Reviewerly/exHarmony huggingface dataset:
Multi-Hop Question Generation with Knowledge Graph-Enhanced Language Model

The task of multi-hop question generation (QG) seeks to generate questions that require a complex reasoning process that spans multiple sentences and answers. Beyond the conventional challenges of what to ask and how to ask, multi-hop QG necessitates sophisticated reasoning from dispersed evidence across multiple sentences. To address these challenges, a knowledge graph-enhanced language model (KGEL) has been developed to imitate human reasoning for multi-hop questions.The initial step in KGEL involves encoding the input sentence with a pre-trained GPT-2 language model to obtain a comprehensive semantic context representation. Next, a knowledge graph is constructed using the entities identified within the context. The critical information in the graph that is related to the answer is then utilized to update the context representations through an answer-aware graph attention network (GAT). Finally, the multi-head attention generation module (MHAG) is performed over the updated latent representations of the context to generate coherent questions. Human evaluations demonstrate that KGEL generates more logical and fluent multi-hop questions compared to GPT-2. Furthermore, KGEL outperforms five prominent baselines in automatic evaluations, with a BLEU-4 score that is 27% higher than that of GPT-2.
Deep labeller: automatic bounding box generation for synthetic violence detection datasets

Abstract Manually labelling datasets for training violence detection systems is time-consuming, expensive, and labor-intensive. Mind wandering, boredom, and short attention span can also cause labelling errors. Moreover, collecting and distributing sensitive images containing violence has ethical implications. Automation is the future for labelling sensitive image datasets. Deep labeller is a two-stage Deep Learning (DL) method that uses pre-trained DL object detection methods on MS-COCO for automatic labelling. The Deep Labeller method labels violent and nonviolent images in WVD and USI. In stage 1, WVD generates weak labels using synthetic images. In stage 2, the Deep labeller method is retrained on weak labels. USI dataset is used to test our method on real-world violence. Deep labeller generated weak and strong labels with an IoU of 0.80036 in stage 1 and 0.95 in stage 2 on the WVD. Automatically generated labels. To test our method’s generalisation power, violent and nonviolent image labels on USI dataset had a mean IoU of 0.7450.
RAFT-MSF: Self-Supervised Monocular Scene Flow Using Recurrent Optimizer

A popular approach to estimate scene flow is to utilize point cloud data from various Lidar scans. However, there is little attention to learning 3D motion from camera images. Learning scene flow from a monocular camera remains a challenging task due to its ill-posedness as well as lack of annotated data. Self-supervised methods demonstrate learning scene flow estimation from unlabeled data, yet their accuracy lags behind (semi-)supervised methods. In this paper, we introduce a self-supervised monocular scene flow method that substantially improves the accuracy over the previous approaches. Based on RAFT, a state-of-the-art optical flow model, we design a new decoder to iteratively update 3D motion fields and disparity maps simultaneously. Furthermore, we propose an enhanced upsampling layer and a disparity initialization technique, which overall further improves accuracy up to 7.2%. Our method achieves state-of-the-art accuracy among all self-supervised monocular scene flow methods, improving accuracy by 34.2%. Our fine-tuned model outperforms the best previous semi-supervised method with 228 times faster runtime. Code will be publicly available to ensure reproducibility.
Low-light image enhancement based on Retinex theory for beam-splitting prism system

Abstract The existing color image enhancement methods are utilized for the most part to directly improve the color image that is obtained through the use of a Bayer array camera, whereas the color image that is obtained through the use of a prism camera is the result of the fusion of three primary color images that have been collected independently. As a result, a method for enhancing color photographs based on three images of primary colors that have a low level of light has been proposed. In this paper, we first transfer RGB to HSV space for image enhancement. In the V channel, we use guided filtering combined with improved SSR method to better preserve edge information. Then the S channel is adjusted adaptively to make the image look more natural. Finally, the image is transferred from HSV space back to RGB space. Compared with other traditional methods, our method achieves better results in PSNR, SSIM and NIQE, and has less computational complexity.
Dataset of bimanual human-to-human object handovers

