Reviewerly/exHarmony huggingface dataset:
Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing

In this paper, we study the effects of several Monte Carlo Tree Search (MCTS) modifications for video game testing. Although MCTS modifications are highly studied in game playing, their impacts on finding bugs are blank. We focused on bug finding in our previous study where we introduced synthetic and human-like test goals and we used these test goals in Sarsa and MCTS agents to find bugs. In this study, we extend the MCTS agent with several modifications for game testing purposes. Furthermore, we present a novel tree reuse strategy. We experiment with these modifications by testing them on three testbed games, four levels each, that contain 45 bugs in total. We use the General Video Game Artificial Intelligence (GVG-AI) framework to create the testbed games and collect 427 human tester trajectories using the GVG-AI framework. We analyze the proposed modifications in three parts: we evaluate their effects on bug finding performances of agents, we measure their success under two different computational budgets, and we assess their effects on human-likeness of the human-like agent. Our results show that MCTS modifications improve the bug finding performance of the agents.
Sports image detection based on FPGA hardware system and particle swarm algorithm

The action of the opposition is conveyed on a fundamental level. Moreover, it finds image detection related information on contenders and football sports Image, which can be realized in the outside image. From the force situation, one can see that image advancement is up. Therefore, this assessment development image as a thing to ponder the usage of image acknowledgment development. Football sports detection is dealing with the whole system. The structure configuration relies upon hardware, including a Field Programmable Gate Array (FPGA). This new computation particle swarm algorithm estimation is implemented to edge recognizable proof, grayscale planning, object get, target affirmation, image area development, etc., which are consolidated into the genuine need of the game video to achieve the various essentials of development image acknowledgment. All the while, it has set itself up as a demonstrating ground to test the suitability of the investigation framework that sees the affirmation of contenders, games affirmation, sports lead judgment, etc. Football sports Image detection results of the relevant investigations have revealed that the existence of the solution.
Deep Reinforcement Learning with Transformers for Text Adventure Games

In this paper, we study transformers for text-based games. As a promising replacement of recurrent modules in Natural Language Processing (NLP) tasks, the transformer architecture could be treated as a powerful state representation generator for reinforcement learning. However, the vanilla transformer is neither effective nor efficient to learn with a huge amount of weight parameters. Unlike existing research that encodes states using LSTMs or GRUs, we develop a novel lightweight transformer-based representation generator featured with reordered layer normalization, weight sharing and block-wise aggregation. The experimental results show that our proposed model not only solves single games with much fewer interactions, but also achieves better generalization on a set of unseen games. Furthermore, our model outperforms state-of-the-art agents in a variety of man-made games.
Medical Image Synthetic Data Augmentation Using GAN

Most medical image datasets are limited and unbalanced. The classification method based on deep neural networks is prone to under-fitting or over-fitting problems on such data sets, which affects the final classification performance. This paper proposes a synthetic data augmentation method based on progressive generative adversarial network, which aims to solve the problem that deep neural networks are difficult to train on small-scale medical image datasets. Experimental results show that the data synthesized by this method is better than existing methods. The classification performance using only classic data augmentation yielded 76.8% sensitivity and 88.4% specificity. By applying the synthetic data augmentation, the results significantly increased to 84.2% sensitivity and 92.1% specificity.
Automated English Digital Essay Grader Using Machine Learning

Automatic essay grader is a program that is designed to grade an essay automatically. Some of automatic essay grader have been made using string kernel, word embedding and reinforced learning method. The objective of this research is to develop an application to help users in grading English digital essays. Grading is done based on 12 score features. Argumentative and narrative essays written by junior high school students are utilized as the dataset. By using XGBoost as the classifier, this research produces an automated essay grader with an average accuracy of 66.87%. Evaluation is conducted using 5-fold cross validation method.
DREAM

Social connections play a vital role in improving the performance of recommendation systems (RS). However, incorporating social information into RS is challenging. Most existing models usually consider social influences in a given session, ignoring that both users? preferences and their friends? influences are evolving. Moreover, in real world, social relations are sparse. Modeling dynamic influences and alleviating data sparsity is of great importance.
Divisive Hierarchical Clustering Based on Adaptive Resonance Theory

