Reviewerly/exHarmony huggingface dataset:
Temporal Incoherence-Free Video Retargeting Using Foreground Aware Extrapolation

Video retargeting is a method of adjusting the aspect ratio of a given video to the target aspect ratio. However, temporal incoherence of video contents, which can occur frequently by video retargeting, is the most dominant factor that degrades the quality of retargeted videos. Current methods to maintain temporal coherence use the entire frames of the input videos; however, these methods cannot be implemented as on-time systems because of their tremendous computational complexity. As far as we know, there is no existing on-time video retargeting method that can avoid spatial distortion while perfectly maintaining temporal coherence. In this paper, we propose a novel on-time video retargeting method that can perfectly maintain temporal coherence and prevent the spatial distortion by using only two consecutive input frames. In our method, the maximum a posteriori-based foreground aware-block matching is used for the extrapolation that extends the side area of a given video to adjust its aspect ratio to the target. To maintain the temporal coherence of the extended area, the result of block matching for backward warping-based extrapolation of the start frame after the scene change occurs, is reused for the other frames until the next scene change occurs. In addition, we propose a scene scenario-adaptive fallback scheme to prevent severe distortions that can occur with reusing block matching results or extrapolation-based side extension. The simulation results showed that the proposed method greatly improved the bidirectional similarity value, which can measure the quality of video retargeting, by up to 10.26 compared with the existing on-time video retargeting methods.
Hierarchical Prediction and Adversarial Learning For Conditional Response Generation

There are a variety of underlying factors influencing what and how people communicate in their daily life. The ability to capture and utilize these factors enables the conversational systems to generate favorable responses and set up amicable connections with users. In this work, we investigate two major factors in response generation, i.e., emotion and intention. To explore the dependency between them, we develop a hierarchical variational model that predicts in sequence the emotion and intention to be conveyed in a response. The response can then be generated word-by-word based on the predictions. We also apply a novel adversarial-augmented inference network to facilitate model training. The experimental results demonstrate the effectiveness of the proposed model as well as the novel adversarial objective. The hypothesis that emotion shapes human communication behavior is also validated.
Evaluating Digital Games for Competitive Music Composition

Digital games are a fertile ground for exploring novel computer music applications. While the lineage of game-based compositional praxis long precedes the advent of digital computers, it flourishes now in a rich landscape of music-making apps, sound toys and playful installations that provide access to music creation through game-like interaction. Characterising these systems is the pervasive avoidance of a competitive game framework, reflecting an underlying assumption that notions of conflict and challenge are somewhat antithetical to musical creativity. As a result, the interplay between competitive gameplay and musical creativity is seldom explored. This article reports on a comparative user evaluation of two original games that frame interactive music composition as a human–computer competition. The games employ contrasting designs so that their juxtaposition can address the following research question: how are player perceptions of musical creativity shaped in competitive game environments? Significant differences were found in system usability, and also creativity and ownership of musical outcomes. The user study indicates that a high degree of musical control is widely preferred despite an apparent cost to general usability. It further reveals that players have diverse criteria for ‘games’ which can dramatically influence their perceptions of musical creativity, control and ownership. These findings offer new insights for the design of future game-based composition systems, and reflect more broadly on the complex relationship between musical creativity, games and competition.
Multi-Batch Scheduling for Improving Performance of Hyperledger Fabric Based IoT Applications

Blockchain technology has been regarded as a paradigm that enables enterprises to deploy decentralized Internet of things (IoT) applications, which require security, mutual truthfulness, privacy, and data reliability. However, the adoption of blockchain technology on IoT applications has been limited due to the lack of blockchain's capability to process a huge amount of transaction requests from IoT devices. In this paper, we propose a new transaction processing mechanism, called Accelerator, which improves the performance of blockchain-based IoT applications in terms of transaction throughput and latency. Two key design ideas of Accelerator lie in: (i) independent modular structure that can be implemented without changing the incumbent blockchain network and (ii) adaptive multi-batch scheduling engine for robust performance. We first provide a mathematical analysis that is used to choose the parameters of Accelerator to maximize the performance. Moreover, we implement Accelerator on a decentralized blockchain network using Hyperledger Fabric and perform the extensive test using Hyperledger Caliper. We show that Accelerator achieves 8x transaction throughput improvement without sacrificing latency.
Scaling Agile Software Development Approach in Government Organization in New Zealand

