Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Method using Gaussian Random Fields for Boilerplate Removal in Web Browsers

Boilerplate removal refers to the problem of removing noisy content from a webpage such as ads and extracting relevant content that can be used by various services. This can be useful in several features in web browsers such as ad blocking, accessibility tools such as read out loud, translation, summarization etc. In order to create a training dataset to train a model for boilerplate detection and removal, labeling or tagging webpage data manually can be tedious and time consuming. Hence, a semi-supervised model, in which some of the webpage elements are labeled manually and labels for others are inferred based on some parameters, can be useful. In this paper we present a solution for extraction of relevant content from a webpage that relies on semi-supervised learning using Gaussian Random Fields. We first represent the webpage as a graph, with text elements as nodes and the edge weights representing similarity between nodes. After this, we label a few nodes in the graph using heuristics and label the remaining nodes by a weighted measure of similarity to the already labeled nodes. We describe the system architecture and a few preliminary results on a dataset of webpages.
Research of Teachers’ Digital Competences in an International Context

Teachers’ digital competences are the key to streamlining the existing processes and tasks of teaching work, as well as a ticket to qualitatively new ways of managing the learning process of pupils and students. The change in the content of education, accompanied by expanding demands on teachers’ competences, is reflected by the number of scientific studies, research plans, and scientific literature. However, there is a lack of systematic summary of research findings in the field of digital competences of teachers, who can be considered the main actors of paradigmatic change in education. It is, therefore, appropriate to analyze the research studies carried out in the area of digital competences of teachers, which will enable the reflection of the current state of knowledge in this area. Therefore, the aim of this paper is to elaborate a systematic review study that provides an insight into the methodology of these researches and their main results. Methodologically, the paper is based on the analysis of published research on the digital competences of teachers. The result is a systematic review study summarizing the basic findings.
Design Patterns for Sorting Algorithms

This Innovative Practice Work-in-Progress Paper presents an alternative approach to teach algorithms using design patterns. The traditional approach to teaching different algorithms is to introduce each algorithm as a stand-alone approach. For example, when one goes over different sorting algorithms, one may start with logically simpler sorts like bubble sort or selection sort, and progress to the more challenging algorithms like heap sort or merge sort. We argue that there is another way to introduce a family of algorithms - identify the design patterns common to a group of algorithms, introduce the patterns first, before differentiating one approach from another. In this way, we focus on overall constructs and operations required, rather than how to complete each one of the sorts separately.This approach allows for an abstract class, such as Sort, to be first identified from a top-down approach, followed by the introduction and identification of which design patterns an actual algorithm falls - say divide-and-conquer for merge sort. The advantage of the approach is to allow the student to explore new realizations of solutions to problems developed from the abstract class, rather than studying a specific implementation of an algorithm.
Rule-based design for Anaphora Resolution of Marathi Sentence

Understanding the natural language is difficult for the computers because natural language is coherently ambiguous. Now-a-days resolving that ambiguity is the main task for the researcher. And one of such ambiguity is the anaphora resolution. Anaphora occurs frequently in the discourse. Anaphora resolution is a complex problem for the researcher. In the application of natural language processing like question answering system, text summarization, natural language generation and in many more application anaphora resolution is a must task. This paper presents the anaphora resolution for Marathi language. The work on anaphora resolution is performed on many Indian languages like, Hindi, Tamil, Telugu, Bengali, and Urdu etc. but not done on the Marathi language. This paper focuses on the Marathi demonstrative pronouns specifically for the name of the persons and trying to resolve the difficulty of the anaphora and the antecedent. For the resolution of anaphora, rule based structure is designed by taking into account the context of Marathi grammar.
Detecting Vehicles’ Relative Position on Two-Lane Highways Through a Smartphone-Based Video Overtaking Aid Application

In this paper we present a smartphone-based real-time video overtaking architecture for vehicular networks. The developed application aims to prevent head-on collisions that might occur due to attempts to overtake when the view of the driver is obstructed by the presence of a larger vehicle ahead. Under such conditions, the driver does not have a clear view of the road ahead and of any vehicles that might be approaching from the opposite direction, resulting in a high probability of accident occurrence. Our application relies on the use of a dashboard-mounted smartphone with the back camera facing the windshield, and having the screen towards the driver. A video is streamed from the vehicle ahead to the vehicle behind automatically, where it is displayed so that the driver can decide if it is safe to overtake. One of the major challenges is the way to pick the right video source and destination among vehicles in close proximity, depending on their relative position on the road. For this purpose, we have focused on two different methods: one relying solely on GPS data, and the other involving the use of the camera and vehicle heading information. Our experiments show that the faster method, using just the location information, is prone to errors due to GPS inaccuracies. A second method that depends on data fusion from the optical sensor and GPS, although accurate over short distances, becomes more computationally intensive, and its performance significantly depends on the quality of the camera.
Particle Filtering for Automotive: A survey

