Reviewerly/exHarmony huggingface dataset:
Data Poisoning Attack Against Federated Learning Based Autonomous Driving Model

Autonomous driving has received much attention from the research community in smart transportation in Internet of Things era. Specifically, autonomous steering angle control is one of most critical autonomous driving task that guides the driving directions, requiring high environment sensing accuracy in real time for precise and timely steering angle decision assistance. Generally, autonomous driving model is trained based on centralized training on a central server with concentrated storage and computing resources. However, centralized training meets data sharing security and decision latency problems and brings huge stress to the central server. Recently, federated learning (FL) is exploited for safer and more efficient autonomous steering angle control in a distributed learning manner. In the FL based autonomous steering angle control system, vehicle ends are close to the data source and still vulnerable to be attacked maliciously, especially the poisoning attacks. Most existing poisoning attacks on FL are applied to classification tasks with discrete output. However, the steering angle control model is a non-linear regression model with continuous output, which is challenging for conducting poisoning attacking against FL. This paper proposed an ATT ack against F ederated L earning based steering angle control of A utonomous V ehicle framework( ATT_FLAV ). This framework targets to improve the attacking effect on all vehicles by only attacking a portion of the vehicles and ultimately evaluate and enhance the robustness of the federated learning based autonomous driving models. In the proposed framework, an A ttack R egion- UCB ( AR-UCB ) strategy is designed for carrying out the data poisoning attack against the non-linear regression detection model. The attack strategy chooses the target attack label region dynamically in each federated learning round based on vehicle end’s historical attack experiences. Experiments are conducted for comparing the attack effects of the proposed attack strategy on the federate learning based steering angle control model with other strategies and multiple parameters. Additional attack robustness experiment is also carried out by verifying the attack effect against commonly used defense scheme in federated learning. Experiment results indicate the proposed poisoning attack strategy can obtain efficient attack rewards in the continuous poisoning federated learning framework.
Convolutional Self-Attention-Based Multi-User MIMO Demapper

In orthogonal frequency division multiplexing (OFDM)-based wireless communication systems, the bit error rate (BER) performance is heavily dependent on the accuracy of channel estimation. It is important for a good channel estimator to be capable of handling the changes in the wireless channel conditions that occur due to the mobility of the users. In recent years, the focus has been on developing complex neural network (NN)-based channel estimators that enable an error performance close to that of a genie-aided channel estimator. This work considers the other alternative which is to have a simple channel estimator but a more complex NN-based demapper for the generation of soft information for each transmitted bit. In particular, the problem of reversing the adverse effects of an imperfect channel estimator is addressed, and a convolutional self-attention-based neural demapper that significantly outperforms the baseline is proposed.
Semi-supervised Medical Image Classification Combining Metric Pseudo-Label and Classification Pseudo-Label

Deep learning networks with good performance often need a lot of data for training. However, in medical image classification, obtaining high-quality label data is time-consuming and depends on the professional knowledge of doctors. In this paper, a new semi-supervised medical image classification framework based on pseudo-labeling, which combines feature metric pseudo-labeling and model classification pseudo-labeling, and obtains supplementary information in the training process through prior information marked by model from unlabeled data. Considering a lot of information is abandoned in feature map extraction, we introduce an effective pseudo-label combination method, which combines the pseudo-labels obtained by different methods to potential information from unlabeled data. Compared with existing pseudo-labeling methods, our method obtains feature metric pseudo-labels by extracting image features, measuring image feature prototype from labeled data, and combines them with the pseudo-labels obtained by model classification. The performance of this model on skin lesion classification is better than most advanced semi-supervised learning methods.
Differential Privacy under Incalculable Sensitivity

Differential privacy mechanisms have been proposed to guarantee the privacy of individuals in various types of statistical information. When constructing a probabilistic mechanism to satisfy differential privacy, it is necessary to consider the impact of an arbitrary record on its statistics, i.e., sensitivity, but there are situations where sensitivity is difficult to derive. In this paper, we first summarize the situations in which it is difficult to derive sensitivity in general, and then propose a definition equivalent to the conventional definition of differential privacy to deal with them. This definition considers neighboring datasets as in the conventional definition. Therefore, known differential privacy mechanisms can be applied. Next, as an example of the difficulty in deriving sensitivity, we focus on the t-test, a basic tool in statistical analysis, and show that a concrete differential privacy mechanism can be constructed in practice. Our proposed definition can be treated in the same way as the conventional differential privacy definition, and can be applied to cases where it is difficult to derive sensitivity.
New Performance Evaluation Method for Data Embedding Techniques for Printed Images Using Mobile Devices Based on a GAN

