Reviewerly/exHarmony huggingface dataset:
Optimal Trajectory Planning for Autonomous Drone Cinematography

This paper presents a method for optimal trajectory planning with applications to drone cinematography. Aerial cinematography with drones is growing fast due to their maneuverability and ability to create unique visual effects. However, planning optimal drone trajectories and camera movements is still a major challenge to autonomous aerial filming. The trajectories must meet the objectives on aesthetic quality of the videos while satisfying several constraints imposed by drone dynamics, gimbal mechanical limits and surrounding obstacles. In this paper, we propose a novel formulation of the problem by decoupling the gimbal and the drone control systems. The problem is formulated as an optimization problem taking into account the gimbal rotation limits and collision avoidance constraints as well as camera angle driven objective functions to ensure feasible and smooth drone trajectories that generate visually pleasing videos. We evaluate the efficacy of our method through simulations and real-world experiments in an outdoor environment.
A New Method to Identify Short-Text Authors Using Combinations of Machine Learning and Natural Language Processing Techniques

Identifying authors by their style of writing is a very challenging task. This problem has several applications, one of which is to identify fake online reviews written by spam accounts. The existence of such fake reviews degrades the credibility of the whole review collection, hence these fake reviews should be identified and removed. This process, however, needs to be automated since it is impossible to perform it manually in large review collections. Current authorship identification approaches identify authors based on large-scale texts such as documents. For this reason, these methods do not scale well to short texts such as online reviews that have limited features to learn from. This paper introduces a new method of author identification in short texts using combinations of machine learning algorithms and natural language processing techniques. The experiments we conducted on Yelp reviews gave promising results.
Learning to Predict Robot Keypoints Using Artificially Generated Images

This work considers robot keypoint estimation on color images as a supervised machine learning task. We propose the use of probabilistically created renderings to overcome the lack of labeled real images. Rather than sampling from stationary distributions, our approach introduces a feedback mechanism that constantly adapts probability distributions according to current training progress. Initial results show, our approach achieves near-human-level accuracy on real images. Additionally, we demonstrate that feedback leads to fewer required training steps, while maintaining the same model quality on synthetic data sets.
Security-enhanced optical nonlinear cryptosystem based on double random phase encoding

Security is the critical requirement for optical encryption. A security-enhanced nonlinear cryptosystem is designed by introducing a time-varying nonlinear term in the double random phase encoding (DRPE) system. The nonlinear term, which is a randomly generated complex distribution, is introduced in the encryption process and has no influence on the decryption of original image. The nonlinear term is not transmitted to receivers of ciphertexts, therefore, there is no need to manage and distribute it. The designed cryptosystem is demonstrated to be resistant to various attacks including ciphertext-only attacks, known-plaintext attacks and chosen-plaintext attacks. In addition, the nonlinear cryptosystem is simple, and has the same optical implementation as that of conventional DRPE system.
Sequence effects in the estimation of software development effort

Currently, little is known about how much the sequence in which software development tasks or projects are estimated affects judgment-based effort estimates. To gain more knowledge, we examined estimation sequence effects in two experiments. In the first experiment, 362 software professionals estimated the effort of three large tasks of similar sizes, whereas in the second experiment 104 software professionals estimated the effort of four large and five small tasks. The sequence of the tasks was randomised in both experiments. The first experiment, with tasks of similar size, showed a mean increase of 10% from the first to the second and a 3% increase from the second to the third estimate. The second experiment showed that estimating a larger task after a smaller one led to a mean decrease in the estimate of 24%, and that estimating a smaller task after a larger one led to a mean increase of 25%. There was no statistically significant reduction in the sequence effect with higher competence. We conclude that more awareness about how the estimation sequence affects the estimates may reduce potentially harmful estimation biases. In particular, it may reduce the likelihood of a bias towards too low effort estimates.
Distributed ant colony optimization based on actor model

The parallelization of metaheuristics and care for the efficient use of the available infrastructure is very popular in the case of population-based algorithms (e.g., evolutionary ones), as many of them have structures intrinsically easy for parallelization. However, swarm computing algorithms (ACO in particular) must use certain global knowledge in order to be properly implemented (e.g., a pheromone matrix in the case of ACO algorithms). Thus, the parallelization of ACO is known to be difficult to realize. In this paper, we propose an actor-based approach for constructing an efficient and robust ACO implementation that leverages the HPC infrastructure. The presented results show the ability to be scaled for up to 30 nodes, and the relevant results support the claim that the implemented algorithm is equal to the original Ant System algorithm. Improving it further and increasing its scalability with the planned asynchrony in the pheromone matrix updates is envisioned as a direct future work.
A Usability Evaluation of Let's Encrypt and Certbot

