Reviewerly/exHarmony huggingface dataset:
Concealed Attack for Robust Watermarking Based on Generative Model and Perceptual Loss

While existing watermarking attack methods can disturb the correct extraction of watermark information, the visual quality of watermarked images will be greatly damaged. Therefore, a concealed attack based on generative adversarial network and perceptual losses for robust watermarking is proposed. First, the watermarked image is utilized as the input of generative networks, and its generating target (i.e. attacked watermarked image) is the original image. Inspired by the U-Net network, the generative networks consist of encoder-decoder architecture with skip connection, which can combine the low-level and high-level information to ensure the imperceptibility of the generated image. Next, to further improve the imperceptibility of the generated image, instead of the loss function based on MSE, a perceptual loss based on feature extraction is introduced. In addition, a discriminative network is also introduced to make the appearance and distribution of generated image similar to those of the original image. The addition of the discriminative network can remove watermark information effectively. Extensive experiments are conducted to verify the feasibility of the proposed concealed attack method. Experimental and analysis results demonstrate that the proposed concealed attack method has better imperceptibility and attack ability in comparison to the existing watermarking attack methods.
Possibilities and Apprehensions in the Landscape of Artificial Intelligence in Education

Artificial intelligence (AI) may be utilized outside of traditional computer settings and is also readily available in low-cost smart devices, making AI easily accessible to general population. Built-in capabilities for conducting complicated computer operations (edge computing), cloud-based services for collaboratively addressing difficult issues, access to huge quantities of open and closed data resources, and conciliatory accession for agile network connections are all available on these low-cost devices. Education is helped by AI in at least two ways: (1) the educational process &#x2013; assistance and modifications to pedagogy and educator's routine function; and (2) the educational ambit and content &#x2013; what kind of education is needed. Author, in this article, explores the challenges and potentialities that AI offers in the field of education. While the focus is on AI's participation, it may be difficult to differentiate it from other technological advancements, especially when it comes to work life. Author conclude that AI (and associated technological advancements) will substitute some professions (didactics will not be required), that other professions will transform impressively (didactic materials will need to be updated), and that a significant number of novel vocations will be created (new-fangled didactics must be constituted). In educational operations &#x2013; the task itself &#x2013; AI will be a reformer as well as a facilitator, altering the characteristics and division of labor.
Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching

Endoscopic imaging plays a very important role in the diagnosis and treatment of lesions. However, the imaging range of endoscopes is small, which may affect the doctors' judgment on the scope and details of lesions. Image mosaic technology can solve the problem well. In this paper, an improved feature-point pair purification algorithm based on SIFT (Scale invariant feature transform) is proposed. Firstly, the K-nearest neighbor-based feature point matching algorithm is used for rough matching. Then RANSAC (Random Sample Consensus) method is used for robustness tests to eliminate mismatched point pairs. The mismatching rate is greatly reduced by combining the two methods. Then, the image transformation matrix is estimated, and the image is determined. The seamless mosaic of endoscopic images is completed by matching the relationship. Finally, the proposed algorithm is verified by real endoscopic image and has a good effect.
Lightweight branched blockchain security framework for Internet of Vehicles

The vision of the Internet of Vehicles (IoV) has acquired widespread acceptance as a result of the development of wireless technology. Its ability to provide drivers with various mobile services has made it an attractive application. Blockchain based security solution is considered good in various applications of IoV till now. But all the frameworks and architectures developed so far in IoV adopt the load of blockchain. So, it is generally deployed on or above edge computing devices, keeping lower structure of the frameworks traditional as managing blockchain with low computing devices on the physical layer is really a challenge. Securing communication in an IoV environment requires ensuring strong authentication and encryption. Although limited efforts are accessible for strong authentication and secure communication but, the lightweight characteristic is absent from this list. This article is proposing a lightweight secured framework for blockchain based IoV that offers strong authentication and secure communication. After securely authorization of the vehicle, it introduces the concept of branched blockchain taking into the consideration of most recently used block, so that the low powered devices can also adopt the security benefits of the blockchain. The existing mining systems are not designed to handle the needs of the IoV at the physical layer. Some of the key features of the proposed framework are lightweight blockchain for physical layer and above, load balancing, scalability, availability and decentralization. According to the relative research, which takes into account factors like computation and transmission costs, our suggested framework outperforms already implemented ones. This framework can act as base of new research verticals where anyone is looking forward for a lightweight blockchain variant applicable on physical layer of IoV.
Multisignal Modulation Classification Using Sliding Window Detection and Complex Convolutional Network in Frequency Domain

