization individually may be enough to gauge its importance in specific scenarios, it ignores the relations between visualizations under a visual analysis context. This paper is to study a strategy via a more general method called VizGRank which models the relations between visualizations as a graph, then calculates the importance of visualizations by adopting a graph-based algorithm. In this model, the relations derived from the visual encoding of the visualizations and the underlying data schema are used for recommendation. Due to the lack of public benchmarks, the effectiveness of the model is evaluated on the synthetic results from an existing public benchmark IDEBench as a workaround. However, since the existing benchmark is specific and synthetic and does not reflect the realistic scenarios of visualization recommendation completely, a new benchmark for visualization recommendation is designed and constructed by collecting real public datasets. Extensive experiments on both the public benchmark and the new benchmark demonstrate that the VizGRank can better capture the relative importance of visualization and outperforms the existing state-of-the-art method.
The Exploration of the Application of VR Technology in Music Education under the Background of Internet+

With the rapid development of science and technology, the “Internet+” national strategy has covered various fields of people's livelihood and faced with diversified new trends. The vocal music teaching mode and teaching methods have also undergone tremendous changes. Similarly, music education will also present a new situation. New music teaching models are emerging in endlessly under the background of “Internet +” such as MOOC, mobile phone APP, WeChat official account, etc., which broke the traditional vocal music teaching mode in the classroom and reorganized the time-space relationship of music teaching. Based on the analysis of current online music education, this article combines big data screening, artificial intelligence, AI technology, 5G network and virtual reality technology (VR) to make a bold exploration of the music education model and make full use of network technology to optimize teaching Mode, promote the development of music education and cultivate more music talents.
Route optimization for autonomous bulldozer by distributed deep reinforcement learning

Since the publication showed DQN based reinforcement learning methods exceeds human's score in Atari 2600 video games, various deep reinforcement learning have bee researched. This paper proposes a method to control bulldozer autonomously by learning the sediment leveling route using PPO that enables distributed deep reinforcement learning. The simulator was originally developed that enables to reproduce the behavior of small and uniform sediment. By incorporating an LSTM that processes the input state as time-series data into the agent network, more than 95% of the sediment in the target area on average was achieved. In addition, the generalization performance for unknown condition was evaluated, by giving unlearned conditions were given as initial setups.
Research on recognition algorithm of LSS based on video in airport clearance area

LSS (Low, Small and Slow) targets have characteristics of low cost, simple operation, easy to carry, low take-off requirements, strong sudden takeoff, and difficult to find and handle. With the development of aviation industry, the cases of LSS flying illegally and used in terrorist attacks are increasing day by day. It will not only cause great negative effects on economy, but also have serious influence on the national security and the normal development of the national economy. In order to eliminate the threat of illegal flight of UAV in the airport clearance area to the safe operation of airport, a LSS recognition algorithm based on the deep learning was proposed in the paper. Compared with other detection types, the video detection was used in the algorithm, which has many advantages such as strong visualization, low cost and fast detection speed. By using the Yolov3 object detection model, one of the most popular deep learning methods, the problem of low accuracy of small objects such as LSS can be solved. The experiments show that the Yolov3 model can improve the accuracy for small objects and can effectively detect the LSS. Thus, the necessary precondition for the subsequent LSS countermeasures can be provided, and the safe operation of the airport can be ensured.
Portrait photography splicing detection using ensemble of convolutional neural networks

Forged portraits of people are widely used for creating deceitful propaganda of individuals or events in social media, and even for cooking up fake pieces of evidence in court proceedings. Hence, it is very important to find the authenticity of the images, and image forgery detection is a significant research area now. This work proposes an ensemble learning technique by combining predictions of different Convolutional Neural Networks (CNNs) for detecting forged portrait photographs. In the proposed method seven different pretrained CNN architectures such as AlexNet, VGG-16, GoogLeNet, Res-Net-18, ResNet-101, Inception-v3, and Inception-ResNet-v2 are utilized. As an initial step, we fine-tune the seven pretrained networks for portrait forgery detection with illuminant maps of images as input, and then uses a majority voting ensemble scheme to combine predictions from the fine-tuned networks. Ensemble methods had been found out to be good for improving the generalization capability of classification models. Experimental analysis is conducted using two publicly available portrait splicing datasets (DSO-1 and DSI-1). The results show that the proposed method outperforms the state-of-the-art methods using traditional machine learning techniques as well as the methods using single CNN classification models.
Pattern recognition of soldier uniforms with dilated convolutions and a modified encoder-decoder neural network architecture

