Reviewerly/exHarmony huggingface dataset:
Meta-Learning Based Hyper-Relation Feature Modeling for Out-of-Knowledge-Base Embedding

Knowledge graph (KG) embedding aims to encode both entities and relations into a continuous vector space. Most existing methods require that all entities should be observed during training while ignoring the evolving nature of KG. Major recent efforts on this issue embed new entities by aggregating neighborhood information from existing entities and relations with Graph Neural Network (GNN). However, these methods rely on the neighbors seen during training and suffer from the embedding of new entities with insufficient triplets or triplets with the unseen-to-unseen form. To relieve this problem, we propose a two-stage learning model referred as Hyper-Relation Feature Learning Network (HRFN) for effective out-of-knowledge-base embedding. For the first stage, HRFN learns pre-representations for emerging entities using hyper-relation features meta-learned from the training set. A novel feature aggregating network that involves an entity-centered Graph Convolutional Network (GCN) and a relation-centered GCN is proposed to aggregate information from both new entities themselves and their neighbors. For stage two, a transductive learning network is employed to learn finer-grained embeddings based on above-mentioned pre-representations of new entities. Experimental results on the link prediction task demonstrate the superiority of our model. Further analysis is also done to validate the effectiveness and efficiency of pre-representing emerging entities with the hyper-relation feature.
Manifold-based aggregation clustering for unsupervised vehicle re-identification

Most vehicle re-identification (V-reID) approaches are based on supervised learning methods which require a considerable amount of tedious and impractical annotations. In this paper, we propose a novel unsupervised V-reID approach based on Manifold-based Aggregation Clustering (MAC) with the unknown number of clusters. The proposed MAC is implemented by alternatively conducting two modules, i.e., deep feature learning module and aggregation clustering module. Specifically, deep feature learning module is responsible for training a convolutional neural network to encourage deep features to be close to the centroids of corresponding clusters which are yielded by an aggregation clustering mechanism based on manifold distance in the feature space. Moreover, the classification-agglomeration loss and manifold-based seeds searching criterion are proposed to improve the discriminative power of the learned features and deal with the problem of varied visual appearance respectively. Note that both annotations and even the certain number of vehicle identities are unknown for the proposed method, which is totally consistent with the real-world unsupervised V-reID condition. Extensive experiments on VehicleID and Veri-776 benchmark datasets show that the proposed method outperforms the state-of-the-art unsupervised V-reID approaches.
Don’t Block the Ground: Reducing Discomfort in Virtual Reality with an Asymmetric Field-of-View Restrictor

Although virtual reality has been gaining in popularity, users continue to report discomfort during and after use of VR applications, and many experience symptoms associated with motion sickness. To mitigate this problem, dynamic field-of-view restriction is a common technique that has been widely implemented in commercial VR games. Although artificially reducing the field-of-view during movement can improve comfort, the standard restrictor is typically implemented using a symmetric circular mask that blocks imagery in the periphery of the visual field. This reduces users' visibility of the virtual environment and can negatively impact their subjective experience. In this paper, we propose and evaluate a novel asymmetric field-of-view restrictor that maintains visibility of the ground plane during movement. We conducted a remote user study that sampled from the population of VR headset owners. The experiment used a within-subjects design that compared the ground-visible restrictor, the traditional symmetric restrictor, and a control condition without FOV restriction. Participation required navigating through a complex maze-like environment using a controller during three separate virtual reality sessions conducted at least 24 hours apart. Results showed that ground-visible FOV restriction offers benefits for user comfort, postural stability, and subjective sense of presence. Additionally, we found no evidence of negative drawbacks to maintaining visibility of the ground plane during FOV restriction, suggesting that the proposed technique is superior for experienced users compared to the widely used symmetric restrictor.
Practical Witness-Key-Agreement for Blockchain-Based Dark Pools Financial Trading