Methods that embed data into printed images and retrieve data from printed images captured using the camera of a mobile device have been proposed. Evaluating these methods requires printing and capturing actual embedded images, which is burdensome. In this paper, we propose a method for reducing the workload for evaluating the performance of data embedding algorithms by simulating the degradation caused by printing and capturing images using generative adversarial networks. The proposed method can represent various captured conditions. Experimental results demonstrate that the proposed method achieves the same accuracy as detecting embedded data under actual conditions.
Improving Face Recognition Using Artistic Interpretations of Prominent Features: Leveraging Caricatures in Modern Surveillance Systems

Advances in computer vision have been primarily motivated by a better understanding of how humans perceive and codify faces. Broadly speaking, progress made in the fields of face recognition and identification has been strongly influenced by the biological mechanisms identified by research in the field of cognitive psychology. Research in cognitive psychology has long acknowledged that human face recognition and identification rely heavily on prominent features and that caricatures are capable of modeling prominent features in a multitude of ways. The field of computer science has done little to no research in the area of application of prominent features to recognition systems. This chapter discusses existing caricature research in cognitive psychology and computer vision, current issues with the practical application of caricatures to face recognition in computer vision, and how caricatures can be used to improve existing surveillance systems.
The condition of masking quantum qutric states

Abstract In this paper, we obtain a general condition of quantum information masking by studying the quantum states in <?CDATA $\mathbb{C}^{3}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi mathvariant="double-struck">C</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> </mml:math> . This condition suggests that quantum information can be masked, if and only if the coefficients of the quantum state satisfy the given system of equations. In this case, we discuss a specific example and give an intuitive representation of the image. For the non-orthogonal case of quantum state in <?CDATA $\mathbb{C}^{3}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi mathvariant="double-struck">C</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> </mml:math> , we acquire the masking quantum information conclusion about the basis. Finally, under the circumstances that one pair of quantum state is not orthogonal, we classify quantum states based on whether they can be masked.
AN EFFECTIVE HYBRID STOCHASTIC GRADIENT DESCENT ARABIC SENTIMENT ANALYSIS WITH PARTIAL-ORDER MICROWORDS AND PIECEWISE DIFFERENTIATION

Social media networking sites, such as Instagram, Facebook, and Twitter, have become an inextricable part of our everyday lives. These social media networks are useful for sharing news, images, and other information. The study of social media sentiment has recently received a lot of attention, especially in Arabic sentiment analysis. Social media sites are distinguished by unusual language that differs from the traditional format of the language. As a result, there is a necessity for efficient ways for analyzing the massive amount of new word variants that appear regularly in the digital world and online world. This study proposes a piecewise Stochastic Gradient Descent (SGD)-based model for sentiment classification. The TF-IDF-based term weighting scheme is employed for textual feature representation. For enhancing the model performance, stemming and partially ordered microword representation of tweets with varying look ahead distances is employed. Also, various n-gram models are considered for textual feature representation, which also improves the model performance. The proposed model is simulated and evaluated with the help of publicly available tweet corpus 9 which is a balanced tweet corpus. The effectiveness of the proposed model is estimated using various performance evaluation metrics. According to the experimental observations, the proposed method accurately categorizes the testing and validation tweets set with an accuracy of up to 92.23%.
A feature matching-based attack on text CAPTCHAs

CAPTCHA is a key defense technology that improves the security of a website. Text CAPTCHA schemes are the first barrier against attacks on major websites because of simplicity and low cost. In this paper, we proposed a novel, simple, and easy-to-operate feature matching approach to attack text CAPTCHAs. Our method combines a localization network and a feature matching network to attack three real-world text CAPTCHA schemes, including Wikipedia, Baidu and JD. We used 2000 samples for our attack experiments, and the results showed that our proposed method achieved high success rates on all three schemes.
Analysis of cyber security rebuts and its rising aims on current technologies

Cyber security executes a useful function in the information technology sector. Recently, keeping the data in the secured area has grown one of the most rebuts recently. In the cyber security field the first thought that would be concerned is a cybercrime that is regular intensifying extremely. Several states and organizations are getting lots of techniques of avoiding these cybercrimes. Besides several techniques cyber security is still larger impact to the entire community. This research basically persist on rebuts endured by cyber security on current technologies. Also, the orientations modify the image of cyber security and it pursues on current ethics and cyber security techniques.
Improved multi-identity fully homomorphic encryption scheme based on LWE

