Reviewerly/exHarmony huggingface dataset:
Local Information Privacy and Its Application to Privacy-Preserving Data Aggregation

In this article, we propose local information privacy (LIP), and design LIP based mechanisms for statistical aggregation while protecting users’ privacy without relying on a trusted third party. The concept of context-awareness is incorporated in LIP, which can be viewed as exploiting of data prior (both in privatizing and post-processing) to enhance data utility. We present an optimization framework to minimize the mean square error of data aggregation while protecting the privacy of each user’s input data or a correlated latent variable by satisfying LIP constraints. Then, we study optimal mechanisms under different scenarios considering the prior uncertainty and correlation with a latent variable. Three types of mechanisms are studied in this article, including randomized response (RR), unary encoding (UE), and local hashing (LH), and we derive closed-form solutions for the optimal perturbation parameters that are prior-dependent. We compare LIP-based mechanisms with those based on LDP, and theoretically show that the former achieve enhanced utility. We then study two applications: (weighted) summation and histogram estimation, and show how proposed mechanisms can be applied to each application. Finally, we validate our analysis by simulations using both synthetic and real-world data. Results show the impact on data utility by different prior distributions, correlations, and input domain sizes. Results also show that our LIP-based mechanisms provide better utility-privacy tradeoffs than LDP-based ones.
Feature Guided CNN for Baby’s Facial Expression Recognition

State-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks (CNNs). However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from a baby’s face image and how difficult is it? In this paper, we first introduce a new face image database, named BabyExp, which contains 12,000 images from babies younger than two years old, and each image is with one of three facial expressions (i.e., happy, sad, and normal). To the best of our knowledge, the proposed dataset is the first baby face dataset for analyzing a baby’s face image, which is complementary to the existing adult face datasets and can shed some light on exploring baby face analysis. We also propose a feature guided CNN method with a new loss function, called distance loss, to optimize interclass distance. In order to facilitate further research, we provide the benchmark of expression recognition on the BabyExp dataset. Experimental results show that the proposed network achieves the recognition accuracy of 87.90% on BabyExp.
Hybrid Cascade Filter With Complementary Features for Visual Tracking

Different features describe different aspects of the object. Individually tailoring proper features for visual tracking is crucial to obtain high performance. In this letter, we propose a hybrid cascade filter to fuse handcrafted and deep features for exploiting their strengths. We complement the deep representation with handcrafted features to achieve better localization accuracy, as well as build a hybrid cascade structure using multiple observation models to achieve better robustness. Furthermore, a coarse-to-fine searching strategy is used for lowering the computational cost. Extensive experimental results on two benchmark datasets show that the proposed method performs favorably against the state-of-the-art trackers.
An Overview of Deepfake: The Sword of Damocles in AI

Deepfakes, which are used to generate and modify human faces, have developed rapidly in recent years. However, as they transfer facial features to target pictures and videos, it is difficult to distinguish the original faces, which has a serious impact on individuals and society. In this survey, we introduce deepfakes technology and classify them into face generation, face swap and facial attribute editing. Moreover, we discuss and summarize the current situation, classic technology, cutting-edge models and public fake datasets of the three aspects. Lastly, we provide an outlook of the development trend of deepfakes in terms of improving authenticity and strengthening confrontation detection.
RARS: Recognition of Audio Recording Source Based on Residual Neural Network

With the popularity of mobile devices and the emergence of various audio-editing tools, it becomes easier to produce and forge audio files. Many criminals will forge false audio information as evidence. Therefore, audio forensics technology becomes particularly important. Audio recording device identification technology, which can verify the authenticity and uniqueness of the evidence obtained, is one of the promising branches of audio forensics technology. In this article, a novel neural-network-based framework using the device noise feature is proposed to identify the source of recording according to the device traces generated by the device during the recording. We also propose a new neural network model RARS (Recognition of Audio Recording Source based on residual neural network). The proposed framework achieves state-of-the-art performance on MOBIPHONE, the only publicly available dataset in this field. Moreover, we build a new dataset based on the latest mobile phones and tablet devices. Our method achieves good performance on both the two datasets, which proves that our model has a certain degree of reusability and robustness.
Extended Kalman Filter Based Modified Elman-Jordan Neural Network for Control and Identification of Nonlinear Systems

