Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-modal Knowledge-aware Event Memory Network for Social Media Rumor Detection

The wide dissemination and misleading effects of online rumors on social media have become a critical issue concerning the public and government. Detecting and regulating social media rumors is important for ensuring users receive truthful information and maintaining social harmony. Most of the existing rumor detection methods focus on inferring clues from media content and social context, which largely ignores the rich knowledge information behind the highly condensed text which is useful for rumor verification. Furthermore, existing rumor detection models underperform on unseen events because they tend to capture lots of event-specific features in seen data which cannot be transferred to newly emerged events. In order to address these issues, we propose a novel Multimodal Knowledge-aware Event Memory Network (MKEMN) which utilizes the Multi-modal Knowledge-aware Network (MKN) and Event Memory Network (EMN) as building blocks for social media rumor detection. Specifically, the MKN learns the multi-modal representation of the post on social media and retrieves external knowledge from real-world knowledge graph to complement the semantic representation of short texts of posts and takes conceptual knowledge as additional evidence to improve rumor detection. The EMN extracts event-invariant features of events and stores them into global memory. Given an event representation, the EMN takes it as a query to retrieve the memory network and output the corresponding features shared among events. With the additional information provided by EMN, our model can learn robust representations of events and consistently perform well on the newly emerged events. Extensive experiments on two Twitter benchmark datasets demonstrate that our rumor detection method achieves much better results than state-of-the-art methods.
Lost & Found: Gaze-based Player Guidance Feedback in Exploration Games

Gaze could be harnessed as a powerful tool for guiding players. By knowing where players are looking, a game could provide support players in finding relevant objects. With this assumption in mind, we made our first steps regarding the investigation of gaze-supported player guidance in a 3D first-person exploration game prototype called Lost & Found. Specifically, we investigated the feedback channels that could be combined with a gaze-based guidance approach. A comparative study was carried out to examine the impact of visual (i.e., vignette effect on screen), auditory (i.e., sound cues), and haptic (i.e., controller vibration) feedback on the players' game experience. Results show that visual and audio feedback appeared to be very appealing for players, While haptic feedback received relatively low scores. The next steps involve the development of more elaborated variants of the visual and auditory feedback.
Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup

This paper considers the task of detecting the ball from a single viewpoint in the challenging but common case where the ball interacts frequently with players while being poorly contrasted with respect to the background. We propose a novel approach by formulating the problem as a segmentation task solved by an efficient CNN architecture. To take advantage of the ball dynamics, the network is fed with a pair of consecutive images. Our inference model can run in real time without the delay induced by a temporal analysis. We also show that test-time data augmentation allows for a significant increase the detection accuracy. As an additional contribution, we publicly release the dataset on which this work is based.
The Impact of Brand Positioning and Information on Emotional and Behavioral Participation of Social Media

The present study attempts to transform brand positioning, using the example of nonprofit museums, to understand and explain how well-known museums around the world use social media to engage in global or local community communication, and how they use information to encourage user participation or interaction. The present study explores the dimensions of user participation and information cues for museums in order to highlight information issues in social media strategies. When the positioning of content and users' perceptions are different, this may produce negative emotional and behavioral effects. Design/methodology/approach -The study looks at the social media pages of the British Museum (United Kingdom), Musée du Louvre (France), and the National Palace Museum (Taiwan), three globally renowned museums but with very different positionings. A total of 3,591 posts were crawled between June 1, 2016, and January 31, 2018, in order to examine how these cultural brands create social media relations and understand how differences between global and local positionings may generate different user perceptions. This study is distinct from brand research for other types of business, instead focusing on the social media activities of nonprofit museums and gathering the contents of posts and data on user behavior and emotions. It compares three museums with different user positionings in order to understand how global positioning and local positioning on social media can achieve successful relationship marketing with users. This study applies an easy-to-understand behavioral model to explore the relationship between information, user emotions, and user behaviors.
Take Back Control: Effects of Player Influence on Procedural Level Generation

Many games use procedural content generation (PCG) to create varied game experiences without having to create all content manually. They allow varying degrees of player influence on generation, from retaining all control to giving full control to players over a number of parameters. Despite the prevalence of PCG in commercial games, little research has examined how player influence on PCG parameters affects their experience. We present a preliminary study examining the effect of three degrees of player influence on PCG parameters of game levels, by means of a dungeon crawler game featuring 22 parameters for level design. Participants played the game with varying degrees of control (none, limited, high) over those parameters and reported subjective player experience measures. The results show that degree of influence affects player experience; high control elicits significantly higher autonomy than the other conditions. While future research disentangling agency and challenge is necessary, our preliminary findings suggest that player control over PCG features potentially improves experience by eliciting increased autonomy.
How Does Social Media Marketing Increase Brand Awareness: A Case Study of the Laboratory of YSU Office Administration

