Reviewerly/exHarmony huggingface dataset:
Comprehensive Survey of Multimedia Steganalysis: Techniques, Evaluations, and Trends in Future Research

During recent years, emerging multimedia processing techniques with information security services have received a lot of attention. Among those trends are steganography and steganalysis. Steganography techniques aim to hide the existence of secret messages in an innocent-looking medium, where the medium before and after embedding looks symmetric. Steganalysis techniques aim to breach steganography techniques and detect the presence of invisible messages. In the modern world, digital multimedia such as audio, images, and video became popular and widespread, which makes them perfect candidates for steganography. Monitoring this huge multimedia while the user communicates with the outside world is very important for detecting whether there is a hidden message in any suspicious communication. However, steganalysis has a significant role in many fields, such as to extract the stego-message, to detect suspicious hidden messages and to evaluate the robustness of existing steganography techniques. This survey provides the general principles of hiding secret messages using digital multimedia as well as reviewing the background of steganalysis. In this survey, the steganalysis is classified based on many points of view for better understanding. In addition, it provides a deep review and summarizes recent steganalysis approaches and techniques for audio, images, and video. Finally, the existing shortcomings and future recommendations in this field are discussed to present a useful resource for future research.
New Genetic Operators for Developing S-Boxes With Low Boomerang Uniformity

The boomerang uniformity measures the resistance of block ciphers to boomerang attacks and has become an essential criterion of the substitution box (S-box).However, the S-boxes created by the Feistel structure have a poor property of boomerang uniformity.The genetic algorithm is introduced to improve the properties of the S-boxes created by the Feistel structure.New genetic operators are designed for the genetic algorithm to improve its searchability.The new genetic algorithm generates some 8 × 8 bijective S-boxes with differential uniformity 6, nonlinearity 108, and boomerang uniformity 10, which has dramatically improved the properties of the S-boxes created by the Feistel structure.Furthermore, the new genetic algorithm also improves the properties of the S-box population created by the Feistel structure as a whole.We compare the S-boxes generated by the new genetic algorithm with those generated by the traditional one.The comparison results show that the S-boxes generated by the new genetic algorithm have better properties than the S-boxes generated by the traditional genetic algorithm, demonstrating the new genetic algorithm's effectiveness and superiority in developing S-boxes.
A graph based recommender system for managing Covid-19 Crisis

The paper aims to present a graph based recommender system for managing the Covid-19 crisis by considering patient and medical staff data. Working with limited number of medical staff, require optimization when creating the appropriate medical staff to assist patient. Patient medical files usually contain more information about the patient diseases and symptoms. In this paper the recommender system at first analyses the patient medical files to find and decide which profile of medical staff could assist efficiency this patient in a crisis situation. Second the recommender system by taking into account the availability of the medical staff will try to propose others doctors with the same profile and the nearest competencies.
Boosting the Security of Blind Signature Schemes

Existing blind signature schemes that are secure for polynomially many concurrent executions of the signing protocol are either inefficient or rely on non-standard assumptions (even in the random-oracle model). We show the first efficient blind signature schemes achieving this level of security based on the RSA, factoring, or discrete logarithm assumptions (in the random-oracle model). Our core technique involves an extension and generalization of a transform due to Pointcheval (Eurocrypt'98) that allows us to convert certain blind signature schemes that are secure for (concurrently) issuing logarithmically many signatures into ones secure for (concurrently) issuing polynomially many signatures.
Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques

Recent years have witnessed the proliferation of social robots in various domains including special education. However, specialized tools to assess their effect on human behavior, as well as to holistically design social robot applications, are often missing. In response, this work presents novel tools for analysis of human behavior data regarding robot-assisted special education. The objectives include, first, an understanding of human behavior in response to an array of robot actions and, second, an improved intervention design based on suitable mathematical instruments. To achieve these objectives, Lattice Computing (LC) models in conjunction with machine learning techniques have been employed to construct a representation of a child's behavioral state. Using data collected during real-world robot-assisted interventions with children diagnosed with Autism Spectrum Disorder (ASD) and the aforementioned behavioral state representation, time series of behavioral states were constructed. The paper then investigates the causal relationship between specific robot actions and the observed child behavioral states in order to determine how the different interaction modalities of the social robot affected the child's behavior.
Weighted RAFT: An Improved Blockchain Consensus Mechanism for Internet of Things Application

