Reviewerly/exHarmony huggingface dataset:
SmartLens

As the most important organs of sense, human eyes perceive 80% information from our surroundings. Eyeball movement is closely related to our brain health condition. Eyeball movement and eye blink are also widely used as an efficient human-computer interaction scheme for paralyzed individuals to communicate with others. Traditional methods mainly use intrusive EOG sensors or cameras to capture eye activity information. In this work, we propose a system named SmartLens to achieve eye activity sensing using zero-power contact lens. To make it happen, we develop dedicated antenna design which can be fitted in an extremely small space and still work efficiently to reach a working distance more than 1 m. To accurately track eye movements in the presence of strong self-interference, we employ another tag to track the user's head movement and cancel it out to support sensing a walking or moving user. Comprehensive experiments demonstrate the effectiveness of the proposed system. At a distance of 1.4 m, the proposed system can achieve an average accuracy of detecting the basic eye movement and blink at 89.63% and 82%, respectively.
Uncovering insecure designs of cellular emergency services (911)

Cellular networks that offer ubiquitous connectivity have been the major medium for delivering emergency services. In the U.S., mobile users can dial an emergency call with 911 for emergency uses in cellular networks, and the call can be forwarded to public safety answer points (PSAPs), which deal with emergency service requests. According to regulatory authority requirements for the cellular emergency services, anonymous user equipment (UE), which does not have a SIM (Subscriber Identity Module) card or a valid mobile subscription, is allowed to access them. Such support of emergency services for anonymous UEs requires different operations from conventional cellular services, and can therefore increase the attack surface of the cellular infrastructure. In this work, we are thus motivated to study the insecurity of the cellular emergency services and then discover four security vulnerabilities from them. Threateningly, they can be exploited to launch not only free data service attacks against cellular carriers, but also data DoS/overcharge and denial of cellular emergency service (DoCES) attacks against mobile users. All vulnerabilities and attacks have been validated experimentally as practical security issues in the networks of three major U.S. carriers. We finally propose and prototype standard-compliant remedies to mitigate the vulnerabilities.
The evolution of the Nigerian prince scam

Purpose The purpose of this paper is to critically examine the evolution of a particular variant of cyber fraud known as the Nigerian prince scam and to demonstrate its socio-cultural impact. These scams are also referred to as yahoo scams. This paper traces the history of the Nigerian prince scam and how the scheme has evolved over the years to become more sophisticated, audacious and damaging to its victims. Design/methodology/approach The analytic approach is conceptual, drawing on institutional anomie theory, social learning theory and routine activity theory to explain this unique typology of cybercrime. Data were collected and analysed from books, journal articles, newspaper articles and other electronic Web sources. Findings The ineffective political leadership of Nigeria’s present elected and past military leaders could be attributed to creating culturally induced pressures to secure monetary success by the scammers. A significant number of people involved in this malfeasance justify these crimes with the argument of being owed reparations for colonisation, which might explain why international and local agencies may not be effective at eradicating this crime problem. Originality/value Despite the continued perpetration of the Nigerian prince scam, current research has not presented an evolutionary view of this fraudulent operation. This paper adds to the literature by explaining the origins of the scam, how they have developed over time and how internet technology has advanced the sophistication of the scam.
Deep Multi-Input Multi-Stream Ordinal Model for age estimation: Based on spatial attention learning

Face aging process is non-stationary since human matures in different ways. This property makes age estimation is an attractive and challenging research topic in the computer vision community. Most of previous work conventionally estimate age from the center area of the aligned face image. However, these methods ignore spatial context information and cannot pay attention to particular domain features due to the uncertainty in deep learning. In this work, we propose a novel Deep Multi-Input Multi-Stream Ordinal (D2MO) Model for facial age estimation, which learns deep fusion feature through a specific spatial attention mechanism. Our approach is motivated by the observations that there are some universal changes, like hair color turning to white and wrinkles increasing, for individuals during aging process. In order to focus these spatial features, our D2MO uses four scales of receptive fields for global and contextual feature learning, and meanwhile, four cropped face patches are utilized for local and detailed feature extraction. Benefiting from a multi-stream CNN architecture, differentiated feature maps are learned separately through each branch and then aggregated together by concatenate layer. We also introduce a novel representation for age label using a multi-hot vector and the final predicted age can be calculated by summing the vector. This representation cast age estimation task to solve a series of binary classification subproblems which is easier to learn and more consistent with human cognition rather than to regress a single age value directly. Finally, we employ a joint training loss to supervise our model to learn the ordinal ranking, label distribution and regression information simultaneously. Extensive experiments show that our D2MO model significantly outperforms other state-of-the-art age estimation methods on MORPH II, FG-NET and UAGD datasets.
Dual-path Network for Liver and Tumor Segmentation in CT Images Using Swin Transformer Encoding Approach

