Reviewerly/exHarmony huggingface dataset:
AGMR-Net: Attention-guided multiscale recovery framework for stroke segmentation

Automatic and accurate lesion segmentation is critical to the clinical estimation of the lesion status of stroke diseases and appropriate diagnostic systems. Although existing methods have achieved remarkable results, their further adoption is hindered by: (1) intraclass inconsistency, i.e., large variability between different areas of the lesion; and (2) interclass indistinction, in which normal brain tissue resembles the lesion in appearance. To meet these challenges in stroke segmentation, we propose a novel method, namely attention-guided multiscale recovery framework (AGMR-Net) in this paper. Firstly, a coarse-grained patch attention (CPA) module in the encoding is adopted to obtain a patch-based coarse-grained attention map in a multistage, explicitly supervised way, enabling target spatial context saliency representation with a patch-based weighting technique that eliminates the effect of intraclass inconsistency. Secondly, to obtain more detailed boundary partitioning to meet the challenge of interclass indistinction, a newly designed cross-dimensional feature fusion (CFF) module is used to capture global contextual information to further guide the selective aggregation of 2D and 3D features, which can compensate for the lack of boundary learning capability of 2D convolution. Lastly, in the decoding stage, an innovative designed multiscale deconvolution upsampling (MDU) is used for enhanced recovery of target spatial and boundary information. AGMR-Net is evaluated on the open-source dataset Anatomical Tracings of Lesions After Stroke, achieving the highest Dice similarity coefficient of 0.594, Hausdorff distance of 27.005 mm, and average symmetry surface distance of 7.137 mm, which demonstrates that our proposed method outperforms state-of-the-art methods and has great potential for stroke diagnosis.
Can I Borrow Your ATM? Using Virtual Reality for (Simulated) In Situ Authentication Research

In situ evaluations of novel authentication systems, where the system is evaluated in its intended usage context, are often infeasible due to ethical and legal constraints. Consequently, researchers evaluate their authentication systems in the lab, which questions the eco-logical validity. In this work, we explore how VR can overcome the shortcomings of authentication studies conducted in the lab and contribute towards more realistic authentication research. We built a highly realistic automated teller machine (ATM) and a VR replica to investigate through a user study (N=20) the impact of in situ evaluations on an authentication system&#x2018;s usability results. We evaluated and compared: Lab studies in the real world, lab studies in VR, in situ studies in the real world, and in situ studies in VR. Our findings highlight 1) VR&#x2018;s great potential to circumvent potential restrictions researchers experience when evaluating authentication schemes and 2) the impact of the context on an authentication system&#x2018;s usability evaluation results. In situ ATM authentications took longer (+24.71% in the real world, +14.17% in VR) than authentications in a traditional (VR) lab environment and elicited a higher sense of being part of an ATM authentication scenario compared to a real-world and VR-based evaluation in the lab. Our quantitative findings, along with participants&#x2018; qualitative feedback, provide first evidence of increased authentication realism when using VR for in situ authentication research. We provide researchers with a novel research approach to conduct (simulated) in situ authentication re-search, discuss our findings in the light of prior works, and conclude with three key lessons to support researchers in deciding when to use VR for in situ authentication research.
Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting

Channel charting is an unsupervised learning method that aims at mapping wireless channels to a so-called chart, preserving as much as possible spatial neighborhoods. In this paper, a model-based deep learning approach to this problem is proposed. It builds on a physically motivated distance measure to structure and initialize a neural network that is subsequently trained using a triplet loss function. The proposed structure exhibits a low number of parameters and clever initialization leads to fast training. These two features make the proposed approach amenable to on-the-fly channel charting. The method is empirically assessed on realistic synthetic channels, yielding encouraging results.
Impact of digital marketing on SMEs performance in Saudi Arabia: Implications on building NEOM