We introduce a new cryptographic scheme, Witness Key Agreement (WKA), that allows a party to securely agree on a secret key with a counter party holding publicly committed information only if the counter party also owns a secret witness in a desired (arithmetic) relation with the committed information. Our motivating applications are over-the-counter (OTC) markets and dark pools, popular trading mechanisms. In such pools investors wish to communicate only to trading partners whose transaction conditions and asset holdings satisfy some constraints. The investor must establish a secure, authenticated channel with eligible traders where the latter committed information matches a desired relation. At the same time traders should be able to show eligibility while keeping their financial information secret. We construct a WKA scheme for languages of statements proven in the designated-verifier Succinct Zero-Knowledge Non-Interactive Argument of Knowledge Proof System (zk-SNARK). We illustrate the practical feasibility of our construction with some arithmetic circuits of practical interest by using data from US$ denominated corporate securities traded on Bloomberg Tradebook.
A Multimodal Deep Framework for Derogatory Social Media Post Identification of a Recognized Person

In today’s era of digitization, social media platforms play a significant role in networking and influencing the perception of the general population. Social network sites have recently been used to carry out harmful attacks against individuals, including political and theological figures, intellectuals, sports and movie stars, and other prominent dignitaries, which may or may not be intentional. However, the exchange of such information across the general population inevitably contributes to social-economic, socio-political turmoil, and even physical violence in society. By classifying the derogatory content of a social media post, this research work helps to eradicate and discourage the upsetting propagation of such hate campaigns. Social networking posts today often include the picture of Memes along with textual remarks and comments, which throw new challenges and opportunities to the research community while identifying the attacks. This article proposes a multimodal deep learning framework by utilizing ensembles of computer vision and natural language processing techniques to train an encapsulated transformer network for handling the classification problem. The proposed framework utilizes the fine-tuned state-of-the-art deep learning-based models (e.g., BERT, Electra) for multilingual text analysis along with face recognition and the optical character recognition model for Meme picture comprehension. For the study, a new Facebook meme-post dataset is created with recorded baseline results. The subject of the created dataset and context of the work is more geared toward multilingual Indian society. The findings demonstrate the efficacy of the proposed method in the identification of social media meme posts featuring derogatory content about a famous/recognized individual.
A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN

Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the optimal structure and hyperparameters for Convolutional Recurrent Neural Networks (CRNNs). In addition, we investigate two types of encoding techniques used to translate the output of optimization to a CRNN recognizer. Finally, we proposed a novel hybridized SSA with Late Acceptance Hill-Climbing (LAHC) to improve the exploitation process. We conducted our experiments on two well-known datasets, IAM and IFN/ENIT, which include both the Arabic and English languages. The experimental results have shown that LAHC significantly improves the SSA search process. Therefore, the proposed DC-CRNN outperforms the handcrafted CRNN methods.
BioFace-3D

Over the last decade, facial landmark tracking and 3D reconstruction have gained considerable attention due to their numerous applications such as human-computer interactions, facial expression analysis, and emotion recognition, etc. Traditional approaches require users to be confined to a particular location and face a camera under constrained recording conditions (e.g., without occlusions and under good lighting conditions). This highly restricted setting prevents them from being deployed in many application scenarios involving human motions. In this paper, we propose the first single-earpiece lightweight biosensing system, BioFace-3D, that can unobtrusively, continuously, and reliably sense the entire facial movements, track 2D facial landmarks, and further render 3D facial animations. Our single-earpiece biosensing system takes advantage of the cross-modal transfer learning model to transfer the knowledge embodied in a high-grade visual facial landmark detection model to the low-grade biosignal domain. After training, our BioFace-3D can directly perform continuous 3D facial reconstruction from the biosignals, without any visual input. Without requiring a camera positioned in front of the user, this paradigm shift from visual sensing to biosensing would introduce new opportunities in many emerging mobile and IoT applications. Extensive experiments involving 16 participants under various settings demonstrate that BioFace-3D can accurately track 53 major facial landmarks with only 1.85 mm average error and 3.38% normalized mean error, which is comparable with most state-of-the-art camera-based solutions. The rendered 3D facial animations, which are in consistency with the real human facial movements, also validate the system's capability in continuous 3D facial reconstruction.
Digital Peter: New Dataset, Competition and Handwriting Recognition Methods

This paper presents a new dataset of Peter the Great’s manuscripts and describes a segmentation procedure that converts initial images of documents into lines. This new dataset may be useful for researchers to train handwriting text recognition models as a benchmark when comparing different models. It consists of 9694 images and text files corresponding to different lines in historical documents. The open machine learning competition ”Digital Peter” was held based on the considered dataset. The baseline solution for this competition and advanced methods on handwritten text recognition are described in the article. The full dataset and all codes are publicly available.
Popularity, novelty and relevance in point of interest recommendation: an experimental analysis