Liver and tumor segmentation from CT images is a complex and crucial step in achieving full-course adaptive radiotherapy and also plays an essential role in computer-aided clinical diagnosis systems. Deep learning-based methods play an important role in achieving automatic segmentation.This research aims to improve liver tumor detection performance by proposing a dual path feature extracting strategy and employing Swin-Transformer.The hierarchical Swin-Transformer is embedded into the encoder and decoder and combined with CNN to form a dual coding path structure incorporating long-range dependencies and multi-scale contextual connections to capture coarse-tuned features at different semantic scales. The features of the two encoding paths and the upsampling path are fused, tested and validated with LITS and in-house datasets.The proposed method has a DG of 97.95% and a DC of 96.2% for liver segmentation; a DG of 80.6% and a DC of 68.1% for tumor segmentation; and a classification study of the tumor dataset shows a DG of 91.1% and a DC of 87.2% for large and continuous tumors and a DG of 71.7% and a DC of 66.4% for small and scattered tumors.Swin-Transformer can be used as a robust encoder for medical image segmentation networks and, combined with CNN networks, can better recover local spatial information and enhance feature representation. Accurate localization before segmentation can achieve better results for small and scattered tumors.
A Decentralized Public Auditing Scheme for Secure Cloud Storage Based on Blockchain

The cloud storage service has brought great convenience to the customer, which can save massive storage and computation resources via outsourcing the data to cloud service provider (CSP). However, the security issues are the biggest challenge such as data integrity. The user can verify the integrity of outsourced data through a remote data auditing solution without retrieving original data from cloud, however, the auditing procedure has heavy computational overhead, which employs third party auditor (TPA) to conduct auditing task on behalf of users. In this paper, we propose a decentralized public auditing scheme for cloud storage based on blockchain, which removes TPA and increases the number of CSP, the auditing task was assigned to multiple CSPs, and the blockchain technology was used to record the audit process. Meanwhile, the structure of e-voting system is utilized to realize the audit result statistics of multiple CSPs via smart contract, which enhanced the credibility and stability of final auditing result. The theoretical analysis and experimental results demonstrate that proposed scheme is secure and efficient.
It's Still Rock and Roll to Me: A Model of Online Browsing Behaviour

ABSTRACT Browsing has long been acknowledged as a critical information seeking strategy. Previous research on information browsing—browsing for books, videos or other items for which decisions have to be made based on an information surrogate—has focused on browsing in a physical context, and much of it predates technology found online today. We lack empirical data from the contemporary digital context to describe how people browse online. This study adopted scenario‐based interviews and observations to investigate people's online information browsing behaviour. Based on a qualitative analysis of the data, we proposed an online browsing journey model formed by four iterative activities: Choose the Browsing Collection, Select Candidates from the Browsing Collection, Inspect the Selected Candidates, Generate and Edit the Candidate list . This model offers novel insight into how people browse in the existing online context, which also forms the basis for further online browsing research.
Diagnosis of Breast Cancer Using Computational Intelligence Models and IoT Applications

The use of computer-aided diagnostic (CAD) models has been proposed to aid in the detection and classification of breast cancer. In this work, we evaluated the performance of multilayer perceptron neural network and nonlinear support vector machine models to classify breast cancer nodules. From the contour of 569 samples, ten morphological features were used as input to the classifiers. The average results obtained in the set of 50 simulations performed show that the proposed models showed good performance (all exceeded 90.0%) in terms of accuracy in the test set. The nonlinear support vector machine algorithm stands out when compared to the proposed multilayer perceptron neural network algorithm, with 99% accuracy and a 2% false-negative rate. The neural network model presented lower performance than the nonlinear support vector machine classifier. With the application of the proposed models, the average results obtained are promising in the classification of breast cancer.
Maintaining Repositories, Databases, and Digital Collections in Memory Institutions: An Integrative Review