We present a multi-sensor dataset of bimanual human-to-human object handovers. The dataset consists of 240 recordings obtained from 12 pairs of participants performing bimanual object handovers with 10 objects, and 120 recordings obtained from the same 12 pairs of participants performing unimanual handovers with 5 of those objects. Each recording includes the giver and receiver's 13 upper-body bone position and orientation trajectories, position trajectories for the 27 markers placed on their upper bodies, object position and orientation trajectories, and two RGB-D data streams. The motion trajectories are recorded at 120Hz and the RGB-D streams are recorded at 30Hz. The recordings are annotated with the three handover phases: reach, transfer, and retreat. The dataset also includes four anthropometric measurements of the participants: height, waistline height, arm span, and weight. Our dataset could help investigations of the bimanual reaching motions and grasps utilized by humans while performing handovers. Also, it can be used to train robots to perform bimanual object handovers with humans.
A Multiscale Deformation Representation

.Motivated by Tadmor, Nezzar, and Vese's work [30] dedicated to multiscale image representation using hierarchical \((BV,L^2)\) decompositions, we propose transposing their approach to the case of registration, a task which consists in determining a smooth deformation aligning the salient constituents visible in an image into their counterpart in another. The underlying goal is to obtain a hierarchical decomposition of the deformation in the form of a composition of intermediate deformations: the coarser one, computed from versions of the two images capturing the essential features, encodes the main structural/geometrical deformation, while iterating the procedure and refining the versions of the two images yields more accurate deformations that faithfully map small-scale features. The proposed model falls within the framework of variational methods and hyperelasticity by viewing the shapes to be matched as Ogden materials. The material behavior is described by means of a specifically tailored strain energy density function, complemented by \(L^{\infty }\) -penalizations ensuring that the computed deformation is a bi-Lipschitz homeomorphism. Theoretical results emphasizing the mathematical soundness of the model are provided, among which the existence of minimizers/asymptotic results, and a suitable numerical algorithm is supplied, along with numerical simulations demonstrating the ability of the model to produce accurate hierarchical representations of deformations. A very preliminary version of this work has been accepted for publication in the Eighth International Conference on Scale Space and Variational Methods in Computer Vision [Springer, Cham, Switzerland, 2021] but it does not include all the theoretical results, nor the detailed related proofs. A more complete and detailed analysis of the numerical experiments is also provided. The theoretical analysis of the numerical algorithm (introduced in section 3 and which is a result in itself) will be the subject of a separate article in preparation.Keywordsmultiscale representationhyperelasticityOgden materialsbi-Lipschitz homeomorphismsasymptotic resultsMSC codes68U104965D1828
Steganography on Color Images Using Least Significant Bit (LSB) Method

In some fields, high data security is required for data transmission.This raises concerns about misuse of data to irresponsible parties.To protect it, efforts were made to hide factual information on top of other information, namely steganography using the Least Significant Bit (LSB) method.This method has the advantage of a well-compressed image that is difficult to detect with the naked eye and has a fast process.In this study, the LSB method has two processes, namely the endcoding and decoding processes.The proposed method is tested and then evaluated MSE and computation time.In this research, each test uses 20 text messages and 20 images.The Text messages that were tested for insertion in images consisted of short messages, medium messages, and long messages.The text message will be inserted into the image with a small and a large image.The test results for the category of short messages inserted into thumbnails produce an average MSE value of 0.28 db, an average encoding and decoding processing time of 14 ms.The medium message category embedded in large images produces an average MSE value of 0.029 db, the average encoding and decoding time is 54ms.The long message category inserted in large images produces an average MSE value of 0.11 db, the average encoding and decoding time 1700 ms.The results of the three tests were that all text messages were successfully inserted into the images.
An Empirical Study About the Instability and Uncertainty of Non-functional Requirements

Abstract Managing non-functional requirements (NFRs) has been a challenge in software development for many years. These requirements are typically used to make important architectural decisions early in the project, which can be problematic if they are uncertain or unstable. When this uncertainty is not considered when designing the software architecture, changes are often costly and sometimes even unfeasible. Some empirical studies on the subject have already been carried out, but few have focused on the perspective of professionals with extensive experience on the changes and uncertainties of NFRs. This work aims to expand the understanding about the management, clarity and validation of NFRs to fill this gap in the literature. To achieve this goal, a survey was carried out with professionals to find out how NFRs were managed and validated. For the research design, instead of generic questions, the questionnaire focused on some specific types of NFRs to induce participants to recall and report concrete situations. As a result, 40 valid responses were obtained, most from professionals with more than 10 years of experience. The results reveal that a significant number of NFRs were defined after the delivery of software increments (more than 30%) and that revision and change occurred in about a third of the NFRs. Hence, this study presents evidence that NFRs, as the functional ones, can also be uncertain and change frequently, requiring agile approaches and techniques to evolve the software architecture to consider this uncertainty.
Object Detection and X-Ray Security Imaging: A Survey

