Reviewerly/exHarmony huggingface dataset:
Situation Mining: Event Pattern Mining for Situation Model Induction

Computational situation assessment (SA) systems support human control center operators in situation monitoring, i.e., detecting and tracking relevant object and event constellations in their observed environment. SA systems frequently employ deductive reasoning techniques implemented in Complex Event Processing or rule engines to solve this real-time pattern recognition problem, by matching data sensed from the monitored environment against templates for those situations, characterizing the event patterns of interest. Hence, they require explicitly formalizing the sought-after types of situations, demanding human domain experts to conceptually model their cognitive situation hypotheses, which represents a time-consuming and non-trivial task. To overcome this situation knowledge acquisition bottleneck, we therefore propose an approach for inductive situation modeling to leverage existing data sets of recorded situations: We contribute a dedicated situation mining algorithm, which bootstraps situation model acquisition by automatically mining behavioral models of situations, so-called situation evolution models, from already observed situation instances. The feasibility of our approach is examined on a case study from the domain of road traffic incident management, to demonstrate how it turns previously implicit knowledge hidden in the situation instances into explicit situation knowledge that can be inspected and queried for situation analytics, and sketch how the derived situation evolution models can be used within a Model-Driven Engineering framework to directly generate the corresponding rule code for automated situation assessment.
Maturation of digital forensics

Photo Response Non-Uniformity (PRNU) is a camera imaging sensor imperfection which has earned a great interest for source device attribution of digital videos. A majority of recent researches about PRNU-based source device attribution for digital videos do not take into consideration the effects of video compression on the PRNU noise in video frames, but rather consider video frames as isolated images of equal importance. As a result, these methods perform poorly on re-compressed or low bit-rate videos. This paper proposes a novel method for PRNU fingerprint estimation from video frames taking into account the effects of video compression on the PRNU noise in these frames. With this method, we aim to determine whether two videos from unknown sources originate from the same device or not. Experimental results on a large set of videos show that the method we propose is more effective than existing frame-based methods that use either only I frames or all (I-B-P) frames, especially on YouTube videos.
A Fast Road Obstacle Detection Using Association and Symmetry recognition

This paper presents a quick road obstacle detection system supported association and symmetry. This approach consists to exploit the edges extracted from consecutive images acquired by a stereo device embedded in an exceedingly moving vehicle. The formula contains 3 main elements: edges detection, association detection and symmetry calculation. The edges detection is achieved by using the canny operator and point corner to extract all possible edges of different objects at the image. The association technique is used to exploit relationship between the edges of two consecutives images by combining it with the moment operator. The symmetry is used as road obstacle validation; the road obstacles like vehicle and pedestrian have a vertical symmetry. The projected approach has been tested on completely different images. The provided results demonstrate the effectiveness of the proposed method.
Iterative Approximate Nonlinear Inference via Gaussian Message Passing on Factor Graphs

Factor graphs are graphical models able to represent the factorization of probability density functions. By visualizing conditional independence statements, they provide an intuitive and versatile interface to sparsity exploiting message passing algorithms as a unified framework for constructing algorithms in signal processing, estimation, and control in a mix-and-match style. Especially, when assuming Gaussian distributed variables, tabulated message passing rules allow for easy automated derivations of algorithms. This letter's contribution consists in the combination of statistical or Jacobian-based linearization approaches to handling nonlinear factors with efficient message parametrizations in a Gaussian message passing setting. Tabulated message passing rules for a multivariate nonlinear factor node are presented that implement a re-linearization about the most current belief (marginal) of each adjacent variable. When utilized in a nonlinear Kalman smoothing setting, the iterated nonlinear modified Bryson-Frazier smoother is recovered, while retaining the flexibility of the factor graph framework. This application is illustrated by deriving an input estimation algorithm for a nonlinear system.
MANiC: Multi-step Assessment for Crypto-miners

Modern Browsers have become sophisticated applications, providing a portal to the web. Browsers host a complex mix of interpreters such as HTML and JavaScript, allowing not only useful functionality but also malicious activities, known as browser-hijacking. These attacks can be particularly difficult to detect, as they usually operate within the scope of normal browser behaviour. CryptoJacking is a form of browser-hijacking that has emerged as a result of the increased popularity and profitability of cryptocurrencies, and the introduction of new cryptocurrencies that promote CPU-based mining. This paper proposes MANiC (Multi-step AssessmeNt for Crypto-miners), a system to detect CryptoJacking websites. It uses regular expressions that are compiled in accordance with the API structure of different miner families. This allows the detection of crypto-mining scripts and the extraction of parameters that could be used to detect suspicious behaviour associated with CryptoJacking. When MANiC was used to analyse the Alexa top 1m websites, it detected 887 malicious URLs containing miners from 11 different families and demonstrated favourable results when compared to related CryptoJacking research. We demonstrate that MANiC can be used to provide insights into this new threat, to identify new potential features of interest and to establish a ground-truth dataset, assisting future research.
Elicitation and evaluation of zoom gestures for touchless interaction with desktop displays

