Reviewerly/exHarmony huggingface dataset:
Emerging approaches to support dynamic mission planning: survey and recommendations for future research

In the Department of Defense, unmanned aerial vehicle (UAV) mission planning is typically in the form of a set of pre-defined waypoints and tasks, and results in optimized plans being implemented prior to the beginning of the mission. These include the order of waypoints, assignment of tasks, and assignment of trajectories. One emerging area that has been recently identified in the literature involves frameworks, simulations, and supporting algorithms for dynamic mission planning, which entail re-planning mid-mission based on new information. These frameworks require algorithmic support for flight path and flight time approximations, which can be computationally complex in nature. This article seeks to identify the leading academic algorithms that could support dynamic mission planning and recommendations for future research for how they could be adopted and used in current applications. A survey of emerging UAV mission planning algorithms and academic UAV flight path algorithms is presented, beginning with a taxonomy of the problem space. Next, areas of future research related to current applications are presented.
Efficient Parameters Estimation Method for the Separable Nonlinear Least Squares Problem

In this work, we combine the special structure of the separable nonlinear least squares problem with a variable projection algorithm based on singular value decomposition to separate linear and nonlinear parameters. Then, we propose finding the nonlinear parameters using the Levenberg–Marquart (LM) algorithm and either solve the linear parameters using the least squares method directly or by using an iteration method that corrects the characteristic values based on the L-curve, according to whether or not the nonlinear function coefficient matrix is ill posed. To prove the feasibility of the proposed method, we compared its performance on three examples with that of the LM method without parameter separation. The results show that (1) the parameter separation method reduces the number of iterations and improves computational efficiency by reducing the parameter dimensions and (2) when the coefficient matrix of the linear parameters is well-posed, using the least squares method to solve the fitting problem provides the highest fitting accuracy. When the coefficient matrix is ill posed, the method of correcting characteristic values based on the L-curve provides the most accurate solution to the fitting problem.
Discovering Causal Structures in Bayesian Gaussian Directed Acyclic Graph Models

Summary Causal directed acyclic graphs (DAGs) are naturally tailored to represent biological signalling pathways. However, a causal DAG is only identifiable up to Markov equivalence if only observational data are available. Interventional data, based on exogenous perturbations of the system, can greatly improve identifiability. Since the gain of an intervention crucially depends on the intervened variables, a natural issue is devising efficient strategies for optimal causal discovery. We present a Bayesian active learning procedure for Gaussian DAGs which requires no subjective specification on the side of the user, explicitly takes into account the uncertainty on the space of equivalence classes (through the posterior distribution) and sequentially proposes the choice of the optimal intervention variable. In simulation experiments our method, besides surpassing designs based on a random choice of intervention nodes, shows decisive improvements over currently available algorithms and is competitive with the best alternative benchmarks. An important reason behind this strong performance is that, unlike non-Bayesian algorithms, our utility function naturally incorporates graph estimation uncertainty through the posterior edge inclusion probability. We also reanalyse the Sachs data on protein signalling pathways from an active learning perspective and show that DAG identification can be achieved by using only a subset of the available intervention samples.
Self-Interfaces: Utilizing Real-Time Biofeedback in the Wild to Elicit Subconscious Behavior Change

Self-Interfaces are interfaces that intuitively communicate relevant subconscious physiological signals through biofeedback to give the user insight into their behavior and assist them in creating behavior change. The human heartbeat is a good example of an intuitive and relevant haptic biofeedback; does not distract and is only felt when the heart beats fast. In this work, we discuss the design and development of a wearable haptic Self-Interface for Electrodermal Activity (EDA). EDA is a covert physiological signal correlated with high and low arousal affective states. We will evaluate the effectiveness of the EDA Self-Interface based on its intuitiveness, its ability to generate useful insight, whether this insight leads to behavior change, and whether the user can develop an intuitive awareness of their EDA over time when the device is removed. We hope the findings from this study will help us establish a series of guidelines for development of other Self-Interfaces in the future.
Do Neural Networks for Segmentation Understand Insideness?

