Reviewerly/exHarmony huggingface dataset:
An Online System for Player-vs-Level Matchmaking in Human Computation Games

We present a fully online system for skill and ratings-based dynamic difficulty adjustment (DDA) in human computation games (RCGs). Prior work used an initial offline phase for collecting level ratings to avoid cold-start when using the system online. Further, such systems set a fixed target score win/lose threshold for each level. In this work, we address these issues by 1) using an ∊-greedy variant of the ratings and skill-based DDA algorithm and 2) applying rating arrays, proposed in past work but not yet used in an online setting. We demonstrate our system in two online matchmaking experiments using two HCGs. Results suggest that the ∊-greedy modification helps address the cold-start and that using rating arrays leads to players completing more levels.
Efficient Integration of XR with Haptic Feedback and 5G Networks

The speedy development of immersive technologies and unexpected situations like the COVID-19 pandemic are expected to boost the application of extended reality (XR) in tourism. Virtualization techniques can create a virtual environment identical to the real one, giving to the user the feeling of actually being there. The aim of haptic systems is to enhance the degree of immersion into a virtual environment by intensifying the skill of physical interaction with people and objects in the distant space. The advancement of cellular networks enables haptic data transmission over high data rate and low latency wireless communication to create ambient haptic experience. The contribution of this research is to describe how XR and haptics are able to reshape tourism industry and present insights for tourism involved parties.
Automated 3D Object Reference Generation for the Evaluation of Autonomous Vehicle Perception

Understanding the surrounding traffic is a challenging task for automated driving systems. A reliable perception is not only mandatory for safe prediction, planning and subsequent operation in traffic but also serves as a basis for post-analysis to identify and collect encountered scenarios. Evaluating a perception component relies mostly on comparing object hypotheses to a reference. These references are often the result of manual labeling processes which are time-consuming, expensive and can be prone to errors. In this work, we propose a process for the automatic generation of dimension and classification references of perceived objects. Our approach post-processes perceived objects under consideration of sensor mounting information and infrastructure elements defined by an HD map. The dimension reference generation considers reliable measurements that correspond to situations that are assessed as favorable for perceiving the analyzed object. The classification reference is generated by investigating objects towards patterns like specific movement profiles or interactions with infrastructure elements. We show process feasibility and evaluate initial results by comparison with manually labelled object classifications and dimensions based on corresponding camera images. The results show an improved correctness up to 93.7% regarding object classifications and accuracy of vehicle length and width (RMSE = {37.51 cm, 24.14 cm} respectively). Finally, we discuss how the proposed approach can facilitate perception evaluation.
Thermal infrared object tracking via unsupervised deep correlation filters

In thermal infrared (TIR) object tracking, the scarcity of annotated TIR image datasets hampers the performance of deep learning-based trackers. Therefore, most trackers still only use handcrafted features. To tackle this problem, we propose an unsupervised deep correlation TIR object tracking framework. Motivated by the forward-backward tracking consistency of robust trackers, we explore a novel approach that learns a lightweight feature extraction network for TIR images in an unsupervised manner. We follow a Siamese correlation filter framework to construct the network and train it based on sample quality-sensitive consistency loss. To the best of our knowledge, this is the first unsupervised end-to-end trained feature extraction network for TIR object tracking. At the tracking stage, to improve tracking performance, we utilize continuous multichannel correlation filters to combine CNN features with handcrafted features. Extensive experiments indicate that the proposed method outperforms state-of-the-art trackers with more than 10% relative gain in EAO, clearly showing its effectiveness and robustness.
Signal Modulation Recognition based on Convolutional Autoencoder and Time-Frequency Analysis

With the development of communication technology, patterns of communication signals have become more complex and diverse. Therefore, the technology of identifying the modulation mode of the communication signal in transmission, especially the modulation recognition technology based on artificial intelligence, has become an extremely important technology in the communication field. For a variety of modulation methods, the traditional method is extremely complicated to implement, and cannot meet the requirements of accurate identification in a short time. In order to increase the speed and reduce the redundancy, this paper proposes a method based on the convolutional autoencoder and the residual network which can realize the denoising, identification and classification of different modulated signals. This method generates ten different modulation types of signals under each signal-to-noise ratio. After the model is trained, the data set is input to the convolutional autoencoder to denoise, and then the data set denoised by the autoencoder is input to the residual network to obtain the classification and recognition accuracy of each modulation type. And an average recognition rate of 92.86% was achieved at -2dB.
Why Maximum-A-Posteriori Blind Image Deblurring Works After All

