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Vertical Flight Society's 76th Annual Forum and Technology Display

作者： Feuerstein, Eric Rasool, Ghulam Bouaynaya, Nidhal Ramachandran, Ravi Johnson, Charles Rowan University GlassboroNJ United States Federal Aviation Administration Egg Harbor TownshipNJ United States

This paper discusses a novel method for improving rotorcraft safety. A new algorithm is proposed for estimating the head position of helicopter pilots and copilots using onboard cockpit videos. Cockpit videos offer the ability for crash analysts or incident investigation to understand not only the aircraft state but also the pilot and copilot's actions in a potentially unsafe situation. In addition, head pose information can also be used to improve the overall scanning techniques used by pilots or to research which technologies can assist the pilots in focusing more attention out of cockpit, rather than down at the instrument panel. Two algorithms were created to provide possible solutions to the problem of head pose estimation: a hybrid computer vision algorithm that utilizes deep learning based detectors, and a purely deep learning algorithm. The purely deep learning algorithm was able to correctly classify 91.49% of copilot head positions in a real-world flight video. © 2020 by the Vertical Flight Society.

关键词： learning algorithms

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A first dataset for film age appropriateness investigation 12

A first dataset for film age appropriateness investigation

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12th International Conference on Language Resources and Evaluation, LREC 2020

作者： Mohamed, Emad Ha, Le An RGCL RIILP University of Wolverhampton Wulfruna Street WolverhamptonWV1 1LY United Kingdom

ISBN: (纸本)9791095546344

Film age appropriateness classification is an important problem with a significant societal impact that has so far been out of the interest of Natural Language Processing and Machine learning researchers. To this end, we have collected a corpus of 17000 film transcripts along with their age ratings. We use the textual contents in an experiment to predict the correct age classification for the United States (G, PG, PG-13, R and NC-17) and the United Kingdom (U, PG, 12A, 15, 18 and R18). Our experiments indicate that gradient boosting machines beat FastText and various Deep learning architectures. We reach an overall accuracy of 79.3% for the US ratings compared to a projected super human accuracy of 84%. For the UK ratings, we reach an overall accuracy of 65.3% (UK) compared to a projected super human accuracy of 80.0%. © European Language Resources Association (ELRA), licensed under CC-BY-NC

关键词： learning algorithms

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Using Web Log Files the Comparative Study of Big Data with Map Reduce Technique

Using Web Log Files the Comparative Study of Big Data with M...

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2020 International Conference on Intelligent Engineering and Management, ICIEM 2020

作者： Khan, Mudassir Basheer, Shakila King Khalid University Department of Computer Science College of Science and Arts Tanumah Saudi Arabia

ISBN: (纸本)9781728140971

This research paper, discusses, how the implementations of a planned algorithm will be processed. The data set is collected from one of the engineering colleges in India. The Datasets are collected from different sources and how the raw data is a preprocessor. The data set is analyzed by using the Map Reducing Technique and that should carry out in naive byes and KNN algorithm. The Map-Reduce technique is parallel to computation using the key/value pair. Thus, the Map-Reduce system helps to analyze the information which will give the data of the potential clients, such as, login time, credit value, and a lot more at least reaction time. Finally, execution estimation has been done by using ROC bend to discover and affirm which algorithm is best similarly as orchestrating the weblog records per the general time, the specific time they have taken on a specific website. The Naive Bayes algorithm if its anticipated characterization. Like that the same process in the KNN algorithm. It is basic to perceive which the greatest classifier method is. To carry out this, the foreseen outcomes are approved during (ROC) Receiver Operating Characteristic curves. © 2020 IEEE.

关键词： learning algorithms

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Tinnitus-like "hallucinations" elicited by sensory deprivation in an entropy maximization recurrent neural network

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PLoS Computational Biology 2021年第12期17卷 e1008664-e1008664页

作者： Dotan, Aviv Shriki, Oren Department of Cognitive and Brain Sciences Ben-Gurion University of the Negev Beer-Sheva Israel Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva Israel Zlotowski Center for Neuroscience Ben-Gurion University of the Negev Beer-Sheva Israel

Sensory deprivation has long been known to cause hallucinations or "phantom" sensations, the most common of which is tinnitus induced by hearing loss, affecting 10–20% of the population. An observable hearing loss, causing auditory sensory deprivation over a band of frequencies, is present in over 90% of people with tinnitus. Existing plasticity-based computational models for tinnitus are usually driven by homeostatic mechanisms, modeled to fit phenomenological findings. Here, we use an objective-driven learning algorithm to model an early auditory processing neuronal network, e.g., in the dorsal cochlear nucleus. The learning algorithm maximizes the network’s output entropy by learning the feed-forward and recurrent interactions in the model. We show that the connectivity patterns and responses learned by the model display several hallmarks of early auditory neuronal networks. We further demonstrate that attenuation of peripheral inputs drives the recurrent network towards its critical point and transition into a tinnitus-like state. In this state, the network activity resembles responses to genuine inputs even in the absence of external stimulation, namely, it "hallucinates" auditory responses. These findings demonstrate how objective-driven plasticity mechanisms that normally act to optimize the network’s input representation can also elicit pathologies such as tinnitus as a result of sensory deprivation. © 2021 Dotan, Shriki. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： learning algorithms

