Reviewerly/exHarmony huggingface dataset:
Differential Privacy Images Protection Based on Generative Adversarial Network

In recent years, as image data are widely used in data analysis tasks, the problem of privacy disclosure is becoming more and more serious. However, the privacy protection technology of image data is still immature. In this paper, we propose a privacy protection framework named dp-WGAN for image data. This framework uses differential privacy and generative adversarial network to train a generative model with privacy protection function. Using this generative model, synthetic data with similar characteristics to sensitive data can be obtained, and synthetic data is published instead sensitive data to complete all kinds of data analysis tasks. Through extensive empirical evaluation on benchmark datasets, we demonstrate that dp-WGAN can provide strong privacy protection for sensitive data and produce high-quality synthetic data.
Using Mixed Reality in Intralogistics - Are we ready yet?

The Industry 4.0 vision proposes a seamless integration of several modern concepts and technologies, such as the Internet of Things (IoT), artificial intelligence, or robotics, to capture and contextualize data to improve the manufacturing processes. In this respect, Industry 4.0 is all about information and connectivity. Mixed reality (MR) takes the data collected by IoT systems and helps workers by visualizing contextualized data in real time. Human-Computer Interaction research has shown that MR can be advantageous in various application scenarios. However, there is a gap between research and practice when it comes to real world scenarios. So the question arises whether MR is already suitable for efficiently supporting employees in their daily work in industrial settings. To answer this question, we examined two maintenance scenarios in the field of intralogistics: (i) the maintenance of roller conveyors and (ii) the alignments of containers in shuttle warehouses using the Microsoft HoloLens. This paper (i) describes the scenarios and the specific challenges they pose, (ii) presents the prototypes that have been developed, and (iii) discusses the results of user studies that have been conducted to evaluate the practical applicability by presenting inhibiting and facilitating factors. In summary, although MR technology itself seems to be very promising in the scenarios presented, further research on hardware ergonomics and intuitive interaction design is needed.
Efficient Blockchain-based IoT Firmware Update Considering Distribution Incentives

The number of Internet of Things (IoT) devices has increased recently and will increase further in the future. IoT devices are vulnerable, so vendors update and distribute firmware. At the time of firmware distribution, a huge number of IoT devices may connect to the firmware server and excessive network traffic may occur. Consequently, a method using a peer-to-peer file sharing system and a blockchain has been proposed. One study proposed an update scheme considering incentives for distributors to help with updating. However, this method requires the IoT devices, which have limited processing power, to decrypt encrypted files, and the distribution nodes to pay the transaction cost. This paper proposes a firmware distribution method that provides incentives for distributors to help with distribution to reduce the gas costs, using a smart contract and access control based on update records. The additional computations performed by IoT devices and distributors key management were reduced compared to those in previous studies by using access control instead of encryption. Further, the proposed approach was implemented and evaluated using a Raspberry Pi and laptop. The evaluation demonstrated that the gas cost per update was successfully lowered compared to the costs in previous studies.
Differential Privacy Preservation in Interpretable Feedforward-Designed Convolutional Neural Networks

Feedforward-designed convolutional neural network (FF-CNN) is an interpretable network. The parameter training of the model does not require backpropagation (BP) and optimization algorithms (SGD). The entire network is based on the statistical data output by the previous layer, and the parameters of the current layer are obtained through one-pass manner. Since the network complexity under the FF design is lower than the BP algorithm, FF-CNN has better utility than the BP training method in the directions of semi-supervised learning, ensemble learning, and continuous subspace learning. However, the FF-CNN training process or model release will cause the privacy of training data to be leaked. In this paper, we analyze and verify that the attacker can obtain the private information of the original training data after mastering the training parameters of FF-CNN and the partial output responses. Therefore, the privacy protection of training data is imperative. However, due to the particularity of the FF-CNN training method, the existing deep learning privacy protection technology is not applicable. So we proposed an algorithm called differential privacy subspace approximation with adjusted bias (DPSaab) to protect the training data in FF-CNN. According to the different contribution of the model filters to the output response, we design the privacy budget allocation according to the ratio of the eigenvalues and allocate a larger privacy budget to the filter with a large contribution, and vice versa. Extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10 datasets show that DPSaab algorithm has better utility than existing privacy protection technologies.
A Video Captioning Method Based on Multi-Representation Switching for Sustainable Computing

Video captioning is a problem that generates a natural language sentence as a video’s description. A video description includes not only words that express the objects in the video but also words that express the relationships between the objects, or grammatically necessary words. To reflect this characteristic explicitly using a deep learning model, we propose a multi-representation switching method. The proposed method consists of three components: entity extraction, motion extraction, and textual feature extraction. The proposed multi-representation switching method makes it possible for the three components to extract important information for a given video and description pair efficiently. In experiments conducted on the Microsoft Research Video Description dataset, the proposed method recorded scores that exceeded the performance of most existing video captioning methods. This result was achieved without any preprocessing based on computer vision and natural language processing, nor any additional loss function. Consequently, the proposed method has a high generality that can be extended to various domains in terms of sustainable computing.
Spelling Error Correction with BERT based on Character-Phonetic

