Reviewerly/exHarmony huggingface dataset:
BRAFT: Recurrent All-Pairs Field Transforms for Optical Flow Based on Correlation Blocks

In this paper, we propose BRAFT, an improved deep network architecture based on the Recurrent All-Pairs Field Transforms (RAFT) for optical flow estimation. BRAFT extracts features for each pixel. According to the characteristics of optical flow, the dense visual similarity is calculated based on the strength-weakness correlation in blocks to establish a more precise 4D correlation volume. Using a single dataset for training, the proposed method achieves better results than the original RAFT. We consider the end-point-error as the performance measure. The results show that the proposed method is 1.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> lower than RAFT on the KITTI 2015 benchmark, and 1.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> lower than MPI Sintel (final). In addition, the errors in BRAFT are mostly small. Therefore, the proposed method is better for performing the edge extraction of moving objects.
CNN and DCGAN for Spectrum Sensors over Rayleigh Fading Channel

Spectrum sensing (SS) has attracted much attention in the field of Internet of things (IoT) due to its capacity of discovering the available spectrum holes and improving the spectrum efficiency. However, the limited sensing time leads to insufficient sampling data due to the tradeoff between sensing time and communication time. In this paper, deep learning (DL) is applied to SS to achieve a better balance between sensing performance and sensing complexity. More specifically, the two-dimensional dataset of the received signal is established under the various signal-to-noise ratio (SNR) conditions firstly. Then, an improved deep convolutional generative adversarial network (DCGAN) is proposed to expand the training set so as to address the issue of data shortage. Moreover, the LeNet, AlexNet, VGG-16, and the proposed CNN-1 network are trained on the expanded dataset. Finally, the false alarm probability and detection probability are obtained under the various SNR scenarios to validate the effectiveness of the proposed schemes. Simulation results state that the sensing accuracy of the proposed scheme is greatly improved.
Research on Global Dynamic Path Planning Method Based on Improved A<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mrow> <mi>∗</mi> </mrow> </msup> </math> Algorithm

Aiming at the optimal path and planning efficiency of global path planning for intelligent driving, this paper proposes a global dynamic path planning method based on improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow> <mi>A</mi> </mrow> <mi>∗</mi> </msup> </math> algorithm. First, this method improves the heuristic function of the traditional <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow> <mi>A</mi> </mrow> <mi>∗</mi> </msup> </math> algorithm to improve the efficiency of global path planning. Second, this method uses a path optimization strategy to make the global path smoother. Third, this method is combined with the dynamic window method to improve the real-time performance of the dynamic obstacle avoidance of the intelligent vehicle. Finally, the global dynamic path planning method of the proposed improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msup> <mrow> <mi>A</mi> </mrow> <mi>∗</mi> </msup> </math> algorithm is verified through simulation experiments and real vehicle tests. In the simulation analysis, compared with the modified <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msup> <mrow> <mi>A</mi> </mrow> <mi>∗</mi> </msup> </math> algorithm and the traditional <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <msup> <mrow> <mi>A</mi> </mrow> <mi>∗</mi> </msup> </math> algorithm, the method in this paper shortens the path distance by 2.5%∼3.0%, increases the efficiency by 10.3%∼13.6% and generates a smoother path. In the actual vehicle test, the vehicle can avoid dynamic obstacles in real time. Therefore, the method proposed in this paper can be applied on the intelligent vehicle platform. The path planning efficiency is high, and the dynamic obstacle avoidance is good in real time.
Knowledge-Aware Dialogue Generation via Hierarchical Infobox Accessing and Infobox-Dialogue Interaction Graph Network

Due to limited knowledge carried by queries, traditional dialogue systems often face the dilemma of generating boring responses, leading to poor user experience. To alleviate this issue, this paper proposes a novel infobox knowledge-aware dialogue generation approach, HITA-Graph, with three unique features. First, open-domain infobox tables that describe entities with relevant attributes are adopted as the knowledge source. An order-irrelevance Hierarchical Infobox Table Encoder is proposed to represent an infobox table at three levels of granularity. In addition, an Infobox-Dialogue Interaction Graph Network is built to effectively integrate the infobox context and the dialogue context into a unified infobox representation. Second, a Hierarchical Infobox Attribute Attention mechanism is developed to access the encoded infobox knowledge at different levels of granularity. Last but not least, a Dynamic Mode Fusion strategy is designed to allow the Decoder to select a vocabulary word or copy a word from the given infobox/query. We extract infobox tables from Chinese Wikipedia and construct an infobox knowledge base. Extensive evaluation on an open-released Chinese corpus demonstrates the superior performance of our approach against several representative methods.
Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion

Deep neural networks need large amounts of labeled data to achieve good performance. In real-world applications, labels are usually collected from non-experts such as crowdsourcing to save cost and thus are noisy. In the past few years, deep learning methods for dealing with noisy labels have been developed, many of which are based on the small-loss criterion. However, there are few theoretical analyses to explain why these methods could learn well from noisy labels. In this paper, we theoretically explain why the widely-used small-loss criterion works. Based on the explanation, we reformalize the vanilla small-loss criterion to better tackle noisy labels. The experimental results verify our theoretical explanation and also demonstrate the effectiveness of the reformalization.
UAV Swarm Navigation Using Dynamic Adaptive Kalman Filter and Network Navigation

Aiming to improve the positioning accuracy of an unmanned aerial vehicle (UAV) swarm under different scenarios, a two-case navigation scheme is proposed and simulated. First, when the Global Navigation Satellite System (GNSS) is available, the inertial navigation system (INS)/GNSS-integrated system based on the Kalman Filter (KF) plays a key role for each UAV in accurate navigation. Considering that Kalman filter’s process noise covariance matrix Q and observation noise covariance matrix R affect the navigation accuracy, this paper proposes a dynamic adaptive Kalman filter (DAKF) which introduces ensemble empirical mode decomposition (EEMD) to determine R and adjust Q adaptively, avoiding the degradation and divergence caused by an unknown or inaccurate noise model. Second, a network navigation algorithm (NNA) is employed when GNSS outages happen and the INS/GNSS-integrated system is not available. Distance information among all UAVs in the swarm is adopted to compensate the INS position errors. Finally, simulations are conducted to validate the effectiveness of the proposed method, results showing that DAKF improves the positioning accuracy of a single UAV by 30–50%, and NNA increases the positioning accuracy of a swarm by 93%.
Prediction and Anticipation Features-Based Intellectual Assistant in Location-Based Services

Intellectual assistant in a location based service (LBS) is considered here as assistive technology in healthcare system for helping users with sensory, physical or intellectual diminished capabilities. The proposed system has the speculative computation module which helps in orientation for predicting the users’ error and alerting them for ensuring a true traveling direction well in advance for the handicapped people of mild to moderate condition of disability in earlier phases of cognitive disabilities. A person with cognitive impairments has the Trajectory (T) associated with them which is a sequenced set of past global positioning system (GPS) points. The implementation and evaluation of the trajectory data mining method have been done for the purpose of achieving the travel route to the user preferences. For the simulation of proposed system, a client server queuing model has been used. The results show the minimization of the overhead with better precision as compared to earlier existing path guiding systems.
Extinguishing of Ground Fires by Fully Autonomous UAVs motivated by the MBZIRC 2020 Competition

In this paper, a system for autonomous extinguishing of ground fires using the placement of fire blankets by Multi-rotor Unmanned Aerial Vehicles (UAVs) is proposed. The proposed system, relying on the fusion of multiple onboard sensors using only onboard computers, is infrastructure independent with a focus on high reliability in safety-critical missions that require power-on-and-go full autonomy. This task was part of the third challenge of MBZIRC 2020 aimed at the development of autonomous robotic systems for extinguishing fires inside and outside of buildings. The MBZIRC competition promotes the development of such robotics applications that are highly demanded by society and, due to their complexity and required robot abilities, go beyond the current robotic state of the art. As far as we are aware, our team was one of only two teams to achieve successful system for placement of fire blankets fully autonomously with vision-based target localization without using Real-time kinematic (RTK)-global navigation satellite system (GNSS), as was required in the competition and also for the real missions of first responders.
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing

Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker. CAT methods adaptively select the next most informative question/item for each student given their responses to previous questions, effectively reducing test length. Existing CAT methods use item response theory (IRT) models to relate student ability to their responses to questions and static question selection algorithms designed to reduce the ability estimation error as quickly as possible; therefore, these algorithms cannot improve by learning from large-scale student response data. In this paper, we propose BOBCAT, a Bilevel Optimization-Based framework for CAT to directly learn a data-driven question selection algorithm from training data. BOBCAT is agnostic to the underlying student response model and is computationally efficient during the adaptive testing process. Through extensive experiments on five real-world student response datasets, we show that BOBCAT outperforms existing CAT methods (sometimes significantly) at reducing test length.
Pose is all you need: the pose only group activity recognition system (POGARS)