With the development of the Internet of Things (IoT), the IoT devices are increasing day by day, resulting in increasingly scarce spectrum resources. At the same time, many IoT devices are facing inevitable malicious attacks. The cognitive Radio-enabled IoT (CR-IoT) is proposed as an effective method for spectrum resource allocation and risk monitoring in the IoT. The signal detection and modulation recognition are the key technologies for CR-IoT, addressing the problem of multisignal detection and automatic modulation classification (AMC) is one of the prerequisites for realizing secure dynamic spectrum access. Based on sliding window and deep learning (DL), this study proposes a multisignal frequency domain detection and recognition method. The frequency spectrum of the time-domain overlapping signal is obtained through the fast Fourier transform (FFT), and the frequency spectrum is segmented based on the signal energy detection method. Finally a complex convolutional neural network (CNN) is constructed for the identification of signal spectrum information. The proposed method can recognize 264 time-domain aliasing and frequency-closed signals with an accuracy of 97.3% under the influence of −2 dB corresponding to the noise of the calibration signal. In addition, the proposed method eliminates the influence of bandwidth, which can effectively detect and recognize the signal types of each component in the frequency band. This method has wide applicability and provides an effective scheme for the IoT cognitive technology.
A Survey on Text Classification: From Traditional to Deep Learning

Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021, focusing on models from traditional models to deep learning. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. We then discuss each of these categories in detail, dealing with both the technical developments and benchmark datasets that support tests of predictions. A comprehensive comparison between different techniques, as well as identifying the pros and cons of various evaluation metrics are also provided in this survey. Finally, we conclude by summarizing key implications, future research directions, and the challenges facing the research area.
Attribute-Based Cloud Data Integrity Auditing for Secure Outsourced Storage

Outsourced storage such as cloud storage can significantly reduce the burden of data management of data owners. Despite of a long list of merits of cloud storage, it triggers many security risks at the same time. Data integrity, one of the most burning challenges in secure cloud storage, is a fundamental and pivotal element in outsourcing services. Outsourced data auditing protocols enable a verifier to efficiently check the integrity of the outsourced files without downloading the entire file from the cloud, which can dramatically reduce the communication overhead between the cloud server and the verifier. Existing protocols are mostly based on public key infrastructure or an exact identity, which lacks flexibility of key management. In this paper, we seek to address the complex key management challenge in cloud data integrity checking by introducing attribute-based cloud data auditing, where users can upload files to cloud through some customized attribute set and specify some designated auditor set to check the integrity of the outsourced data. We formalize the system model and the security model for this new primitive, and describe a concrete construction of attribute-based cloud data integrity auditing protocol. The new protocol offers desirable properties namely attribute privacy-preserving and collusion-resistance. We prove soundness of our protocol based on the computational Diffie-Hellman assumption and the discrete logarithm assumption. Finally, we develop a prototype of the protocol which demonstrates the practicality of the protocol.
Cecoin: A decentralized PKI mitigating MitM attacks

For numerous applications, it is essential to reliably link a public key with its owner. The current solution is to employ the well-known Public Key Infrastructure (PKI), represented by a trusted certificate authority (CA), to fulfill this assignment by signing the certificate for the public key after validating its owner. However, due to the centralized architecture, it raises the single-point failure problem with unpredictable threats. In this paper, we present a distributed certificate scheme, referred to as Cecoin which is inspired by the well-known Bitcoin by employing its irreversible unforgeability and public verifiability. In Cecoin, the certificates can be treated as currencies and recorded on block chain, which removes the single point failure problem. The miners can verify the validity of certificates following a set of rules to ensure ownership consistency, and allow an identity to bind multiple public-key certificates. For efficient retrieval and verification of certificates, and quick operations, we incorporate the modified Merkle Patricia tree and employ it to implement a distributed Certificate Library. To allow the owner to transfer the possession of identity, we design an online fair exchange protocol without a trusted third party. Security and efficiency analyses show that our Cecoin provides strong security with desirable efficiency.
Neural Network Training With Levenberg–Marquardt and Adaptable Weight Compression