In order to solve the problem of high proportion of erroneous strings caused by spelling errors in the process of official document writing, this paper proposes a Character-Phonetic BERT model based on the structural transformation of BERT. Which uses the BiLSTM network to detect the location of error characters, and then add the pinyin prior knowledge of the error location to the BERT network to achieve end-to-end spelling error detection and correction. The experimental results show that the Character-Phonetic BERT model in this paper improves the effect by about 5% compared with the traditional language error correction model; Compared with the Bert-Finetune model without considering the pinyin information of the error location, Character-Phonetic BERT model increases by 2.1%. It achieves more accurate Chinese text automatically corrects errors.
A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations

Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.
Combining Deep Learning and Linear Processing for Modulation Classification and Symbol Decoding

Deep learning has been recently applied to many problems in wireless communications including modulation classification and symbol decoding. Many of the existing end-to-end learning approaches demonstrated robustness to signal distortions like frequency and timing errors, and outperformed classical signal processing techniques with sufficient training. However, deep learning approaches typically require hundreds of thousands of floating points operations for inference, which is orders of magnitude higher than classical signal processing approaches and thus do not scale well for long sequences. Additionally, they typically operate as a black box and without insight on how their final output was obtained, they can't be integrated with existing approaches. In this paper, we propose a novel neural network architecture that combines deep learning with linear signal processing typically done at the receiver to realize joint modulation classification and symbol recovery. The proposed method estimates signal parameters by learning and corrects signal distortions like carrier frequency offset and multipath fading by linear processing. Using this hybrid approach, we leverage the power of deep learning while retaining the efficiency of conventional receiver processing techniques for long sequences. The proposed hybrid approach provides good accuracy in signal distortion estimation leading to promising results in terms of symbol error rate. For modulation classification accuracy, it outperforms many state of the art deep learning networks.
A simplex approach in Italian teacher education programmes to promote inclusive practices

Stemming from the premise that no single solution can be adequate to deal with today’s intricate school contexts and that this complexity cannot be overcome, the teachers’ pivotal role in guaranteeing quality inclusive education has been extensively acknowledged. As a result, literature on teacher competency profiles has flourished in recent years. Yet, there is still much debate as to which set of competences teachers should possess in order to feel well-equipped to act professionally and effectively. From a neuro-physiological perspective, Berthoz’s theory of simplexity provides innovative insight on how Complex Adaptive Systems thrive by harmoniously putting into action an ensemble of biological devices that appeared in the course of evolution. Following its application to better comprehend teacher agency and action, this theory has been experimented in teacher education programmes at the University of Salerno. The aim was to raise awareness among teachers about their inner resources and identify ways how they can make use of these simplex properties and principles to face their day-to-day challenges. The article outlines the theory and provides concrete examples of its multi-faceted potential and possible application in different education systems and cultural contexts.
Geometric correction code‐based robust image watermarking

Abstract Digital image watermarking is one of the effective schemes to protect the copyrights of still images. However, the existing watermarking schemes are still not robust enough to the common geometric transformation attacks such as arbitrary rotation, scaling and shifting with desirable hiding capacity. To address this issue, we propose a robust watermarking scheme based on geometric correction codes (GCCs). In this scheme, the watermark and pre‐set GCCs are combined and embedded into a cover image to obtain the watermarked image. At the stage of watermark extraction, the watermarked image, under a variety of geometric transformation attacks, can be geometrically corrected by minimising the difference between the extracted and the original GCCs, then the watermark is extracted from the watermarked image. The experiments demonstrate that, compared to the typical watermarking schemes, the proposed scheme achieves much higher robustness to the common geometric transformation attacks and comparable invisibility with the same embedding capacity.
Automatic lumen border detection in IVUS images using dictionary learning and kernel sparse representation

Intravascular ultrasound (IVUS) images display the cross-sectional information of the lumen, from which the diameter and length of the lumen are accurately measured to calculate the volume. More importantly, they provide the tissue information of the plaque, thus assisting the diagnosis of coronary heart disease and effective interventional therapy. In this study, a fully automatic method is presented for the detection of the lumen contours in IVUS images of the coronary artery. First, texture feature vectors are extracted from the original images with a patch size of 3 × 3. The sparse coding and kernel dictionary learning are used to employ the features to construct two dictionaries for positive and negative tissues, respectively. Then a series of preprocessing helps to reduce the impact of artifacts, calculation cost and get approximate Region of interest (ROI). A kernel-cluster algorithm based on linear discrimination method is developed to classify the pixels in the ROI. In the end, morphological operations are used to improve the detection quality. Publicly available evaluation indicators are applied to the proposed algorithm with 326 test images of different structures. Mean value of the total results (JACC: 0.87, HD: 0.35, PAD: 0.09) outperforms the other automatic methods of the participants in the challenge. Besides, compared with the recent methods used on the same dataset, the proposed method shows good performance and high accuracy. Furthermore, kernel method and preprocessing steps are effective in acquiring better detection results by reducing the influences of artifacts.
A GAN-based input-size flexibility model for single image dehazing