The correct configuration of HTTPS is a complex set of tasks, which many administrators have struggled with in the past. Let's Encrypt and Electronic Frontier Foundation's Certbot aim to improve the TLS ecosystem by offering free trusted certificates (Let's Encrypt) and by providing user-friendly support to configure and harden TLS (Certbot). Although adoption rates have increased, to date, there has been only a little scientific evidence of the actual usability and security benefits of this semi-automated approach. Therefore, we conducted a randomized control trial to evaluate the usability of Let's Encrypt and Certbot in comparison to the traditional certificate authority approach. We performed a within-subjects lab study with 31 participants. The study sheds light on the security and usability enhancements that Let's Encrypt and Certbot provide. We highlight how usability improvements aimed at administrators can have a large impact on security and discuss takeaways for Certbot and other security-related tasks that experts struggle with.
Unsupervised outlier detection techniques for well logs and geophysical data

Outliers in well logs and other borehole-based subsurface measurements are often due to poor borehole condition, problems in data acquisition, irregularity in operating procedures, the presence of rare geological formations, or certain rare process/phenomenon in the subsurface. Detection of outliers is an important step prior to building a robust data-driven or machine learning-based model. We perform a comparative study of the performances of four unsupervised outlier detection techniques (ODTs) on various original and synthetic well-log datasets. The four unsupervised ODTs compared in this study are isolation forest (IF), one-class SVM (OCSVM), local outlier factor (LOF), and density-based spatial application with noise (DBSCAN). The unsupervised ODTs are evaluated on four labeled outlier-prone validation datasets using precision-recall curve, F1 score, area under the curve (AUC) score, and receiver operating characteristic (ROC) curve. Isolation forest is the most robust unsupervised ODT for detecting various types of outliers, whereas DBSCAN is particularly effective in detecting noise in a well-log dataset. Efficient feature engineering and feature selection is important to ensure robust detection of outliers in well-log and subsurface measurements using unsupervised outlier detection methods.
Voice-activated solutions for agile retrospective sessions

Retrospective (retro) sessions are an important part of Agile/Scrum process for software development. In theory, conducting retro sessions each sprint should improve team dynamics and productivity. In praxis, retro sessions also have some disadvantages/hurdles that are hard to overcome: they are non-anonymous and time consuming. The goal of our project, conducted in collaboration between Shine Solutions and RMIT University, is to analyse the existing hurdles from industry-prospective and to provide an AI-based solution to overcome them. The two main outcomes of the project are (1) a web-based solution to support retro-sessions, and (2) a qualitative analysis of two speech recognition tools, Google Home and Amazon Alexa, to be connected with the elaborated solution to populate the retro board and help in time boxing a retro by using voice activated commands.
DeepFakes: uncovering hardcore open source on GitHub

ABSTRACT In November 2017, a subreddit dedicated to ‘DeepFakes’ brought the work of machine-learning video face-swapping to widespread attention: the new ease of pornographic fakeswapping (focused primarily on celebrities, but also used more broadly to generate a type of revenge porn) became a content regulation topic of discussion on sites such as Pornhub and Reddit. Although the spaces dedicated to sharing these videos were gradually banned or moderated in early 2018, the essential project remains: a series of codebases shared on GitHub, actively situated for open source contributions. In this case study, we will examine the role of GitHub as a platform in the spread of DeepFakes, and interrogate the intersections of the open source ethos and Reddit's approach to moderation, ethics, and anonymity. Ultimately, we find that the DeepFakes project has intentionally spread to spaces with limited moderation, which has allowed the continued creation of non-consensual pornography.
An Improved Probabilistic Roadmap Algorithm with Potential Field Function for Path Planning of Quadrotor

Probabilistic Roadmap Method (PRM) is a path planning method based on random sampling strategy, which can solve the problem that effective paths are difficult to construct with most algorithms in high-dimensional space. In order to achieve the obtain all the possibilities, the PRM algorithm requires a large amount of sampling data, which leads to double increase of the computing time and low efficiency in the narrow passage planning. In this paper, an improved PRM algorithm is proposed with the introduction of potential field in the whole planning space, called P-PRM. The sampled potential field strength is used to select valuable nodes to avoid collision detection for the sampling points. Cost function is set to avoid the algorithm falling into local optimum in narrow channels or target areas. Experiments are performed for quadrotor unmanned vehicle of path planning in 2D environment, and it is proved that the proposed P-PRM algorithm is superior to the traditional PRM algorithm as the planning efficiency of narrow channel is higher and calculation time or path length is lower. Moreover, the feasibility of the proposed improved algorithm has been verified by simulation in 3D space.
The “Why Did You Do That?” Button: Answering Why-Questions for End Users of Robotic Systems