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Dynamic Difficulty Adjustment via Fast User Adaptation 33

Dynamic Difficulty Adjustment via Fast User Adaptation

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33rd Annual ACM Symposium on User Interface Software and Technology, UIST 2020

作者： Moon, Hee-Seung Seo, Jiwon Yonsei University Incheon Korea Republic of

ISBN: (纸本)9781450375153

Dynamic difficulty adjustment (DDA) is a technology that adapts a game's challenge to match the player's skill. It is a key element in game development that provides continuous motivation and immersion to the player. However, conventional DDA methods require tuning in-game parameters to generate the levels for various players. Recent DDA approaches based on deep learning can shorten the time-consuming tuning process, but require sufficient user demo data for adaptation. In this paper, we present a fast user adaptation method that can adjust the difficulty of the game for various players using only a small amount of demo data by applying a meta-learning algorithm. In the video game environment user test (n=9), our proposed DDA method outperformed a typical deep learning-based baseline method. © 2020 Owner/Author.

关键词： learning algorithms

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Chronic nicotine increases midbrain dopamine neuron activity and biases individual strategies towards reduced exploration in mice

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NATURE COMMUNICATIONS 2021年第1期12卷 6945-6945页

作者： Dongelmans, Malou Durand-de Cuttoli, Romain Nguyen, Claire Come, Maxime Durante, Etienne K. Lemoine, Damien Britto, Raphael Yahia, Tarek Ahmed Mondoloni, Sarah Didienne, Steve Bousseyrol, Elise Hannesse, Bernadette Reynolds, Lauren M. Torquet, Nicolas Dalkara, Deniz Marti, Fabio Mourot, Alexandre Naude, Jeremie Faure, Philippe Sorbonne Univ INSERM CNRS Neurosci Paris SeineInst Biol Paris Seine NPS IB F-75005 Paris France PSL Res Univ Brain Plast Unit ESPCI Paris CNRS F-75005 Paris France Sorbonne Univ Inst Vis CNRS INSERM Paris France Icahn Sch Med Mt Sinai Nash Family Dept Neurosci New York NY 10029 USA

Chronic nicotine exposure impacts various components of decision-making processes, such as exploratory behaviors. Here, the authors identify the cellular mechanism and show that chronic nicotine exposure increases the tonic activity of VTA dopaminergic neurons and reduces exploration in mice. Long-term exposure to nicotine alters brain circuits and induces profound changes in decision-making strategies, affecting behaviors both related and unrelated to drug seeking and consumption. Using an intracranial self-stimulation reward-based foraging task, we investigated in mice the impact of chronic nicotine on midbrain dopamine neuron activity and its consequence on the trade-off between exploitation and exploration. Model-based and archetypal analysis revealed substantial inter-individual variability in decision-making strategies, with mice passively exposed to nicotine shifting toward a more exploitative profile compared to non-exposed animals. We then mimicked the effect of chronic nicotine on the tonic activity of dopamine neurons using optogenetics, and found that photo-stimulated mice adopted a behavioral phenotype similar to that of mice exposed to chronic nicotine. Our results reveal a key role of tonic midbrain dopamine in the exploration/exploitation trade-off and highlight a potential mechanism by which nicotine affects the exploration/exploitation balance and decision-making.

关键词： Neural circuits Reward learning algorithms

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Improvement of dark channel defogging algorithm based on duided filtering

Improvement of dark channel defogging algorithm based on dui...

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2020 International Conference on Virtual Reality and Intelligent Systems, ICVRIS 2020

作者： Chang, Jianli Du, Lixia Lanzhou Jiaotong University School of Electronic and Information Engineering Lanzhou730070 China

ISBN: (纸本)9781728196367

Aiming at the problem that the dark channel prior defogging algorithm has the problems that the dark channel fails in large areas such as sky or white objects and the guided filtering algorithm is not completely defogging, a single image defogging improvement based on dark channel prior and guided filtering is proposed algorithm. First obtain the minimum image of each color channel of the foggy image and the rough estimate of the transmittance according to the dark channel theory, use the guide filter for the minimum image to obtain the guide image containing the edge information, and then use the guide filter again to optimize the initial transmittance. The problem of underestimation of the transmittance of the foggy sky area is to determine the range of the sky area and make certain adjustments to its transmittance, and use the quadtree search algorithm to obtain accurate atmospheric light values. Finally, the fog-free image is recovered through the atmospheric physical model, and the algorithm is compared with other defogging algorithms. The results show that the algorithm can effectively process foggy images containing sky areas, and the time complexity is low. © 2020 IEEE.