Divisive hierarchical clustering is a powerful tool for extracting knowledge from data with a pluralistic and appropriate information granularity. Recent developments of hierarchical clustering algorithms apply Growing Neural Gas (GNG) to data divisive mechanisms. However, GNG-based algorithms tend to generate nodes excessively and sensitive to the input order of data points. Furthermore, the plasticity-stability dilemma is another unavoidable problem. In this paper, we propose a divisive hierarchical clustering algorithm based on Adaptive Resonance Theory-based clustering. Simulation experiments show that the proposed algorithm can generate an appropriate tree structure depending on data while improving the performance of hierarchical clustering.
Codewords Detection in Microblogs Focusing on Differences in Word Use Between Two Corpora

In recent years, drug trafficking using microblogs has risen and become a social problem. A common method of cyber patrols for cracking down on crimes, such as drug trafficking, involves searching for crime-related keywords. However, criminals who post crime-inducing messages make maximum use of "codewords" rather than keywords, such as enjo kosai, marijuana, and methamphetamine, to camouflage their criminal intentions. Research suggests that these codewords change once they become popular; therefore, searching for a specific word requires significant effort to keep track of the latest codewords. In this study, we focused on the appearance of codewords and those likely to be included in incriminating posts with aim to detect codewords with the high likelihood of inclusion in incriminating posts. We proposed new methods for detecting codewords based on differences in word usage and conducted experiments on concealed-word detection in order to evaluate method effectiveness. The results showed that the proposed method was capable of detecting concealed words other than those in the initial list and to better degree relative to baseline methods. These findings demonstrated the ability of the proposed method to rapidly and automatically detect codewords that change over time and blog posts that induce crimes, thereby potentially reducing the burden of continuous monitoring of codewords.
OpenSense: A Platform for Multimodal Data Acquisition and Behavior Perception

Automatic multimodal acquisition and understanding of social signals is an essential building block for natural and effective human-machine collaboration and communication. This paper introduces OpenSense, a platform for real-time multimodal acquisition and recognition of social signals. OpenSense enables precisely synchronized and coordinated acquisition and processing of human behavioral signals. Powered by the Microsoft's Platform for Situated Intelligence, OpenSense supports a range of sensor devices and machine learning tools and encourages developers to add new components to the system through straightforward mechanisms for component integration. This platform also offers an intuitive graphical user interface to build application pipelines from existing components. OpenSense is freely available for academic research.
On Measuring RPKI Relying Parties

In this paper, we introduce a framework to observe RPKI relying parties (i.e., those that fetch RPKI data from the distributed repository) and present insights into this ecosystem for the first time. Our longitudinal study of data gathered from three RPKI certification authorities (AFRINIC, APNIC, and our own CA) identifies different deployment models of relying parties and (surprisingly) prevalent inconsistent fetching behavior that affects Internet routing robustness. Our results reveal nearly 90% of relying parties are unable to connect to delegated publication points under certain conditions, which leads to erroneous invalidation of IP prefixes and likely widespread loss of network reachability.
Self-supervised monocular depth estimation with direct methods

Depth estimation is crucial to understanding the geometry of a scene in robotics and computer vision. Traditionally, depth estimators can be trained with various forms of self-supervised stereo data or supervised ground-truth data. In comparison to the methods that utilize stereo depth perception or ground-truth data from laser scans, determining depth relation using an unlabeled monocular camera proves considerably more challenging. Recent work has shown that CNN-based depth estimators can be learned using unlabeled monocular video. Without needing the stereo data or ground-truth depth data, learning with monocular self-supervised strategies can utilize much larger and more varied image datasets. Inspired by recent advances in depth estimation, in this paper, we propose a novel objective that replaces the use of explicit ground-truth depth or binocular stereo depth with unlabeled monocular video sequence data. No assumptions about scene geometry or pre-trained information are used in the proposed architecture. To enable a better pose prediction, we propose the use of an improved differentiable direct visual odometry (DDVO), which is fused with an appearance-matching loss. The auto-masking approach is introduced in the DDVO depth predictor to filter out the low-texture area or occlusion area, which can easily reduce matching error, from one frame to the subsequent frame in the monocular sequence. Additionally, we introduce a self-supervised loss function to fuse the auto-masking segment and the depth-prediction segment accordingly. Our method produces state-of-the-art results for monocular depth estimation on the KITTI driving dataset, even outperforming some supervised methods that have been trained with ground-truth depth.
Complexity of finite-variable fragments of products with <b>K</b>

