Reviewerly/exHarmony huggingface dataset:
A Novel Dictionary Generation Methodology for Contextual-Based Password Cracking

It has been over 50 years since the concept of passwords was introduced and adopted in our society as a digital authentication method.Despite alternative authentication methods being developed since, it is reasonable to assume that this prevailing method of authentication will not be toppled anytime soon.Naturally, each password is tightly connected to its creator.This connection has given rise to advanced techniques aimed at exploiting user habits for password cracking.Such techniques are often generic approaches leveraging large datasets of human created passwords.A 2021 study showed that the online identity of almost one in three Americans was stolen in the last year alone, with a further 13% not being sure if their credentials were also breached [1].Recent research has underlined the influence that context can have during password selection for a user.Such information could be of significant added value when digital investigators need to target a specific user or group of users during a criminal investigation.Besides manual techniques, there are no automated approaches that can extract and utilize contextual information during password cracking processes.In this paper, a methodology and framework for creating custom dictionary lists for dictionary attacks are introduced, with a specific focus on leveraging the contextual information encountered during an investigation.Furthermore, a detailed explanation the framework's implementation is provided and the benefits of the approach are demonstrated with the use of test cases.This demonstrates the benefits of context in password cracking.
Guided Erasable Adversarial Attack (GEAA) Toward Shared Data Protection

In recent years, there has been increasing interest in studying the adversarial attack, which poses potential risks to deep learning applications and has stimulated numerous researches, e.g. improving the robustness of deep neural networks. In this work, we propose a novel double-stream architecture &#x2013; Guided Erasable Adversarial Attack (GEAA) &#x2013; for protecting high-quality labeled data with high commercial values under data-sharing scenarios. GEAA contains three phases, the double-stream adversarial attack, denoising reconstruction, and watermark extraction. Specifically, the double-stream adversarial attack injects erasable perturbations into the training data to avoid database abuse. The denoising reconstruction rebuilds the traceable denoising data from adversarial examples. The watermark extraction recovers identity information from the denoised data for copyright protection. Additionally, we introduce the annealing optimization strategy to balance these phases and a boundary constraint to degrade the availability of adversarial examples. Through extensive experiments, we demonstrate the effectiveness of the proposed framework in data protection. The Pytorch&#x00AE; implementations of GEAA can be downloaded from an open-source Github project https://github.com/Dlut-lab-zmn/ GEAA-for-data-protection.
A Matrix Factorization Recommendation System-Based Local Differential Privacy for Protecting Users’ Sensitive Data

The recommendation system (RS) predicts user ratings by collecting user information, but the users’ private information may be exposed in this process. Thus, it is crucial to achieving a balance between recommendation performance and privacy-preserving of RSs. Aiming to solve the above problem, this article proposes a novel matrix factorization (MF) algorithm. The algorithm predicts the user rating through a linear weighting of global average rating, item average rating, user average rating, and MF, which improves the prediction accuracy. Then, based on the above algorithm, this article proposes a MF RS for preserving user privacy by using local differential privacy technology. In this algorithm, the rating data are normalized on the user side to reduce global sensitivity. Then, Laplace noise is added to sensitive data before it is sent to the aggregator. Finally, based on the disturbed data, rating prediction is realized by using the MF algorithm. The proposed method is compared with five well-known recommendation methods on four public datasets. The experimental results show that the proposed algorithm achieves better recommendation performance at the same privacy-preserving level.
A Hybrid Deep Neural Network for Multimodal Personalized Hashtag Recommendation

Users share information on social media platforms by posting visual and textual contents. Due to the massive influx of user-generated content, hashtags are extensively used to manage, organize, and categorize the content. Despite the usability of hashtags, many social media users refrain from assigning hashtags to their posts owing to the uncertainty in choosing appropriate hashtags. Several methods have been proposed to recommend hashtags using content-based information. However, multimodality and personalization aspects of hashtag recommendation have rarely been addressed. In light of the above, we propose a multimoDal pErSonalIzed hashtaG recommeNdation (DESIGN) method that incorporates relevant information embedded in textual and visual modalities of social media posts and models user interests to recommend a plausible set of hashtags. We use word-level attention (WA) on the textual modality followed by a parallel co-attention (PCA) mechanism to model the interaction between textual and visual modalities. Unlike the existing works, we present a hybrid deep neural network that capitalizes hashtags from multilabel classification (MLC) and sequence generation (SG) to recommend candidate hashtags for social media posts. We perform our experiments on social media datasets containing textual, visual, and user information. Experimental results show that the proposed method outperforms the state-of-the-art methods.
Development of AIS Using Simulated Learners, Bayesian Networks and Knowledge Elicitation Methods