In this paper, we study a deep learning (DL)-based multimodal technology for military, surveillance, and defense applications based on a pixel-by-pixel classification of soldier’s image dataset. We explore the acquisition of images from a remote tactical-robot to a ground station, where the detection and tracking of soldiers can help the operator to take actions or automate the tactical-robot in battlefield. The soldier detection is achieved by training a convolutional neural network to learn the patterns of the soldier’s uniforms. Our CNN learns from the initial dataset and from the actions taken by the operator, as opposed to the old-fashioned and hard-coded image processing algorithms. Our system attains an accuracy of over 81% in distinguishing the specific soldier uniform and the background. These experimental results prove our hypothesis that dilated convolutions can increase the segmentation performance when compared with patch-based, and fully connected networks.
A microservices persistence technique for cloud-based online social data analysis

Social data analysis has become a vital tool for businesses and organisations for mining data from social media and analysing for diverse purposes such as customer opinion mining, pattern recognition and predictive analytics. However, the high level of volatility for social data means application updates due to analytical results requires spontaneous integration. In addition, while cloud computing has been hugely utilised to address computational overhead issues due to the volume of social data, results obtained still fall short of expected levels. Hence, a persistence mechanism for rapid deployment and integration of software updates for the analytical process is proposed. The persistence mechanism constitutes a significant component within a novel methodology which also leverages cloud computing, microservices and orchestration for online social data analysis, one which fully maximises cloud capabilities and fosters optimisation of cloud computing resources. The proposed methodology provides means of delivering real-time, persistent social data analytics as a cloud service, thereby facilitating spontaneous integration of solutions to maximise expectations from targeted social media audience.
Crime Analysis and Prediction Using Graph Mining

Crime investigation and counteractive action is a deliberate methodology for distinguishing and examining examples and patterns in crime. Our framework can foresee regions which have a high likelihood for crime event and can predict crime-prone regions. With the expanding approach of mechanized frameworks, crime information investigators can help the law authorization officers to accelerate the way toward identifying violations. Utilizing the idea of information mining, we can extract beforehand, uncertain valuable data from unstructured information. Crimes are a social aggravation and cost our general public beyond all doubt in a few different ways. Any study that can help in explaining crime quicker will pay for itself. About 10 percent of the criminals carry out about half of the violations. Here we utilize graph mining techniques for gathering information to distinguish the crime instances and accelerate the way toward enlightening crime. Graph mining is done with the help of identifying the structure of the graph to obtain frequent patterns of information. With the help of graph database, we could store the past criminal records and infer important information from it. Our project aims to store the data in a graph database and try to determine the important patterns on the graph which can be used to predict the regions which have a high probability of crime occurrence and can help the law enforcement officers to enhance the speed of the process of solving crimes.
Low Cost Point to Point Navigation System

This document describes a novel low-cost methodology called “Towards and Tangent” for point to point navigation in robotics systems. The system navigates in an unknown environment, and it avoids obstacles to reach the desired point, using only laser range sensor, without any integration with mems or another kind of data. It is composed by two simple steps: head toward the goal and select the direction with the lower angle to overcome the obstacle. In this way, compared to the state of the art algorithms, it requires less computational resources, since it does not need to detect obstacles discontinuities and do not need to follow an obstacle's boundaries. Moreover, a simple state machine can handle both obstacle avoidance and point to point navigation. Even if the system is easy to implement and requires low resources, it reaches high performances, in line with more sophisticated algorithms and works very well in real-time.
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation

In imitation learning from observation (IfO), a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. In this work, we hypothesize that we can incorporate ideas from model-based reinforcement learning with adversarial methods for IfO in order to increase the data efficiency of these methods without sacrificing performance. Specifically, we consider time-varying linear Gaussian policies, and propose a method that integrates the linear-quadratic regulator with path integral policy improvement into an existing adversarial IfO framework. The result is a more data-efficient IfO algorithm with better performance, which we show empirically in four simulation domains: using far fewer interactions with the environment, the proposed method exhibits similar or better performance than the existing technique.
Designing of a Smart Wheelchair for People with Disabilities

There are many people with disabilities in this day and age who find it is impossible to learn or perform day to day tasks. Some of these kinds of people rely on others for their assistance. Whichever way, they can become autonomous and conduct a few routine exercises with the assistance of helpful gadgets. The most used support devices are wheelchairs. The main purpose of wheelchairs is to provide help to people who are unable to walk because of sickness, handicaps or damage. However, there are also elderly persons with weak appendages and seals who cannot move the wheelchair. Smart wheelchairs will therefore gain a tone for them, and anyone else in the public eye. Intelligent wheelchairs are electrically operated wheelchairs with many other components, such as a PC and sensors, which help the consumer and clockmaker to handle wheelchairs safely and efficiently. Signal growth and autonomous mechanics contribute to the creation of new wheelchairs in the fields of artificial intelligence. This paper is designed to improve the maneuvering tasks of an intelligently self-contained wheelchair. In addition to computer-based treatment, no human intervention during navigation and perception is needed in the wheelchair.