Maximum-a-posteriori (MAP) methods, while being a standard choice for many estimation problems, have been considered problematic for blind image deblurring: They have been suspected of preferring blurry images to sharp ones. Alternative methods without this apparent defect have been proposed instead. Reservations about MAP methods for blind image deblurring persist even as their close relation to these alternatives has become evident. We revisit the literature on this topic and argue that the original rejection of MAP methods was ill-founded. We show that the MAP approach can prefer sharp images over blurry ones. Furthermore, we show experimentally that the MAP approach can in principle achieve deblurring results that are competitive with the allegedly superior methods. We thereby challenge some traditional notions of the relevant causes underlying successful blind deblurring to obtain a more accurate understanding of the blind image deblurring problem.
Application of Artificial Intelligence Technology in Information Retrieval of University Library

In the context of information technology, artificial intelligence technology has been rapid development, and has been widely used in various fields. In recent years, the introduction of artificial intelligence technology in university library to build a new intelligent information retrieval system is a major trend of the reform and development of university library. However, there are still many deficiencies in the existing intelligent information retrieval technology. Therefore, this paper puts forward the research on the application of artificial intelligence technology in information retrieval of university library. This paper makes an in-depth investigation on the existing intelligent information retrieval system of university library, and analyzes the main problems in the current intelligent information retrieval technology. In view of these problems, based on the actual needs of university library information retrieval, combined with the latest artificial intelligence technology, this paper puts forward innovative solutions. The core of the solution lies in the use of existing artificial intelligence technology achievements, trying to refer to the field of university library information retrieval, in order to solve the existing defects. According to the relevant survey results, the information retrieval technology of University Library Based on artificial intelligence has greatly improved the comprehensive management ability of university library, and brought better user experience.
Tutoring Reinforcement Learning via Feedback Control

We introduce a control-tutored reinforcement learning (CTRL) algorithm. The idea is to enhance tabular learning algorithms by means of a control strategy with limited knowledge of the system model. By tutoring the learning process, the learning rate can be substantially reduced. We use the classical problem of stabilizing an inverted pendulum as a benchmark to numerically illustrate the advantages and disadvantages of the approach.
Topic-Guided Knowledge Graph Construction for Argument Mining

Decision-making tasks usually follow five steps: identifying the problem, collecting data, extracting evidence, iden-tifying arguments, and making the decision. This paper focuses on two steps of decision-making: extracting evidence by building knowledge graphs (KGs) of specialized topics and identifying sentences' arguments through sentence-level argument mining. We present a hybrid model that combines topic modeling using latent Dirichlet allocation (LDA) and word embeddings to obtain external knowledge from structured and unstructured data. We use a topic model to extract topic- and sentence-specific evidence from the structured knowledge base Wikidata. A knowledge graph is constructed based on the cosine similarity between the entity word vectors of Wikidata and the vector of the given sentence. A second graph based on topic-specific articles found via Google supplements the general incompleteness of the structured knowledge base. Combining these graphs, we obtain a graph-based model that, as our evaluation shows, successfully capitalizes on both structured and unstructured data.
Research and Development of Multipurpose Unmanned Aerial Vehicle (Flying Drone)

Technology in the unmanned aerial vehicle (UAV) can solve many emergency problems in civilian and military sectors by doing the proper implementation [1]. However, this is not commercially used on large scale till now in many countries because of many security factors. Proper implementation of the drone can utilize the problem of emergency medical goods delivery on inaccessible roads, quick surveillance for military and government law enforcement agencies, and much more. Traditional transportation infrastructure might be affected in the same way by delivery drones. This paper is about a new drone model. With the suggested design of a drone, multipurpose work including emergency delivery and surveillance network will facilitate more time efficiency and much more economical to potentially save lives. Moreover, in the current COVID-19 pandemic situation, it will be very helpful to supply medicine and goods to the lockdown areas.
Detection of Data Breaching Websites using Machine Learning