Since its introduction more than two decades ago, the particle filter has become an established technique for nonlinear state estimation, due to its capability to cope with severe nonlinearities and non-Gaussian noise. More recently, there has also been rapid development in particle filtering for learning, either for real-time estimation of unknown parameters in the system, or for offline system identification. Due to the increased flexibility the particle filter has been applied in such diverse areas as meteorology, medical imaging, video analysis, robotics, self-driving cars, and aerial vehicles. In this paper, we survey particle filters in vehicle applications, with particular focus on autonomous vehicles. We describe the particle filter and its relation to marginalization and conjugate priors, which are key enablers in several important applications. Based on our own research, we present three recent use cases of particle filtering in the automotive industry and give an outlook on potential research directions.
Coalescing Artificial Intelligence with Augmented Reality to vitalize Smart-Glasses

Over the last decade, artificial intelligence has become a core for success of Augmented Reality. This paper will discuss the rise of Artificial Intelligence, birth of Augmented Reality and the dwindling of Smart glasses. We will talk about what went right and what went wrong when the wave of smart glasses started in 2013 and how Artificial Intelligence and Augmented Reality; two of its major segments will enable it to flourish. Several other technologies have been discussed which have major impact on development of smart glasses. As well as a concept of smart glasses has been discussed, with this concept, we introduce about some new features which will be a major assistance to the further improvement of smart glasses in the future.
An IoT Reconfiguration Framework Applied Ontology-based Modeling and Bayesian-based Reasoning for Context Management

The rapid evolution in Internet of Things (IoTs), the increasing of the complexity of the sensors, and the dynamic changes and unpredicted events in the IoTs environments pose new challenges to IoTs reconfiguration. In this paper, we propose an IoTs reconfiguration framework, namely ROB-IoT, for the reconfiguration of IoTs applications upon the changes of the complex contexts to improve our previous study. The approach to build the intelligent context management component of ROB-IoT is using ontology for modelling and the Bayesian network for reasoning. A Bayesian network can flexibly handle the uncertain environments of frequent changes in IoTs devices configuration, the proposed framework can adjust to the changing Internet of Things environment, making it more flexible. To validate our proposed framework, experiments have been carried out in the smart campus application by changing the behaviors of the IoTs application. Results show that our proposed framework for improving the timing of re-configured decision making is better for the dynamic reconfiguration of IoTs applications.
Saliency Detection via Robust Seed Selection of Foreground and Background Priors

Recently, saliency detection has become a research hotspot in both the computer-vision and image-processing fields. Among the diverse saliency-detection approaches, those based on the foreground and background-based model can achieve promising performance. Reliable seed selection for the foreground and background priors is a critical step for successful saliency detection. In this paper, we firstly exploit the spatial distribution of the extracted directional patches to predict the centroid of each salient object in an image. Then, we adopt the located centroids as the visual-attention center of the whole image to compute the superpixel-based center prior, which can facilitate the subsequent seed selection for the foreground and background-prior generation. Finally, the two individual foreground-based and background-based saliency maps are combined together into a plausible and authentic saliency map. Extensive experimental assessments on publicly available datasets demonstrate the effectiveness of our proposed model.
Phone-Aware Multi-task Learning and Length Expanding for Short-Duration Language Recognition

In the language recognition, the phonetic information has shown great potential for neural network to learn the high-level representations. In this paper, we explore two significant aspects to improve the system performance on oriental language recognition (OLR) challenge under the short-duration condition. Firstly, we propose to learn the language information and phonetic information jointly with multi-task learning. The classified networks can learn the extra phonetic representation from a frame-level phone-task and extract the language embedding at the segment level. Furthermore, we propose to introduce length expanding strategy to provide supplemental information of short-duration utterances by dithering the short duration evaluation utterances at different speeds. The evaluation results of the 3rd OLR Challenge showed that our proposed methods obtained the best results on the short-duration condition.
Automatic classification of medical X-ray images with convolutional neural networks

