Reviewerly/exHarmony huggingface dataset:
User Context Ontology for Adaptive Mobile-Phone Interfaces

The Adaptive User Interface (AUI) adapts to the changes in the context of use and provides improved interaction abilities for different users. The adaptivity in the user interfaces requires in depth knowledge of context. There is a need to enrich user profiles to achieve the personalized services with the ability to adapt the user's context. The context can be reflected in a particular kind of knowledge and hence modeled as ontology. Ontology based context models are effective means to handle complex situations that support the sharing or integration of context information. This paper presents ontology based context model using OWL for adaptive mobile devices. It models the context over its four major elements including device, user, environment (location and time) and activity. The proposed ontology was derived in different classes, relationships, associations, dependencies and constraints to model dynamic context. Ontologies present a standardized, consistent and shareable context model. The context model and consequent context snapshots can be acknowledged by AUI to present a suitable user interface. The ontology was developed using Protégé on the basis of each context type having different values. Semantic querying (SPARQL) was used for knowledge acquisition. Moreover, the Pellet and HermiT Reasoner were used to verify the rules, relations and constraints to avoid the inconsistency between classes. Comparative to other context models for adaptive interfaces, ontological model provides more of scalability and growth with learning new context in to the shared context knowledge.
Statistical Learning in Word Reading and Spelling across Languages and Writing Systems

Learning to read and spell involves learning about the written forms of words and how these are linked to language. Writing systems include formal patterns, which pertain to the appearance of written words, and functional patterns, which pertain to links between units of writing and units of language. We review the evidence that learners of a variety of writing systems extract some of these patterns through statistical learning. Statistical learning is often slow and incomplete, however, and direct instruction improves performance. Many studies have focused on statistical-learning skill as an ability of individual learners. We discuss some problems with this approach, arguing that it is more productive to attend to the statistical patterns that writing systems embody. Studies of these patterns and their learning can help explain similarities and differences in learning to read and spell across writing systems.
Automatic modeling and fault diagnosis of car production lines based on first-principle qualitative mechanics and semantic web technology

Fault diagnosis is critical for intelligent manufacturing by monitoring the status of a production line and preventing financial loss. Model-based fault diagnosis has the advantage of being able to explain the cause and propagation of faults over model-free diagnosis, but would need knowledge about the configuration model and context-specific information of the production line. Ontology modelling can provide context-specific information on top of a configuration model to benefit fault diagnosis. Typically ontologies are manually constructed and then used by a reasoner based on a set of predefined rules. From the perspective of fault diagnosis, this approach works as an expert system where both the ontology models and predefined rules are specific to a given system. Once the system has changed which happens from time to time as repairs and updates in a production line, or in the case of a different system, the ontology models and predefined rules would need to be manually modified or reconstructed. Here a model-based method is proposed to automate generation of configuration models with context-specific information using semantic web technology when a production line is healthy, and to use the generated configuration model and information for diagnosis when the production line has a fault. The method does not rely on predefined rules and reasoners, but rather uses dynamics models that are based on first-principle qualitative mechanics. It uses numerical optimization to minimize the discrepancy between sensor data from the production line and from simulation running the dynamics model to achieve automatic configuration modelling and fault diagnosis. With three use cases commonly found for a production line, i.e. automatic sensor placement modeling or misplacement diagnosis, motor fault diagnosis with single sensor modality, and motor fault diagnosis with sensory substitution, the feasibility of the proposed method is demonstrated. The method's faster computational speed and comparable accuracy to a quantitative model-based approach suggests it may complement and accelerate the latter with early-stage selection of candidate models for both modelling and fault diagnosis.
XAI Evaluation: Evaluating Black-Box Model Explanations for Prediction

The results of evaluating explanations of the black-box model for prediction are presented. The XAI evaluation is realized through the different principles and characteristics between black-box model explanations and XAI labels. In the field of high-dimensional prediction, the black-box model represented by neural network and ensemble models can predict complex data sets more accurately than traditional linear regression and white-box models such as the decision tree model. However, an unexplainable characteristic not only hinders developers from debugging but also causes users mistrust. In the XAI field dedicated to “opening” the black box model, effective evaluation methods are still being developed. Within the established XAI evaluation framework (MDMC) in this paper, explanation methods for the prediction can be effectively tested, and the identified explanation method with relatively higher quality can improve the accuracy, transparency, and reliability of prediction.
An Overview of Blockchain Technology: Applications, Challenges and Future Trends