Difficult experiments in training neural networks often fail to converge due to what is known as the flat-spot problem, where the gradient of hidden neurons in the network diminishes in value, rending the weight update process ineffective. Whereas a first-order algorithm can address this issue by learning parameters to normalize neuron activations, the second-order algorithms cannot afford additional parameters given that they include a large Jacobian matrix calculation. This paper proposes Levenberg-Marquardt with weight compression (LM-WC), which combats the flat-spot problem by compressing neuron weights to push neuron activation out of the saturated region and close to the linear region. The presented algorithm requires no additional learned parameters and contains an adaptable compression parameter, which is adjusted to avoid training failure and increase the probability of neural network convergence. Several experiments are presented and discussed to demonstrate the success of LM-WC against standard LM and LM with random restarts on benchmark data sets for varying network architectures. Our results suggest that the LM-WC algorithm can improve training success by 10 times or more compared with other methods.
Emotion-oriented requirements engineering: A case study in developing a smart home system for the elderly

Smart home technology has received growing interest in recent years with a focus on automation and assistance, for example, Alexa, Google Home, Apple HomePod, and many cheap IoT devices. Better supporting elderly people to continue live in their home using smart home technology is a key target application. However, most of the existing smart home solutions for the elderly are not designed with people's emotional goals in mind, leading to lack of adoption, lack of engagement, and failure of the technology. In this paper, we introduce an emotion-oriented requirements engineering approach to help identifying, modeling and evaluating emotional goals. We also explain how we used this technique to help us develop SofiHub - a new smart home platform for elderly people. SofiHub comprises a range of devices and software for sensing, interaction, passive monitoring, and emergency assistance. We have conducted multiple trials including initial field trials for elderly people in real houses. We have used our emotion-oriented requirements techniques to evaluate the participants' emotional reactions before, during, and after trials to understand the impact of such technology on elderly people's emotions to the SofiHub solution. Our analysis shows that SofiHub successfully alleviates their loneliness, makes them feel safer and cared about. We also found that the trial participants developed a strong relation with the system and hence, felt frustrated when SofiHub did not respond in ways expected or desired. We reflect on the lessons learned from the trials related to our emotion-oriented design and evaluation experimental approach, including refining our set of evaluation tools.
Blind Video Quality Assessment With Weakly Supervised Learning and Resampling Strategy

Due to the 3D spatiotemporal regularities of natural videos and small-scale video quality databases, effective objective video quality assessment (VQA) metrics are difficult to obtain but highly desirable. In this paper, we propose a general-purpose no-reference VQA framework that is based on weakly supervised learning with a convolutional neural network (CNN) and a resampling strategy. First, an eight-layer CNN is trained by weakly supervised learning to construct the relationship between the deformations of the 3D discrete cosine transform of video blocks and the corresponding weak labels judged by a full-reference (FR) VQA metric. Thus, the CNN obtains the quality assessment capacity converted from the FR-VQA metric, and the effective features of the distorted videos can be extracted through the trained network. Then, we map the frequency histogram calculated from the quality score vectors predicted by the trained network onto the perceptual quality. Especially, to improve the performance of the mapping function, we transfer the frequency histogram of the distorted images and videos to resample the training set. The experiments are carried out on several widely used VQA databases. The experimental results demonstrate that the proposed method is on a par with some state-of-the-art VQA metrics and has promising robustness.
Joint multi-label classification and label correlations with missing labels and feature selection