In this paper, the Extended Kalman Filter (EKF) is used for online training of a recurrent neural network (RNN) model since the EKF outperforms the first order gradient-based algorithms as a second order method. The modified Elman-Jordan Neural Network model with one hidden layer is adopted as the RNN structure. Self-connections are added in context units to investigate their effects. Then, this model is utilized for identification and online control of a nonlinear single input single output (SISO) process model. The performance of the proposed structure is evaluated by simulation results. The effects of some parameters and the number of hidden units to the performance are also examined.
Parameter-Adaptive Compensation (PAC) for Processing Underwater Selective Absorption

The quality of visual information of underwater images is mainly limited by selective absorption and scattering in water. Usually, most researches of underwater image recovery focus on addressing the scattering via inverting an optical model, but less on compensating for the unbalance attenuation caused by the selective absorption. For this reason, the recovery results are still subject to artifacts and color shifting. In this letter, a parameter-adaptive compensation method is proposed to mitigate the selective absorption. Different from the fixed parameter utilized compensation operations, the proposed method utilizes an adaptation parameter to compensate the attenuation. Further, a rigorous gradient relation between the dominant color channel and the compensated channel is established to avoid over-compensation or under-compensation. The final recovery results show the parameter-adaptive compensation method is beneficial to improve the visual quality and the Underwater Image Quality Measurement (UIQM) scores of recovered underwater images.
Towards Learning to Read Like Humans

Previous NLP works were successful in using human gaze behavior to improve task performance on their data sets. However, having to repeatedly collect gaze data for every new data set is impractical. Thus, there is a need for a method that will allow the utilization of available gaze data without the overhead. Our work presents a novel attempt to directly predict gaze features for each word with respect to its sentence. We take on a multi-corpus and task-agnostic approach: using four different eye-tracking data sets, regardless of reading task, material, and experiment design. Using only the word sequence as input to a 2-layer bidirectional LSTM, we achieve $$R^2$$ scores in the range of 76.80 to 95.59 for the following five gaze features: Number of Fixations (NFIX), First Fixation Duration (FFD), Total Reading Time (TRT), Go-Past Time (GPT), and Gaze Duration (GD). In addition, we use the model to predict gaze features for words in seen and unseen sentences in an attempt to improve performance in two NLP tasks. This led to a slight increase in performance, supporting the potential of such a model. Our paper presents an exploratory experiment into this methodology.
SideInfNet: A Deep Neural Network for Semi-Automatic Semantic Segmentation with Side Information

Fully-automatic execution is the ultimate goal for many Computer Vision applications. However, this objective is not always realistic in tasks associated with high failure costs, such as medical applications. For these tasks, semi-automatic methods allowing minimal effort from users to guide computer algorithms are often preferred due to desirable accuracy and performance. Inspired by the practicality and applicability of the semi-automatic approach, this paper proposes a novel deep neural network architecture, namely SideInfNet that effectively integrates features learnt from images with side information extracted from user annotations. To evaluate our method, we applied the proposed network to three semantic segmentation tasks and conducted extensive experiments on benchmark datasets. Experimental results and comparison with prior work have verified the superiority of our model, suggesting the generality and effectiveness of the model in semi-automatic semantic segmentation.
Impact Propagation in Airport Systems

Abstract The effective protection of critical infrastructure against cyber and physical security threats involves many different steps from initially the identification of risks to finally the implementation of counter measures in the infrastructure. To derive counter measures and to come to intelligent decisions facing the identified risks, the impact calculation plays a central role. The impact of a specific threat can propagate through the systems of the infrastructure and thus needs to be analysed carefully. In this paper, the role of impact propagation of cyber-physical threats for infrastructure protection is discussed, exemplified for airport systems. In the ongoing EU-H2020 project SATIE (Security of Air Transport Infrastructure of Europe) a toolkit is developed containing two tools for impact propagation, namely the Business Impact Assessment (BIA) and the Impact Propagation Simulation (IPS). Both tools are described and for a small test case the propagation of a cyber threat and the transformation into a physical threat is demonstrated in a network representation as well as an agent-based model of the airport’s systems employing the IPS.
K-Nearest Neighbors Algorithm (KNN)