The development of adaptive instructional systems (AIS) is an iterative process where both empirical data on human performance and learning, and experimentation using computer simulations can play a role. The paper presents our current efforts to advance adaptive instructional system technology conceived as self-improvement systems [37]. The paper describes our methodological approach for informing the design and implementation of adaptive instructional systems by conducting concurrent research activities using 1) Bayesian networks for modelling learning processes, 2) knowledge elicitation of expert instructors, and 3) simulated learners and tutors to explore AIS system design options. Each activity fulfills separate but complementary objectives. Bayesian networks modelling of learners' performance provides the means to implement predictions of learners' performance, and selection of adaptive learning content. Knowledge elicitation methods are fundamental in understanding human capabilities and limitations in the context of AIS systems design that support and regulate the cognitive demands of the learner and instructor. Simulated learner and tutor interactions enable the specification of detailed cognitive process models of learning and instructions.
Human-Machine Duality: What’s Next In Cognitive Aspects Of Artificial Intelligence?

The goal of the paper is to find means for the unification of human-machine duality in collective behavior of people and machines, by conciliating approaches that proceed in opposite directions.The first approach proceeds top-down from non-formalizable, cognitive, uncaused, and chaotic human consciousness towards purposeful and sustainable human-machine interaction.The second approach proceeds bottom-up from intelligent machines towards high-end computing and is based on formalizable models leveraging multi-agent architectures.The resulting work reviews the extent, the merging points, and the potential of hybrid artificial intelligence frameworks that accept idea of strong artificial intelligence.These models concern the pairing of connectionist and cognitive architectures, conscious and unconscious actions, symbolic and conceptual realizations, emergent and brain-based computing, automata and subjects.The special authors' convergent methodology is considered, which is based on the integration of inverse problem-solving on topological spaces, cognitive modelling, quantum field theory, category theory methods, and holonic approaches.It aims to a more purposeful and sustainable human-machine interaction in form of algorithms or requirements, rules of strategic conversations or network brainstorming, and cognitive semantics.The paper addresses reducing the impact of AI development on breaking ethics.The findings are used to provide perspectives on the shaping of societal, ethical, and normative aspects in the symbiosis between humans and machines.Implementations in real practice are represented.
Data-to-text Generation with Variational Sequential Planning

Abstract We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input. We focus on generating long-form text, that is, documents with multiple paragraphs, and propose a neural model enhanced with a planning component responsible for organizing high-level information in a coherent and meaningful way. We infer latent plans sequentially with a structured variational model, while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Experiments on two data-to-text benchmarks (RotoWire and MLB) show that our model outperforms strong baselines and is sample-efficient in the face of limited training data (e.g., a few hundred instances).
An Improved H-Algorand Consensus Mechanism With Credibility Assessment Applying in EMR Storage System

In order to enhance the security and throughput of medical records storage system, a model using an improved H-Algorand consensus mechanism with credibility assessment is proposed.In this study, the model applies blockchain technology to realize distributed storage and privacy protection of medical data.To ensure the effective operation of the blockchain system, a credibility assessment algorithm is used to obtain the trust value of each medical institution node, and replaces the account balances in the H-Algorand consensus mechanism with the trust value as the node weight.Meanwhile, this paper designs a privacy protection scheme, through the searchable encryption algorithm and asymmetric encryption algorithm to achieve system data storage and sharing.Compared with the existing medical record storage model, the proposed model has higher security and throughput efficiency.
Cover Selection for Steganography Using Image Similarity

Existing cover selection methods for steganography mainly focus on embedding distortion of each image, but ignore the similarity between images. When the cover images are similar, a number of relevant samples are provided to steganalysis, which is disadvantageous to steganography. This paper proposes a new cover selection method to joint image similarity and embedding distortion. Due to the difference between steganography and other image processing tasks, e.g., image reconstruction, image recognition, we propose a customized method to calculate image similarity for steganography based on SVD (singular value decomposition). Importantly, the small singular values (instead of the large ones) are employed, since it is suitable for the properties of steganography. In addition, embedding distortion is calculated by the current distortion minimization framework. The obtained image similarity and embedding distortion are combined to form a new cover selection strategy. As a result, the properties of batch images can be fully used. Experimental results show that our scheme outperforms the state-of-the-art cover selection methods when they are checked by modern steganalytic tools.
A Wavelet-Based Robust Medical Image Watermarking Technique Using Whale Optimization Algorithm for Data Exchange Through Internet of Medical Things