This study aimed at developing a laboratory of the office administration using social media marketing and explaining the influence of an Instagram account, as a social media marketing platform, on brand awareness. Conducted from February to July 2018, this quantitative descriptive research collected the data using an online questionnaire created using a Google form. The research object was adpfeuny Instagram account while the research sample was the followers of this account. The sample was selected using quota sampling technique through Slovin's formula. The data analysis aimed to describe the development of adpfeuny Instagram account by following the stages of descriptive analysis. To determine the influence of social media marketing on brand awareness, this study carried out a regression analysis. The findings showed that (1) in developing laboratory through social media, the adpfeuny Instagram account had 576 followers and 57 content posts, (2) social media marketing through adpfeuny Instagram account showed a positive and significant effect on brand awareness of the laboratory of YSU office administration.
Doença do disco intervertebral cervical em cães: 28 casos (2003-2008)

The aim of this study was to evaluate the medical records of dogs with cervical intervertebral disk disease (IDD), submitted to surgical treatment at the Hospital Veterinario Universitario of Universidade Federal de Santa Maria (HVU-UFSM), between January 2003 and October 2008. The animal data included breed, sex, age, neurological signs and their durations, response to surgical treatment, complications, time for functional recovery after surgery, and recurrence. All dogs showed cervical hyperaesthesia confirmed by the neck movement test. Regarding the degree of neurological dysfunction, the following were verified: degree I (eight dogs, 28.5%), degree II (three dogs, 10.7%), degree III (five dogs, 17.8%), and degree V (twelve dogs, 42.8%). In relation to the duration of neurological signs prior to surgery, seven dogs (25%) showed neurological deficits for up to 15 days, fourteen dogs (50%) for 15 to 30 days and the other seven dogs (25%) showed signs of neurological deficiencies for more than 30 days. All dogs (n=21) showed satisfactory recovery and no recurrence. It can be concluded that most affected dogs are middle aged, males and condrodystrophic and noncondrodystrophic breeds including large-breed dogs and the main neurological sign is cervical hyperaesthesia. The surgical treatment promotes satisfactory functional recovery and no recurrence, the main complications intraoperative are venous plexus hemorrhage, bradycardia and hypotension and the duration of clinical signs prior to surgery do not interfere with the recovery time of the dogs.
Fluid Intake and Hydration Status of Forest Workers -- A Preliminary Investigation

Abstract Dehydration and its milder form hypohydration have both short term and long term health effects. In the short term poor, body hydration impairs cognitive performance, physical strength and aerobic power, rendering the worker prone to injury and heat illness. In the long term the potential consequences of hypohydration are kidney stones and bladder cancer. The aim of this study was to evaluate hydration status of forest workers in New Zealand and their preferred fluid replacement. The specific gravity (sg) of urine was used as an indicator of body fluid status. In addition daily fluid loss was compared with a tested algorithm of sweat rate to better understand if workers are hydrating at the desired rate. The results of this preliminary study clearly demonstrate that loggers are working at sub-optimal hydration levels and are consuming inappropriate fluids to replace sweat losses. The hypohydrated state of these workers may pose both an immediate and long term health and safety risk.
Treating Explosive Kids: The Collaborative Problem Solving Approach

This book by Ross Greene and his colleague, Stuart Ablon, is an excellent manual for clinicians dealing with a very common problem in child psychiatry; namely, children with significant oppositional defiant behaviour and explosive outbursts of temper and rage. It is a follow up to Ross Greene’s very successful and popular book written in the late nineties called “The Explosive Child. ” The current diagnosis for these children in DSM IV is that of oppositional defiant disorder. However, as the authors note these children at different times have been called defiant, aggressive, angry, raging, intransigent, resistant, willful, non-compliant, challenging, stubborn and so on. In the first chapter of the book titled “The Need for a Different Paradigm” Ross Greene outlines his philosophy of children and dealing with these challenging children. It is the premise of his book that explosive kids have lagging skills in the global domains of flexibility or adaptability, frustration tolerance and problem solving. He presents a deficit model for understanding and dealing with these challenging children. He sees these children as having deficits in a variety of skills, including executive skills, language processing skills, emotional regulation, cognitive flexibility, social skills and recommends a skills teaching intervention for these children and their families.
Multi-modal Multi-layer Fusion Network with Average Binary Center Loss for Face Anti-spoofing