The insideness problem is an aspect of image segmentation that consists of determining which pixels are inside and outside a region. Deep neural networks (DNNs) excel in segmentation benchmarks, but it is unclear if they have the ability to solve the insideness problem as it requires evaluating long-range spatial dependencies. In this letter, we analyze the insideness problem in isolation, without texture or semantic cues, such that other aspects of segmentation do not interfere in the analysis. We demonstrate that DNNs for segmentation with few units have sufficient complexity to solve the insideness for any curve. Yet such DNNs have severe problems with learning general solutions. Only recurrent networks trained with small images learn solutions that generalize well to almost any curve. Recurrent networks can decompose the evaluation of long-range dependencies into a sequence of local operations, and learning with small images alleviates the common difficulties of training recurrent networks with a large number of unrolling steps.
CREMON: Cryptography Embedded on the Convolutional Neural Network Accelerator

Due to their excellent performance, tremendous progress has been made in the development of convolutional neural network (CNN) algorithms and efficient CNN accelerators for edge devices. At the same time, security concerns about CNN processing have increased regarding user privacy and safety. In this brief, we focus on developing an efficient data ciphering system embedded in a CNN accelerator. The number of operations of CNN and security workloads, AES-128 in our system, constantly changes during execution, thereby varying their relative ratio. To efficiently support various convolution/AES workloads, we propose CREMON, a reconfigurable system with a cryptography reconfigurable processing element (CRPE). A test chip with the proposed scheme was implemented and tested for performance verification. As a result, the CREMON prototype chip achieved state-of-the-art performance/area efficiency for AES and improved energy efficiency by up to 44.1% in processing CNN/AES workloads.
Dynamic facial presentation attack detection for automated border control systems

Millions of passengers travel every day, border crossing being one of their most common activities. At these points it is extremely important that security is completely guaranteed. However, the maintaining adequate security levels is a very demanding issue. This has promoted the development of systems capable of providing support to border authorities by automatising some of their tasks. Thus, Automated Border Control (ABC) systems have become a key tool. These systems increase the flow of travellers as they can achieve fast evaluations of individuals through their machine-readable travel documents. However, this has motivated the appearance of attacks that try to avoid the identity detection of individuals by these systems. Presentation Attack Detection (PAD) algorithms have emerged to mitigate such a problem. This paper presents the On-the-Fly Presentation Attack Detection (FlyPAD) framework that implements a set of dynamic PAD techniques. It allows multiple types of attacks to be detected as the traveller approaches the ABC system, rather than being static in front of the cameras. Several experiments have been carried out, both in the laboratory and in real environments, obtaining promising results.
The future of human-artificial intelligence nexus and its environmental costs

The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al. (2019) in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. The gratuitous generalisation capability refers to a discrepancy between the cognitive demands of a task to be accomplished and the performance (accuracy) of a used ML/AI model. If the latter exceeds the former because the model was optimised to achieve the best possible accuracy, it becomes inefficient and its operation harmful to the environment. The future dominated by the non-anthropic induction describes a use of ML/AI so all-pervasive that most of the inductive inferences become furnished by ML/AI generalisations. The paper argues that the present debate deserves an expansion connecting the environmental costs of research and ineffective ML/AI uses (the issue of gratuitous generalisation capability) with the (near) future marked by the all-pervasive Human-Artificial Intelligence Nexus.
Zoning Feature Extraction for Handwritten Javanese Character Recognition

A Javanese character used to replace Javanese, Madurese, Sundanese, Balinese and Sasak texts. Later then during Dutch colonialism era, Javanese character was replaced by Latin letters. Various efforts have been made by the government to preserve cultural heritage. For example, Javanese character reading and writing skills are also taught in schools, from elementary school to junior high school, especially in Central Java and East Java. Even so, currently not many people can use Javanese character. Javanese script does have a variety of forms so it requires understanding and memorization. To make it easier to understand each character, the technology of Javanese translation into Latin letters is one of the solutions. This technology called Character Recognition. There is an important stage in character recognition called feature extraction. This stage will determine the result of the classification process. In this paper, the adoption of feature extraction methods introduced by Elima Hussain called Zoning Feature Extraction is applied. This method works by dividing an image into several zones and counts the number of black pixel from each zone. Based on the experiment, this method with the number of zone 16 can extract features of Javanese Character.
Developing the Assessment Questions Automatically to Determine the Cognitive Level of the E-Learner Using NLP Techniques