Starting from the basic concept of blockchain technology, the paper introduces the architecture of blockchain technology and the typical application of blockchain technology, and finally summarizes the challenges faced by blockchain technology, so as to provide some reference for the technological path innovation and data value reconstruction of deep integration of blockchain technology and various fields.
Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels

Deep neural networks (DNNs) based digital receivers can potentially operate in complex environments. How-ever, the dynamic nature of communication channels implies that in some scenarios, DNN-based receivers should be periodically retrained in order to track temporal variations in the channel conditions. To this aim, frequent transmissions of lengthy pilot sequences are generally required, at the cost of substantial overhead. In this work we propose a DNN-aided symbol detector, Meta-ViterbiNet, that tracks channel variations with reduced overhead by integrating three complementary techniques: 1) We leverage domain knowledge to implement a model-based/data-driven equalizer, ViterbiNet, that operates with a relatively small number of trainable parameters; 2) We tailor a meta-learning procedure to the symbol detection problem, optimizing the hyperparameters of the learning algorithm to facilitate rapid online adaptation; and 3) We enable online training with short-length pilot blocks and coded data blocks. Numerical results demonstrate that Meta-ViterbiNet operates accurately in rapidly-varying channels, outperforming the previous best approach, based on ViterbiNet or conventional recurrent neural networks without meta-learning, by a margin of up to 0.6dB in bit error rate in various challenging scenarios.
A clustering‐based anonymization approach for privacy‐preserving in the healthcare cloud

Abstract Today, modern healthcare systems rely on advanced computational technologies, including cloud‐based systems, to gather and examine personal health data on a large scale. The use of advanced cloud services technologies, such as software as a service, application as a service, is challenging for end‐users of cloud systems to protect sensitive data in their health applications. According to the importance of publishing data in the cloud, the information should be recorded and handled somehow so that any individuals' identity remains hidden. Therefore, one of the critical privacy challenges is protecting the quality of published data and privacy‐preserving on the healthcare cloud simultaneously. The K‐anonymity technology is one of the prevalent methods used for privacy‐preserving. In this article, we suggest a novel approach based on the clustering process using the K‐means++ method to achieve an optimal k‐anonymity algorithm. Also, we use the normal distribution function to delete data that is less frequent, which can be improved the quality of anonymized data. Extensive experiments show the proposed method has been able to reduce information loss 1.5 times and execution time 3.5 times compared to AKA and GCCG algorithms. Also, it is highly scalable than others.
SP-SMOTE: A novel space partitioning based synthetic minority oversampling technique

Traditional machine learning algorithms are always trapped by the class-imbalance problem due to they are biased to the majority class. As one of the most efficient techniques to solve the class-imbalance problem, oversampling technique has attracted many researchers’ attention. An obvious observation of an imbalanced dataset is that there is a clear density difference between minority class and majority class. In view of this, we propose a new density-adaptive space partition method called Dannoy. It can distinguish minority class from dataset easily. After that, a novel space partitioning based synthetic minority oversampling technique named SP-SMOTE is also proposed in this paper to deal with the class imbalance problem. Experiments on four synthetic and fifteen real-world datasets are performed and the results on all real-world datasets demonstrate that the average performances (Accuracy, F1-measure, G-mean and AUC) of SP-SMOTE is superior to the other existing popular algorithms SMOTE, ADASYN, K-means SMOTE, Borderline-SMOTE (1 & 2), polynom_fit_SMOTE and ProWSyn.
Towards End-to-End Text Spotting in Natural Scenes

Text spotting in natural scene images is of great importance for many image understanding tasks. It includes two sub-tasks: text detection and recognition. In this work, we propose a unified network that simultaneously localizes and recognizes text with a single forward pass, avoiding intermediate processes such as image cropping and feature re-calculation, word separation, and character grouping. The overall framework is trained end-to-end and is able to spot text of arbitrary shapes. The convolutional features are calculated only once and shared by both the detection and recognition modules. Through multi-task training, the learned features become more discriminative and improve the overall performance. By employing a 2D attention model in word recognition, the issue of text irregularity is robustly addressed. The attention model provides the spatial location for each character, which not only helps local feature extraction in word recognition, but also indicates an orientation angle to refine text localization. Experiments demonstrate that our proposed method can achieve state-of-the-art performance on several commonly used text spotting benchmarks, including both regular and irregular datasets. Extensive ablation experiments are performed to verify the effectiveness of each module design.
Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks

Authorship classification is a method of automatically determining the appropriate author of an unknown linguistic text.Although research on authorship classification has significantly progressed in highresource languages, it is at a primitive stage in the realm of resource-constraint languages like Bengali.This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing.For this purpose, this work develops a new embedding corpus (named WEC) and a Bengali authorship classification corpus (called BACC-18), which are more robust in terms of authors' classes and unique words.Using three text embedding techniques (Word2Vec, GloVe and FastText) and combinations of different hyperparameters, 90 embedding models are created in this study.All the embedding models are assessed by intrinsic evaluators and those selected are the 9 best performing models out of 90 for the authorship classification.In total 36 classification models, including four classification models (CNN, LSTM, SVM, SGD) and three embedding techniques with 100, 200 and 250 embedding dimensions, are trained with optimized hyperparameters and tested on three benchmark datasets (BACC-18, BAAD16 and LD).Among the models, the optimized CNN with GloVe model achieved the highest classification accuracies of 93.45%, 95.02%, and 98.67% for the datasets BACC-18, BAAD16, and LD, respectively.
Semi-quantum private comparison protocol of size relation with d-dimensional GHZ states*

A novel efficient semi-quantum private comparison protocol based on the d-dimensional GHZ states is proposed. With the assistance of semi-honest third party, two classical participants can compare the size relation of their secretswithout any information leakage. To reduce the consumption of quantum devices, the qubit efficiency of our protocol isimproved by introducing the semi-quantum conception via the existing semi-quantum privatecomparisons. Furthermore,it is unnecessary to prepare the secure classical authentication channel among participants in advance. It is shown that our protocol is not only correct and efficient, but also free from external and internal attacks.
Consensus-Based Labeled Multi-Bernoulli Filter for Multitarget Tracking in Distributed Sensor Network

This article introduces a novel consensus-based labeled multi-Bernoulli (LMB) filter to tackle multitarget tracking (MTT) in a distributed sensor network (DSN), whose sensor nodes have limited and different fields of view (FoVs). Although consensus-based algorithms are effective for distributed fusion and MTT, it may be problematic when distributed sensor nodes have different FoVs. To deal with this issue, the proposed method constructs an extended label space mapping to overcome the "label space mismatching" phenomenon; after that, the model of the undetected multitargets is established so that the tracks can be initialized outside the FoV of local sensors; finally and most important, weight selection and evolution mechanism are proposed such that the fusion weights are automatically tuned for each track at each time step and consensus step. The efficiency and robustness of the proposed algorithm are demonstrated in a distributed MTT scenario via numerical simulations.
Detection and Evaluation of Machine Learning Bias

Machine learning models are built using training data, which is collected from human experience and is prone to bias. Humans demonstrate a cognitive bias in their thinking and behavior, which is ultimately reflected in the collected data. From Amazon’s hiring system, which was built using ten years of human hiring experience, to a judicial system that was trained using human judging practices, these systems all include some element of bias. The best machine learning models are said to mimic humans’ cognitive ability, and thus such models are also inclined towards bias. However, detecting and evaluating bias is a very important step for better explainable models. In this work, we aim to explain bias in learning models in relation to humans’ cognitive bias and propose a wrapper technique to detect and evaluate bias in machine learning models using an openly accessible dataset from UCI Machine Learning Repository. In the deployed dataset, the potentially biased attributes (PBAs) are gender and race. This study introduces the concept of alternation functions to swap the values of PBAs, and evaluates the impact on prediction using KL divergence. Results demonstrate females and Asians to be associated with low wages, placing some open research questions for the research community to ponder over.
Certainty driven consistency loss on multi-teacher networks for semi-supervised learning

One of the successful approaches in semi-supervised learning is based on the consistency regularization. Typically, a student model is trained to be consistent with teacher prediction for the inputs under different perturbations. To be successful, the prediction targets given by teacher should have good quality, otherwise the student can be misled by teacher. Unfortunately, existing methods do not assess the quality of the teacher targets. In this paper, we propose a novel Certainty-driven Consistency Loss (CCL) that exploits the predictive uncertainty in the consistency loss to let the student dynamically learn from reliable targets. Specifically, we propose two approaches, i.e. Filtering CCL and Temperature CCL to either filter out uncertain predictions or pay less attention on them in the consistency regularization. We further introduce a novel decoupled framework to encourage model difference. Experimental results on SVHN, CIFAR-10, and CIFAR-100 demonstrate the advantages of our method over a few existing methods.
Efficient white-box fairness testing through gradient search