Face anti-spoofing detection is critical to guarantee the security of biometric face recognition systems. Despite extensive advances in facial anti-spoofing based on single-model image, little work has been devoted to multi-modal anti-spoofing, which is however widely encountered in real-world scenarios. Following the recent progress, this paper mainly focuses on multi-modal face anti-spoofing and aims to solve the following two challenges: (1) how to effectively fuse multi-modal information; and (2) how to effectively learn distinguishable features despite single cross-entropy loss. We propose a novel Multi-modal Multi-layer Fusion Convolutional Neural Network (mmfCNN), which targets at finding a discriminative model for recognizing the subtle differences between live and spoof faces. The mmfCNN can fully use different information provided by diverse modalities, which is based on a weight-adaptation aggregation approach. Specifically, we utilize a multi-layer fusion model to further aggregate the features from different layers, which fuses the low-, mid- and high-level information from different modalities in a unified framework. Moreover, a novel Average Binary Center (ABC) loss is proposed to maximize the dissimilarity between the features of live and spoof faces, which helps to stabilize the training to generate a robust and discriminative model. Extensive experiments conducted on the CISIA-SURF and 3DMAD datasets verify the significance and generalization capability of the proposed method for the face anti-spoofing task. Code is available at: https://github.com/SkyKuang/Face-anti-spoofing.
A Comparative Analysis of Information Provided in the Universities' Website for International Students: Malaysian Public and Private Universities

The higher education industry is an important growth sector for Malaysia and the enrollment of more international students in the universities is essential. As of 2018, there is a higher enrolment of international students in Malaysia private universities than that seen in the Malaysia public universities. This present study will investigate how both the Malaysian public and private universities provide the immigration policies information through their website especially those related to socioeconomics topics which are deemed essential by the international students when deciding on a destination country for further studies. Ten universities in Malaysia were selected for this study; five public universities and five private universities in Malaysia with a student population range of 6,000 to 21,000. It was determined that both the Malaysian public and private universities websites provided similar immigration policies information with relations to the limitations on immigration, dependents, scholarship and higher tuition rates for international students. However, private university websites provide much more information on possible employment opportunities in Malaysia to prospective international students as compared to the websites of public universities studied in this research. The findings are in line with the key stand of this study which is the university website information relating to socioeconomic issues in the host country could influence the international students' decision on the choice location for further study. From the practitioner perspective, this study provides university management and leadership a better understanding of the on the importance of the university website as a platform to reach potential international students.
Blending Science and Practice: A Collaborative Approach for Evaluating the Value of Heart Rate Measurement

We report on our collaboration between the user research team of Ubisoft Blue Byte (UBB) Düsseldorf and the Entertainment Computing Research Group (ECG) of the University of Duisburg-Essen. Based on a shared interest in exploring the use of biometrics for evaluating the player experience, we opted for heart rate (HR) monitoring in order to gain initial experience with general requirements for psychophysiological methods. We conducted two subsequent studies: the first at the university to evaluate the method in general, the second at UBB to gain insights into the particular requirements of playtests in practice. This two-stage process was chosen to reduce resource requirements on the company's side and to refine the procedure before testing it in the field. Experiences with guidelines to use biometrics are shared and discussed regarding opportunities for collaboration between academics and practitioners.
Farmland Recognition of High Resolution Multispectral Remote Sensing Imagery using Deep Learning Semantic Segmentation Method

Farmland mapping is an important step for estimating grain yields. However extraction of farmland from multispectral remote sensing images (RSIs) is still a challenging work, as farmland is located on not only plains but also mountains, which displays divergent and confusing characteristics in RSIs. To solve the problem of lacking the multispectral remote sensing image dataset for pretraining, we extend Deep Feature Aggregation Net (DFANet) with fewer network parameters, a semantic segmentation network, to automatically map farmland from 3-band to multispectral images in a pixel-wise strategy. In this network, we first utilize more information aggregation. The fully-connected attention module is then replaced by a proposed convolution attention module. Finally, a new proposed decoder is used to recover the details of the feature map. Experimental results show that the model with multispectral RSIs outperforms the baselines.
Teaching Undergraduate Nursing Courses via Videoconference: All that Glitters is not Gold

