Reviewerly/exHarmony huggingface dataset:
Verification of integrity of deployed deep learning models using Bayesian Optimization

Artificial Intelligence (AI) is hugely driven by machine learning (ML). Popular ML algorithms like deep neural networks (DNN) are used in wide variety of applications. Due to excellent predictive capabilities, these models are used in many realworld applications. To accommodate the huge service demands from these models, they are hosted on cloud platforms. Hosting a model in cloud raises security concerns. Malicious third party can modify the model when in transit from local system to cloud platform or after being uploaded in the cloud. We propose Bayesian Compromise Detection (BCD) algorithm to detect such model compromises by generating sensitive samples. Finding a sensitive sample is essentially an optimization problem that aims to maximize the prediction differences between the original and the compromised model. The optimization problem is challenging as (1) a cloud customer can only have a blackbox access to the compromised model (2) sensitive sample has to be searched in the distribution of the training inputs, which is most likely a high-dimensional space and (3) the optimization problem is non-convex. To tackle these obstacles, we use Variational Autoencoder (VAE) to map high-dimensional data to a non-linear low-dimensional space and then use Bayesian Optimization (BO) to find the globally optimum sensitive sample. Proposed algorithm generates a sensitive sample that can detect model compromise without incurring much cost by multiple queries. Experiments with multiple datasets demonstrate the effectiveness of our proposed method. Our method outperforms the state-of-the-art method in terms of detection rate of the compromised models on cloud platforms.
Prediction of Short-Term Photovoltaic Power Via Self-Attention-Based Deep Learning Approach

Abstract Photovoltaic (PV) is characterized by random and intermittent. As increasing popularity of PV, it makes PV power prediction increasingly significant for efficiency and stability of the power grid. At present, prediction models of PV power based on deep learning show superior performance, but they ignore the interdependent mechanism of prediction error along the input characteristics of the neural network. This paper proposed a self-attention mechanism (SAM)-based hybrid one-dimensional convolutional neural network (1DCNN) and long short-term memory (LSTM) combined method (named 1DCNN-LSTM-SAM). In the proposed model, SAM redistributes the neural weights in 1DCNN-LSTM, and then 1DCNN-LSTM further extracts the space-time information of effective PV power. The polysilicon PV arrays data in Australia are employed to test and verify the proposed model and other five competition models. The results show that the application of SAM to 1DCNN-LSTM improves the ability to capture the global dependence between inputs and outputs in the learning process and the long-distance dependence of its sequence. In addition, mean absolute percentage error of the 1DCNN-LSTM-SAM under sunny day, partially cloudy day, and cloudy day weather types has increased by 24.2%, 14.4%, and 18.3%, respectively, compared with the best model among the five models. Furthermore, the weight distribution mechanism of self-attention to the back end of LSTM was analyzed quantitatively and the superiority of SAM was verified.
Freely Available Arabic Corpora: A Scoping Review

Corpora play a vital role when training machine learning (ML) models and building systems that use natural language processing (NLP). It can be challenging for researchers to access corpora in a language other than English, and even more so if the corpora are not available for free of cost. The Arabic language is used by more than 1.5 billion Muslims and is the native language of over 250 million people as the Quran, the core text of Islam, is written in Arabic. To highlight peer-reviewed literature reporting free and accessible Arabic corpora. We aimed to benefit researchers by providing insights into freely available Arabic and accessible corpora, allowing them to achieve their research goals with ease. By conducting a scoping review using PRISMA guidelines, we searched the most common information technology (IT) databases and identified free of cost and accessible Arabic corpora. We identified a total of 48 accessible corpora sources available free of cost in the Arabic language, we present our findings according to categories to further help readers understand the corpora with direct links where available. The results were classified by corpora type into five categories based on their primary purpose. Arabic is underrepresented considering freely available corpora as most such corpora are available in English. Although previous studies have performed searches for corpora, ours is the first of its kind as it follows the PRISMA guidelines and includes peer-reviewed articles in the literature, obtained by searching the most common IT databases and source recommendations from language experts.
Joint semantics and data-driven path representation for knowledge graph reasoning