The integration of blockchain technology and RAFT consensus algorithm could address many of the IoT application security and privacy issues. However, each node in RAFT consensus has equivalent probability of being elected as a leader, which takes no consideration on the communication and computing status of the node. Therefore, in this paper, we propose a weighted RAFT consensus algorithm for IoT applications, which allows IoT gateway with better capacity to have a higher possibility of being elected as the leader compared to the normal RAFT mechanism. Thus, the latency of uploading IoT data on blockchain can be reduced a lot. Simulation results show that the weighted RAFT can reduce the system forwarding latency by 24% at most.
Pose graph parsing network for human-object interaction detection

The detection of interactions between humans and objects is one of the core issues in the area of scene understanding in image analysis. The conventional method is to pair the human body with the object as an entity and pay attention to the human spatial area and object. However, this method does not consider two key aspects: humans use certain body parts to interact with objects, and correlations exist between different body parts. Thus, in this paper, we propose a pose graph parsing network (PGPN) for human-object interaction detection. Specifically, we construct a multibranch network to study high-level semantic features. In addition to emphasizing the appearance area of each instance in an image, feature propagation based on a pose graph is further adopted to consider the features of correlation between different body parts. Furthermore, a branch refines and captures the relationship between human parts and an object using a human pose. We validate this approach on the V-COCO and HICO-DET datasets and compare it with the state-of-the-arts. In comparison to other models, the PGPN superior and significantly improves the performance of human-object interaction detection.
Double-Branch Dehazing Network based on Self-Calibrated Attentional Convolution

Single image dehazing is an essential preprocessing task to improve the image’s visual effect. In this paper, we propose a Double-Branch Dehazing Network based on Self-Calibrated Attentional Convolution (DBDN-SCAC), which effectively captures the multi-scale features of important information for solving dehazing problems. More specifically, our proposed DBDN-SCAC uses an encoder–decoder architecture based on a Laplacian Pyramid to learn information at different scales. At the same time, the self-calibration attention convolution module is incorporated after each scaling to expand the receptive field and enrich the output feature. Furthermore, to avoid information loss caused by downsampling, a new self-calibration branch is established to help the image recover more detailed information. Finally, a Content-aware Upsampling module is added in two branches to allow the network in reconstructing better images. The effectiveness of our approach, which is compared with the state-of-the-art methods in the public datasets, is justified by our achieving the best results.
Contrastive Instance Association for 4D Panoptic Segmentation Using Sequences of 3D LiDAR Scans

Scene understanding is critical for autonomous navigation in dynamic environments. Perception tasks in this domain like segmentation and tracking are usually tackled individually. In this letter, we address the problem of 4D panoptic segmentation using LiDAR scans, which requires to assign to each 3D point in a temporal sequence of scans a semantic class and for each object a temporally consistent instance ID. We propose a novel approach that builds on top of an arbitrary single-scan panoptic segmentation network and extends it to the temporal domain by associating instances across time. We propose a contrastive aggregation network that leverages the point-wise features from the panoptic network. It generates an embedding space in which encodings of the same instance at different timesteps lie close together and far from encodings belonging to other instances. The training is inspired by contrastive learning techniques for self-supervised metric learning. Our association module combines appearance and motion cues to associate instances across scans, allowing us to perform temporal perception. We evaluate our proposed method on the SemanticKITTI benchmark and achieve state-of-the-art results even without relying on pose information.
Using MLSTM and Multioutput Convolutional LSTM Algorithms for Detecting Anomalous Patterns in Streamed Data of Unmanned Aerial Vehicles

In this article, we present a comparative study of two existing deep learning tools that are used in a novel way to detect anomalies in the streamed data of the unmanned aerial vehicle (UAV). Detecting anomalies is very vital to predict potential faults that are caused by hardware and software faults and may prevent the UAV from hazardous accidents. Therefore, we suggest using multiple long short-term memory and multioutput convolutional long short-term memory (LSTM) to detect anomalies in UAV data. LSTM networks attracted many researchers in several domains, as it is a useful tool for learning dynamic temporal patterns and long-range dependencies in sequential data, which cannot be achieved using traditional neural networks. However, utilizing multiple LSTM networks would result in too much redundancy. The redundancy issue can be solved by incorporating one convolutional LSTM (ConvLSTM) network with multiple outputs. The ConvLSTM is suitable for analyzing multivariate temporal data; due to its convolutional architecture and the advantage of preserving the benefits of the LSTM networks. We evaluated and compared the two approaches using well-known indicators such as the detection rate, the false alarm rate, the precision, and the F.score indicators. The two methods exhibited promising results in predicting different types of faults, for instance (sensor-impulse and sensor-cut). However, the multioutput ConvLSTM was faster in training and testing phases, and its results were superior in predicting (sensor-stuck and sensor-drift) faults.
Applying benefits and avoiding pitfalls of 3D computational modeling-based machine learning prediction for exploration targeting: Lessons from two mines in the Tongling-Anqing district, eastern China