The issue of explainability for autonomous systems is becoming increasingly prominent. Several researchers and organisations have advocated the provision of a “Why did you do that?” button which allows a user to interrogate a robot about its choices and actions. We take previous work on debugging cognitive agent programs and apply it to the question of supplying explanations to end users in the form of answers to why-questions. These previous approaches are based on the generation of a trace of events in the execution of the program and then answering why-questions using the trace. We implemented this framework in the agent infrastructure layer and, in particular, the Gwendolen programming language it supports – extending it in the process to handle the generation of applicable plans and multiple intentions. In order to make the answers to why-questions comprehensible to end users we advocate a two step process in which first a representation of an explanation is created and this is subsequently converted into natural language in a way which abstracts away from some events in the trace and employs application specific predicate dictionaries in order to translate the first-order logic presentation of concepts within the cognitive agent program in natural language. A prototype implementation of these ideas is provided.
Features-Enhanced Multi-Attribute Estimation with Convolutional Tensor Correlation Fusion Network

To achieve robust facial attribute estimation, a hierarchical prediction system referred to as tensor correlation fusion network (TCFN) is proposed for attribute estimation. The system includes feature extraction, correlation excavation among facial attribute features, score fusion, and multi-attribute prediction. Subnetworks (Age-Net, Gender-Net, Race-Net, and Smile-Net) are used to extract corresponding features while Main-Net extracts features not only from an input image but also from corresponding pooling layers of subnetworks. Dynamic tensor canonical correlation analysis (DTCCA) is proposed to explore the correlation of different targets’ features in the F7 layers. Then, for binary classifications of gender, race, and smile, corresponding robust decisions are achieved by fusing the results of subnetworks with those of TCFN while for age prediction, facial image into one of age groups, and then ELM regressor performs the final age estimation. Experimental results on benchmarks with multiple face attributes (MORPH-II, Adience Benchmark datasets, LAP-2016, and CelebA) show that the proposed approach has superior performance compared to state of the art.
Query is GAN: Scene Retrieval With Attentional Text-to-Image Generative Adversarial Network

Scene retrieval from input descriptions has been one of the most important applications with the increasing number of videos on the Web. However, this is still a challenging task since semantic gaps between features of texts and videos exist. In this paper, we try to solve this problem by utilizing a text-to-image Generative Adversarial Network (GAN), which has become one of the most attractive research topics in recent years. The text-to-image GAN is a deep learning model that can generate images from their corresponding descriptions. We propose a new retrieval framework, “Query is GAN”, based on the text-to-image GAN that drastically improves scene retrieval performance by simple procedures. Our novel idea makes use of images generated by the text-to-image GAN as queries for the scene retrieval task. In addition, unlike many studies on text-to-image GANs that mainly focused on the generation of high-quality images, we reveal that the generated images have reasonable visual features suitable for the queries even though they are not visually pleasant. We show the effectiveness of the proposed framework through experimental evaluation in which scene retrieval is performed from real video datasets.
A Low-Cost Interactive Writing Board for Primary Education using Distinct Augmented Reality Markers

Educational institutions demand cost-effective and simple-to-use augmented reality systems. ARToolKit, an open-source computer tracking library for the creation of augmented reality applications that overlay virtual imagery on the real world, is such a system. It uses a simple camera and black-and-white markers printed on paper. However, due to inter-marker confusion, if the marker distinctions are not ensured, the markers are often miss-recognized. This paper presents an ARToolKit-based Interactive Writing Board (IWB) with a simple mechanism for designing confusion-free marker libraries. The board is used for teaching single characters of Arabic/Urdu to primary level students. It uses a simple ARToolKit marker for the recognition of each character. After marker recognition, the IWB displays the corresponding image, helping students with character understanding and pronunciation. Experimental results reveal that the system improves students’ motivation and learning skills.
Sketchforme

Sketching is an effective communication medium that augments and enhances what can be communicated in text. We introduce Sketchforme, the first neural-network-based system that can generate complex sketches based on text descriptions specified by users. Sketchforme's key contribution is to factor complex sketch rendering into layout and rendering subtasks using neural networks. The sketches composed by Sketchforme are expressive and realistic: we show in our user study that these sketches convey descriptions better than human-generated sketches in several cases, and 36.5% of those sketches were identified as human-generated. We develop some interactive applications using these generated sketches, and show that Sketchforme can significantly improve language learning applications and support intelligent language-based sketching assistants.
Capturing the change in topical interests of personas over time