Medical image plays a vital role in altering the diagnostic process. Using the Internet of Things (IoT) in healthcare applications, the diagnosis process will be more successful in medical image analysis. However, to accomplish privacy-preserving medical image exchange, we need a solid and secure approach. The primary objective of the proposed model in this chapter is to watermark the medical image by preserving the privacy of individuals based on a hybrid approach for telemedicine and the use of the IoT. Firstly, discrete wavelet transform (DWT) estimates the coefficients to incorporate the singular values. The optimum embedding coefficients are then determined using the Whale Optimization Algorithm’s cost function evaluation (WOA). The watermark medical image is embedded using the singular value decomposition (SVD) and inverse SVD (ISVD) methods. Despite the combination of different attack methods, PSNR and NC were associated with a 22% and 17% improvement in the watermark process, respectively. The proposed watermarking method generates high PSNR values and produces results of outstanding quality with preserving privacy. Additionally, it presents high correlation results when combined attacks occur and requires less computational processing time.
Improving Throttled Load Balancing Algorithm in Cloud Computing

The service and resource delivery model through high-speed internet is called as Cloud Computing. Users of Cloud Computing has increased exponentially due to specific characteristics like pay per usage and use anywhere, any time without human intervention. The emergence of Cloud Computing has reduced the initial investment from the service providers' point of view and this, in turn, results in low-cost service for service users. Cloud Computing has many issues associated with it, out of which performance is a major challenge. The elasticity of resources without paying a premium increased the traffic on the internet rapidly. Such a rapid increased workload overloads the server and leads to performance inefficiency. One of the techniques to overcome this challenge is using load balancing. This needs to be done cautiously because of failure in any one of the virtual machines can lead to the unavailability. We compare performance of the three well-known algorithms: Equally Spread Current Execution, Round Robin and Throttled load balancing and the result shows Throttled performs better that the two but it still need improvement. The proposed algorithm improves performance of Throttled algorithm queues introducing two queues. One of the queues is used to store available virtual machines and the other queue, priority based, is used to store busy virtual machines. When a request arrives the load balancer pop the front virtual machine from available queue, assign the request to it and then push it too busy queue. Cloud Analyst simulator is used for simulation and the result shows the proposed algorithm improves the response time and resource utilization of the cloud computing environment.
A Detection Model of the Complex Dynamic Traffic Environment for Unmanned Vehicles

It has always been an important and arduous task to detect the complex dynamic traffic environment, especially for unmanned driving. Although the existing advanced detection models have reached the speed requirements for detection, the detection accuracy needs to be further elevated to improve the unmanned driving’s safety. How to balance the accuracy and speed of detecting the complex dynamic traffic environment is still the primary problem to be solved for unmanned vehicles. Therefore, this article proposes a detection model of the complex dynamic traffic environment for unmanned vehicles by following the framework idea of YOLOv3. Firstly, we regard MobileNetv3 as the backbone and replace the traditional convolution with the depthwise separable convolution in the whole model to reduce the number of parameters and calculations. Secondly, in enhanced feature fusion layers, we perform the multi-scale fusion of four feature maps by the compress-and-expand module, the SPP module, and the cross-layer bidirectional module of feature fusion to improve the locating accuracy and reduce false detections. Thirdly, we add an IoU loss to improve the accuracy of model regression. Then, we employ the improved clustering algorithm to re-cluster anchor boxes, reducing the time overhead while improving the clustering accuracy. Finally, we compare the proposed model with other advanced detection models in the processed BDD dataset and the KITTI dataset. We verify that the mAP of the proposed model improves notably without loss of detection speed, the number of parameters and calculations decreases dramatically, and the proposed model exhibits a more superior performance.
FR-DETR: End-to-End Flowchart Recognition With Precision and Robustness