Deep learning (DL) systems are increasingly deployed for autonomous decision-making in a wide range of applications. Apart from the robustness and safety, fairness is also an important property that a well-designed DL system should have. To evaluate and improve individual fairness of a model, systematic test case generation for identifying individual discriminatory instances in the input space is essential. In this paper, we propose a framework EIDIG for efficiently discovering individual fairness violation. Our technique combines a global generation phase for rapidly generating a set of diverse discriminatory seeds with a local generation phase for generating as many individual discriminatory instances as possible around these seeds under the guidance of the gradient of the model output. In each phase, prior information at successive iterations is fully exploited to accelerate convergence of iterative optimization or reduce frequency of gradient calculation. Our experimental results show that, on average, our approach EIDIG generates 19.11% more individual discriminatory instances with a speedup of 121.49% when compared with the state-of-the-art method and mitigates individual discrimination by 80.03% with a limited accuracy loss after retraining.
Incremental semi-supervised Extreme Learning Machine for Mixed data stream classification

With an explosive growth of data generated in the Internet and other fields, the data stream classification has sparked broad interest recently. Nowadays, some of the challenges in data streams, such as concept drift detection and supervised data stream classification, have been well-developed. However, when confronted with mixed data streams (containing categorical and numerical values) or limited available labeled samples, many data stream methods cannot achieve a satisfying performance or even cannot work. To tackle these two problems, we proposed an Incremental Semi-supervised Extreme Learning Machine for Mixed data stream classification (MIS-ELM). To be specific, for the issue of mixed data in data streams, we designed a novel soft one-hot encoding method by combining the coupling object similarity method and the one-hot encoding method, which can embed categorical data into high-quality numerical data and is used in the data preprocessing phase of MIS-ELM; for the issue of limited labeled samples, we introduced an incremental learning method based on unlabeled data, which is employed in the training classifier phase of MIS-ELM. When no concept drift occurs in the data stream, MIS-ELM uses only unlabeled data for incremental learning to fine-tune the classifier trained in the previous sliding window. Also, MIS-ELM instinctively inherits the fast computability of ELM, so it is very suitable for the real-time processing of data streams. Finally, we evaluated the representation performance of the soft one-hot encoding and the classification performance of MIS-ELM, within real data streams. The experimental results demonstrate the superiority of the proposed methods over the state-of-the-art techniques in their areas, respectively.
Capturing detailed deformations of moving human bodies

We present a new method to capture detailed human motion, sampling more than 1000 unique points on the body. Our method outputs highly accurate 4D (spatio-temporal) point coordinates and, crucially, automatically assigns a unique label to each of the points. The locations and unique labels of the points are inferred from individual 2D input images only, without relying on temporal tracking or any human body shape or skeletal kinematics models. Therefore, our captured point trajectories contain all of the details from the input images, including motion due to breathing, muscle contractions and flesh deformation, and are well suited to be used as training data to fit advanced models of the human body and its motion. The key idea behind our system is a new type of motion capture suit which contains a special pattern with checkerboard-like corners and two-letter codes. The images from our multi-camera system are processed by a sequence of neural networks which are trained to localize the corners and recognize the codes, while being robust to suit stretching and self-occlusions of the body. Our system relies only on standard RGB or monochrome sensors and fully passive lighting and the passive suit, making our method easy to replicate, deploy and use. Our experiments demonstrate highly accurate captures of a wide variety of human poses, including challenging motions such as yoga, gymnastics, or rolling on the ground.
Long-term hybrid prediction method based on multiscale decomposition and granular computing for oxygen supply network

An accurate long-term prediction of oxygen demand can provide guidance for planning and scheduling the real production of the steel industry. In this paper, a polynomial-feature granulation prediction method based on long short-term memory network (GLSTM) is first developed and uses quadratic polynomials to form linguistic descriptors for data granules and perform prediction with LSTM. Considering the multiscale characteristics of oxygen flow data, a long-term oxygen demand prediction method based on multiscale decomposition and granular computing for oxygen supply network is further proposed. In this method, oxygen flow data are decomposed into low-, middle-, and high-frequency sublayers by empirical wavelet transformation. After removing the high-frequency noise sublayers, the GLSTM algorithm is employed to predict the low-frequency sublayers, while the k-SVM is employed to predict the middle-frequency sublayers. Finally, the effectiveness and performance of the proposed method is demonstrated in an industrial case study.