While videoconferencing is noted for facilitating access to education, it is also a technology that requires considerable management while classes delivered by videoconferencing demand extra pedagogical planning and support. In the following case study, the role of videoconferencing in delivering nursing research courses co-taught by two School of Nursing faculty at X University and taken by undergraduate nursing students at Y College is described. Resume Alors que la visioconference est reconnue pour faciliter l’acces a l’education, il s’agit aussi d’une technologie qui requiert une gestion considerable puisque les cours dispenses par visioconference exigent une planification et un soutien pedagogiques supplementaires. Dans l’etude de cas suivante, on decrit le role de la visioconference dans la diffusion de cours de recherche en sciences infirmieres enseignees conjointement par deux professeurs de l’Ecole de sciences infirmieres d’une universite et suivis par des etudiants en sciences infirmieres de premier cycle dans un college.
TC-Net for iSBIR: Triplet Classification Network for Instance-level Sketch Based Image Retrieval

Sketch has been employed as an effective communication tool to express the abstract and intuitive meaning of object. While content-based sketch recognition has been studied for several decades, the instance-level Sketch Based Image Retrieval (iSBIR) task has attracted significant research attention recently. In many previous iSBIR works -- TripletSN, and DSSA, edge maps were employed as intermediate representations in bridging the cross-domain discrepancy between photos and sketches. However, it is nontrivial to efficiently train and effectively use the edge maps in an iSBIR system. Particularly, we find that such an edge map based iSBIR system has several major limitations. First, the system has to be pre-trained on a significant amount of edge maps, either from large-scale sketch datasets, e.g., TU-Berlin~\citeeitz2012hdhso, or converted from other large-scale image datasets, e.g., ImageNet-1K\citedeng2009imagenet dataset. Second, the performance of such an iSBIR system is very sensitive to the quality of edge maps. Third and empirically, the multi-cropping strategy is essentially very important in improving the performance of previous iSBIR systems. To address these limitations, this paper advocates an end-to-end iSBIR system without using the edge maps. Specifically, we present a Triplet Classification Network (TC-Net) for iSBIR which is composed of two major components: triplet Siamese network, and auxiliary classification loss. Our TC-Net can break the limitations existed in previous works. Extensive experiments on several datasets validate the efficacy of the proposed network and system.
An Attentional-LSTM for Improved Classification of Brain Activities Evoked by Images

Multimedia stimulation of brain activities is not only becoming an emerging area for intensive research, but also achieved significant progresses towards classification of brain activities and interpretation of brain understanding of multimedia content. To exploit the characteristics of EEG signals in capturing human brain activities, we propose a region-dependent and attention-driven bi-directional LSTM network (RA-BiLSTM) for image evoked brain activity classification. Inspired by the hemispheric lateralization of human brains, the proposed RA-BiLSTM extracts additional information at regional level to strengthen and emphasize the differences between two hemispheres. In addition, we propose a new attentional-LSTM by adding an extra attention gate to: (i) measure and seize the importance of channel-based spatial information, and (ii) support the proposed RA-BiLSTM to capture the dynamic correlations hidden from both the past and the future in the current state across EEG sequences. Extensive experiments are carried out and the results demonstrate that our proposed RA-BiLSTM not only achieves effective classification of brain activities on evoked image categories, but also significantly outperforms the existing state of the arts.
SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation

Point cloud analysis has drawn broader attentions due to its increasing demands in various fields. Despite the impressive performance has been achieved on several databases, researchers neglect the fact that the orientation of those point cloud data is aligned. Varying the orientation of point cloud may lead to the degradation of performance, restricting the capacity of generalizing to real applications where the prior of orientation is often unknown. In this paper, we propose the point projection feature, which is invariant to the rotation of the input point cloud. A novel architecture is designed to mine features of different levels. We adopt a PointNet-based backbone to extract global feature for point cloud, and the graph aggregation operation to perceive local shape structure. Besides, we introduce an efficient key point descriptor to assign each point with different response and help recognize the overall geometry. Mathematical analyses and experimental results demonstrate that the proposed method can extract strictly rotation-invariant representations for point cloud recognition and segmentation without data augmentation, and outperforms other state-of-the-art methods.
Video-based Analysis of Soccer Matches

With the increasingly detailed investigation of game play and tactics in invasive team sports such as soccer, it becomes ever more important to present causes, actions and findings in a meaningful manner. Visualizations, especially when augmenting relevant information directly inside a video recording of a match, can significantly improve and simplify soccer match preparation and tactic planning. However, while many visualization techniques for soccer have been developed in recent years, few have been directly applied to the video-based analysis of soccer matches. This paper provides a comprehensive overview and categorization of the methods developed for the video-based visual analysis of soccer matches. While identifying the advantages and disadvantages of the individual approaches, we identify and discuss open research questions, soon enabling analysts to develop winning strategies more efficiently, do rapid failure analysis or identify weaknesses in opposing teams.