The traditional hierarchical identity fully homomorphic encryption scheme can only satisfy the homomorphic operation under a single identity, and the dimension of the lattice will increase synchronously with the increase of the layering depth. Aiming at the problem of high algorithm complexity due to the high-precision real number iterative operation used in the trapdoor derivation process of the existing scheme, this paper proposes an improved multi-identity fully homomorphic encryption scheme (mIBFHE): first, we use the features of new MP12 trapdoor to optimize the Gaussian sampling of the original scheme; secondly, the encryption and decryption structure of the original scheme is modified on the basis of the dual Regev system, so that the improved hierarchical identity encryption (HIBE) scheme meets the conversion format of the GSW compiler; finally, the transformed HIBFHE scheme is further transformed into a multi-identity fully homomorphic encryption scheme through the Mask system that meets the conditions. The hardness of our scheme can be reduced to the LWE problem, and IND-ID-CPA security is achieved under the random oracle model. After comparative analysis, the size of the master private key of the mIBFHE scheme in this paper is significantly reduced compared to the size of the same type of IBE scheme, which is 1/4 of the original scheme size. Our scheme is more efficient than traditional IBFHE scheme that it has smaller size than similar schemes in both the public key and the ciphertext when satisfying the attributes of hierarchical, homomorphism, and multiple identities and at the same time.
Image self-embedding semi-fragile watermarking algorithm based on block truncation coding

Digital image self embedding watermarking technology is one of the main technical means to solve the problem of image tamper detection and recovery. The existing self embedding watermarking technology has the problem of low authentication granularity. In this paper, an image self embedding semi fragile watermarking algorithm based on block truncation coding is proposed. Watermark generation and embedding: the image is divided into two levels at the block level, and the granularity is 4×4 and 2×2 image block; block truncation coding is used for each 4×4 image blocks are compressed and encoded to generate 2×2. Restore the watermark of the image block, and hash the restored watermark for the authentication watermark of the image block. Tamper authentication and recovery: a 4×4. Compare the watermark extracted from the image block with the watermark generated by the block to judge the two of the image block×whether the sub block two passes the authentication; restore the image block according to the authentication result.
Modeling hybrid systems based on combination of SysML and Modelica

The hybrid system is composed of discrete component and continuous component. However, there is no modeling language that can describe the discrete and dynamic characteristics of it at the same time. Hence, we propose a combined modeling method for hybrid system. SysML and Modelica are used to model discrete and physical components of hybrid systems respectively. The structure diagram of SysML and port are used to realize the communication between SysML model and Modelica model, and the complete semi-formal model of the system can be obtained. Since it is impossible to directly make verification on a semi-formal model, we propose a transformation algorithm to transform it into the formal model, and the correctness of the algorithm can be demonstrated by bisimulation. In addition, we select the hybrid interface automata as the formal model for describing the dynamic behavior of a hybrid system and the interface properties between subsystems. In order to obtain the complete formal model of the system, we propose a combination algorithm of HIA based on SysML and Modelica. An automatic air refueling system is used to illustrate the correctness of the combined modeling way and the algorithms proposed in the paper.
Automatic summarization of medical conversations based on episodic memory network hierarchical labels

Summarizations to the patients’ problems and the doctors’ treatments in the medical conversations can be highly useful, in terms of helping other patients with similar problems have a precise reference for potential medical solutions. The patient's first question in the medical conversation often contains critical information that determines the diagnosis. The previous simple weight division for the role of dialogue determines that doctors and patients' discourses do not affect each other, which makes it incomplete for the model to obtain the important discourses in the dialogue. Therefore, it is proposed to use episodic memory network to enhance the influence of the patient's first sentence on the model's acquisition of important words, and re-divide the sentence role weight according to the first sentence vector. The experiment proves that using episodic memory network can be more sensitive to the information between utterances, acquire important utterances more accurately, and generate high-quality abstracts.
A privacy-preserving data collection scheme for smart agriculture based on edge blockchain