In pandemic situation, the world has upgraded to virtual mode. From classes to work everything turned to be online mode. So, the usage of internet and online services are rising to a greater extent. People lack knowledge about privacy risks and data breaching websites. These websites paved the way for lot of cybercrimes like data breaching. Many methodologies have been used to identify data breaching websites earlier. But phishers are succeeded in finding loop holes. This paved way to create solution to overcome security checks. Most of the cyber-attacks had same pattern. These patterns will give a brief knowledge about these cyber-attacks. For identifying data breaching websites, machine learning algorithms will be the better choice of selection. This project deals with design and development of a model to identify data breaching websites. Algorithm chosen for this project is Random Forest Algorithm, the best suited algorithm for classification of large number of characters. There are 3 major steps involved in this project such as data collection, feature extraction and testing and training. Important step in this process is feature extraction from website URL.A set of 17 features are extracted for analysis. Following the feature extraction, there will 2 phase such as training and testing. By the end, trained machine learning model will be obtained. This trained model can be used in future works. Numerous data breaching websites are created in current days. This machine learning technique will identify those data breaching websites in an effective manner.
Trust Beyond Border: Lightweight, Verifiable User Isolation for Protecting In-Enclave Services

Due to the absence of in-enclave isolation, today's trusted execution environment (TEE), specifically Intel's Software Guard Extensions (SGX), does not have the capability to securely run different users' tasks within a single enclave, which is required for supporting real-world services, such as an in-enclave machine learning model that classifies the data from various sources, or a microservice (e.g., data search) that performs a very small task (within sub-seconds) for a user and therefore cannot afford the resources and the delay for creating a separate enclave for each user. To address this challenge, we developed Liveries, a technique that enables lightweight, verifiable in-enclave user isolation for protecting time-sharing services. Our approach restricts an in-enclave thread's privilege when configuring an enclave, and further performs integrity check and sanitization on critical enclave data upon user switches. For this purpose, we developed a novel technique that ensures the protection of sensitive user data (e.g., session keys) even in the presence of the adversary who may have compromised the enclave. Our study shows that the new technique is lightweight (1% overhead) and verifiable (about 3200 lines of code), making a step towards assured protection of real-world in-enclave services.
MDMD options discovery for accelerating exploration in sparse-reward domains

One of the main challenges in reinforcement learning is how an agent explores the environment with sparse rewards to learn the optimal policy. Although many option discovery methods have been proposed to improve exploration in sparse-reward domains, it is still an open question how to accelerate exploration in a near-optimal manner. Recently, covering options was proposed to find a set of options that reduce the expected cover time of environment—the expected number of steps required to visit every state in the environment. Specifically, covering options constructs options by eigenvectors of the graph Laplacian matrix to minimize the environment’s expected cover time. However, calculating the whole graph Laplacian matrix directly has high computational time complexity usually, especially for a large sparse graph, so this method does not well solve the problem of accelerating exploration in sparse-reward domains. In this paper, we propose a new option discovery method, Min Degree and Max Distance (MDMD) options, to accelerate exploration in sparse-reward domains by reducing the expected cover time of the environment. Specifically, our algorithm heuristically selects state transition matrix’s two nonadjacent vertices with the minimum degree and the maximum distance as options. The generated options can provably reduce the environment’s expected cover time by using the transition function learned by the agent. Without calculating the graph Laplacian matrix and its eigenvectors, our method can accelerate exploration in sparse-reward domains. In six challenging sparse-reward environments, experimental results show that our approach significantly accelerates exploration and thus obtains a higher total cumulative reward than other option discovery methods.
Named Entity Recognition Method in Network Security Domain Based on BERT-BiLSTM-CRF

With the increase of the number of network threats, the knowledge graph is an effective method to quickly analyze the network threats from the mass of network security texts. Named entity recognition in network security domain is an important task to construct knowledge graph. Aiming at the problem that key Chinese entity information in network security related text is difficult to identify, a named entity recognition model in network security domain based on BERT-BiLSTM-CRF is proposed to identify key named entities in network security related text. This model adopts the BERT pre-training model to obtain the word vectors of the preceding and subsequent text information, and the obtained word vectors will be input to the subsequent BiLSTM module and CRF module for encoding and sorting. The test results show that this model has a good effect on the data set of network security domain. The recognition effect of this model is better than that of LSTM-CRF, BERT-LSTM-CRF, BERT-CRF and other models, and the F1&#x003D;93.81&#x0025;.
Multi-Scale Feature Distillation for Anomaly Detection