The classification of medical images is an important step for image-based clinical decision support systems. With the number of images taken per patient scan rapidly increasing, there is a need for automatic medical image classification systems that are accurate because manual classification and annotation is time-consuming and prone to errors. This paper focuses on automatic classification of X-ray image from the ImageCLEF 2009 dataset based on anatomical and biological information using the InceptionV3 model. The X-ray images are prepared and preprocessed with two different padding techniques, two image enhancement techniques and layering to convert the grey-scale images to 3-channel images to prepare them for InceptionV3. In terms of classification loss, constant padding with no enhancements had the best performance with an accuracy of 68.67% and a loss of 1.442. In terms of classification accuracy, constant padding with enhancement had the best performance with an accuracy of 71.34% and a loss of 1.608.
Virtual and Augmented Reality in Science Teaching and Learning

The present research investigates the state-of-the-art concerning virtual and augmented reality lab environments, in science teaching and learning. Both environments are suggested as most appropriate for science education. The study explores the extent to which research has progressed concerning these new tools, while focusing on the extent to which actual educational trials in science classrooms have been completed. To this effect, 19 research papers were identified and reviewed. Conclusions are drawn as regards the effectiveness of these two lab environment types as a function of the age of students, today. The creation of educational VR and AR that resembles reality so closely that it is hard to differentiate between virtual, augmented, and real, thus creating a unified continuum, seems to finally be within our grasp, yet further research is needed as to their optimum use.
Efficient Mapping for VM Allocation Scheme in Cloud Data Center

The allocation of virtual machines in the cloud data center has been a challenge in recent years. The needed resources for virtual machine (VMs) allocation map to the available resources of physical machines (PMs). The efficient allocation of the virtual machines is one of the optimization problems in order to resolve the underutilization of resources in cloud data center. The recently studies emphasized this optimization problem. This paper presents the proposed efficient mapping scheme of virtual machines (VMs) allocation in the cloud data center and then evaluates the efficiency of the proposed allocation scheme. In the presented mapping scheme, the VM allocation problem solved as Bin-packing with BestFit algorithm (BF) and the binary search concept is integrated. The efficiency of the proposed mapping scheme of VMs allocation is verified through the CloudSim simulator. According to the experiment results, the proposed binary search based VMs allocation requires less processing time than the existing one.
High-accuracy Range Image Generation by Fusing Binocular and Motion Stereo Using Fisheye Stereo Camera

This paper proposes a method that fuses two stereo measurements: binocular and motion stereo. The method is implemented for a fisheye stereo camera. The two stereo measurements have difficult characteristics. In case of the binocular stereo, the length of the baseline is usually small due to the installation conditions to a robot or a car. On the other hand, in the case of the motion stereo, the baseline is the distance the camera moves between two consecutive frames, and thus, the length of the baseline is usually longer than binocular stereo. In addition, the area-based approach is used for binocular stereo and the feature-based approach is used for motion stereo in the corresponding point search. In summary, the two stereo measurements have differences in the length of the baseline and the accuracy of the corresponding point search. Therefore, their fusion is studied to improve the stereo measurement. A bilateral-like filter, which is a weighted averaging method, is proposed for the fusion of the two measurements. In motion stereo, the proposed method is verified by outdoor experiments.
Layered data aggregation with efficient privacy preservation for fog‐assisted IIoT

Summary The emergence of fog computing facilitates industrial Internet of Things (IIoT) to be more real‐time and efficient; in order to achieve secure and efficient data collection and applications in fog‐assisted IIoT, it usually sacrifices great computation and bandwidth resources. From the low computation and communication overheads perspective, this paper proposes a layered data aggregation scheme with efficient privacy preservation (LDA‐EPP) for fog‐assisted IIoT by integrating the Chinese remainder theorem (CRT), modified Paillier encryption, and hash chain technology. In LDA‐EPP scheme, the entire network is divided into several subareas; the fog node and cloud are responsible for local and global aggregations, respectively. Specially, the cloud is able to obtain not only the global aggregation result but also the fine‐grained aggregation results of subareas, which enables that can provide fine‐grained data services. Meanwhile, the LDA‐EPP realizes data confidentiality by the modified Paillier encryption, ensures that both outside attackers and internal semi‐trusted nodes (such as, fog node and cloud) are unable to know the privacy data of individual device, and guarantees data integrity by utilizing simply hash chain to resist tempering and polluting attacks. Moreover, the fault tolerance is also supported in our scheme; ie, even though some IIoT devices or channel links are failure, the cloud still can decrypt incomplete aggregation ciphertexts and derive expected aggregation results. Finally, the performance evaluation indicates that our proposed LDA‐EPP has less computation and communication costs.
3D face model reconstructing from its 2D images using neural networks