Reasoning on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering. The path-based reasoning models can leverage much information over paths other than pure triples in the KG but face several challenges. Firstly, all the existing path-based methods are data-driven, lacking explainability, namely how the path representations and the reasoning results are obtained with human-understandable explanations. Besides, some approaches either consider only relational paths or ignore the heterogeneity between entities and relations both in paths, which cannot capture the rich semantics of paths well. To address the above challenges, in this work, we propose a novel joint semantics and data-driven path representation that balances explainability and generalization in the framework of KG embedding. Specifically, we inject horn rules to obtain the condensed paths through a transparent and explainable path composition procedure. The entity converter is designed to transform entities along paths into the representations in the semantic level similar to relations for reducing the heterogeneity between entities and relations. The KGs, both with and without type information, are considered. Our proposed model is evaluated on two classes of tasks: link prediction and path query answering. The experimental results show that our model obtains significant performance gains over several state-of-the-art baselines.
The impact of social class and service type on preference for AI service robots

Purpose The research aims to explore the interaction effect of consumer social class and service type on consumers' preference for robot services, as well as the mediating role of risk aversion in this interaction effect. Design/methodology/approach Experiment 1 is a field experiment with service type being the independent variable. The participants were divided into two groups based on the services they received (diagnostic dental services vs. hotel room services). 93 consumers participated voluntarily in the blind experiment and were asked if they would choose to allow a robot to perform the focal services. Experiment 2 employs a 2 × 2 factorial design: personal fitness trainer services at the gym vs wait staff services in a casual dining restaurant × higher- vs lower-social class, with 196 participants. Findings Results from the two experiments show that participants in the higher-social classes were more willing than participants in the lower-social classes to choose robot services in credence-based service settings. More significantly, risk aversion mediated the interaction effect of social class and service type on participants' preference for robot services. Originality/value Based on the credence-experience typology, this research is the first to discuss the weight of social class in consumer decision-making regarding preference for different types of robot services. Furthermore, by extending risk aversion to the robot services field, the current research sheds new light on this underlying mechanism that can inform future studies.
Optimal Feature Selection for Imbalanced Text Classification

Textual data suffers from two main problems, large number of features and class imbalance. Many conventional approaches and their variants exist in literature to solve both these problems. The classic synthetic minority oversampling technique (SMOTE) is the most explored technique for balancing the dataset. We introduced a new algorithm to balance the dataset, named distributed SMOTE (D_SMOTE), which overcomes the problem of lack of density and reducing the formation of small disjuncts. Further, another problem handled is the large number of features or high-dimensionality. To solve high-dimensionality, a novel feature selection technique is introduced known as modified biogeography-based optimization (M_BBO). The proposed model, M_BBO, performs modification in ranking of variables using feature weighting algorithm rather than randomly ranking. We have proposed two new expressions in D_SMOTE and one new expression in M_BBO. The extensive experimental results are computed out on four text classification datasets with four machine learning classifiers. The results are concluded using three performance measures, area under curve, G-mean, and F1-score. Our empirical and statistical observation for four class-imbalanced datasets shows that the proposed D_SMOTE outperforms the other similar oversampling technique. We have also compared our proposed algorithm, M_BBO+D_SMOTE, with other models on 17 imbalanced text classification datasets. Our model outperformed the other models in 14 datasets. We have also compared our model with bidirectional encoder representations from transformers. To validate the experimental analysis, statistical Friedman test is employed.
Discriminative Feature Mining and Enhancement Network for Low-Resolution Fine-Grained Image Recognition

Existing fine-grained image recognition methods are difficult to learn complete discriminative features from low-resolution (LR) data, because the original subtle inter-class distinctions become slimmer with the reduction of the image resolution. Besides, existing methods of LR fine-grained image recognition and general LR image recognition only consider the restoration and extraction of global discriminative features, ignoring unreliable local fine-grained details can be detrimental to final recognition. To address the above problems, we propose a multi-tasking framework, discriminative feature mining and enhancement network (DME-Net), for the LR fine-grained image recognition task, which aims to capture the reliable object descriptions from macro and micro perspectives, respectively. Macroscopically, we train the framework’s ability to recover and extract global discriminative features based on the whole images. Microscopically, we purposefully reinforce the framework’s ability to repair and capture the local discriminative details on the mined informative parts. To precisely excavate the most potential parts, we design an informative part mining (IPM) module, in which we firstly employ a part generation layer to predict several part masks that focus on different discriminative parts under the guidance of discrepancy loss and discriminant loss. Then we introduce a part selection (PS) submodule to further screen out a group of most informative parts from the predicted part masks according to their corresponding scores, which measure the semantic correlation degree of each part to the others. Experimental results on three benchmark datasets and one retail product dataset consistently show that our proposed framework can significantly boost the performance of the baseline model. Besides, extensive ablation studies are conducted, which further prove the effectiveness of each component of our designs.
A novel micro-expression detection algorithm based on BERT and 3DCNN

