Reviewerly/exHarmony huggingface dataset:
Questions or questioners: Factors affecting response quantity on academic social Q&amp;A sites

ABSTRACT With the continuous development of academic social network sites, academic social Q&amp;A platforms have also become a venue for scholars to obtain academic information. This study explores the factors that influence the response quantity for questions on an academic social Q&amp;A platform. Using 130 questions from the library and information services domain on ResearchGate Q&amp;A, we adopt content analysis and multiple linear regression analysis to investigate the relationship between question characteristics and response quantity. We find that, for academic questions, the questioner's authority does not affect response quantity but questions that provide academic resources and contain fewer personal opinions from the questioner tend to attract more answers.
Coverage Path Planning Design of Mapping UAVs Based on Particle Swarm optimization Algorithm

Mapping UAVs have played a more important role in regional mapping than ever before, and coverage path planning is an important part of mapping tasks. However, there are few studies on single-region path planning in complex polygonal region. In this paper, we proposed a method that searches the optimal yaw-angle and flight height of mapping UAVs based on particle swarm optimization algorithm. Compared with traditional algorithms such as cattle farming algorithm, there are less redundant coverage and turning, and the flight path planned by the algorithm in this paper is more reasonable and convenient for photographic processing.
Colorless Video Rendering System via Generative Adversarial Networks

In today's society, even though the technology is so developed, the coloring of computer images has remained at the manual stage. As a carrier of human culture and art, film has existed in our history for hundred years. With the development of science and technology, movies have developed from the simple black-and-white film era to the current digital age. There is a very complicated process for coloring old movies. Aside from the traditional hand-painting techniques, the most common method is to use post-processing software for coloring movie frames. This kind of operation requires extraordinary skills, patience and aesthetics, which is a great test for the operator. In recent years, the extensive use of machine learning and neural networks has made it possible for computers to intelligently process images. Since 2016, various types of generative adversarial networks models have been proposed to make deep learning shine in the fields of image style transfer, image coloring, and image style change. In this case, the experiment uses the generative adversarial networks principle to process pictures and videos to realize the automatic rendering of old documentary movies.
Efficient Rationalization of Triplet Halfband Filter Banks and its Application to Image Compression

This paper presents a novel approach for rationalization of irrational coefficients of filters designed using three-step lifting scheme. The existing three-step lifting schemes result in the irrational filter coefficients which require infinite precision for implementation. In this work, Euler-Frobenius halfband polynomials are designed to obtain the required kernels of three-step lifting structure. Next, the efficient rationalization of the designed filter banks (FBs) is proposed to reduce the arithmetic complexity. The proposed rational FBs preserve perfect reconstruction, near-orthogonality and regularity properties of wavelet FBs. These filters with rational coefficients are then used in image compression algorithms to compress the test images in well-known Classic, EPFL, RAISE, FiveK datasets and chromosome image datasets. The performance of the proposed rational FBs is compared with the existing rational FBs. It is observed that the performance of the proposed rational FBs is improved in terms of computational cost, encoding-decoding time and compression ratio as compared to existing rational FBs.
Visual Tracking Based on Siamese Network of Fused Score Map

Nowadays, visual object tracking becomes a hotspot and difficulty to achieve a real-time and accurate target tracking, but the Siamese network has solved these difficulties because of its good tracking effect and real-time performance. The location of the target in the previous frame is the template, and the similarity matching is carried out in the search area of the current frame. However, it uses Alexnet network with simple structure and fewer layers to extract features, and just uses a score map to predict the final position of the object. Aiming at these problems, in this paper, we propose the Siamese network of fused response map that use the Alexnet network with fine tuning to extract target features, and weight fusion of score maps to estimate the final position of object. Sufficient experiments on the VOT2015 and OTB100 benchmarks validate that our tracker can improve tracking performance, and perform at 60FPS.
Mobile Application for Crowdsourced Gamification of Automated External Defibrillator (AED) Locations

Automated External Defibrillators (AEDs) are crucial for out-of-hospital cardiac arrests (OHCAs). Therefore, it is vital that AEDs are working and ready for use. However, knowledge of the locations of publicly accessible AEDs is limited, leading to difficulty in tracking, managing as well as obtaining one during an emergency situation. There is also often poor maintenance of AEDs (e.g., depleted batteries, expiration, or vandalism and theft). An AED locator mobile app was created to explore the feasibility of educating users on the location of AEDs and to motivate users to check the condition of the devices via gamification. An initial pilot study was conducted on the system's usability, effectiveness of gamification methods and users' motivation. Results suggest that gamification is effective in educating users on AED locations and motivates users to assess device condition
Octave Deep Plane-Sweeping Network: Reducing Spatial Redundancy for Learning-Based Plane-Sweeping Stereo