ABSTRACT Database maintenance and migration are critical but under‐supported activities in libraries, archives, museums (LAMs), and other scholarly spaces. Existing guidelines for digital curation rarely account for the maintenance needed to keep digital curation infrastructures functioning over time. Though many case studies have been published describing individual instances of migration, there has been little generalizable research done in this area. Thus, it is challenging to understand overall trends or best practices in this space. We bridge this gap by conducting an integrative literature review of papers describing database migrations and maintenance in LAMs and other scholarly contexts. By qualitatively coding 75 articles from 58 publication venues, we identify common motivations for database migrations and maintenance actions. We find that databases are migrated to support changing user needs as well as to ward off technological obsolescence; we also find that common challenges include schema crosswalking and a need for data cleaning. Practitioners describe community collaboration as key in surmounting these challenges. Through this integrative review, we build a base for further best practices development and identify a need to better model database curation as part of the digital curation lifecycle.
A Comparison of Source Distribution and Result Overlap in Web Search Engines

ABSTRACT When it comes to search engines, users generally prefer Google. Our study aims to find the differences between the results found in Google compared to other search engines. We compared the top 10 results from Google, Bing, DuckDuckGo, and Metager, using 3,537 queries generated from Google Trends from Germany and the US. Google displays more unique domains in the top results than its competitors. Wikipedia and news websites are the most popular sources overall. With some top sources dominating search results, the distribution of domains is also consistent across all search engines. The overlap between Google and Bing is always under 32%, while Metager has a higher overlap with Bing than DuckDuckGo, going up to 78%. This study shows that the use of another search engine, especially in addition to Google, provides a wider variety in sources and might lead the user to find new perspectives.
Unsupervised Hierarchical Graph Pooling via Substructure-Sensitive Mutual Information Maximization

Graph pooling plays a vital role in learning graph embeddings. Due to the lack of label information, unsupervised graph pooling has received much attention, primarily via mutual information (MI). However, most existing MI-based pooling methods only preserve node features while overlooking the hierarchical substructural information. In this paper, we propose SMIP, a novel unsupervised hierarchical graph pooling method based on substructure-sensitive MI maximization. SMIP reconstructs a hard-style substructure encoder based on cluster-based pooling paradigm, and trains it with two substructure-sensitive MI-based objectives, i.e., node-substructure MI and node-node MI. The node-substructure MI guides to transfer maximum node feature information into corresponded substructures and the node-node MI guarantees a more accurate node allocation. Moreover, to avoid extra computation of augmented graphs and prevent noise information during MI estimation, we propose a local-scope contrastive MI estimation method, making SMIP more potent in capturing intrinsic features of the input graph. Experiments on six benchmark graph classification datasets demonstrate that our hierarchical deep learning approach outperforms all state-of-the-art unsupervised GNN-based methods and even surpasses the performance of nine supervised ones. Generalization study shows that the proposed substructure-sensitive MI objective can be successfully embedded into other cluster-based pooling methods to improve their performance.
Learning Chinese Word Embeddings By Discovering Inherent Semantic Relevance in Sub-characters

Learning Chinese word embeddings is important in many tasks of Chinese language information processing, such as entity linking, entity extraction, and knowledge graph. A Chinese word consists of Chinese characters, which can be decomposed into sub-characters (radical, component, stroke, etc). Similar to roots in English words, sub-characters also indicate the origins and basic semantics of Chinese characters. So, many researches follow the approaches designed for learning embeddings of English words to improve Chinese word embeddings. However, some Chinese characters sharing the same sub-characters have different meanings. Furthermore, with more cultural interaction and the popularization of the Internet and web, many neologisms, such as transliterated loanwords and network terms, are emerging, which are only close to the pronunciation of their characters, but far from their semantics. Here, a tripartite weighted graph is proposed to model the semantic relationship among words, characters, and sub-characters, in which the semantic relationship is evaluated according to the Chinese linguistic information. So, the semantic relevance hidden in lower components (sub-characters, characters) can be used to further distinguish the semantics of corresponding higher components (characters, words). Then, the tripartite weighted graph is fed into our Chinese word embedding modelinsideCC to reveal the semantic relationship among different language components, and learn the embeddings of words. Extensive experimental results on multiple corpora and datasets verify that our proposed methods outperform the state-of-the-art counterparts by a significant margin.
Tutorial on Deep Learning Interpretation

Deep learning models have achieved exceptional predictive performance in a wide variety of tasks, ranging from computer vision, natural language processing, to graph mining. Many businesses and organizations across diverse domains are now building large-scale applications based on deep learning. However, there are growing concerns, regarding the fairness, security, and trustworthiness of these models, largely due to the opaque nature of their decision processes. Recently, there has been an increasing interest in explainable deep learning that aims to reduce the opacity of a model by explaining its behavior, its predictions, or both, thus building trust between human and complex deep learning models. A collection of explanation methods have been proposed in recent years that address the problem of low explainability and opaqueness of models. In this tutorial, we introduce recent explanation methods from a data perspective, targeting models that process image data, text data, and graph data, respectively. We will compare their strengths and limitations, and offer real-world applications.
Multi-granularity Fatigue in Recommendation