Smart agriculture is a new agricultural production model integrating Internet, cloud computing and Internet of things, which provides precision planting and online services for agricultural production. The accuracy and reliability of agricultural data collection affect agricultural decision-making, so it is very necessary to protect agricultural privacy data. Existing solutions usually require the cooperation of trusted institutions, which is not practical. Blockchain adopts the PBFT consensus algorithm and does not require a trusted third party. Edge computing can greatly improve the efficiency of data aggregation and save data transmission time. Hence, this paper proposes a data collection scheme with a faulttolerant property based on blockchain and edge computing to protect sensitive information in smart agriculture. It is proved to be data security, privacy protection, and fault tolerance with the performance simulation and security analysis.
Audio adversarial attack: HIS attack

The adversarial example is an input carefully designed by the attacker to tamper with the output of the neural network model. The emergence of audio adversarial example is to tamper with the end-to-end automatic speech recognition (ASR) system results. Due to the high difficulty of tampering with the ASR recognition results, the noise produced by today’s audio adversarial example generation methods is still easy to be noticed by humans and machines. In this paper, we propose an audio adversarial example generation method that makes audio adversarial example difficult to be perceived based on time-domain restriction. This method hides the adversarial perturbation noise in the speech part of audio by limiting the adversarial perturbation noise in the time domain. Our proposed method is more difficult to be detected by detection methods of audio adversarial example with equal attack performance compared to existing methods. We call the proposed method the HIS (hide in speech) attack.
Applications of the Document Towers method of representing document structures

Abstract This article argues in favor of representing the spatial distribution of information within and between documents, by surveying a broad variety of potential applications, including the entire document lifecycle, multiple sensory modalities, and a large spectrum of tasks and users. The theoretical explanations of this richness are a further facet of the article, and can be summarized as follows: (1) insights emerge from focusing on information structure, rather than information meaning; (2) spatializing information creates new information; (3) simplification increases the polyvalence of representation models; (4) introducing mystery in communication channels motivates discovery and diversifies insights; (5) approaching information design as a Gesamtkunstwerk multiplies the applications; (6) information is a manifestation of a link between structures and the actions these enable, while information design is the art and science of creating such links. The argument is developed around the concrete example of a document structure visualization, the Document Towers, which uses the metaphor of architectural models to represent documents.
Improved semantic segmentation algorithm based on attention mechanism and pyramid module

In the semantic segmentation of images, the problems of incoherence of segmented objects, inconsistency of small objects, and mismatch between objects and labels have become the bottleneck restricting the application of semantic segmentation. Accordingly, an encoder-decoder network is designed with an improved residual network, and an improved model combining attention mechanism and pyramid module is proposed. Among them, the attention module enhances the position information of objects, and the pyramid module improves the segmentation accuracy of object outlines and small objects. The simulation experiments were carried out on the PASCAL VOC 2012 dataset, and the results showed that the MIoU (mean intersection over union, average intersection ratio) reached 79.5% in the improved network structure on the VOC 2012 dataset, which was higher than the accuracy of the basic algorithm. 2.8%, which verifies the effectiveness of the proposed method.
Research on the construction method of encrypted malicious traffic detection system based on machine learning

With the development of social science and technology, the communication process is often accompanied by a variety of encrypted malicious traffic intrusion into the network system. Therefore, it is necessary to build a malicious traffic detection system to solve this problem. This paper proposes a construction scheme of malicious traffic detection system based on machine learning. The malicious traffic data selects the public cic-ids-2017 dataset, and carries out feature engineering processing on the traffic data, which is transformed into a data feature set that is easy to be trained by machine learning model. The performance of various machine learning models are tested and compared. Finally, the optimal random forest model is selected as the malicious traffic detection model. Experiment shows that the model can effectively detect encrypted malicious traffic.
Context-aware adaptation of mobile video decoding resolution

While the evolution of mobile computing is experiencing considerable growth, it is at the same time seriously threatened by the limitations of battery technology, which does not keep pace with the evergrowing increase in energy requirements of mobile applications. Yet, with the limits of human perception and the diversity of requirements that individuals may have, a question arises of whether the effort should be made to always deliver the highest quality result to a mobile user? In this work we investigate how a user's physical activity, the spatial/temporal properties of the video, and the user's personality traits interact and jointly influence the minimal acceptable playback resolution. We conduct two studies with 45 participants in total and find out that the minimal acceptable resolution indeed varies across different contextual factors. Our predictive models inferring the lowest acceptable playback resolution, together with the reduced power consumption we measure at lower resolutions, open an opportunity for saving a mobile's energy through context-adaptable approximate computing.