The key objective of the teaching-learning process (TLP) is to impart the knowledge to the learner. In the digital world, the computer-based system emphasis teaching through online mode known as e-learning. The expertise level of the learner in learned subjects can be measured through e-assessment in which multiple choice questions (MCQ) is considered to be an effective one. The assessment questions play the vital role which decides the ability level of a learner. In manual preparation, covering all the topics is difficult and time consumable. Hence, this article proposes a system which automatically generates two different types of question helps to identify the skill level of a learner. First, the MCQ questions with the distractor set are created using named entity recognizer (NER). Further, based on blooms taxonomy the Subjective questions are generated using natural language processing (NLP). The objective of the proposed system is to generate the questions dynamically which helps to reduce the occupation of memory concept.
Robust composite sine-trapezoidal phase-shifting algorithm for nonlinear intensity

Phase-shifting profilometry (PSP) is an efficient three-dimensional (3D) measurement technique. The accuracy of the PSP based 3D measurement system is generally affected by the nonlinear intensity response of the projector. In general, nonlinear errors can be reduced by projecting more fringe patterns, but this approach significantly increases the measuring time. To improve the measurement accuracy and speed, a new composite sine-trapezoidal phase-shifting profilometry technique that suppresses nonlinear error is proposed in this study. The main idea of the proposed method is projecting a set of sinusoidal fringe patterns and two sets of composite sine-trapezoidal fringe patterns with different intensity ranges in sequence. Among these patterns, the sinusoidal fringe is used to encode the pixel position, and the order of the wrapping phase is determined by the region code of the composite fringe. The flat intensity parts of the composite fringe are used to encode the nonlinear intensity response of the measurement system. Through system identification, the response parameters of the pixels can be obtained, and the gray value of the pixels can be corrected to gain a better phase distribution. Compared to the straight edges of the trapezoidal fringe, the sinusoidal edges of the composite fringe are more robust to the projector defocus, which improves the accuracy of the identification of the system intensity response model. Simulation results show that the proposed method can achieve a higher phase retrieval accuracy than the trapezoidal PSP and the sine PSP. The experiment results indicate that the proposed method has a better measurement accuracy than those of the standard 3-step and 4-step sine PSP methods.
Causes of merge conflicts

Software branching and merging allows collaborative development and creating software variants, commonly referred to as clone & own. While simple and cheap, a trade-off is the need to merge code and to resolve merge conflicts, which frequently occur in practice. When resolving conflicts, a key challenge for developer is to understand the changes that led to the conflict. While merge conflicts and their characteristics are reasonably well understood, that is not the case for the actual changes that cause them.
StudieMe: College Recommendation System

In this work, we present a novel web platform for a college selection process. Having a recommendation system as a helping hand to give them detailed information about the options they have and the best options to choose from according to their caliber is a huge requirement. In this paper, we worked on a designing a recommendation system that could understand the skill set and interest of a user through the data from the User's Profile to suggest recommended options of colleges for the users to select. We have developed the college recommendation system as a web platform which gives the result as top matched colleges for a particular user.
Support vector machine based prediction of photovoltaic module and power station parameters

The uncertainty in the output power of the photovoltaic (PV) power generation station due to variation in meteorological parameters is of serious concern. An accurate output power prediction of a PV system helps in better design and planning. The present study is carried out for the prediction of output power of PV generating station by using Support Vector Machines. Two cases are considered in the present study for prediction. Case-I deals with the prediction of PV module parameters such as Voc, Ish, Rs, Rsh, Imax, Vmax, Pmax, and case-II deals with the prediction of power generation parameters such as PDC, PAC, and system efficiency. Historical data of PV power station with an installed capacity of 10 MW and weather information are used as input to develop four different seasons-based SVM models for all parameters. The performance results of the models are presented in terms of Mean Relative Error (MRE) and Root Mean Square Error (RMSE). Additionally, the performance results obtained with polynomial and Radial Based Function kernel are also compared to show that which kernel has better prediction accuracy, and practicability. The result shows that the minimum average RMSE and MRE for case-I with Radial Based Function kernel are 0.034%, 0.055%, 0.002%, 1.726%, 0.044%, 0.047%, 2.342%, and 0.005%, 0.014%, 0.079%, 0.885%, 0.005%, 0.007%, 0.013%, and for case-II with poly kernel are 0.014%, 0.016%, 0.149% and 0.011%, 0.0175, 1.03%, respectively. The present study will be helpful to provide technical guidance to the prediction of the PV power System.
Comparison of Computer Vision Approaches in Application to the Electricity and Gas Meter Reading