Abstract Recommender Systems (RSs) are often assessed in off-line settings by measuring the system precision in predicting the observed user’s ratings or choices. But, when a precise RS is on-line, the generated recommendations can be perceived as marginally useful because lacking novelty. The underlying problem is that it is hard to build an RS that can correctly generalise, from the analysis of user’s observed behaviour, and can identify the essential characteristics of novel and yet relevant recommendations. In this paper we address the above mentioned issue by considering four RSs that try to excel on different target criteria: precision, relevance and novelty. Two state of the art RSs called and follow a classical Nearest Neighbour approach, while the other two, and are based on Inverse Reinforcement Learning. and optimise precision, tries to identify the characteristics of POIs that make them relevant, and , a novel RS here introduced, is similar to but it also tries to recommend popular POIs. In an off-line experiment we discover that the recommendations produced by and optimise precision essentially by recommending quite popular POIs. can be tuned to achieve a desired level of precision at the cost of losing part of the best capability of to generate novel and yet relevant recommendations. In the on-line study we discover that the recommendations of and are liked more than those produced by . The rationale of that was found in the large percentage of novel recommendations produced by , which are difficult to appreciate. However, excels in recommending items that are both novel and liked by the users.
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification

Abstract Real-world classification domains, such as medicine, health and safety, and finance, often exhibit imbalanced class priors and have asynchronous misclassification costs. In such cases, the classification model must achieve a high recall without significantly impacting precision. Resampling the training data is the standard approach to improving classification performance on imbalanced binary data. However, the state-of-the-art methods ignore the local joint distribution of the data or correct it as a post-processing step. This can causes sub-optimal shifts in the training distribution, particularly when the target data distribution is complex. In this paper, we propose Radial-Based Combined Cleaning and Resampling (RB-CCR). RB-CCR utilizes the concept of class potential to refine the energy-based resampling approach of CCR. In particular, RB-CCR exploits the class potential to accurately locate sub-regions of the data-space for synthetic oversampling. The category sub-region for oversampling can be specified as an input parameter to meet domain-specific needs or be automatically selected via cross-validation. Our $$5\times 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>5</mml:mn> <mml:mo>×</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> cross-validated results on 57 benchmark binary datasets with 9 classifiers show that RB-CCR achieves a better precision-recall trade-off than CCR and generally out-performs the state-of-the-art resampling methods in terms of AUC and G-mean.
Sentiment analysis from email pattern using feature selection algorithm

Abstract Today number of applications are available on mobile devices and computers for electronic mail (email) conversations. The demand for email communication is increasing day‐by‐day. Therefore the incoming and outgoing messages are also getting increased. However, extracting the sentiments from the emails is now demanding. Therefore in the proposed method, the pattern classification and sentiment clustering are carried out in two phases. Initially, the pattern classification is performed using support vector regression, then the sentiments from such classified patterns are clustered using a unsupervised fuzzy‐model‐based Gaussian clustering algorithm. Finally, the experimental analysis is performed in Python tool. The proposed sentiment clustering from email patterns has attained a better accuracy result of 97.13%, which is found higher than other existing techniques. Along with the parametric analysis, non‐parametric statistical analysis using the Wilcoxon rank‐sum test is also carried out to identify the proposed sentiment analysis architecture's effectiveness.
Introducing a new approach of geochemical anomaly intensity index (GAII) for increasing the probability of exploration of shear zone gold mineralization