Abstract We show that products and expanding relativized products of propositional modal logics where one component is the minimal monomodal logic K are polynomial-time reducible to their single-variable fragments. Therefore, the known lower-bound complexity and undecidability results for such logics are extended to their single-variable fragments. Similar results are obtained for products where one component is a polymodal logic with a K-style modality; these include products with propositional dynamic logics.
Blockchain Framework for Social Media DRM Based on Secret Sharing

Huge adoption of the Internet into our daily lives generates at least few MBs of data. Social media in modern age enabled every single user with a smartphone and Internet to be a marketer, journalist, publisher and content creator. Ensuring protection and copyright of social media data like images, videos and audios are extremely demanding. Copyright infringement happens easily, and protecting the content for its originality and authenticity should be simplified. In this paper, we are proposing a blockchain framework with smart contracts to protect social media contents using IPFS, a modern decentralized file storage system and a secret sharing scheme. This framework through its decentralized and immutability feature offers limitless opportunities for managing copyright rights of content on decentralized social media.
Interactive Exploration and Refinement of Facial Expression using Manifold Learning

Posing expressive 3D faces is extremely challenging. Typical facial rigs have upwards of 30 controllable parameters, that while anatomically meaningful, are hard to use due to redundancy of expression, unrealistic configurations, and many semantic and stylistic correlations between the parameters. We propose a novel interface for rapid exploration and refinement of static facial expressions, based on a data-driven face manifold of natural expressions. Rapidly explored face configurations are interactively projected onto this manifold of meaningful expressions. These expressions can then be refined using a 2D embedding of nearby faces, both on and off the manifold. Our validation is fourfold: we show expressive face creation using various devices; we verify that our learnt manifold transcends its training face, to expressively control very different faces; we perform a crowd-sourced study to evaluate the quality of manifold face expressions; and we report on a usability study that shows our approach is an effective interactive tool to author facial expression.
Planning for Fish Net Inspection with an Autonomous OSV

In aquaculture farming, escaping fish can lead to large economic losses and major local environmental impacts. As such, the careful inspection of fishnets for breaks or holes presents an important problem. In this paper, we extend upon our previous work in the design of an omnidirectional surface vehicle (OSV) for fishnet inspection by incorporating AI (artificial intelligence) planning methods. For large aquaculture sites, closely inspecting the surface of the net may lead to inefficient performance as holes may occur infrequently. We leverage a hierarchical task network planner to construct plans on when to evaluate a net closely and when to evaluate a net at a distance in order to survey the net with a wider range. Simulation results are provided.
Putting DNS in Context

Internet traffic generally relies on the Domain Name System (DNS) to map human-friendly hostnames into IP addresses. While the community has studied many facets of the system in isolation, this paper aims to study the DNS in context. With data from a residential ISP we study DNS along with both activity before an application needs a given mapping and the subsequent application transaction. We find that a majority of applications transactions (i) incur no direct DNS costs and (ii) for those that do the cost is minimal.
Geno

Supporting voice commands in applications presents significant benefits to users. However, adding such support to existing GUI-based web apps is effort-consuming with a high learning barrier, as shown in our formative study, due to the lack of unified support for creating multi-modal interfaces. We develop Geno---a developer tool for adding the voice input modality to existing web apps without requiring significate NLP expertise. Geno provides a unified workflow for developers to specify functionalities to support by voice (intents), create language models for detecting intents and the relevant information (parameters) from user utterances, and fulfill the intents by either programmatically invoking the corresponding functions or replaying GUI actions on the web app. Geno further supports references to GUI context in voice commands (e.g., "add this to the playlist"). In a study, developers with little NLP expertise were able to add the multi-modal support for two existing web apps using Geno.
PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation

Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach. However, we observe that in a continuous action space, PPO can prematurely shrink the exploration variance, which leads to slow progress and may make the algorithm prone to getting stuck in local optima. Drawing inspiration from CMA-ES, a black-box evolutionary optimization method designed for robustness in similar situations, we propose PPO-CMA, a proximal policy optimization approach that adaptively expands the exploration variance to speed up progress. With only minor changes to PPO, our algorithm considerably improves performance in Roboschool continuous control benchmarks. Our results also show that PPO-CMA, as opposed to PPO, is significantly less sensitive to the choice of hyperparameters, allowing one to use it in complex movement optimization tasks without requiring tedious tuning.
Enhanced Modeling and Control for Multilinked Aerial Robot With Two DoF Force Vectoring Apparatus