关键词： learning algorithms

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The Multiplayer Colonel Blotto Game 20

The Multiplayer Colonel Blotto Game

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21st ACM Conference on Economics and Computation, EC 2020

作者： Boix-Adserà, Enric Edelman, Benjamin L. Jayanti, Siddhartha Massachusetts Institute of Technology Cambridge United States Harvard University Cambridge United States

ISBN: (纸本)9781450379755

We initiate the study of the natural multiplayer generalization of the classic continuous Colonel Blotto *** two-player Blotto game, introduced by Borel as a model of resource competition across nsimultaneous fronts, has been studied extensively for a century and seen numerous applications throughout the social sciences. Our work defines the multiplayer Colonel Blotto gameand derives Nash equilibria for various settings of k(number of players) and n(number of battlefields) - -in particular, we mostly solve the symmetric three-player case. We also introduce a "Boolean" version of multiplayer Blotto. The main technical difficulty of our work, as in the two-player theoretical literature, is the challenge of coupling various marginal distributions into a joint distribution satisfying a strict sum constraint. In contrast to previous works in the continuous setting, we derive our couplings algorithmically in the form of efficient sampling algorithms. The full paper can be found at https://***/abs/2002.05240. © 2020 Owner/Author.

关键词： learning algorithms

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Age and gender predictions using artificial intelligence algorithm 3

Age and gender predictions using artificial intelligence alg...

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3rd International Conference on Intelligent Sustainable Systems, ICISS 2020

作者： Ghildiyal, Anirudh Sharma, Sachin Verma, Ishita Marhatta, Urvi Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9781728170893

Gender is still a central aspect of personality, and in social life it is still an important factor. Gender and age projections for artificial intelligence can be used in many areas, such as the development of smart human-machine interfaces, fitness, cosmetics, e-commerce, etc. The prediction of age and gender is an ongoing and active research question for individuals from their facial images. A number of approaches to solving this issue have been suggested by the researchers, but the criteria and actual performance are still insufficient. This paper proposes a mathematical approach to recognition patterns in order to solve this problem. The Convolution Neural Network (ConvNet / CNN) deep learning algorithm is used as a feature extractor in the proposed solution. CNN takes input images and assigns value to and can distinguish between various aspects / objects (learnable weights and biases) of the image. ConvNet needs much less pre-processing than other classification algorithms. While the filters are hand-made in primitive methods, ConvNet can learn these filters / features with adequate training. In this research, face images of individuals have been trained with convolution neural networks, and age and sex with a high rate of success have been predicted. More than 20, 000 images are containing age, gender and ethnicity annotations. The images cover a wide range of poses, facial expression, lighting, occlusion, and resolution. © 2020 IEEE.

关键词： learning algorithms

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An Enhanced Two-Speed, Radix-4 Multiplier using Spurious Power Suppression Technique

An Enhanced Two-Speed, Radix-4 Multiplier using Spurious Pow...

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2020 International Conference on Smart Electronics and Communication, ICOSEC 2020

作者： Soni, Naveen Chinthala, Ramesh Amrita School of Engineering Dept. of Electronics and Communication Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781728154619

This paper presents the application of the Spurious Power Suppression Technique (SPST) on the Two-Speed Multiplier (TSM) that finds application in digital filters, artificial neural networks (ANN), and other machine learning algorithms. It is a radix-4, serial-parallel multiplier utilizing the concept of SPST to reduce the dynamic power by filtering out unwanted signal transitions in the arithmetic block. The control circuit of the multiplier dynamically skips addition operation for zero booth encodings and does computation only for non-zero booth encodings, thus making delay depends upon input bit pattern. This paper also discusses the merits and demerits of available Parallel Prefix Adders (PPAs) which can be used in the multiplier. Kogge Stone adder (KSA) with an improved prefix-computation stage to reduce the hardware complexity is used for Least Significant (LSP) and hybrid Han Carlson adder (HCA) with better area^{\ast}power is used for Most Significant (MSP) computations. TSM utilizes two sub-circuits with different critical paths which further improves the delay and throughput for a sub-set of inputs over the existing multipliers. The proposed SPST based modified TSM is implemented on an Intel Cyclone V 5CSEMA5F31A7 FPGA device and is evaluated against Booth serial, parallel-parallel and Serial-Parallel (SP) multipliers. Proposed SPST based TSM achieves 1.43x-1. 52x improvement in power-delay product and 1.096x-l.l3x improvement in area-power-delay product over the standard two-speed multiplier.. © 2020 IEEE.

关键词： learning algorithms

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