The cryptocurrency is the first implementation of blockchain technology. This technology provides a set of tracks and innovation in scientific research, such as use of data either to detect anomalies either to predict price in the Bitcoin and the Ethereum. Furthermore, the blockchain technology provide a set of technique to automate the business process. This chapter presents a review of some research works related to cryptocurrency. A model with a KNN algorithm is proposed to detect illicit transaction. The proposed model uses both the elliptic dataset and KNN algorithm to detect illicit transaction. Furthermore, the elliptic dataset contains 203,769 nodes and 234,355 edges; it allows to classify the data into three classes: illicit, licit, or unknown. Each node has associated 166 features. The first 94 features represent local information about the transaction. The remaining 72 features are called aggregated features. The accuracy exceeded 90% with k=2 and k=4, the recall reaches 56% with k=3, and the precision reaches 78% with k=4.
HFNet: A Novel Model for Human Focused Sports Action Recognition

Action recognition has attracted much attention recently and progressed remarkably. However, as a special kind of actions, sports action recognition is more difficult and deserves more attention. Our goal in this paper is to distinguish fine-grained human-focused sport actions. Sport actions can always be decomposed into sub-actions by body parts and it's necessary to establish the relationships among body parts and combine them together to perform classification. Besides, sport actions are usually fine-grained and subclasses a re similar which are hard to distinguish. Another tough problem in practice is to locate the actor in complicated circumstances. However, current methods in action recognition always pay attention to the whole image, thus failing to capture details and constructing relationships in images. In this paper, we propose a novel model to construct visual relationships in images through graph convolutions. We make use of patches cropped around body joints as input for graph nodes. Thus our model is able to pay attention to the changes and details of body parts. Then, we carefully design model to learn connections among graph nodes adaptively and empirically. We also provide another method to construct visual relationships for graph nodes. By specially focusing on relationships and details, our model achieves start-of-the-art performance on complex human-focused sports datasets FSD-10 and Diving48.
SELI: statistical evaluation based leaker identification stochastic scheme for secure data sharing

Data leakage has become a critical challenge for any organisation, especially when the third party is involved with legitimate permissions to access its confidential data. This study provides a statistical evaluation-based leaker identification (SELI) scheme to identify the malicious entity when the data is leaked by any agent at some unauthorised place. A new distribution strategy for object and agent selection which enriches the guilty agent recognition is introduced. The three algorithms named SELI–first come first serve, SELI–round Robin (SELI–RR) and SELI–shortest request first (SELI–SRF) are presented for the agent selection. Furthermore, the spurious data objects are added while distributing the data sets among the agents to uniquely identify the leaker. The SELI scheme estimates the guilty entity based on the allocated data among the agents using bigraph. The proposed solution achieves an improvement of up to 25.37 and 58.18% for average probability and average success rate respectively in case of SELI–SRF and 163.68% in case of SELI–RR for detection rate compared to the prior work. In addition to this, the SELI scheme proved its efficiency by securing up to 99.82% accuracy, 99.92% precision, 99.4% recall and 99.97% specificity.
Design and application of learner emotion recognition for classroom

Abstract The emotion recognition of learners in the classroom plays an important role in improving classroom efficiency. At present, the recognition methods based on traditional image processing generally have problems such as low recognition accuracy, difficulty in feature extraction. In order to effectively solve the above problems, a deep learning-based method is proposed to perform emotion recognition for learners. This method first replaces the DarkNet-53 of YOLO v3 by introducing the convolution structure of MobileNet, which makes the model lighter and reduces the parameters. Then improve the loss function of the model by using GIoU loss. Finally, K-means clustering algorithm is used to obtain an anchor suitable for the emotion recognition scene on the self-made dataset. Thus, a high-accuracy and lightweight learner emotion recognition model ER_YOLO is obtained. Experimental results show that the improved model mAP increased by 4%, F1 score increased by 3.2%, detection time reduced by 1/3, and parameters reduced by 1/10.
Correctness-by-construction for feature-oriented software product lines

Software product lines are increasingly used to handle the growing demand of custom-tailored software variants. They provide systematic reuse of software paired with variability mechanisms in the code to implement whole product families rather than single software products. A common domain of application for product lines are safety-critical systems, which require behavioral correctness to avoid dangerous situations in-field. While most approaches concentrate on post-hoc verification for product lines, we argue that a stepwise approach to create correct programs may be beneficial for developers to manage the growing variability. Correctness-by-construction is such a stepwise approach to create programs using a set of small, tractable refinement rules that guarantee the correctness of the program with regard to its specification. In this paper, we propose the first approach to develop correct-by-construction software product lines using feature-oriented programming. First, we extend correctness-by-construction by two refinement rules for variation points in the code. Second, we give a proof for the soundness of the proposed rules. Third, we implement our technique in a tool called VarCorC and show the applicability of the tool by conducting two case studies.
Inferring Absolutely Non-Circular Attribute Grammars with a Memetic Algorithm