Digital marketing has replaced traditional marketing approaches due to an increase in the access to internet and communication technologies across the globe. In addition, it has created an opportunity for SMEs with limited market and funding to promote their brand across different regions, supporting balanced regional development. In this context, Saudi Arabia has been promoting the growth of SMEs through various initiatives as a part of its vision 2030 programme. Accordingly, NEOM city, a multi-billion project was launched to support businesses, innovators, entrepreneurs, and researchers by proposing the city as a hub for international entrepreneurs. However, it is unclear how the local SMEs and their approaches toward digital marketing would be impacted, and how the city can support them. This paper analyses and evaluates the impact of digital marketing on SMEs performance in Saudi Arabia, and its implications for developing NEOM city. An online survey questionnaire instrument was used for collecting data from 358 entrepreneurs and business owners categorised under SMEs. Low-cost marketing mix with techniques such as email marketing, social media marketing are preferred by SMEs. Cultural, economic, and technological are the major factors identified influencing the adoption of digital marketing by SMEs. Digital marketing reflected more positive impact on strategic performance (customers and employees’ satisfaction) than financial performance (profitability and sustainability) of Saudi Arabian SMEs. Saudi Arabian SMEs reflect their readiness and preparedness to reap the benefits from the Saudization programme, with a few minor issues related to socio-cultural, technological, and labour challenges.
AI-Generated Characters: Putting Deepfakes to Good Use

AI generated characters, i.e., realistic renderings of human faces, voices, and mannerisms that appear authentic to a human being [7] are made possible through advancements in generative machine learning. In addition to character creation, neural networks have recently also been used for the hyper-realistic synthesis and modification of prose, images, audio, and video data.
Mobile Robot Path Planning Method Based on Deep Reinforcement Learning Algorithm

Path planning is an important part of the research field of mobile robots, and it is the premise for mobile robots to complete complex tasks. This paper proposes a reflective reward design method based on potential energy function, and combines the ideas of multi-agent and multi-task learning to form a new training framework. The reflective reward represents the quality of the agent’s current decision relative to the past historical decision sequence, using the second-order information of the historical reward sequence. The policy or value function update of the master agent is then assisted by the reflective agent. The method proposed in this paper can easily extend the existing deep reinforcement learning algorithm based on value function and policy gradient, and then form a new learning method, so that the agent has the reflective characteristics in human learning after making full use of the reward information. It is good at distinguishing the optimal action in the corresponding state. Experiments in pathfinding scenarios verify the effectiveness of the algorithm in sparse reward scenarios. Compared with other algorithms, the deep reinforcement learning algorithm has higher exploration success rate and stability. Experiments in survival scenarios verify the improvement effect of the reward feature enhancement method based on the auxiliary task learning mechanism on the original algorithm. Simulation experiments confirm the effectiveness of the proposed algorithm for solving the path planning problem of mobile robots in dynamic environments and the superiority of deep reinforcement learning algorithms. The simulation results show that the algorithm can accurately avoid unknown obstacles and reach the target point, and the planned path is the shortest and the energy consumed by the robot is the least. This demonstrates the effectiveness of deep reinforcement learning algorithms for local path planning and real-time decision making.
The Future of Emotion in Human-Computer Interaction

Emotion has been studied in HCI for two decades, with specific traditions interested in sensing, expressing, transmitting, modelling, experiencing, visualizing, understanding, constructing, regulating, manipulating or adapting to emotion in human-human and human-computer interactions. This CHI 2022 workshop on the Future of Emotion in Human-Computer Interaction brings together interested researchers to take stock of research on emotion in HCI to-date and to explore possible futures. Through group discussion and collaborative speculation we will address questions such as: What are the relationships between digital technology and human emotion? What roles does emotion play in HCI research? How should HCI researchers conceptualize emotion? When should HCI researchers use interdisciplinary theories of emotion or create new theory? Can specific emotions be designed for, and where is this knowledge likely to be applied? What are the implications of emotion research for design, ethics and wellbeing? What is the future of emotion in human-computer interaction?
Antecedents of QR code acceptance during Covid-19: Towards sustainability

This is an exploratory study of Quick Response (QR) code usage in India in Bangalore city during the Covid-19. QR is a ubiquitous tool extensively used in many developed countries to attract customers through sustainability marketing. The significant penetration among smartphone users in developing countries like India has increased the usage of QR codes for various services like digital information sharing, digital payments, digital shopping, digital coupons, etc. The digital payment method is one of the top 17 agendas of United Nations sustainable development goals. This research paper aims to identify the critical elements of behavioural intention towards QR code acceptance (BIQRA) among Indian smartphone users. This research uses survey data from 432 smartphone users from Bangalore city during COVID-19. This research paper focuses on the knowledge gap of sustainability marketing using QR codes and helps beginners understand how PLS-SEM uses sustainability marketing research. The research finding found increased QR-code usage among the customers during the Covid-19 in Bangalore city.
A Survey on Machine Learning Algorithms for Detecting Fake Instagram Accounts