The multilinked or modular structure is one of the state-of-the-art topics in aerial robot field. One type of the multilinked aerial robot called DRAGON containing a two degree-of-freedom (DoF) force vectoring apparatus in each link has been developed in our previous work to augment both maneuvering and manipulation ability. However, several types of invalid robot poses, which are due to the mechanical structure, the previous control method and the interrotor aerodynamic interference, significantly reduce the region of deformation. Thus, enhanced modeling and control methods are necessary to overcome these problems. In this letter, we first develop an integrated thrust and vectoring control method with a rigorous allocation to solve the control singularity. Second, we present a vectoring configuration planning method, which solves the mechanical singularity by locking one of the axes in the two DoF vectoring apparatus. Finally, we propose an estimation method and an active compensation control regarding the aerodynamic interference to improve the flight stability. In the end, simulation studies and experiments involving the hovering and deformation along a vertical plane are performed to evaluate the whole modeling and control framework.
Global Optimization: Combining Local Loss With Result Ranking Loss in Remote Sensing Image Retrieval

With the explosive growth of remote sensing big data, large-scale remote sensing image retrieval (RSIR) has become one of the most challenging tasks in data mining, attracting more and more attention from researchers. In recent years, deep learning has achieved fruitful results in RSIR. However, there are three major problems. The first problem is due to the imbalanced categories in RSIR data sets. Accordingly, a novel sample mining method is proposed. This method is based on top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> “misplaced” samples (Top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> MS) along the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbor decision boundary, rather than setting boundary threshold of sample mining and reducing the impact of artificial and objective factors on network training as usual. The second problem comes from the inconsistency between the loss reduction direction and the optimization direction. In response, we additionally propose a novel result ranking loss (RRL) according to the retrieval results in a batch. RRL is used as a plug-in and combined with other local losses. As a result, the retrieval accuracy improved significantly. For the third problem, processing a large number of high-resolution images will increase training time and cause network freeze. To solve the problem, this article proposes a global optimization model based on feature space and retrieval results (FSRR). More specifically, FSRR consists of local loss based on feature space and RRL based on retrieval results, and can be optimized in an end-to-end manner. Experimental results show that our proposed algorithm achieves the best performance when compared with other state-of-the-art deep RSIR algorithms on two benchmark data sets.
Contextualized interpretable machine learning for medical diagnosis

research-article Share on Contextualized interpretable machine learning for medical diagnosis Authors: Wagner Meira Universidade Federal de Minas Gerais, Belo Horizonte, Brazil Universidade Federal de Minas Gerais, Belo Horizonte, BrazilView Profile , Antonio L. P. Ribeiro Universidade Federal de Minas Gerais, Belo Horizonte, Brazil Universidade Federal de Minas Gerais, Belo Horizonte, BrazilView Profile , Derick M. Oliveira Universidade Federal de Minas Gerais, Belo Horizonte, Brazil Universidade Federal de Minas Gerais, Belo Horizonte, BrazilView Profile , Antonio H. Ribeiro Universidade Federal de Minas Gerais, Belo Horizonte, Brazil Universidade Federal de Minas Gerais, Belo Horizonte, BrazilView Profile Authors Info & Claims Communications of the ACMVolume 63Issue 11November 2020 pp 56–58https://doi.org/10.1145/3416965Online:22 October 2020Publication History 4citation2,628DownloadsMetricsTotal Citations4Total Downloads2,628Last 12 Months297Last 6 weeks19 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
RUArt: A Novel Text-Centered Solution for Text-Based Visual Question Answering

Text-based visual question answering (VQA) requires to read and understand text in an image to correctly answer a given question. However, most current methods simply add optical character recognition (OCR) tokens extracted from the image into the VQA model without considering contextual information of OCR tokens and mining the relationships between OCR tokens and scene objects. In this paper, we propose a novel text-centered method called RUArt (Reading, Understanding and Answering the Related Text) for text-based VQA. Taking an image and a question as input, RUArt first reads the image and obtains text and scene objects. Then, it understands the question, OCRed text and objects in the context of the scene, and further mines the relationships among them. Finally, it answers the related text for the given question through text semantic matching and reasoning. We evaluate our RUArt on two text-based VQA benchmarks (ST-VQA and TextVQA) and conduct extensive ablation studies for exploring the reasons behind RUArt’s effectiveness. Experimental results demonstrate that our method can effectively explore the contextual information of the text and mine the stable relationships between the text and objects.