Agile methods are based on an iterative and incremental cycles of development where requirements and solutions are developed through collaboration and coordination between cross-functional teams and their customer. Agile software development focuses on flexibility, allowing changes in requirements to occur during the software development process. The use of Agile software development is increasing in both the private and public sectors. However, there is little knowledge about the use of Agile in the public sector. This paper presents important factors such as communication, transparency, feedback, product owner engagement, confidence and organization culture that contribute to the outcome of Agile Software development projects in public sector organisations. This study compares the main challenges in the public and the private sector. Transparency and Product Owner engagement were found to be the main difference in the challenges in the public and private sectors.
Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words for Predicting Medical Codes

Word embeddings are a useful tool for extracting knowledge from the free-form text contained in electronic health records, but it has become commonplace to train such word embeddings on data that do not accurately reflect how language is used in a healthcare context. We use prediction of medical codes as an example application to compare the accuracy of word embeddings trained on health corpora to those trained on more general collections of text. It is shown that both an increase in embedding dimensionality and an increase in the volume of health-related training data improves prediction accuracy. We also present a comparison to the traditional bag-of-words feature representation, demonstrating that in many cases, this conceptually simple method for representing text results in superior accuracy to that of word embeddings.
Cross-View Contextual Relation Transferred Network for Unsupervised Vehicle Tracking in Drone Videos

Recently CNN-centric object tracking methods have been gaining tremendous success in ground-view videos, however, it remains hard to cope with vehicle tracking in unmanned aerial vehicle (UAV) videos. The key difficulties mainly stem from lacking large-scale well-labeled training datasets and view-invariant appearance model for fast-moving drone-view vehicles. We enhance the vehicle's cross-view feature by exploring relations between the pivotal context and the target to facilitate unsupervised vehicle tracking. The relation is modeled as the relevance of the target and its contextual regions in the tracking task. Specifically, we propose a contextual relation actor-critic (CRAC) framework integrates an actor-critic agent with a dual GAN learning mechanism, which aims to dynamically search the related contextual regions and transfer the relations from ground-view to drone-view videos while retaining the discriminative features. We demonstrate that CRAC could be applied to several state-of-the-art trackers by extensive experiments and ablation studies on four public benchmarks. All the experiments confirm that, our CRAC can improve the performance of state-of-the-art methods in terms of accuracy, robustness, and versatility.
Conversational Structure Aware and Context Sensitive Topic Model for Online Discussions

Millions of online discussions are generated everyday on social media platforms. Topic modelling is an efficient way of better understanding large text datasets at scale. Conventional topic models have had limited success in online discussions, and to overcome their limitations, we use the discussion thread tree structure and propose a "popularity" metric to quantify the number of replies to a comment to extend the frequency of word occurrences, and the "transitivity" concept to characterize topic dependency among nodes in a nested discussion thread. We build a Conversational Structure Aware Topic Model (CSATM) based on popularity and transitivity to infer topics and their assignments to comments. Experiments on real forum datasets are used to demonstrate improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.
Fuzzy case‐based‐reasoning‐based imputation for incomplete data in software engineering repositories

Abstract Missing data is a serious issue in software engineering because it can lead to information loss and bias in data analysis. Several imputation techniques have been proposed to deal with both numerical and categorical missing data. However, most of those techniques used is simple reuse techniques originally designed for numerical data, which is a problem when the missing data are related to categorical attributes. This paper aims (a) to propose a new fuzzy case‐based reasoning (CBR) imputation technique designed for both numerical and categorical data and (b) to evaluate and compare the performance of the proposed technique with the k ‐nearest neighbor (KNN) imputation technique in terms of error and accuracy under different missing data percentages and missingness mechanisms in four software engineering data sets. The results suggest that the proposed fuzzy CBR technique outperformed KNN in terms of imputation error and accuracy regardless of the missing data percentage, missingness mechanism, and data set used. Moreover, we found that the missingness mechanism has an important impact on the performance of both techniques. The results are encouraging in the sense that using an imputation technique designed for both categorical and numerical data is better than reusing methods originally designed for numerical data.
Removing Unvoiced Segment to Improve Text Independent Speaker Recognition

Speaker recognition is one of the biometric technologies to recognize humans, such as identification of fingerprints, DNA, and iris because the characteristics of human speech are unique. The characteristics of speech are dominated by voiced segment. Meanwhile, the unvoiced segments those commonly have random waveform can be seen as noise for the speaker recognition. Therefore, in this paper, a simple procedure to remove unvoiced parts is proposed to improve the accuracy. This procedure is simply implemented using the short time zero-crossing rate (STZCR) to detect and remove the unvoiced parts. It is applied before the Mel Frequency Cepstral Coefficients (MFCC)-based feature extraction and the Gaussian Mixture Model (GMM)-based classification. Evaluation on VoxForge dataset using 4-fold cross-validation show that the proposed procedure is capable of improving the accuracy from 99.94% to 100%.
An Automated Machine Learning Approach for Sentiment Classification of Bengali E-Commerce Sites