With the tremendous increase in internet usage since its inception, cybercrime and online scamming has become extremely abundant. This surge in online scams is only helped by the presence of social media, which serves as one of the most popular platforms for the scammers to target their victims. Social media platforms such as Instagram, Facebook, Whatsapp, Twitter, have become the primary hub for scamsters, and these platforms facilitate their dangerous activities. The fraudsters carry out such scams using fake accounts which help them hide their true identity. To prevent such scams, we need a tool that helps us differentiate between fake and legitimate profiles. The machine learning models like Artificial neural network, AdaBoost, Multi-Layer Perceptron, Random Forest and Stochastic Gradient Descent (SGD) were implemented and their performance was compared in this paper to differentiate between the fake and legitimate profiles. We compare the results of these algorithms using various parameters and find Random Forest to be the most efficient.
Neural Graph Matching for Pre-training Graph Neural Networks

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Neural Graph Matching for Pre-training Graph Neural NetworksYupeng Hou, Binbin Hu, Wayne Xin Zhao, Zhiqiang Zhang, Jun Zhou, and Ji-Rong WenYupeng Hou, Binbin Hu, Wayne Xin Zhao, Zhiqiang Zhang, Jun Zhou, and Ji-Rong Wenpp.172 - 180Chapter DOI:https://doi.org/10.1137/1.9781611977172.20PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Recently, graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. However, when adapted to downstream tasks, it usually requires abundant task-specific labeled data, which can be extremely scarce in practice. A promising solution to data scarcity is to pre-train a transferable and expressive GNN model on large amounts of unlabeled graphs or coarse-grained labeled graphs. Then the pre-trained GNN is fine-tuned on downstream datasets with task-specific fine-grained labels. In this paper, we present a novel Graph Matching based GNN Pre-Training framework, called GMPT. Focusing on a pair of graphs, we propose to learn structural correspondences between them via neural graph matching, consisting of both intra-graph message passing and inter-graph message passing. In this way, we can learn adaptive representations for a given graph when paired with different graphs, and both node- and graph-level characteristics are naturally considered in a single pre-training task. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training. We further propose an approximate contrastive training strategy to significantly reduce time/memory consumption. Extensive experiments on multi-domain, out-of-distribution benchmarks have demonstrated the effectiveness of our approach. The code is available at: https://github.com/RUCAIBox/GMPT. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
Database Search Results Disambiguation for Task-Oriented Dialog Systems

As task-oriented dialog systems are becoming increasingly popular in our lives, more realistic tasks have been proposed and explored.However, new practical challenges arise.For instance, current dialog systems cannot effectively handle multiple search results when querying a database, due to the lack of such scenarios in existing public datasets.In this paper, we propose Database Search Result (DSR) Disambiguation, a novel task that focuses on disambiguating database search results, which enhances user experience by allowing them to choose from multiple options instead of just one.To study this task, we augment the popular task-oriented dialog datasets (MultiWOZ and SGD) with turns that resolve ambiguities by (a) synthetically generating turns through a pre-defined grammar, and (b) collecting human paraphrases for a subset.We find that training on our augmented dialog data improves the model's ability to deal with ambiguous scenarios, without sacrificing performance on unmodified turns.Furthermore, pre-fine tuning and multi-task learning help our model to improve performance on DSR-disambiguation even in the absence of in-domain data, suggesting that it can be learned as a universal dialog skill.Our data and code will be made publicly available.
Intelligent Psychology Teaching System Based on Adaptive Neural Network

In order to study the intelligent psychology system, this paper proposes the role of adaptive neural network based on it and uses the ICAP learning method to compare with it. Firstly, the basic structure of the neural network in the teaching system is introduced, the psychological teaching algorithm based on the adaptive neural network is introduced, the ideas are formulated, and the four learning methods and the design elements of the adaptive neural network are described. The corresponding relationship between the four learning methods and the adaptive neural network is explained. The most popular and advanced adaptive neural network module usage statistics are made. The network model on the right is more advanced than the left, and the classification accuracy is higher. The interactive learning elements used by the network model from left to right gradually increase, and the performances of the network model are gradually enhanced. Among them, the number of interactive learning elements inception modules used by the network models GoogLeNet, Inception-v2, Inception-v4, and Inception-ResNet-v2 are 9, 10, 14, and 20, respectively. Inception-v4 also employs 2 interactive learning element reduction modules. Inception-ResNet-v2 uses 2 interactive learning element reduction modules and 20 residual modules. The ICAP classification method is experimentally studied. The design of the experiment adopts passive method (P), active method (A), constructive method (C), and interactive method (I), respectively, to learn a short text in materials science. By analyzing the learning effect and comparing the data before and after the test, it can be concluded that the learning performance of the four learning methods gradually increased by 8%-10%, and the learning effect increased significantly. With the gradual increase of educational psychological learning elements in the adaptive neural network, the network learning level is continuously improved, and the classification accuracy is gradually improved.
Three numerical approaches to find mutually unbiased bases using Bell inequalities