Personalized recommendation aims to provide appropriate items according to user preferences mainly from their behaviors. Excessive homogeneous user behaviors on similar items will lead to fatigue, which may decrease user activeness and degrade user experience. However, existing models seldom consider user fatigue in recommender systems. In this work, we propose a novel multi-granularity fatigue, modeling user fatigue from coarse to fine. Specifically, we focus on the recommendation feed scenario, where the underexplored global session fatigue and coarse-grained taxonomy fatigue have large impacts. We conduct extensive analyses to demonstrate the characteristics and influence of different types of fatigues in real-world recommender systems. In experiments, we verify the effectiveness of multi-granularity fatigue in both offline and online evaluations. Currently, the fatigue-enhanced model has also been deployed on a widely-used recommendation system of WeChat.
Latent Coreset Sampling based Data-Free Continual Learning

Catastrophic forgetting poses a major challenge in continual learning where the old knowledge is forgotten when the model is updated on new tasks. Existing solutions tend to solve this challenge through generative models or exemplar-replay strategies. However, such methods may not alleviate the issue that the low-quality samples are generated or selected for the replay, which would directly reduce the effectiveness of the model, especially in the class imbalance, noise, or redundancy scenarios. Accordingly, how to select a suitable coreset during continual learning becomes significant in such setting. In this work, we propose a novel approach that leverages continual coreset sampling (CCS) to address these challenges. We aim to select the most representative subsets during each iteration. When the model is trained on new tasks, it closely approximates/matches the gradient of both the previous and current tasks with respect to the model parameters. This way, adaptation of the model to new datasets could be more efficient. Furthermore, different from the old data storage for maintaining the old knowledge, our approach choose to preserving them in the latent space. We augment the previous classes in the embedding space as the pseudo sample vectors from the old encoder output, strengthened by the joint training with selected new data. It could avoid data privacy invasions in a real-world application when we update the model. Our experiments validate the effectiveness of our proposed approach over various CV/NLP datasets under against current baselines, and we also indicate the obvious improvement of model adaptation and forgetting reduction in a data-free manner.
ITSM-GCN

Recently, graph convolutional network (GCN) has become one of the most popular and state-of-the-art collaborative filtering (CF) methods. Existing GCN-based CF studies have made many meaningful and excellent efforts at loss function design and embedding propagation improvement. Despite their successes, we argue that existing methods have not yet properly explored more effective sampling strategy, including both positive sampling and negative sampling. To tackle this limitation, a novel framework named ITSM-GCN is proposed to carry out our designed Informative Training Sample Mining (ITSM) sampling strategy for the learning of GCN-based CF models. Specifically, we first adopt and improve the dynamic negative sampling (DNS) strategy, which achieves considerable improvements in both training efficiency and recommendation performance. More importantly, we design two potentially positive training sample mining strategies, namely a similarity-based sampler and score-based sampler, to further enhance GCN-based CF. Extensive experiments show that ITSM-GCN significantly outperforms state-of-the-art GCN-based CF models, including LightGCN, SGL-ED and SimpleX. For example, ITSM-GCN improves on SimpleX by 12.0%, 3.0%, and 1.2% on [email protected] for Amazon-Books, Yelp2018 and Gowalla, respectively.
GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction

Short video has witnessed rapid growth in the past few years in e-commerce platforms like Taobao. To ensure the freshness of the content, platforms need to release a large number of new videos every day, making conventional click-through rate (CTR) prediction methods suffer from the item cold-start problem. In this paper, we propose GIFT, an efficient Graph-guIded Feature Transfer system, to fully take advantages of the rich information of warmed-up videos to compensate for the cold-start ones. Specifically, we establish a heterogeneous graph that contains physical and semantic linkages to guide the feature transfer process from warmed-up video to cold-start videos.Specifically, we establish a heterogeneous graph that contains physical and semantic linkages to guide the feature transfer process. The physical linkages consist of the explicit relationships (e.g., produced by the same author, or showcasing the same product etc.), and the semantic linkages measure the proximity of multi-modal representations of two videos. We elaborately design the feature transfer function to make aware of different parts of transferred features (e.g., id representations and historical statistics) from different types of nodes and edges along the metapath on the graph. We conduct extensive experiments on a large real-world dataset, and the results show that our GIFT system outperforms SOTA methods significantly and brings a 6.82% lift on CTR in the homepage of Taobao App.