The 3D computational modeling-based machine learning (ML) prediction is an innovative methodology for exploration-targeting. This paper presents our studies on its application in the Fenghuangshan (FHS) and Anqing (AQ) mines, its benefits and pitfalls and the strategies for applying benefits and avoiding pitfalls. The 3D geological models of the FHS and AQ orefields show that the topography and attitudes of the intrusions’ contact zones have spatial constraints on orebodies. The 3D variation of resistivity can provide some ambiguous evidences for inferring contact zone and orebodies. The dynamic simulations of the intrusions’ cooling processes suggest that the dilatant deformation produced by the coupled mechano-thermo-hydrological (MTH) processes is favor for mineralization. Based on the results of 3D geometric and geodynamic modeling, we conducted the random forest (RF) ML model in the FHS mine and the ML models respectively of artificial neural network (ANN), support vector machine (SVM) and RF in the AQ mine to predict mineral potentials. The RF prediction indicates that there is no significant potential in the FHS mine down to depth of 1400 m. The drills of the FHS Deep Drilling Program (FHSDDP) are all at the locations with low RF prediction probability (PP) of mineralization, which can explain why the FHSDDP had not discovered any orebodies and verifies the reliability of the RF prediction. The ANN, SVM and RF models of AQ mine gave different predictions, although the same data were imputed. The RF prediction indicates that there is no significant potential there down to −1520 m, whilst the SVM prediction shows a few small high potentials there, and ANN prediction shows prominent high potentials there. The drills of the AQ Mine Outer Drilling Program (AQMODP) that failed in ore discovery are all at the locations with low RF PP, but with high ANN PP, suggesting that the RF prediction is more reliable than that of ANN. Such inconsistent predictions of different algorithms and the unsuccessful exploration stories in FHS and AQ mines demonstrate that the 3D computational modeling-based ML technologies are a two edged sword. On the one hand, they can facilitate mineral exploration by characterizing the mineralization system in 3D visual and quantitative prediction of ore potentials. But on the other hand, they have some hidden pitfalls to impede or misguide mineral exploration, such as over extrapolation producing false model, over interpolation creating false big data, overfitting generating false knowledge and over simplification causing false prediction. The right strategies for applying benefits and avoiding pitfalls of 3D computational modeling-based ML prediction are pursuing high quality data, applying a capable ML algorithm, reinforcing field geology-based investigation and ratiocination, and keeping full feedback coupling with exploration processes to ensure correct decision-making and timely updating of data.
A two‐phase knowledge distillation model for graph convolutional network‐based recommendation

Graph convolutional network (GCN)-based recommendation has recently attracted significant attention in the recommender system community. Although current studies propose various GCNs to improve recommendation performance, existing methods suffer from two main limitations. First, user–item interaction data is generally sparse in practice, highlighting these methods' ineffectiveness in learning user and item feature representations. Second, they usually perform a dot-product operation to model and calculate user preferences on items, leading to inaccurate user preference learning. To address these limitations, this study adopts a design idea that sharply differs from existing works. Specifically, we introduce the knowledge distillation concept into GCN-based recommendation and propose a two-phase knowledge distillation model (TKDM) improving recommendation performance. In Phase I, a self-distillation method on a graph auto-encoder learns the user and item feature representations. This auto-encoder employs a simple two-layer GCN as an encoder and a fully connected layer as a decoder. On this basis, in Phase II, a mutual-distillation method on a fully connected layer is introduced to learn user preferences on items with triple-based Bayesian personalized ranking. Extensive experiments on three real-world data sets demonstrate that TKDM outperforms classic and state-of-the-art methods related to GCN-based recommendation problems.
Efficient Robot Motion Planning Using Bidirectional-Unidirectional RRT Extend Function