Mutually unbiased bases correspond to highly useful pairs of measurements in quantum information theory. In the smallest composite dimension, six, it is known that between three and seven mutually unbiased bases exist, with a decades-old conjecture, known as Zauner's conjecture, stating that there exist at most three. Here we tackle Zauner's conjecture numerically through the construction of Bell inequalities for every pair of integers $n,d \ge 2$ that can be maximally violated in dimension $d$ if and only if $n$ MUBs exist in that dimension. Hence we turn Zauner's conjecture into an optimisation problem, which we address by means of three numerical methods: see-saw optimisation, non-linear semidefinite programming and Monte Carlo techniques. All three methods correctly identify the known cases in low dimensions and all suggest that there do not exist four mutually unbiased bases in dimension six, with all finding the same bases that numerically optimise the corresponding Bell inequality. Moreover, these numerical optimisers appear to coincide with the "four most distant bases" in dimension six, found through numerically optimising a distance measure in [P. Raynal, X. L\"u, B.-G. Englert, Phys. Rev. A, 83 062303 (2011)]. Finally, the Monte Carlo results suggest that at most three MUBs exist in dimension ten.
Pandemics, Polycentricity and Public Perceptions: Lessons from the Djokovic Saga.

Since the early 1960s the analytical lens of polycentricity has provided an opportunity to understand complex systems and how they intersect in a variety of environments. With a contemporary origin in governance, regulation and political science scholarship, polycentricity analysis has focused on overlap and conflict in systems and the potential for reduction in effectiveness of service provision. This paper reviews contemporary thinking on polycentricity, including in the context of the COVID-19 pandemic. It instances the unfortunate events leading to the failed application for judicial review in the Full Court of the Federal Court of Australia by the tennis player, Novak Djokovic (Djokovic v Minister for Immigration, Citizenship, Migrant Services and Multicultural Affairs [2022] FCAFC 3) as an example of the efforts that need to be made in the midst of a public health crisis in the interests of community trust in government to ensure that there is co-ordination and consistency of messaging and decision-making.
Block-Skim: Efficient Question Answering for Transformer

Transformer models have achieved promising results on natural language processing (NLP) tasks including extractive question answering (QA). Common Transformer encoders used in NLP tasks process the hidden states of all input tokens in the context paragraph throughout all layers. However, different from other tasks such as sequence classification, answering the raised question does not necessarily need all the tokens in the context paragraph. Following this motivation, we propose Block-skim, which learns to skim unnecessary context in higher hidden layers to improve and accelerate the Transformer performance. The key idea of Block-Skim is to identify the context that must be further processed and those that could be safely discarded early on during inference. Critically, we find that such information could be sufficiently derived from the self-attention weights inside the Transformer model. We further prune the hidden states corresponding to the unnecessary positions early in lower layers, achieving significant inference-time speedup. To our surprise, we observe that models pruned in this way outperform their full-size counterparts. Block-Skim improves QA models' accuracy on different datasets and achieves 3 times speedup on BERT-base model.
ARcall: Real-Time AR Communication using Smartphones and Smartglasses

Augmented Reality (AR) smartglasses are increasingly regarded as the next generation personal computing platform. However, there is a lack of understanding about how to design communication systems using them. We present ARcall, a novel Augmented Reality-based real-time communication system that enables an immersive, delightful, and privacy-preserving experience between a smartphone user and a smartglasses wearer. ARcall allows a remote friend (Friend) to send and project AR content to a smartglasses wearer (Wearer). The ARcall system was designed with the practical limits of existing AR glasses in mind, including shorter battery life and a reduced field of view. We conduct a qualitative evaluation of the three main components of ARcall: Drop-In, ARaction, and Micro-Chat. Our results provide novel insights for building future AR-based communication methods, including, the importance of context priming, user control over AR content placement, and the feeling of co-presence while conversing.