E-commerce reviews and comments about specific products disclose consumer's perceptions as well as attitudes. This attitudes expressed by the consumer's seem to be most useful for the new customers who is interested on any product. Meanwhile, an ever increasing number of reviews and comments are being stored daily and the amount of people buying goods online are increasing in a great extent. User emotions record associated with every products is beneficial for both the makers as well as customers. But with the increasing number of datasets in the e-commerce websites, it has almost become impossible for manual analysis and without automated machine learning approach it can't be even imagined. Therefore, improvement in the field of machine learning approaches must be accomplished. Realizing the worth of this, this work proposes a hybrid machine learning approach by incorporating different machine learning approaches. Own Bangla StopWords database consisting of approximately 900words have also been concentrated in this work. Initially, input data are tokenized using python NLTK library and filtered using StopWords created. Then conversion of data to numerical from string are conducted using TF-IDF (Term Frequency-Inverse Document Frequency) information retrieval mechanism and finally trained using K-nearest neighbor (KNN) with Support Vector Machine (SVM). Our proposed approach Normalization along with StopWords filtering embedded KNN based SVM demonstrates superiority after a comparative study with Principle Component Analysis (PCA) with Convolutional neural network (CNN), Random Forest, Logistic Regression.
A theorem on a product of lognormal variables and hybrid models for children’s exposure to soil contaminants

This study developed hybrid Bayesian models to investigate the modeling process for children's exposure to soil contaminants, which involves the intrinsic uncertainty of the exposure model, people's judgments regarding random variables, and limited data resources. A hybrid Bayesian p-box was constructed, which was facilitated by a multiple integral dimensionality reduction (MIDR) theorem. The results indicated that exposure frequency (EF) dominated the exposure dose. The hybrid Bayesian p-box for the Frequentist-Bayesian (F-B) model at the 95th percentile of the simulated average daily dose (ADD) values corresponded to a 4.40 order-of-magnitude difference between the upper and lower bounds of the p-box. This considerable uncertainty was magnified by the combination of the highest posterior density (HPD) regions for three groups of the distribution parameters. For the Interior-Bayesian (I-B) hybrid model, the uncertainty of the outcomes, namely, [1.75 × 10-8, 2.18 × 10-8] mg kg-1d-1, was limited by the HPD regions for only one parameter unless the hyperparameters for the variables' distributions were further evaluated. It was concluded that the hybrid models could provide a novel understanding of the complexity of the exposure modeling process compared to the traditional modeling method.
Machine Learning in the Wild: The Case of User-Centered Learning in Cyber Physical Systems

Smart environments, such as smart cities and smart homes, are Cyber-Physical-Systems (CPSs) which are becoming an increasing part of our everyday lives. Several applications in these systems, such as energy management through home appliance identification or activity recognition, adopt Machine Learning (ML) as a practical tool for extracting useful knowledge from raw data. These applications are usually characterized by a sequential stream of data, unlike the classical ML scenario in which the entire data is available during training. For such applications, Stream-based Active Learning (SAL) has been designed as a type of supervised ML in which an expert is asked to label the most informative instances as they arrive. Previous SAL techniques assume that the expert is always available and always labels the data correctly. However, in several applications, such as those mentioned above, the SAL activity interweaves with the everyday life of regular residents, who are often not experts, and may also not always be willing to participate in the labeling process. In this paper, we discuss the importance of user-centered ML, and show how taking into account realistic models of user behavior significantly improves the accuracy and reduces the training period of smart environment applications based on SAL. We consider two use cases, namely appliance identification and activity recognition. Results based on real data sets show an improvement in terms of accuracy up to 55.38%.
Underwater Object Tracking Benchmark and Dataset

While there has been tremendous advancement in object tracking for open air visual data, much less work has been done for underwater object tracking. This is due to the low quality of underwater visual data. Underwater visual data suffers distortions in contrast and sharpness, as a result of refraction and absorption of light, and particles, which all vary dependent on the depth, color and nature of water. Although there currently exists several object tracking algorithms with proven record of high speed, precision and success rate, these algorithms work best for open air tracking, and considerably degrade in performance when tracking targets in underwater environments, as it is presented in this paper. The advancement made in open air tracking has been facilitated by availability of multiple benchmark and dataset. However, no such benchmark and dataset exist for underwater tracking, and this lack of data has hindered development of dedicated underwater tracking algorithms. In this paper, we present: a) the first underwater tracking benchmark dataset consisting of 32 videos, and a total of 24241 annotated frames, averaging 29.15 seconds and 757.53 frames per video, to help improve underwater tracking; and b) a comparative performance analysis of existing tracking algorithms in underwater environment as opposed to open air.
Software Wallet Based Secure Participation in Hyperledger Fabric Networks