As a special type of facial expressions, the spontaneous micro-expressions can reveal the genuine emotions that people attempt to hide, therefore can provide potential information in criminal detection, lie detection, etc. Compared to ordinary facial expressions, micro-expressions are involuntary, transient and of low intensity. Consequently, micro-expression detection is difficult and overly dependent on expert experiences. Thus, we propose a novel micro-expression detection method based on the Bidirectional Encoder Representation from Transformers (BERT) network, namely R3D_BERT + Group, which includes the candidate segment generation module, spatio-temporal feature extraction module and grouping module. Specifically, the candidate segments are generated by the candidate segment generation module, then each candidate segment is divided into smaller time slots by the spatio-temporal feature extraction module, where spatio-temporal features are extracted through 3DCNN and BERT network. Finally, consecutive segments are merged and overlapping segments are suppressed by the grouping module to locate the position of the onset and offset frames of the micro-expression more accurately. Comprehensive experiments on CASME 2 and SDU_spotting databases firmly demonstrate the effectiveness of our method over other state-of-the-art detection methods.
Social media analytics system for action inspection on social networks

Social networks are increasingly used for discussing all kinds of topics, including those related to politics, serving as a virtual arena. Consequently, analysing online conversations, for example, to predict election outcomes, is becoming a popular and challenging research area. On social networking sites, citizens express themselves spontaneously regarding political topics, often driven by specific events in social life. Real-time analysis of social media can provide valuable feedback and insights to both politicians and news agencies. In this paper, we discuss the design and implementation of a system for tracking and analysing social media. The SocMINT system provides an easy-to-use, visual dashboard to monitor the discussion on specific topics, to capture trends in communities and, by iteratively applying multidimensional data analysis and filtering, to deeply analyse posts and influencers. SocMINT aggregates data from multiple social sources and performs sentiment analysis on textual, visual and mixed content via a specifically designed neural network architecture. The system was applied in a real context by administrative staff of a political party to effectively analyse candidates' political communication on Facebook, Instagram and Twitter and the related online community reactions and discussion. In the paper, we report on this real-world case study, showing how the system meaningfully captures trends in public opinion, comparing the main KPIs provided by SocMINT with the outcomes of traditional polls.
Effect of the use of augmented reality applications on academic achievement of student in science education: meta analysis review

It was aimed to review the effect of the use of augmented reality applications on the academic achievement of students in science education in this study. In line with this aim, the experimental researches reviewing the effect of use of augmented reality applications within the scope of science course on student achievement were evaluated with meta-analysis method. 16 studies included into this research were accessed from ERIC, Google Scholar, ScienceDirect, SpringerLink, Taylor & Francis, Web of Science databases in line with certain criteria. In order to reach these studies from databases, the following key words were used; “‘augmented reality’ & ‘achievement’” and “‘augmented reality’ & ‘achievement & science’”. Funnel plot, Orwin’s Fail-Safe N and Egger tests were used for detecting the publication bias and it was determined that there is no publication bias. At the end of the meta-analysis, it was determined that the effect of the use of the augmented reality applications on the student achievement in the science course is moderate (d = 0,643) in favor of the experimental group. Thus, it was presented that the use of the augmented reality applications affects the student achievement positively in the science course.
Dense Video Captioning With Early Linguistic Information Fusion

Dense captioning methods generally detect events in videos first and then generate captions for the individual events. Events are localized solely based on the visual cues while ignoring the associated linguistic information and context. Whereas end-to-end learning may implicitly take guidance from language, these methods still fall short of the power of explicit modeling. In this paper, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Visual-Semantic Embedding (ViSE) Framework</i> that models the word(s)-context distributional properties over the entire semantic space and computes weights for all the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-grams</i> such that higher weights are assigned to the more informative <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-grams</i> . The weights are accounted for in learning distributed representations of all the captions to construct a semantic space. To perform the contextualization of visual information and the constructed semantic space in a supervised manner, we design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Visual-Semantic Joint Modeling Network (VSJM-Net)</i> . The learned <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ViSE</i> embeddings are then temporally encoded with a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hierarchical Descriptor Transformer (HDT)</i> . For caption generation, we exploit a transformer architecture to decode the input embeddings into natural language descriptions. Experiments on the large-scale ActivityNet Captions dataset and YouCook-II dataset demonstrate the efficacy of our method.
Multi-expert learning for fusion of pedestrian detection bounding box