Multi-label classification problem is a key learning task where each instance may belong to multiple class labels simultaneously. However, there exists four main challenges: (a) designing an effective multi-label classifier, (b) learning the high-order asymmetric label correlations automatically, (c) reducing the dimensionality of feature space, (d) dealing with both the full labels and missing labels cases. In this paper, we directly address the above four problems in a unified learning framework, and propose a novel Multi-Label classification approach joint with label correlations, Missing labels and Feature selection, named MLMF. The proposed MLMF not only makes the joint learning of independent binary classifiers, but also allows the joint learning of multi-label classification and label correlations. Meanwhile, the shared sparse feature structure among labels are selected by l2,1-norm. Furthermore, MLMF can also handle missing labels. Experimental results on sixteen multi-label data sets in terms of six evaluation criteria demonstrate that MLMF outperforms the state-of-the-art multi-label classification algorithms.
Prediction of survival and metastasis in breast cancer patients using machine learning classifiers

BackgroundBreast cancer (BC) is one of the most common malignancies in women. Early diagnosis of BC and metastasis among the patients based on an accurate system can increase survival of the patients to >86%. This study aimed to compare the performance of six machine learning techniques two traditional methods for the prediction of BC survival and metastasis.MethodsWe used a dataset that include the records of 550 breast cancer patients. Naive Bayes (NB), Random Forest (RF), AdaBoost, Support Vector Machine (SVM), Least-square SVM (LSSVM) and Adabag, Logistic Regression (LR) and Linear Discriminant Analysis were used for the prediction of breast cancer survival and metastasis. The performance of the used techniques was evaluated with sensitivity, specificity, likelihood ratio and total accuracy.ResultsOut of 550 patients, 83.4% were alive and 85% did not experience metastasis. In prediction of survival, the average specificity of all techniques was ≥94% and the SVM and LDA have greater sensitivity (73%) in comparison to other techniques. The greater total accuracy (93%) belonged to the SVM and LDA. For metastasis prediction, the RF had the highest specificity (98%), the NB had highest sensitivity (36%) and the LR and LDA had the highest total accuracy (86%).ConclusionsOur finding showed that the SVM outperformed other machine learning methods in prediction of survival of the patients in terms of several criteria. Nevertheless, the LDA technique as a classical method showed similar performance.
Just Google it! Digital literacy and the epistemology of ignorance

In this paper we examine digital literacy and explicate how it relates to the philosophical study of ignorance. Using data from a study which explores the knowledge producing work of undergraduate students as they wrote course assignments, we argue that a social practice approach to digital literacy can help explain how epistemologies of ignorance may be sustained. If students are restricted in what they can know because they are unaware of exogenous actors (e.g. algorithms), and how they guide choices and shape experiences online, then a key issue with which theorists of digital literacy should contend is how to educate students to be critically aware of how power operates in online spaces. The challenge for Higher Education is twofold: to understand how particular digital literacy practices pave the way for the construction of ignorance, and to develop approaches to counter it.
A survey on metaheuristic optimization for random single-hidden layer feedforward neural network

Random single-hidden layer feedforward neural network (RSLFN) is currently a popular learning algorithm proposed for improving traditional gradient-based model due to its fast learning speed and acceptable performance. For RSLFN, the input weights and/or other parameters are randomly initialized, and the other ones are iteratively or non-iteratively trained. However, the performance of RSLFN is sensitive to the number of hidden neurons and randomly initialized parameters. Numerous methods have been successfully employed to improve the RSLFN from various perspectives. Because of their favourable search ability, metaheuristic optimization approaches gradually attract more and more attentions. Metaheuristic algorithms usually formulate the random parameters of RSLFN into an optimization model, and then provide a near-optimum solution which could be converted into RSLFN with better generalization performance. The hybrid method for optimizing RSLFN therefore shows considerable potential in intelligent computing and artificial intelligence. However, there is no comprehensive survey on RSLFN with metaheuristic in the research area, which ultimately leads to lost opportunities for an advancement. This paper firstly introduces the basic principles of RSLFN along with several metaheuristic algorithms. Secondly, it provides a comprehensive survey of the state-of-the-art contributions in the area. Finally, current challenges are highlighted and promising research directions are also presented.
Detecting Traffic Information From Social Media Texts With Deep Learning Approaches

Mining traffic-relevant information from social media data has become an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. First, we apply the continuous bag-of-word model to learn word embedding representations based on a data set of three billion microblogs. Compared to the traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next, we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on a bag of n-gram features, the SVM model based on word vector features, and the multi-layer perceptron model based on word vector features. Experiments show the effectiveness of the proposed deep learning approaches.