Identity plays an important role in blockchain networks, both permissioned and permissionless. Enterprise applications built on permissioned blockchains, associate access control and privileges with user, organization and blockchain node identities. The identity, backed by cryptographic keys is thus important to safeguard one's data as well as for the sanctity of the network. Securing the keys without trusting and delegating key management to another is critical to extend the trust paradigm all the way to the end-user. In this paper, we present a secure software wallet which resides on the user's mobile device and using which one participates in the blockchain network. We demonstrate the use of the software wallet in a Hyperledger Fabric network and especially in conjunction with the transaction flow of Hyperledger Fabric. We show that software wallets can be seamlessly incorporated into enterprise applications to extend the security and enable the end-users to manage their identities.
Malicious URL Classification Using Machine Learning Algorithms and Comparative Analysis

Exponential expansion in the application of the internet in each and every field has resulted in the escalation of data traffic over the internet. In the vastness of this data it has become important for engineers to classify the data as malicious and non-malicious so that different traffic can be treated differently. Rule-based and port-based classification exhibited a number of limitations which ultimately led to the steep decline in their usage to classify the internet traffic and gave rise to the machine learning techniques which are more promising and efficient. In this paper four popularly known machine learning classifiers: KNN, Naive Bayes, Decision Trees and Random forest have been implemented to classify the internet traffic based on whether the traffic is malicious or not and then compare their results on the basis of their accuracy score.
A High-Performance Pedestrian Detector and Its Implementation on Embedded Systems for Hypermarket Environment

Enabling autonomous driving in hypermarket environments is a new challenge. The whole scenario is very different from traditional outdoor autonomous driving. To navigate in hypermarket environments, the vehicles need to know where all the surrounded pedestrians are. In addition, the detection model must be small enough to be executed in real-time speed on embedded systems. Therefore, we present a high-performance convolutional neural network for detecting moving indoor pedestrians as well as its implementation on embedded systems in this paper. The proposed CNN model can achieve the same high accuracy as YOLO v3 at the cost of only 27% of the original model size. When implemented on an embedded system, i.e., Jetson Xavier, this work achieves 30 fps @ 360p video format.
Real-time Control System for a Tracked Robot

In this paper the designing of a mobile tracked robot's real-time control system is considered. The developed system embodies algorithms for the robot's remote control, or its autonomous movement to the target with an obstacle avoidance, with simultaneous localization and mapping in real time. To implement the control system, the real-time operating system ChibiOS and the operating system for robots ROS are used.
Minimal Kinematics: An All k and n Peek into Trop<sup>+</sup>G(k,n)

In this note we present a formula for the Cachazo-Early-Guevara-Mizera (CEGM) generalized biadjoint amplitudes for all k and n on what we call the minimal kinematics.We prove that on the minimal kinematics, the scattering equations on the configuration space of n points on CP k-1 has a unique solution, and that this solution is in the image of a Veronese embedding.The minimal kinematics is an all k generalization of the one recently introduced by Early for k = 2 and uses a choice of cyclic ordering.We conjecture an explicit formula for m (k) n (I, I) which we have checked analytically through n = 10 for all k.The answer is a simple rational function which has only simple poles; the poles have the combinatorial structure of the circulant graph C (1,2,...,k-2) n. Generalized biadjoint amplitudes can also be evaluated using the positive tropical Grassmannian Tr + G(k, n) in terms of generalized planar Feynman diagrams.We find perfect agreement between both definitions for all cases where the latter is known in the literature.In particular, this gives the first strong consistency check on the 90 608 planar arrays for Tr + G(4, 8) recently computed by Cachazo, Guevara, Umbert and Zhang.We also introduce another class of special kinematics called planar-basis kinematics which generalizes the one introduced by Cachazo, He and Yuan for k = 2 and uses the planar basis recently introduced by Early for all k.Based on numerical computations through n = 8 for all k, we conjecture that on the planarbasis kinematics m (k) n (I, I) evaluates to the multidimensional Catalan numbers, suggesting the possibility of novel combinatorial interpretations.For k = 2 these are the standard Catalan numbers.