Touchless gestural interaction has been widely studied and adopted in many contexts. Furthermore, the growing availability of low-cost enabling devices, such as Kinect or Leap Motion, boosted up the interest in such interaction both for commercial and scientific purposes, both for large public displays and for personal displays. The problem of choosing the right touchless gesture for the right action is thus still an open issue, depending on several aspects, such as context, purpose, users' culture, etc. In this work, we first present the results of a gesture elicitation study that allowed us to identify a set of touchless gestures for performing zoom actions while interacting with desktop displays. Next, we present a second user study for evaluating perceived workload, usability and effectiveness of the elicited gestures, showing a clear preference towards one.
Store Sign Text Recognition for Wearable Navigation Assistance System

Abstract With the development of computer vision, wearable computing technologies not only have changed our lifestyle, but also have provided much convenience for vulnerable road users, especially the Visually Impaired (VI) pedestrians. VI people have difficulties in locating and socializing due to the limitations of traditional assistive tools, e.g., the inability to recognize text. Text plays a significant role in various aspects, which can convey abundant semantic information of the scene. In recent years, text detection and recognition has made huge progress which makes it possible for VI people to understand the surroundings by using scene text information. In this paper, a text recognition system is proposed to help VI people to perceive store sign text. Firstly, we locate the text on the sign with the aim of leading VI pedestrian to reach the destination store. Towards this end, an objection detection network is integrated into the system to extract Regions of Interest (ROI) in complex real-world scenarios. In order to fulfil real-time assistance, an efficient detection network named Single Shot MultiBox Detector (SSD) has been made light-weight and embedded in the wearable system. Secondly, we leverage an open-source optical character recognition (OCR) instrument to recognize the detected text. Afterwards, we introduce the collected dataset and critical training tips for the task. Finally, a comprehensive set of experiments on our dataset demonstrates that our approach significantly improves the precision and make the recognition robust even in real-world settings. Based on our approach, the wearable system can feedback the recognized text in real time and assist the VI people during their every independent navigation.
An Impact of Different Images Color Spaces on the Efficiency of Convolutional Neural Networks

A common standard while working with convolutional neural networks is using an RGB color space as an input of the network. All popular benchmarks datasets use this standard with three channels (red, green and blue) and with 8 bits per channel. We can modify images and use lower bits size per channel or use different color spaces standards like HSV, CMYK or YIQ. The main contribution of this paper is testing the influence of an image color space on convolutional neural networks. In our experiments we use DenseNet - a state of the art model and CIFAR 10 - a image benchmark dataset. Our experiments show that choosing proper color space have an impact on the final efficiency of the network and using the most popular RGB color space might not always be the best choice.
A novel deep learning diagnosis scheme for rotating machinery using adaptive local iterative filtering and ensemble hierarchical extreme learning machine

There are many hyper-parameters to be tuned in both machine learning model and deep learning model, and the structure of the deep learning model is large and complicated, making it extremely difficult for fault-feature extraction and classification. In order to address these two problems, a deep learning diagnosis method is proposed in this study by combining adaptive local iterative filtering and ensemble hierarchical extreme learning machine. Adaptive local iterative filtering and entropy feature matrix are used to extract fault features, and an ensemble hierarchical extreme learning machine with deep learning architecture is proposed for unsupervised feature learning and supervised classification. The proposed deep learning diagnosis scheme is tested on fault benchmark datasets under different severity conditions to verify its effectiveness and accuracy. The test results show that the proposed method performs better than traditional extreme learning machine and other variants.
Learning in virtual physics laboratories assisted by a pedagogical agent

Abstract We present the concrete realization of a virtual laboratory equipped with a pedagogical agent. Its functionality and media didactics takes into account the results of an usability test on a prototype system, and the students’ demand on such an automated assistance as obtained from a preliminary survey. The pedagogical agent mediates between the content and the learner by activating him or her. To provide information about the learner’s skills, we propose a pragmatic and simplified competence model that is based on fundamental representations in physics (experiment, figure, text and equation). Moreover, an automated feedback relates the student’s self-assessment with the submitted answer to the correctness of the respective task. In consequence, the pedagogical agent enables mental reflection for a crucial review of the own learning process. Interestingly, learning pathways can be envisioned, thus, giving valuable insight into individual strengths and weaknesses.
10‐2: Waveguide Manufacturing for AR Displays: Past, Present, and Future