The most common methods for reconstruction of 3D face models are considered, their quantitative estimates are analyzed and determined, the most promising approach - 3D Morphable Model is highlighted. The necessity of its modification is substantiated in order to improve the results of reconstruction based on the analysis of the main components and the use of a neural network. One of the advantages of using the 3D Morphable Model with the analysis of the main components is the representation of only a probable solution, when the space of solutions is limited, thereby simplifying the problem being solved. While the original approach involves manual initialization. The main result, is an approach for creating three-dimensional face models from their 2D images, having least time and satisfactory root mean square error. The tasks of further research are determined.
An Unsupervised Method for Web User Interest Analysis

In this paper, we propose an unsupervised method to determine dominating user interests from browsing behavior data. This method identifies group of users with similar interests by partitioning a similarity graph, in which nodes are users and edges represent the similarities in browsing behavior between the users. Two users are considered similar if they have viewed the same content. We consider and compare several types of similarity measures, which are calculated at different granularity levels of content: at web page level, page category level, and topic level as identified by topic modeling techniques. A hierarchical graph clustering algorithm is used to perform partitioning process to capture the natural hierarchy of user clusters and producing intuitive features for understanding the content linked to each cluster. For evaluation, we perform an experiment on a browsing behavior dataset from a university's Web portal. Our system can discover interesting information like web user interests, who is interested in which content.
Lucas-Kanade Optical Flow Based Camera Motion Estimation Approach

This paper presents an alternative of our previously proposed lightweight camera motion estimation method based on descriptor matching. First, based on the FAST feature and Lucas-Kanade Optical flow approach, we obtain tracked feature point pairs of two frames of input image. Secondly, according to the obtained pixel coordinates of the tracking point pairs, the motion of the camera can be estimated by the epipolar geometry method. The experiment results indicate that our proposed work can provide a 25% improvement in time-efficiency relative to our previous work. So that it can have higher suitability and possibility of implementation in low-power computing platforms.
Incidents are Meant for Learning, Not Repeating: Sharing Knowledge About Security Incidents in Cyber-Physical Systems

Cyber-physical systems (CPSs) are part of many critical infrastructures such as industrial automation and transportation systems. Thus, security incidents targeting CPSs can have disruptive consequences to assets and people. As incidents tend to re-occur, sharing knowledge about these incidents can help organizations be more prepared to prevent, mitigate or investigate future incidents. This paper proposes a novel approach to enable representation and sharing of knowledge about CPS incidents across different organizations. To support sharing, we represent incident knowledge ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incident patterns</i> ) capturing incident characteristics that can manifest again, such as incident activities or vulnerabilities exploited by offenders. Incident patterns are a more abstract representation of specific incident instances and, thus, are general enough to be applicable to various systems - different from the one in which the incident originally occurred. They can also avoid disclosing potentially sensitive information about an organization's assets and resources. We provide an automated technique to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">extract</i> an incident pattern from a specific incident instance. To understand how an incident pattern can manifest again in other cyber-physical systems, we also provide an automated technique to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">instantiate</i> incident patterns to specific systems. We demonstrate the feasibility of our approach in the application domain of smart buildings. We evaluate correctness, scalability, and performance using two substantive scenarios inspired by real-world systems and incidents.
Extremely Tiny Siamese Networks with Multi-level Fusions for Visual Object Tracking

Siamese architectures have enhanced the performance of visual object tracking a lot these years. Though their great influence, less work focuses on designing tiny networks for tracking. In this paper, we propose a novel tiny Siamese (TinySiam) architecture with extremely tiny parameters and computations. Due to the limited computation requirement, the tracker could run in an extremely fast speed and has the potential to be exploited directly in embedded devices. For efficient designs in the tiny network, we first utilize the layer-level fusion between different layers by concatenating their features in the building block, which ensures the information reusing. Second, we use channel shuffle and channel split operations to ensure the channel-level feature fusion in different convolution groups, which increases the information interaction between groups. Third, we utilize the depth-wise convolution to effectively decrease convolution parameters, which benefits fast tracking a lot. The final constructed network (24K parameters and 59M FLOPs) drastically lowers model complexity. Experimental results on GOT-10k and DTB70 benchmarks for both ordinary and aerial tracking illustrate the excellently real-time attribute (129 FPS on GOT-10k and 166 FPS on DTB70) and the robust tracking performance of our TinySiam Tracker.