In this paper, we propose the octave deep plane-sweeping network (OctDPSNet). OctDPSNet is a novel learning-based plane-sweeping stereo, which drastically reduces the required GPU memory and computation time while achieving a state-of-the-art depth estimation accuracy. Inspired by octave convolution, we divide image features into high and low spatial frequency features, and two cost volumes are generated from these using our proposed plane-sweeping module. To reduce spatial redundancy, the resolution of the cost volume from the low spatial frequency features is set to half that of the high spatial frequency features, which enables the memory consumption and computational cost to be reduced. After refinement, the two cost volumes are integrated into a final cost volume through our proposed pixel-wise “squeeze-and-excitation” based attention mechanism, and the depth maps are estimated from the final cost volume. We evaluate the proposed model on five datasets: SUN3D, RGB-D SLAM, MVS, Scenes11, and ETH3D. Our model outperforms previous methods on five datasets while drastically reducing the memory consumption and computational cost. Our source code is available at https://github.com/matsuren/octDPSNet.
SeFra

Electronic health information is an efficient technique for providing health care services to society. Patient health information is stored in the cloud, to allow access of eHealth information from anywhere, and at any time, but the technical problems are security, privacy, etc. Sharing the medical data in a trustless environment is overcome by the proposed framework SeFra. The proposed work provides a secure framework to manage the eHealth record by using blockchain (SeFra). For authentication purposes, a temporal shadow is used and the integrity of health records is ensured by blockchain technology.
Running Event Visualization using Videos from Multiple Cameras

Visualizing the trajectory of multiple runners with videos collected at different points in a race could be useful for sports performance analysis. The videos and the trajectories can also aid in athlete health monitoring. While the runners unique ID and their appearance are distinct, the task is not straightforward because the video data does not contain explicit information as to which runners appear in each of the videos. There is no direct supervision of the model in tracking athletes, only filtering steps to remove irrelevant detections. Other factors of concern include occlusion of runners and harsh illumination. To this end, we identify two methods for runner identification at different points of the event, for determining their trajectory. One is scene text detection which recognizes the runners by detecting a unique 'bib number' attached to their clothes and the other is person re-identification which detects the runners based on their appearance. We train our method without ground truth but to evaluate the proposed methods, we create a ground truth database which consists of video and frame interval information where the runners appear. The videos in the dataset was recorded by nine cameras at different locations during the a marathon event. This data is annotated with bib numbers of runners appearing in each video. The bib numbers of runners known to occur in the frame are used to filter irrelevant text and numbers detected. Except for this filtering step, no supervisory signal is used. The experimental evidence shows that the scene text recognition method achieves an F1-score of 74. Combining the two methods, that is - using samples collected by text spotter to train the re-identification model yields a higher F1-score of 85.8. Re-training the person re-identification model with identified inliers yields a slight improvement in performance(F1 score of 87.8). This combination of text recognition and person re-identification can be used in conjunction with video metadata to visualize running events.
Smart Surveillance System for Detection of Suspicious Behaviour Using Machine Learning

A method for detecting suspicious behaviour and activities in live surveillance is presented in the following paper. Recent works in the field of convolutional neural networks have shown considerable progress in the areas of object detection and recognition, especially in images. Labels are required as learning signals in the convolutional neural networks. The proposed method finds difference between two consecutive video frames in order to find the motion history image by eliminating the use of labels. We detect suspicious behaviour in videos and live surveillance using a spatiotemporal architecture. The architecture for prediction of video frames is based on convolutional neural networks. Classification of normal and abnormal behaviour is attained for most of the scenarios. An anomalous batch of frames within the videos is identified and an alarm is generated to alert people about the malicious activities. In this way, they can be prevented on the spot instead of taking action later.
Crowd-based Study of Gameplay Impairments and Player Performance in DOTA 2