Current AR waveguide fabrication processes, including Surface Relief Gratings (SRG), holographic and cascaded mirrors, are reviewed. We present DigiLens's Switchable Bragg Grating (SBG)‐based manufacturing process which transforms classical holographic contact copying into a versatile, cost‐effective printing process for AR waveguides of any size and field‐of‐view.
Heterogeneous Defect Prediction Using Ensemble Learning Technique

One of the quite frequently used approaches that programmers adhere to during the testing phase is the software defect prediction of the life cycle of the software development, this testing becomes utmost important as it identifies potential error before the product is delivered to the clients or released in the market. Our primary concern is to forecast the errors by using an advanced heterogeneous defect prediction model based on ensemble learning technique which incorporates precisely eleven classifiers. Our approach focuses on the inculcation of supervised machine learning algorithms which paves the way in predicting the defect proneness of the software modules. This approach has been applied on historical metrics dataset of various projects of NASA, AEEEM and ReLink. The dataset has been taken from the PROMISE repository. The assessment of the models is done by using the area under the curve, recall, precision and F-measure. The results obtained are then compared to the methods that exist for predicting the faults.
AWBING plus algorithm for generic object proposal generation

Binarized normed gradients (BING) can be utilized as a preprocessing step for generic object proposal generation, and has attracted great attention because of its fast running and appropriate generalization performance. Recently, although some modified schemes were presented to improve the proposal localization quality, the mechanism of enhancing the performance is still an open problem. In this paper, Adaptive weighted binary normed gradients plus (AWBING Plus) algorithm is proposed, based on the BING method, which replaces the support vector machine (SVM) with adaptive weighted extreme learning machine (Adaptive WELM) to reduce the number of proposals, as well as comparable performance, by using the multi-thresholding straddling expansion (MTSE) as the post-processing stage to enhance the localization quality. We explain the methodology of WELM applied to BING, and analyzed the effect of the improved WELM algorithm, which is named Adaptive WELM. The experimental results from PASCAL VOC2007, Microsoft COCO2014 and ILSVRC2013 show that the proposed approach achieved superior performance compared with other advanced methods on generic object proposal generation, and it runs faster as well.
Spatio-Temporal Deep Q-Networks for Human Activity Localization

Human activity localization aims to recognize category labels and detect the spatio-temporal locations of activities in video sequences. Existing activity localization methods suffer from three major limitations. First, the search space is too large for three-dimensional (3D) activity localization, which requires the generation of a large number of proposals. Second, contextual relations are often ignored in these target-centered methods. Third, locating each frame independently fails to capture the temporal dynamics of human activity. To address the above issues, we propose a unified spatio-temporal deep Q-network (ST-DQN), consisting of a temporal Q-network and a spatial Q-network, to learn an optimized search strategy. Specifically, the spatial Q-network is a novel two-branch sequence-to-sequence deep Q-network, called TBSS-DQN. The network makes a sequence of decisions to search the bounding box for each frame simultaneously and accounts for temporal dependencies between neighboring frames. Additionally, the TBSS-DQN incorporates both the target branch and context branch to exploit contextual relations. The experimental results on the UCF-Sports, UCF-101, ActivityNet, JHMDB, and sub-JHMDB datasets demonstrate that our ST-DQN achieves promising localization performance with a very small number of proposals. The results also demonstrate that exploiting contextual information and temporal dependencies contributes to accurate detection of the spatio-temporal boundary.
Direct image reconstruction from raw measurement data using an encoding transform refinement-and-scaling neural network

Direct reconstruction of raw measurement data into a final image using a neural network is currently an uncommon approach to the use of deep learning in medical imaging. One reason may be the relatively recent adoption of deep learning. Another reason may be the computational requirements associated with performing the domain transform using fully connected perceptron layers. We propose an AUTOMAP inspired multi-segment Encoding Transform Refinement-and-Scaling (ETRS) neural network that allows reconstruction of full size 512x512 images compared to the 128x128 image size of AUTOMAP.
An Text Document Retrieval System for University Support Service on a High Performance Distributed Information System