As flowchart images become diverse and complex, existing flowchart recognition methods no longer achieve satisfactory recognition accuracy, particularly for images that contain rarely used symbols and texture backgrounds.Existing deep-learning-based object detectors and line segment detectors are promising in recognizing symbols and connecting edges separately.However, using two separate detectors for symbol and edge detection will inevitably cause unnecessary training and inference costs.Moreover, the insufficient volume and diversity of available dataset further limit the overall recognition accuracy.To address these issues, this paper proposes an end-to-end multi-task network FR-DETR (Flowchart Recognition DETection TRansformer) and a new dataset for precise and robust flowchart recognition.FR-DETR comprises a CNN backbone and a shared multi-scale Transformer structure with two prediction heads for symbol detection and edge detection respectively.The multi-scale Transformer encodes and decodes feature maps with different resolutions to jointly detect symbols and edges in a coarse-to-fine refinement process.The coarse stage uses features with low resolution and suggests candidate regions that contain potential targets for the fine stage to produce accurate predictions using features with high resolution.At each stage, every task detects targets using shared features and its respective prediction head.A new dataset is constructed to provide more symbol types and complex backgrounds for network training and evaluation.It contains more than 1000 machine-generated flowchart images, 25K+ symbol instances with nine categories, and 20K+ line segments.The experiments show that FR-DETR achieves an overall precision and recall of 94.0% and 93.1% on the proposed dataset, and 98.7% and 98.1% on the CLEF-IP dataset, respectively, which all outperform the prior methods. INDEX TERMS Flowchart recognition, multi-task learning, multi-scale Transformer
Objective-Constraint Mutual-Guided Surrogate-Based Particle Swarm Optimization for Expensive Constrained Multimodal Problems

Expensive constraint multimodal optimization problems (ECMMOPs) have such characteristics as expensive objectives and constraints, and multiple optimal modalities simultaneously, which pose severe challenges to evolutionary optimization methods. This article studies an objective-constraint mutual-guided surrogate-assisted particle swarm optimization algorithm for the kind of problem, aiming to discover multiple competing feasible optimal solutions at a lower calculation cost. The algorithm designs first a new two-layer cooperative surrogate model framework based on heterogeneous database to effectively adjust the prediction accuracies of objective surrogates and constraint surrogates on different search regions. An objective-constraint mutual-guided partial evaluation strategy (O-C-PES) is developed to generate high-quality infilling samples for objective and constraint surrogates, respectively, based on which the number of unnecessary real evaluations can be significantly reduced. Moreover, a position feature-guided hybrid update mechanism (PF-HUM) is proposed to find more optimal solutions by searching excellent infeasible and feasible areas at the same time, and a feasible ratio-driven local search (FR-LS) strategy is proposed to improve the algorithm’s exploitation. Compared with four existing surrogate-assisted evolutionary algorithms (EAs) and one constraint multimodal EAs on 21 benchmark problems and three engineering instances, experiment results show that the proposed algorithm can simultaneously obtain multiple highly-competitive feasible optimal solutions with less computational cost.
Using Human Body Capacitance Sensing to Monitor Leg Motion Dominated Activities with a Wrist Worn Device

Inertial measurement unit (IMU) is currently the dominant sensing modality in sensor-based wearable human activity recognition. In this work, we explored an alternative wearable motion-sensing approach: inferring motion information of various body parts from the human body capacitance (HBC). While being less robust in tracking the body motions, HBC has a property that makes it complementary to IMU: It does not require the sensor to be placed directly on the moving part of the body of which the motion needs to be tracked. To demonstrate the value of HBC, we performed exercise recognition and counting of seven machine-free leg-alone exercises. The HBC sensing shows significant advantages over the IMU signals in both classification(0.89 vs 0.78 in F-score) and counting.
Blockchain technologies in the digital supply chain

The application of blockchain or, more generally, distributed ledger technology in logistics and supply chain management has created a huge amount of interest among academics and practitioners. Blockchain's inherent characteristics include immutable data, seamless information flows, and shared access to data. Also, the potential to deploy smart contracts (program code executed automatically) with blockchain has raised high hopes for improving the effectiveness, efficiency, and sustainability of supply chains. In this chapter, we clarify the meaning of terms used in the blockchain ecosystem and present the results from an extensive literature review from which we summarize current findings. We highlight the principal drivers influencing blockchain adoption—traceability, trust and transparency, supply chain integration, data security, privacy, and sustainability. We note technical, organizational, and regulatory barriers that may inhibit adoption of blockchain in supply chain applications, including scalability, investment costs versus perceived benefits, data sharing and interoperability challenges, and lack of standards or regulations. Although blockchain adoption is still in its early stages, an increasing number of applications are being reported. We identify reported supply chain applications classified by industry sector. This chapter equips scholars and practitioners with an understanding of how blockchain can provide value in contemporary supply chains.