ABSTRACT In this research, we collect monthly content consumption and demographic data from YouTube over two years for a large media publisher. We use automation to generate 15 personas each month and examine the consistency of the generated personas over time. We find that there are 35 unique personas in total for the entire period, reflecting the changes in the underlying audience population. For each persona, we generate topics of interest and identify the top three monthly topics for each of the 35 personas following an identical algorithmic approach each month. We then compare the sets of topical interests of the personas month‐over‐month for the entire two‐year period. Findings show that there is an average 20.2% change in topical interests and that 68% of the personas experience more topical change than topical consistency. Findings suggest that the topical interests of online audiences are fluid and changes in the underlying audience data can occur within a relatively short period, resulting in the need for constant updating of personas using data‐driven methods. The implications for organizations seeking to understand their online audience are that they should employ routine data analysis to detect changes in the audience interests and investigate ways to automate their persona generation processes.
An Assessment of Blockchain Identity Solutions: Minimizing Risk and Liability of Authentication

Personally Identifiable Information (PII) is often used to perform authentication and acts as a gateway to personal and organizational information. One weak link in the architecture of identity management services is sufficient to cause exposure and risk identity. Recently, we have witnessed a shift in identity management solutions with the growth of blockchain. Blockchain—the decentralized ledger system—provides a unique answer addressing security and privacy with its embedded immutability. In a blockchain-based identity solution, the user is given the control of his/her identity by storing personal information on his/her device and having the choice of identity verification document used later to create blockchain attestations. Yet, the blockchain technology alone is not enough to produce a better identity solution. The user cannot make informed decisions as to which identity verification document to choose if he/she is not presented with tangible guidelines. In the absence of scientifically created practical guidelines, these solutions and the choices they offer may become overwhelming and even defeat the purpose of providing a more secure identity solution.
CATSLU: The 1st Chinese Audio-Textual Spoken Language Understanding Challenge

Spoken language understanding (SLU) is a key component of conversational dialogue systems, which converts user utterances into semantic representations. The previous works almost focus on parsing semantic from textual inputs (top hypothesis of speech recognition and even manual transcripts) while losing information hidden in the audio. We herein describe the 1st Chinese Audio-Textual Spoken Language Understanding Challenge (CATSLU) which introduces a new dataset with audio-textual information, multiple domains and domain knowledge. We introduce two scenarios of audio-textual SLU in which participants are encouraged to utilize data of other domains or not. In this paper, we will describe the challenge and results.
Gradient conjugate priors and multi-layer neural networks

The paper deals with learning probability distributions of observed data by artificial neural networks. We suggest a so-called gradient conjugate prior (GCP) update appropriate for neural networks, which is a modification of the classical Bayesian update for conjugate priors. We establish a connection between the gradient conjugate prior update and the maximization of the log-likelihood of the predictive distribution. Unlike for the Bayesian neural networks, we use deterministic weights of neural networks, but rather assume that the ground truth distribution is normal with unknown mean and variance and learn by the neural networks the parameters of a prior (normal-gamma distribution) for these unknown mean and variance. The update of the parameters is done, using the gradient that, at each step, directs towards minimizing the Kullback–Leibler divergence from the prior to the posterior distribution (both being normal-gamma). We obtain a corresponding dynamical system for the prior's parameters and analyze its properties. In particular, we study the limiting behavior of all the prior's parameters and show how it differs from the case of the classical full Bayesian update. The results are validated on synthetic and real world data sets.
Mind the Gap: Gap Affordance Judgments of Children, Teens, and Adults in an Immersive Virtual Environment

Immersive Virtual Environments (IVEs) are powerful tools that can be used to investigate whether adults' action capabilities or affordances are scaled to the physical dimensions of their body and how these affordances are influenced by environmental context. However, less is known about affordance judgments in middle-aged children and adolescents in IVEs. Differences in rate of growth, decision criteria, and perceived risk could influence affordance judgments for children. In Experiment 1, children, teens, and adults were asked to view gaps of different widths that were presented at either ground level or at a height of 15 meters within the IVE. Across all age groups, estimates of gap crossing were underestimated at the higher height compared to the ground, consistent with reports of fear and risk of falling. Children, compared to adults, underestimated their maximum crossable gap compared to their actual crossable gap. To test whether this difference was specific to IVEs or a more generalized age effect, children and adults were tested on gap estimates in the real world in Experiment 2. This real world study showed no difference between children and adults, suggesting a unique contribution of the IVE to children’s affordance judgments. We discuss the implications for using IVEs to study children’s affordances.