Abstract We introduce a novel deep learning-based group activity recognition approach called the Pose Only Group Activity Recognition System (POGARS), designed to use only tracked poses of people to predict the performed group activity. In contrast to existing approaches for group activity recognition, POGARS uses 1D CNNs to learn spatiotemporal dynamics of individuals involved in a group activity and forgo learning features from pixel data. The proposed model uses a spatial and temporal attention mechanism to infer person-wise importance and multi-task learning for simultaneously performing group and individual action classification. Experimental results confirm that POGARS achieves highly competitive results compared to state-of-the-art methods on a widely used public volleyball dataset despite only using tracked pose as input. Further, our experiments show by using pose only as input, POGARS has better generalization capabilities compared to methods that use RGB as input.
Applications of Deep Learning in News Text Classification

The advancement in technology is taking place with an accelerating pace across the globe. With the increasing expansion and technological advancement, a vast volume of text data are generated everyday, in the form of social media platform, websites, company data, healthcare data, and news. Indeed, it is a difficult task to extract intriguing patterns from the text data, such as opinions, summaries, and facts, having varying length. Because of the problems of the length of text data and the difficulty of feature value extraction in news, this paper proposes a news text classification method based on the combination of deep learning (DL) algorithms. In order to classify the text data, the earlier approaches use a single word vector to express text information and only the information of the relationship between words were considered, but the relationship between words and categories was ignored which indeed is an important factor for the classification of news text. This paper follows the idea of a customized algorithm which is the combination of DL algorithms such as CNN, LSTM, and MLP and proposes a customized DCLSTM-MLP model for the classification of news text data. The proposed model is expressed in parallel with word vector and word dispersion. The relationship among words is represented by the word vector as an input of the CNN module, and the relationship between words and categories is represented by a discrete vector as an input of the MLP module in order to realize comprehensive learning of spatial feature information, time-series feature information, and relationship between words and categories of news text. To check the stability and performance of the proposed method, multiple experiments were performed. The experimental results showed that the proposed method solves the problems of text length, difficulty of feature extraction in the news text, and classification of news text in an effective way and attained better accuracy, recall rate, and comprehensive value as compared to the other models.
Online Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network

Online credit payment fraud detection plays a critical role in financial institutions due to the growing volume of fraudulent transactions. Recently, researchers have shown an increased interest in capturing users’ dynamic and evolving fraudulent tendencies from their behavior sequences. However, most existing methodologies for sequential modeling overlook the intrinsic structure information of web pages. In this paper, we adopt multi-scale behavior sequence generated from different granularities of web page structures and propose a model named SAH-RNN to consume the multi-scale behavior sequence for online payment fraud detection. The SAH-RNN has stacked RNN layers in which upper layers modeling for compendious behaviors are updated less frequently and receive the summarized representations from lower layers. A dual attention is devised to capture the impacts on both sequential information within the same sequence and structural information among different granularity of web pages. Experimental results on a large-scale real-world transaction dataset from Alibaba show that our proposed model outperforms state-of-the-art models. The code is available at https://github.com/WangliLin/SAH-RNN.
Understanding the Emotional Impact of GIFs on Instagram through Consumer Neuroscience

The ability of GIFs to generate emotionality in social media marketing strategies is analyzed. The aim of this work is to show how neuroscience research techniques can be integrated into the analysis of emotions, improving the results and helping to guide actions in social networks. This research is structured in two phases: an experimental study using automated biometric analysis (facial coding, GSR and eye tracking) and an analysis of declared feelings in the comments of Instagram users. Explicit valence, type of emotion, length of comment and proportion of emojis are extracted. The results indicate that the explicit measure of emotional valence shows a higher and more positive emotional level than the implicit one. This difference is influenced differently by the engagement and the proportion of emojis in the comment. A further step has been taken in the measurement of user emotionality in social media campaigns, including not only content analysis, but also providing new insights thanks to neuromarketing.
On Sampled Metrics for Item Recommendation (Extended Abstract)

Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates such sampled metrics and shows that they are inconsistent with their exact counterpart, in the sense that they do not persist relative statements, e.g., recommender A is better than B, not even in expectation. We show that it is possible to improve the quality of the sampled metrics by applying a correction. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. Our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the estimates.
Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts

This paper investigates the model-based methods in multi-agent reinforcement learning (MARL). We specify the dynamics sample complexity and the opponent sample complexity in MARL, and conduct a theoretic analysis of return discrepancy upper bound. To reduce the upper bound with the intention of low sample complexity during the whole learning process, we propose a novel decentralized model-based MARL method, named Adaptive Opponent-wise Rollout Policy Optimization (AORPO). In AORPO, each agent builds its multi-agent environment model, consisting of a dynamics model and multiple opponent models, and trains its policy with the adaptive opponent-wise rollout. We further prove the theoretic convergence of AORPO under reasonable assumptions. Empirical experiments on competitive and cooperative tasks demonstrate that AORPO can achieve improved sample efficiency with comparable asymptotic performance over the compared MARL methods.
Sending or Not-Sending Twin-Field Quantum Key Distribution with Flawed and Leaky Sources

Twin-field quantum key distribution (TF-QKD) and its variants can overcome the fundamental rate-distance limit of QKD which has been demonstrated in the laboratory and field while their physical implementations with side channels remains to be further researched. We find the external modulation of different intensity states through the test, required in those TF-QKD with post-phase compensation, shows a side channel in frequency domain. Based on this, we propose a complete and undetected eavesdropping attack, named passive frequency shift attack, on sending or not-sending (SNS) TF-QKD protocol given any difference between signal and decoy states in frequency domain which can be extended to other imperfections with distinguishable decoy states. We analyze this attack by giving the formula of upper bound of real secure key rate and comparing it with lower bound of secret key rate under Alice and Bob's estimation with the consideration of actively odd-parity pairing (AOPP) method and finite key effects. The simulation results show that Eve can get full information about the secret key bits without being detected at long distance. Our results emphasize the importance of practical security at source and might provide a valuable reference for the practical implementation of TF-QKD.
Deep-Sequence–Aware Candidate Generation for e-Learning System

Recently proposed recommendation systems based on embedding vector technology allow us to utilize a wide range of information such as user side and item side information to predict user preferences. Since there is a lack of ability to use the sequential information of user history, most recommendation system algorithms fail to predict the user’s preferences more accurately. Therefore, in this study, we developed a novel recommendation system that takes advantage of sequence and heterogeneous information in the candidate-generation process. The principle underlying the proposed recommendation model is that the new sequence based embedding layer in the model catches the sequence pattern of user history. The proposed deep-learning model may improve the prediction accuracy using user data, item data, and sequential information of the user’s profile. Experiments were conducted on datasets of the Korean e-learning platform, and the empirical results confirmed the capability of the proposed approach and its superiority over models that do not use the sequences of the heterogeneous information of users and items for the candidate-generation process.
THE DIGITAL TRANSFORMATION REVOLUTION

In the context of sweeping social, economic, technological, and demographic changes, digital transformation is a series of deep and coordinated culture, workforce, and technology shifts that enable new educational and operating models and transform an institution’s operations, strategic directions. Digital transformation is a destination that is practically mandatory for most organizations. The interconnectivity that it fosters will make the communication flow between students and teachers more fluid. It also helps to improve the relationship and promotes the best use of available resources, especially in higher education. So, the researchers shed light on digital transformation, its importance and the challenges it faces, especially in higher education institutions Therefore, higher education institutions must keep pace with all these changes and take them into account if they want to develop and compete with their peers.
Procedural Dungeon Generation: A Survey

Procedural content generation (PCG) is a method of content creation entirely or partially done by computers. PCG is popularly employed in game development to produce game content, such as maps and levels. Representative examples of games using PCG are Rogue (1998), which introduced the rogue­like genre, and No Man’s Sky (2016), which generated whole worlds with fauna and flora. PCG may generate final contents, ready to be added to a game, or intermediate contents, which might be polished by human designers or work as an input level sketch to be interpreted by a level translator. In this paper, we survey the current state of procedural dungeon generation (PDG) research, a PCG subarea, applied in the context of games. For each work we selected in this survey, we examined and compared how they created game features, what type of level structure and representation they propose, which content generation strategy they applied, and, finally, we classify them according to the taxonomy of procedural content generation proposed by Togelius et al. (2016). The most relevant findings of our survey are: (1) PDG for 3D levels has been little explored; (2) few works supported levels with barriers, a game mechanic which temporarily blocks the player progression, and; (3) mixed-initiative approaches, i.e., software that helps human designers by making suggestions to the levels being created, are little explored.