Performance of pedestrian detection, which is one of the essential tasks in automatic drive, relies heavily on a large number of labels. Some researchers proposed unsupervised domain adaptive frameworks to improve the detection accuracy in wild datasets to reduce the need for labels. However, it is not a down-to-earth and cost-effective way for deploying these frameworks in practical engineering because it needs both source and target data for training. Unlike the former research, this work presents a new fine-tuning method without using source and target data for unsupervised detection. In this work, different well-trained models from the source domain are regarded as less-accurate experts in the wild domain, where a multi-expert learning algorithm is applied to learn from the difference between these models and fuse bounding boxes to present more accurate detection results. Experimental results on three common pedestrian detection datasets show that our method can efficiently improve the detection accuracy under unsupervised settings. Our method can also achieve better performance without source and target data involved compared with state-of-the-art works.
Deep Learning-Based Perceptual Video Quality Enhancement for 3D Synthesized View

Due to occlusion among views and temporal inconsistency in depth video, spatio-temporal distortion occurs in 3D synthesized video with depth image-based rendering. In this paper, we propose a deep Convolutional Neural Network (CNN)-based synthesized video denoising algorithm to reduce temporal flicker distortion and improve perceptual quality of 3D synthesized video. First, we analyze the spatio-temporal distortion, and model eliminating spatio-temporal distortion as a perceptual video denoising problem. Then, a deep learning-based synthesized video denoising network is proposed, in which a CNN-friendly spatio-temporal loss function is derived from a synthesized video quality metric and integrated with a single image denoising network architecture. Finally, specific schemes, i.e., specific Synthesized Video Denoising Networks (SynVD-Nets), and a general scheme, i.e., General SynVD-Net (GSynVD-Net), based on existing CNN-based denoising models, are developed to handle synthesized video with different distortion levels more effectively. Experimental results show that the proposed SynVD-Net and GSynVD-Net can outperform deep learning-based counterparts and conventional denoising methods, and significantly enhance perceptual quality of 3D synthesized video.
GARAT: Generative Adversarial Learning for Robust and Accurate Tracking

Object tracking by the Siamese network has gained its popularity for its outstanding performance and considerable potential. However, most of the existing Siamese architectures are faced with great difficulties when it comes to the scenes where the target is going through dramatic shape or environmental changes. In this work, we proposed a novel and concise generative adversarial learning method to solve the problem especially when the target is going under drastic changes of appearance, illumination variations and background clutters. We consider the above situations as distractors for tracking and joint a distractor generator into the traditional Siamese network. The component can simulate these distractors, and more robust tracking performance is achieved by eliminating the distractors from the input instance search image. Besides, we use the generalized intersection over union (GIoU) as our training loss. GIoU is a more strict metric for the bounding box regression compared to the traditional IoU, which can be used as training loss for more accurate tracking results. Experiments on five challenging benchmarks have shown favorable and state-of-the-art results against other trackers in different aspects.
Blockchain-based automated and robust cyber security management

We initiate the study on the problem of automated and robust Cyber Security Management (CSM). We exemplify the problem by investigating how CSM should respond to the discovery of cyber intelligence that identifies new attackers, victims, or defense capabilities. Given the complexity of CSM, we divide it into three classes, referred to as Network-centric (N-CSM), Tools-centric (T-CSM) and Application-centric (A-CSM). These lead to a range of functions for examining whether, and to what extent, a network has been compromised. Moreover, we propose to incorporate blockchain (via Hyperledger Fabric) to build a decentralized CSM system, dubbed B2CSM, that ensures the retrieval of valid invocation results for CSM purposes. We also integrate B2CSM with a decentralized storage network (DSN), instantiated by InterPlanetary File System (IPFS), to reduce on-chain storage costs without hindering its robustness. We present the design and implementation of the prototype B2CSM system. Experiments with real-world datasets show that the CSM solutions and system are effective and efficient.