Network-induced QoS impairments can directly affect one's satisfaction with a video game. Most studies on this topic have been performed in dedicated lab environments with a limited number of participants, often due to geographic restrictions. In this work, we present a crowdsourcing-based approach to objectively assess the impact of game impairments on the player performance at a large scale outside of a traditional lab environment. We illustrate how game modifications can be used in this context. Our preliminary results --- that delay negatively affects the player performance --- are in line with existing lab results. While the presented method shows promise, we also identify some challenges and limitations, like the limited influence on the number of voluntary participants.
Gaze Data Visualizations for Educational VR Applications

VR displays (HMDs) with embedded eye trackers could enable better teacher-guided VR applications since eye tracking could provide insights into student’s activities and behavior patterns. We present several techniques to visualize eye-gaze data of the students to help a teacher gauge student attention level. A teacher could then better guide students to focus on the object of interest in the VR environment if their attention drifts and they get distracted or confused.
Moving Target Detection and Tracking Based on Improved FCM Algorithm

With the rapid development of computer intelligence technology, the majority of scholars have a great interest in the detection and tracking of moving targets in the field of video surveillance and have been involved in its research. Moving target detection and tracking has also been widely used in military, industrial control, and intelligent transportation. With the rapid progress of the social economy, the supervision of traffic has become more and more complicated. How to detect the vehicles on the road in real time, monitor the illegal vehicles, and control the illegal vehicles effectively has become a hot issue. In view of the complex situation of moving vehicles in various traffic videos, the authors propose an improved algorithm for effective detection and tracking of moving vehicles, namely improved FCM algorithm. It combines traditional FCM algorithm with genetic algorithm and Kalman filter algorithm to track and detect moving targets. Experiments show that this improved clustering algorithm has certain advantages over other clustering algorithms.
Blockchain: Opportunities and Challenges in the Educational Context

A blockchain is a new technology that provides a distributed append-only ledger, which basically it means that it is almost unfeasible to modify or delete data once recorded. Furthermore, blockchains are maintained and governed by multiple nodes, which decide the state of the system following a consensus protocol. In this way, blockchains not only avoid single points of failure, but also create transparent systems that cannot be tampered or manipulated by a reduced group of entities with admin rights (unlike centralized systems). Attracted by these benefits, in the recent years many projects from many different sectors are proposing to use a blockchain to improve existing services or to deploy new business cases that require the consensus, the involvement and/or the cooperation of several actors. This chapter explores prominent proposals related to education and academia. In this context, trends are moving towards: financial applications, administrative efficiency, certification, immutable public registry, reputation systems, and identity systems and privacy. Moreover, this chapter also takes into account the problems associated with blockchains and discusses the main difficulties and challenges that proposals embracing this technology will have to address.
Feature Selection for Analogy-Based Learning to Rank

Learning to rank based on principles of analogical reasoning has recently been proposed as a novel method in the realm of preference learning. Roughly speaking, the method proceeds from a regularity assumption as follows: Given objects A, B, C, D, if A relates to B as C relates to D, and A is preferred to B, then C is presumably preferred to D. This assumption is formalized in terms of so-called analogical proportions, which operate on a feature representation of the objects. Consequently, a suitable feature representation is an important prerequisite for the success of analogy-based learning to rank. In this paper, we therefore address the problem of feature selection and adapt common feature selection techniques, including forward selection, correlation-based filter techniques, as well as Relief-based methods, to the case of analogical learning. The usefulness of these approaches is shown in experiments with synthetic and benchmark data.
An Improved Privacy Protection Method Based on k-degree Anonymity in Social Network

To preserve the privacy of social networks, most existing methods are applied to satisfy different anonymity models, but there are some serious problems such as huge large information losses and great structural modifications of original social network. Therefore, an improved privacy protection method called k-subgraph is proposed, which is based on k-degree anonymous graph derived from k-anonymity to keep the network structure stable. The method firstly divides network nodes into several clusters by label propagation algorithm, and then reconstructs the sub-graph by means of moving edges to achieve k-degree anonymity. Experimental results show that our k-subgraph method can not only effectively improve the defense capability against malicious attacks based on node degrees, but also maintain stability of network structure. In addition, the cost of information losses due to anonymity is minimized ideally.
GraphInception: Convolutional Neural Networks for Collective Classification in Heterogeneous Information Networks