This chapter presents comparison of computer vision approaches in application to the meter reading process for the standard (non-smart) electricity and gas. In this work, we analyse four techniques, Google Cloud Vision, AWS Rekognition, Tesseract OCR, and Azure's Computer Vision. Electricity and gas meter reading is a time consuming task, which is done manually in most cases. There are some approaches proposing use of smart meters that report their readings automatically. However, this solution is expensive and requires both replacement of the existing meters, even when they are functional and new, and extensive changes of the whole meter reading system dealing.
User experience of stereo and spatial audio in 360° live music videos

360° music videos are becoming prevalent in music entertainment. Still, academic studies of the 360° live music experience covering both audio and visual experience are scarce. In this paper, we present a study of user experience of stereo and spatial audio in a 360° live music video setting with two different display types. The research was conducted in the form of a laboratory experiment, in which 20 participants watched and evaluated stereo and spatial audio versions of the same music video using a flat computer display and a head-mounted display (HMD). Based on the results, spatial audio combined with HMD scored highest in the quantitative metrics of perceived audio quality, presence, and overall listening experience. However, qualitative findings reveal that this combination does not fit well with users' listening habits. While nine participants preferred to use headphones to listen to music, thirteen participants viewed music listening as a secondary task - making the use of HMDs less suitable.
Effective Prediction of Transmission of Solar Irradiance through Dusty Solar Panels using Atmospheric Aerosol Data for Lahore, Pakistan

The effect of soiling on the performance of photovoltaic (PV) panels can be difficult to predict as the process is highly dependent on the site specific climate parameters. Among various climate parameters, highest correlation to the soiling losses have been found for the airborne particulate matter (PM) concentration and the duration of dry period (time between events of significant rain). Here, we study the effect of the size distribution of PM and show that it can significantly modulate the PV soiling loss for a given mass concentration of PM. The PM size distribution from satellite aerosol data for the last 15 years for Lahore is used to predict the PV soiling loss using a model based on Mie scattering theory and Lambert-Beer law. The PV soiling loss is experimentally measured for Lahore to validate the model prediction. For a moderate soiling mass density of ~1.0 g/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , we show that variations in PM size distribution can modulate the transmission loss by ~5%. The soiling loss across the year for Lahore is predicted to be at its maximum of 19% in June and minimum of 12% in January for a constant mass density of 1.5 g/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . We further apply the model to predict soiling losses for the particle size distributions extracted from the naturally soiled PV panels reported in literature. A nice match is found between the experimental results and model based predictions.
Video Text Processing Method Based on Image Stitching

This paper studies the video of text with large field of view, solves the problem of text acquisition when a single frame cannot obtain complete text information. This paper proposes a method for acquiring video text panoramas, which automatically processes video text content by natural scene text positioning, text tracking, image stitching and other methods. First, the text is detected using the YOLO target detection framework. Second, use ECO tracking method to keep track of text. Finally, the local matching and global stitching method are used to obtain a text panorama. Experiments show that the proposed method can automatically process video and obtain high-resolution panoramic images with text parts, which greatly improves the text processing speed and effect of video content.
Social Movements

We present the iterative design and final implementation of a real-time full-body control system of the sound score by performers in an immersive contemporary dance performance. Digital musical instruments for dance require different considerations than for music, particularly in contemporary, non-proscenium and participatory audience contexts. Arising from dramaturgical research around social movement and civic participation in the digital age, this case study presents a fusion of design choices that consider artistic themes, performer interaction and audience experience. Three generations of sound and technical design are presented, with intermittent performances, challenges and learnings at each stage. We believe that the collaborative evolution of compositional choices, computer vision techniques, sound mapping, performer interaction and staging reveals important insights into developing interaction systems for kinesthetic empathy. Rather than placing the focus on human-computer interaction, our final production employed technology as a bridge to encourage human-human interaction around themes of occupation, resistance and resilience.
Ethics of Food Recommender Applications

The recent unprecedented popularity of food recommender applications has raised several issues related to the ethical, societal and legal implications of relying on these applications. In this paper, in order to assess the relevant ethical issues, we rely on the emerging principles across the AI & Ethics community and define them tailored context specifically. Considering the popular Food Recommender Systems (henceforth F-RS) in the European market cannot be regarded as personalised F-RS, we show how merely this lack of feature shifts the relevance of the focal ethical concerns. We identify the major challenges and propose a scheme for how explicit ethical agendas should be explained. We also argue how a multi-stakeholder approach is indispensable to ensure producing long-term benefits for all stakeholders. After proposing eight ethical desiderata points for F-RS, we present a case-study and assess it based on our proposed desiderata points.