University support service strategy is to place customers at the center, which is a future insight that customers are the primary source for the growth. The rapid growth of text documents and organizing the data efficiently to support students, and other stakeholders of the organizations is a key challenge. We have built a text document search support system on High Performance Distributed Information System which resolves data handling and serve customers. In this paper, we describe the system architecture, data distribution technique and the retrieval system we have built. Text document classification is carried out using the convolutional neural network (CNN) for efficient retrieval and indexing of the data for crawling. The information is distributed and retrieved based on the identification key using an API based micro-service architecture. The system provides the platform to extract knowledge and channel the data to use by the organization and create on-demand service at the support centers.
Toward Real-Time System Adaptation Using Excitement Detection from Eye Tracking

Users' performance is known to be impacted by their emotional states. To better understand this relationship, different situations could be simulated during which the users' emotional reactions are analyzed through sensors like eye tracking and EEG. In addition, virtual reality environments provide an immersive simulation context that induces high intensity emotions such as excitement. Extracting excitement from EEG provides more precise measures then other methods, however it is not always possible to use EEG headset in virtual reality environment. In this paper we present an alternative approach to the use of EEG for excitement detection using only eye movements. Results showed that there is a correlation between eye movements and excitement index extracted from EEG. Five machine learning algorithms were used in order to predict excitement trend exclusively from eye tracking. Results revealed that we can detect the offline excitements trend directly from eye movements with a precision of 92% using deep neural network.
Simultaneous interaction with a large display by many users

We propose a large public display that extracts the skeletons of multiple users and that enables many users to simultaneously interact with the display using gesture operation. The result of experiments using a "whack-a-mole" game showed that all the players were able to join the game and got higher scores with the increase of the number of players in both cases with four children and with ten adults.
Frequent Item Set Mining Algorithm Based on Bit Combination

A frequent item set mining algorithm based on bit combination is proposed in this paper. Frequent item set mining algorithm based on bit combination is an algorithm that searches for possible frequent item set by transforming data into binary bit representation and adding data representing the combination of regulatory elements step by step, and then mining frequent item set by bit and calculation. In the process of data mining, the algorithm is optimized by pruning, preprocessing and frequent item set culling. Because the recursive method used by traditional frequent item set mining algorithms such as FP-Growth algorithm can't effectively parallel a large number of data, the greatest advantage of this algorithm is that it facilitates the parallel computation of data and provides a new idea for improving the efficiency of frequent item set mining. The parallel acceleration of the algorithm is realized by using OpenMP technology to verify the parallel feasibility of the algorithm in this paper.
Distributed image retrieval with colour and keypoint features

Content-based image retrieval poses many problems to computer systems. The content of images has to be described by some feature extraction methods. As image databases are often very large, they are sometimes to complex to be processed by traditional computing methods. We have to use big data solutions to fast retrieve images. The paper presents a system for retrieving images in relational databases in a distributed environment. Content of the query image and images in the database is compared using global colour information and local image keypoints. Image keypoint descriptors are indexed by fuzzy sets directly in a relational database by our algorithm. The process is distributed to several machines thanks to the Apache Hadoop software framework with HDFS.
Detection of Replay Attack Based on Normalized Constant Q Cepstral Feature

Since the voice is easy to be recorded and replayed, the replay attack is considered a major threat to the voiceprint authentication system. However, few works have focused on the text-independent detection. We found that there exist differences in spectral features between the original voice and the replayed voice. Hence, constant Q cepstral coefficients which can well describe the spectral features are extracted as acoustic features. Then the cepstral mean and variance normalization is used as the postprocessing method to eliminate the impact of channel noise on detection performance. Finally, the Gaussian Mixture Model determines whether the suspected voice is replayed or not. The experimental results on the ASVspoof 2017 database indicate the proposed algorithm can significantly reduce the equal error rate and keep high robustness when the replayed voice came from various spoofing devices.
High Precision Implicit Modeling for Patient-Specific Coronary Arteries

High precision geometric reconstruction of patient-specific coronary arteries plays a crucial role in visual diagnosis, treatment decision-making, and the evaluation of the therapeutic effect of interventions in coronary artery diseases. It is also a fundamental task and a basic requirement in the numerical simulation of coronary blood flow dynamics. In this paper, a new implicit modeling technique for the geometric reconstruction of patient-specific coronary arteries has been developed. In the proposed method, the coronary arteries geometry is reconstructed segment by segment using radial basis functions with ellipsoid constraint from the point cloud obtained with a volumetric vascular image segmentation method, and the individually reconstructed coronary branches are then combined using a shape-preserving implicit blending operation to form a complete coronary artery surface. The experiment results and validations indicate that the reconstructed vascular shapes are of high smoothness and faithfulness.