Due to the diversity and scarcity of defective samples, anomaly detection is usually taken as one of the main solutions of defect detection in intelligent manufacturing industry. Unsupervised anomaly detection usually faces two main challenges: (1) Without effective knowledge transferring, diversity and scarcity of defect samples led to model's training difficulties, especially for deep CNN model; (2) Large scale variations of defects led to the lack of generalization ability for typical CNN models with single or a few scale represented features. In this work, we propose a simple yet powerful unsupervised anomaly detection method by: i) The teacher and student networks are introduced to train deep CNN-based detection networks. With a powerful deep pre- trained neural network as teacher, and one proposed combined- distance based loss function, a student network with the same architecture is developed to learn teacher's knowledge, and the defective regions are recognized by the larger feature distances compared with the normal regions; ii) Multi-scale feature distillation is developed to against the scale variations. Unlike most knowledge distillation methods that only distill feature output by the last layer, we distill the features on different levels of feature maps output by the intermediate layers, and develop one combined-distance and Gaussian Kernel based receptive field up-sampling mechanism, to generate high resolution anomaly heatmap. Evaluation of the widely used MVTec anomaly detection dataset shows that our method is competitive to the state-of-the-art methods with over 10 times inference speed increase: from 2.8fps@256 × 256 to 33.2fps; The experiments on other two anomaly detection datasets, ShanghaiTech Campus dataset (STC) and Magnetic Tile Defects dataset (MTD), are also given to verify the generalization of the proposed work.
Virtual user geography of library websites

The author generalizes the results of library user survey. The survey was held within the framework of the study “Webometric monitoring of libraries”. The study goal was to assess the virtual geography of library website users and to specify target audience. The user audience of 11 library websites was assessed by following criteria: geographical data (user location, probable user residence area); user demographic data (sex, age); loyalty indicators (bounce rate, visit depth, visit duration); website user desired actions (estimation of desired action conversion). Based on the findings, the author concludes that: library offline services impact website user geography; there is a close interdependence between library location and website traffic; users resident in the library region make the most loyal website users; women prevail in the library user audience. The target website user audience is identified; recommendations are offered for the further strategic development in the web-space. The study conclusions and recommendations would facilitate generated demanded content and efficient promotion of library information resources and services in the web-space.
Modeling Non-Stationarity with Deep Gaussian Processes: Applications in Aerospace Engineering

With the rising trend in hierarchical models, Deep Gaussian Processes (DGP) has introduced itself competitive amongst its counterparts by offering a probabilistic framework for deep learning based on the Gaussian Process (GP). An interesting byproduct of DGP, as observed in numerous studies, is its ability to outperform the standard GP with regard to non-stationary functions. Of particular interest to the present study is that of discontinuous-like functions. However, with the current state-of-the-art Doubly Stochastic DGP utilizing a sparse inducing point variational framework, issues regarding its approximate Gaussian posterior and the loss of the interpolation property has propped up in recent works. To that end, a new approach to inference dubbed the DGP stochastic imputation (DGP-SI) has been recently proposed, citing its ability to interpolate. The present study considers the task of surrogate modelling and uncertainty quantification with DGP-SI on three problems with discontinuous-like features compared with a stationary GP. Results indicate that, on average, DGP-SI performs better and shows promise. However, at such an early stage in its development, DGP-SI is sensitive to hyperparameter optimization and thus may result in a model slightly worse than the standard GP.
On the Opinion Score Consistency in Repeated 360° Video Quality Assessment for Standing and Seated Viewing on Head-Mounted Displays

The development of immersive media systems and services relies on subjective tests that provide ground truth on the quality of experience as perceived by humans. In this paper, we investigate the opinion score consistency through a repeated subjective 360&#x00B0; video quality assessment experiment. The test stimuli are presented on a head-mounted display with participants given the task to rate the video quality in three repeated sessions for both standing and seated viewing. The statistical analysis of the data from the subjective tests aims at revealing if participants change their rating behavior over time or keep their opinion scores given to the test stimuli in standing and seated viewing consistent in a statistical sense. The experimental results are reported in terms of histograms of opinion scores, skewness and kurtosis of opinion scores, mean opinion scores, standard deviation of opinion scores (SOS), SOS fitting functions, and analysis of variance tests. The statistical analysis supports the conjecture that each participant has its own but consistent rating behavior throughout the respective sessions for standing and seated viewing. The finding that the quality rating behavior of the individual participant does not fundamentally change over time may, e.g., assist in scheduling subjective tests under pandemic conditions where experimental campaigns may need to be stalled for an unknown period of time.