Image-to-image translation based on generative adversarial network (GAN) has achieved state-of-the-art performance in various image restoration applications. Single image dehazing is a typical example, which aims to obtain the haze-free image of a haze one. This paper concentrates on the challenging task of single image dehazing. Based on the atmospheric scattering model, a novel model is designed to directly generate the haze-free image. The main challenge of image dehazing is that the atmospheric scattering model has two parameters, i.e., transmission map and atmospheric light. When they are estimated respectively, the errors will be accumulated to compromise the dehazing quality. Considering this reason and various image sizes, a novel input-size flexibility conditional generative adversarial network (cGAN) is proposed for single image dehazing, which is input-size flexibility at both training and test stages for image-to-image translation with cGAN framework. A simple and effective U-connection residual network (UR-Net) is proposed to combine the generator and adopt the spatial pyramid pooling (SPP) to design the discriminator. Moreover, the model is trained with multi-loss function, in which the consistency loss is a novel designed loss in this paper. Finally, a multi-scale cGAN fusion model is built to realize state-of-the-art single image dehazing performance. The proposed models receive a haze image as input and directly output a haze-free one. Experimental results demonstrate the effectiveness and efficiency of the proposed models.
A Genetic Algorithm to Combine Deep Features for the Aesthetic Assessment of Images Containing Faces

The automatic assessment of the aesthetic quality of a photo is a challenging and extensively studied problem. Most of the existing works focus on the aesthetic quality assessment of photos regardless of the depicted subject and mainly use features extracted from the entire image. It has been observed that the performance of generic content aesthetic assessment methods significantly decreases when it comes to images depicting faces. This paper introduces a method for evaluating the aesthetic quality of images with faces by encoding both the properties of the entire image and specific aspects of the face. Three different convolutional neural networks are exploited to encode information regarding perceptual quality, global image aesthetics, and facial attributes; then, a model is trained to combine these features to explicitly predict the aesthetics of images containing faces. Experimental results show that our approach outperforms existing methods for both binary, i.e., low/high, and continuous aesthetic score prediction on four different image databases in the state-of-the-art.
Knowledge Graph Visualization: Challenges, Framework, and Implementation

A knowledge graph (KG) is a rich resource representing real-world facts. Visualizing a knowledge graph helps humans gain a deep understanding of the facts, leading to new insights and concepts. However, the massive and complex nature of knowledge graphs has brought many longstanding challenges, especially to attract non-expert users. This paper discusses these challenges; we turned them into a generic knowledge-graph visualization framework, namely KGViz, consisting of four dimensions: modularity, intuitive user interface, performance, and access control. Our implementation of KGViz is a high-capacity, extendable, and scalable KG visualizer, which we designed to promotes community contributions.
Blockchain technology using consensus mechanism for IoT-based e-healthcare system

Abstract In healthcare systems IoT device has to give sensitive and private information about the patients, it should be maintained extremely confidential and personal. Most of the IoT-healthcare devices based on existing centralized server data management technology don’t give any guarantee of authenticity and security. Blockchain is a public tamperproof ledger that is decentralized network system in which transaction of data is distributed among the nodes. Blockchain with its consensus algorithm can solve problems by providing decentralized computation for IoT data. The combination of IoT and blockchain technologies with consensus algorithm can become a best choice of designing the secured distributed e-healthcare systems. This paper reviews the blockchain technology and the basic principle and characteristics of a consensus algorithm for IoT- based e- healthcare system.
CrossNet: Detecting Objects as Crosses

With the use of deep learning, object detection has achieved great breakthroughs. However, existing object detection methods still can not cope with challenging environments, such as dense objects, small objects, and object scale variations. To address these issues, this paper proposes a novel keypoint-based detection framework, called CrossNet, which significantly improves detection performance with minimal costs. In our approach, an object is modeled as a cross that consists of a center keypoint and a specific size, which eliminates the need of hand-craft anchor design. The proposed CrossNet outputs three types of maps: the center map, size map, and offset map, where both center map and offset map are to predict the center keypoints of objects and the size map is to estimate the sizes (width and height) of objects. Specifically, we first design a cascaded center prediction method that introduces a coarse-to-fine idea to improve center prediction. Furthermore, since center prediction considered as a classification task is easier than size regression relatively, we design a center-attention size regression module that uses the detection results of centers to assist the size prediction. In addition, a slightly modified hourglass network is designed to enhance the quality of feature maps for center and size prediction. Extensive experiments are conducted to demonstrate the effectiveness of CrossNet on the challenging PASCAL VOC, COCO, KITTI, and WiderFace datasets. Empirical studies show that CrossNet achieves competitive results with top-ranked one-stage and two-stage detectors while being time-efficient.
Several classes of complete permutation polynomials with Niho exponents

In this paper, eight classes of complete permutation polynomials with Niho exponents are proposed. Based on certain polynomials over Fq2 whose (q−1)-th powers are 1 or monomials on the unit circle, three classes of complete permutation polynomials are obtained. In addition, we completely characterize the complete permutation properties of five classes of known permutation trinomials.