Increasing the prediction rate in the identification of mineralization zones using the stream sediment geochemical data is an essential issue in the regional exploration stage. The various univariate (such as fractal and probability plot (PP) methods) and multivariate methods (such as principal component analysis (PCA)) have been performed for interpreting the geochemical data and detecting the mineralization areas. In this study, a new geochemical criterion named geochemical anomaly intensity index (GAII) was proposed for geochemical anomaly mapping. This approach was developed based on the PCA method and the catchment basin coefficient (CBC). The GAII as a weighted geochemical index is calculated using the mineralization principal component (MPC) scores and CBC. GAII can be mapped and utilized for geochemical anomaly mapping and detecting the mineralization areas. Besides, GAII can identify paragenesis elements better than the current methods. In this research, GAII was successfully used to generate geochemical anomaly maps on shear zone gold mineralization in the southwest of Saqqez, NW Iran. The geochemical data have been divided into three groups based on catchment basins and the host rock type. Then the MPCs and paragenesis elements of Au mineralization have been obtained individually using PCA. Three mineralization paragenesis groups consisting of (Au, Sn), (Au, W), and (Au, As, Sb and Ba) have been recognized for different catchment basins of the southwest of Saqqez district using PCA. GAII was calculated and mapped based on the CBC(Au, Sn), CBC(Au, W), CBC(Au, As, Sb, Ba), and their MPC scores. GAII accurately detected the Au mineralization zones and improved the geochemical anomaly map in this area compared to the PP method, concentration-area fractal model, and U-spatial statistics method. The results demonstrated that GAII was successfully used for (a) identifying the mineralization paragenesis elements, (b) intensifying the geochemical anomaly, and (c) increasing the prediction rate of mineralization zones. The shear zone gold mineralization areas in the southwest of Saqqez district were effectively detected using this new data analysis approach. GAII has provided better results than the current PP method, concentration-area fractal model, and U-spatial statistics method.
Image transformer for explainable autonomous driving system

In the last decade, deep learning (DL) approaches have been used successfully in computer vision (CV) applications. However, DL-based CV models are generally considered to be black boxes due to their lack of interpretability. This black box behavior has exacerbated user distrust and therefore has prevented widespread deployment DLCV models in autonomous driving tasks even though some of these models exhibit superiority over human performance. For this reason, it is essential to develop explainable DL models for autonomous driving task. Explainable DL models are able to not only boost user trust in autonomy but also serve as a diagnostic approach to identify the defects and weaknesses of the model during the system development phase. In this paper, we propose such an explainable end-to-end autonomous driving system using “Transformer,” a state-of-the-art (SOTA) self-attention based model, to map visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations. The results demonstrate the efficacy of our proposed model as it outperforms the benchmark model by a significant margin in terms of actions and explanations prediction with lower computational cost.
In-air hand gesture signature using transfer learning and its forgery attack

In-air hand gesture signature (HGS) has become a new and emerging technique for dynamic signature recognition due to its advantageous touchless acquisition procedure. Unlike the conventional dynamic signature, HGS creates general images that carrying the spatial and temporal information of the signing action. Deep learning algorithms are prominent to learn these image features. However, they require tremendous amount of data to reach an optimal model which make the collection process computationally expensive. Transfer learning becomes an alternative solution for small sample size problems. This paper aims to investigate the feasibility of transfer learning in classifying a hand gesture-based signature. In our system, the hand region is detected and segmented from each depth image. Then, the salient spatial and temporal features are formed from various images. The knowledge of a pre-trained model is transferred and reused to classify the new seen image features. In this paper, we further investigate the robustness of the proposed approach against two common forgery attacks, (1) random forgeries and (2) skilled forgeries. Empirical results demonstrate that the proposed approach can achieves 99.03% of precision and 98.89% of recall in classifying HGS. On top of this, the proposed approach also manifests its robustness in handling different kinds of forgery attacks, i.e. achieving low error rates of 0.78% in random forgery attack and 4.88% in skilled forgery attack.
Mining Skyline Frequent-Utility Itemsets with Utility Filtering

Skyline frequent-utility itemsets (SFUIs) can provide more actionable information for decision-making with both frequency and utility considered. In this paper, the problem of mining SFUIs by filtering utilities from different perspectives is studied. First, filtering by frequency is considered. The max utility array (MUA) structure is designed, which is proved to have a size no larger than the size of arrays in state-of-the-art algorithms. Using the MUA, the utility-list is verified to prune unpromising itemsets and their extensions. Second, filtering using transaction-weighted utilization is applied. The minimum utility of SFUIs is proposed and the proof that this concept can be used as a pruning strategy in the early stage of search space traversal is provided. Finally, filtering using utility itself is also considered. The minimum utility of extension is presented, and its use as a pruning strategy during the extension stage of search space traversal is validated. Based on these filtering methods, a novel algorithm called SFUIs mining based on utility filtering (SFUI-UF) is proposed. Extensive experimental results show that the SFUI-UF algorithm can discover all correct SFUIs with high efficiency and low memory usage.