Collective classification has attracted considerable attention in the last decade, where the labels within a group of instances are correlated and should be inferred collectively, instead of independently. Conventional approaches on collective classification mainly focus on exploiting simple relational features (such as count and exists aggregators on neighboring nodes). However, many real-world applications involve complex dependencies among the instances, which are obscure/hidden in the networks. To capture these dependencies in collective classification, we need to go beyond simple relational features and extract deep dependencies between the instances. In this paper, we study the problem of deep collective classification in Heterogeneous Information Networks (HINs), which involve different types of autocorrelations, from simple to complex relations, among the instances. Different from conventional autocorrelations, which are given explicitly by the links in the network, complex autocorrelations are obscure/hidden in HINs, and should be inferred from existing links in a hierarchical order. This problem is highly challenging due to the multiple types of dependencies among the nodes and the complexity of the relational features. In this study, we proposed a deep convolutional collective classification method, called GraphInception, to learn the deep relational features in HINs. And we presented two versions of the models with different inference styles. The proposed methods can automatically generate a hierarchy of relational features with different complexities. Extensive experiments on four real-world networks demonstrate that our approach can improve the collective classification performance by considering deep relational features in HINs.
(“Oops! Had the silly thing in reverse”)—Optical injection attacks in through LED status indicators

It is possible to attack a computer remotely through the front panel LEDs. Following on previous results that showed information leakage at optical wavelengths, now it seems practicable to inject information into a system as well. It is shown to be definitely feasible under realistic conditions (by infosec standards) of target system compromise; experimental results suggest it further may be possible, through a slightly different mechanism, even under high security conditions that put extremely difficult constraints on the attacker. The problem is of recent origin; it could not have occurred before a confluence of unrelated technological developments made it possible. Arduino-type mi-crocontrollers are involved; this is an Internet of Things (IoT) vulnerability. Unlike some previous findings, the vulnerability here is moderate-at present-because it takes the infosec form of a classical covert channel. However, the architecture of several popular families of microcontrollers suggests that a Rowhammer-like directed energy optical attack that requires no malware might be possible. Phase I experiments yielded surprising and encouraging results; a covert channel is definitely practicable without exotic hardware, bandwidth approaching a Mbit/s, and the majority of discrete LEDs tested were found to be reversible on GPIO pins. Phase II experiments, not yet funded, will try to open the door remotely.
Examination of online information search stopping behaviors and stopping rules by task type

ABSTRACT This poster aims to understand how task type could influence when people would stop searching and what stopping rules people would use in the following three stopping behaviors: query stopping, read stopping, and task stopping during online information search. We conducted a pilot user experiment with four participants; each performed two different types of search tasks. The results show two different patterns of stopping behavior on well‐structured and decomposable tasks and poorly structured and indecomposable tasks: sequential mode, and simultaneous mode; and people use different stopping rules in three stopping behaviors.
CalliScan

In this work, a solution for handwriting text extraction from images with visual user assistance is proposed. Use of end-to-end systems that pipe together text detection and recognition is often awkward because the user cannot influence the detection stage. On the other hand, glossing over the word's regions to help system with text localization requires a manual job and can be unacceptable. This paper proposes a solution that gives visual cues to the user during a detection stage. These hints differ from traditional bounding boxes in two ways. Firstly, the found text is surrounded with polygonal bounding reflecting a possible complex nature of text blocks. Secondly, TextRadar scanning effect provides a non-overloaded camera view, helping the user to capture the most relevant part of the text on image on-the-fly. CalliScan works on-device and keeps the user's privacy. The evaluation study has shown that users need such a solution, but it is necessary to carefully handle the text layout complexity.
Applying Self-attention for Stance Classification

Stance classification is the task of automatically identify the user's positions about a specific topic. The classification of stance may help to understand how people react to a piece of target information, a task that is interesting in different areas as advertising campaigns, brand analytics, and fake news detection, among others. The rise of social media has put into the focus of this task the classification of stance in online social networks. A number of methods have been designed for this purpose showing that this problem is hard and challenging. In this work, we explore how to use self-attention models for stance classification. Instead of using attention mechanisms to learn directly from the text we use self-attention to combine different baselines' outputs. For a given post, we use the transformer architecture to encode each baseline output exploiting relationships between baselines and posts. Then, the transformer learns how to combine the outputs of these methods reaching a consistently better classification than the ones provided by the baselines. We conclude that self-attention models are helpful to learn from baselines' outputs in a stance classification task.