When valid syntactical structures are additionally constrained with context-sensitive information the Grammar Inference needs to be extended to the Semantic Inference. In this paper, it is shown that a complete compiler/interpreter for small Domain-Specific Languages (DSLs) can be generated automatically solely from given programs and their associated meanings using Semantic Inference. In this work a wider class of Attribute Grammars has been learned, while only S-attributed and L-attributed Grammars have previously been inferred successfully. Inferring Absolutely Non-Circular Attribute Grammars (ANC-AG) with complex dependencies among attributes has been achieved by integrating a Memetic Algorithm (MA) into the LISA.SI tool. The results show that the proposed Memetic Algorithm is at least four times faster on the selected benchmark than the previous method.
Radiometric mapping and spectral based classification of rocks using remote sensing data analysis: The Precambrian basement complex, NW Nigeria

In this study, radiometric datasets which are best interpreted in conjunction with multispectral remote sensing data were applied in discriminating and classifying the lithologies of the Kebbi–Sokoto basement terrain for effective lithological mapping of the region. K/Th/U radiometric and Landsat-8 remote sensing data were enhanced through several processing techniques to display the characteristics of surface responses needed as input parameters in the classification of the surface rocks. Potassium radioelement concentration, Th/K channel ratio, normalization and ternary maps were produced using radiometric data while band combination and band ratio images were obtained from the Landsat-8 data. Several band ratios highlighted the major rock–forming minerals in the area such as feldspar, quartz, hematite, goethite, illite, kaolinite, biotite, amphibolite and dolomite. The major lithological expressions deduced from the combined data analysis namely; sandstone, felsic gneiss, clay, migmatite, granitoid and carbonates were used as representative classes to train the classification algorithm for the lithological interpretation. Supervised classification algorithm employing maximum likelihood classifier was used to obtain the lithological classification for basement rock units in the region. The classification results which were tested on band ratio b6/b2, b6/b7, b6/b5*b4/b5 image, b4/b2, b5/b6, b6/b7 image and b4/b2, b6/b7, b6/b5 image using a contingency table produced overall classification accuracy values of 79%, 92%, and 88% respectively. The classified images displayed quite similar geological feature discrimination with the radiometric ternary map and geological map of the area. The study emphasized the benefit of using geophysical and multispectral datasets to produce a more complete classification of the surficial Precambrian rocks exposed in the region.
Multi-scale Attention Module U-Net liver tumour segmentation method

Liver tumour is a general term for tumour lesions occurring in the liver, which is the third leading cause of tumour death in the world. Most liver tumours are small in size, variable in shape and unfixed in position, and the existing neural network is not fully utilized in the segmentation of liver tumours. In order to make up for the problem of traditional tumour segmentation, improve the precision and effect of tumour treatment. The multi-scale Attention Module U-NET (MAM U-NET) was proposed by analyzing CT image data of liver tumours. The MAM U-NET model consists of a newly designed Combination module and an MAM attention module. Each MAM attention module is composed of two parts: spatial attention model and channel attention model, which enable the model to learn more extensive and rich context-dependent information and spatial information. Finally, a comparison experiment was conducted in the LITS data set to verify the performance of the model. Liver segmentation achieved the optimal result, and the average Dice coefficient reached 96.8282%. The segmentation results of liver tumours were second only to nnU-Net, and the average Dice coefficient was 60.5991%, which proved that the MAM U-NET model had better performance in the segmentation of liver tumours.
Multiview clustering via exclusive non-negative subspace learning and constraint propagation

Multiview clustering partitions a set of data into groups by exploring complementary information of multiple views. The mainstream tries to project the multiview data into a commonly shared subspace and further discover the true data structure. Ideally, clusters in the subspace should share less semantics with each other so that distinct groups can be obtained while this exclusivity is not guaranteed in previous works. To this end, this paper proposes a non-negative matrix factorization based subspace learning method for exclusive multiview clustering, where the double-orthogonal constraints are imposed for the cluster exclusivity. Moreover, to boost the clustering performance, the proposed method also exploits the available labeled data and is extended into a semi-supervised manner. Particularly, by incorporating the propagated semi-supervised manifold regularizations, the limited supervised information is enriched and encoded in our method to guide the learning process. The formulated optimization problem can be solved by the derived iterative updating rules. Experimental results on seven public datasets demonstrate its promising performance against other state-of-the-art approaches.