In this article, based on the rapidly-exploring random tree (RRT), we propose a novel and efficient motion planning algorithm using bidirectional RRT search. First, a RRT extend function is used to organize the sampled states under kinodynamic constraints. Meanwhile, the bidirectional search strategy is implemented to grow a forward tree and backward tree simultaneously in the tree extension process. When these two trees meet each other, the backward tree will act as a heuristic to guide the forward tree to continuously grow toward the goal state, where the algorithm switches to unidirectional search mode. Therefore, the two-point boundary value problem (BVP) in the connection process is avoided, and the extension process gets much accelerated. We also prove that probabilistic completeness is guaranteed. Numerical simulations are conducted to demonstrate that the proposed algorithm performs much better than the state-of-the-art algorithms in different environments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this work is to develop an efficient sampling-based motion planning algorithm for mobile robots. Conventional sampling-based algorithms are time-consuming to find a feasible solution under differential constraints. When applying bidirectional search strategy to improve them, the complex 2-point BVP is required to solve. In this article, the backward free is regarded as a heuristic to guide the tree growth. On the one hand, the advantage of bidirectional search is retained. On the other hand, the 2-point BVP is avoided. Therefore, the bidirectional-unidirectional technique can achieve efficient robot motion planning. The proposed algorithm can be extended to other specified sampling-based algorithms to further improve their performance. Besides, it can be also applied to autonomous driving, service robot and medical robots to achieve efficient motion planning.
Guidelines and Regulatory Framework for Machine Learning in Aviation

Automation and eventually autonomy are regarded as the enabler for upcoming Urban Air Mobility (UAM) / Advanced Air Mobility segment. Only they could enable unprecedented opportunities for scaling drones and air taxis to a large number of vehicles, making the services available for everyone. Artificial Intelligence (AI) in general, Machine Learning (ML) in particular promise a huge leap towards achieving high levels of automation and further autonomy. Nevertheless, the safety concerns and challenges regarding compliance to the existing software standards is now pressing more then ever. Existing regulatory framework for hardware and software items fail to provide adequate acceptable means of compliance for AI-based systems. Hence, there are currently number of ongoing efforts to update and augment the current standards. This paper will give an overview of the existing and upcoming regulatory framework for certifying AI-based systems. It will elaborate the EASA documents, artificial intelligence roadmap, Concepts of Design Assurance for Neural Networks (CoDANN), CoDANN II, as well as the concept paper on first usable guidance for level I machine learning applications. Furthermore, suitable guidance from EuroCAE, RTCA, ASTM and AVSI will be discussed.
An improvement of both security and reliability for AES implementations

Cryptographic circuits, because they contain confidential information, are subject to fraudulent manipulations, commonly called attacks, by ill-intentioned people. Several attacks have been identified and analyzed. One of the most efficient attacks, called DFA (Differential Fault Analysis) cryptanalysis, exploits the presence of faults, injected voluntarily by the attacker, for example with a laser, in the calculations. Countermeasures are then developed and validated to protect the hardware implementation of cryptographic algorithms such as AES algorithm. The present paper aims to protect the AES algorithm against fault injection attacks. In this paper, an efficient fault detection method for the AES algorithm has been developed. In our proposed design, the AES round architecture is devised into three parts and two pipelines registers are inserted in between. Our simulations show that fault detection capabilities of the proposed method for random fault archives 99.539%. The error detection structure can detect the inserted faults with good coverage using the proposed approach. In order to verify the accuracy of our results, we compare our implementation result with the results presented in the literature. Note that the results obtained are competitive with existing implementations. Our fault detection method has the ability to achieve a compromise of safety level and low implementation cost.
ScoreGAN: A Fraud Review Detector Based on Regulated GAN With Data Augmentation

The promising performance of Deep Neural Networks (DNNs) in text classification has attracted researchers to use them for fraud review detection. However, the lack of trusted labeled data has limited the performance of the current solutions in detecting fraud reviews. The Generative Adversarial Network (GAN) as a semi-supervised method has been demonstrated to be effective for data augmentation purposes. The state-of-the-art solutions utilize GANs to overcome the data scarcity problem. However, they fail to incorporate the behavioral clues in fraud generation. Additionally, state-of-the-art approaches overlook the possible bot-generated reviews in the dataset. Finally, they also suffer from a common limitation in the generalization and stability of the GAN, slowing down the training procedure. In this work, we propose ScoreGAN for fraud review detection that makes use of both review text and review rating scores in the generation and detection process. Scores are incorporated through Information Gain Maximization (IGM) into the loss function for three reasons. One is to generate score-correlated reviews based on the scores given to the generator. Second, the generated reviews are employed to train the discriminator, allowing the discriminator to correctly label the possible bot-generated reviews through joint representations learned from the concatenation of GLobal Vector for Word representation (GLoVe) extracted from the text and the score. Finally, it can be used to improve the stability and generalization of the GAN. Results show that the proposed framework outperformed the existing state-of-the-art FakeGAN framework, in terms of AP by 7%, and 5% on the Yelp and TripAdvisor datasets, respectively.