Security is paramount in public places such as subways, airports, and train stations, where security screeners use X-ray imaging technology to check passengers’ luggage for potential threats. To streamline this process and make it more efficient, researchers have turned to object detection techniques with the help of deep learning. While some progress has been made, there are few comprehensive literature reviews. This paper provides a comprehensive overview of the standard object detection algorithms and principles in X-ray dangerous goods detection. The article begins by classifying and describing the more popular deep learning object detection techniques in detail and presenting the commonly used publicly available datasets and metrics. And then go on to summarize previous applications of deep learning techniques in X-ray dangerous goods detection, highlighting their successes and limitations. Finally, based on an analysis of the experimental results, it summarizes some of the limitations of deep learning in X-ray baggage detection thus far. It offers insights into the future of this exciting field. With this review, we hope to provide valuable insights and guidance for those seeking to improve public safety through X-ray imaging and deep learning technology.
Neural network based feedback optimal control for pinpoint landers under disturbances

Imitation learning leverages example demonstrations to teach a policy to replicate a desired behavior. In this investigation, the example demonstrations consist of open-loop optimal trajectories calculated off-line. This paper introduces the use of imitation learning to demonstrate optimal feedback control of two different high-fidelity 6-degree-of-freedom (6DOF) lander models. A loss of optimality is shown when disturbances are applied to policies trained only with nominal trajectories. Methodologies such as multi-phase optimal control and triple-single-phase optimal control are applied to include disturbances in the optimal trajectory generation, and the policies are trained to mitigate loss of optimality when disturbances are applied. Monte Carlo simulations show that loss of optimality can be mitigated by including disturbances in the optimal trajectory generation and training data.
‘Is This a Hate Speech?’ The Difficulty in Combating Radicalisation in Coded Communications on Social media Platforms

Abstract The use of the Internet for the purposes of radicalisation is well understood. The use of social media platforms to spread messages of hate and intolerance has become mainstreamed, both as a means of communication and a focus of academic attention. The purpose of this paper is to focus on the complications in addressing these radicalisation efforts where the means of communication is through Internet memes, in which humour and coded language are used as means of radicalising an audience. While existing legal frameworks provide for online platforms to tackle this problem through a combination of assuring immunity from liability for taking action when content is brought to a platforms attention, as well as engaging in voluntary proactive measures, this paper explores the difficulty of addressing content that is more borderline in nature and where arguments concerning humour and freedom of expression may be raised by those spreading these messages in a system providing for significant discretion on the part of online platforms. Considering developments under the Digital Services Act, and an increased focusing on the algorithmic control of content, this article will argue that even these new measures may find the countering of radicalising content conveyed through humour quite difficult.
Geochemical balance of the mineral fraction and statistical analyzes of the Ebolowa municipal lake’s sediments (central Africa): Implication for early diagenesis process

The Ebolowa Municipal Lake (EML) (South Cameroon) in order to identify the early diagenesis processes taking place in the lake and the factors influencing them. To this end, 21 samples were collected. In situ, hydrogen potential, redox potential, conductivity, dissolved oxygen content, and turbidity were measured. In the laboratory, the samples were subjected to mineralogical analysis by X-ray diffraction, geochemical analysis by X-ray fluorescence and ICP-MS, and statistical analysis. The coefficient of variation (Qi) was calculated from the geochemical data. In the water column, OD > 2 mg/L, pH > 7 and Eh < 0 mV. In sediments: pH < 7, Eh values are lower. The contents of 2.08 ≤ TOC ≤ 12.65%. The mineralogical procession consists of quartz, kaolinite, gibbsite, goethite, and siderite. The latter is only present in the EML. The sediments are dominated by SiO2 (60.44-89.47%), Al2O3 (6.55-18.17%), and Fe2O3 (1.15-6.21%). The Qi values range from 0.73 to 2.31. The Mn/Fe ratio values are below 0.40. Qi > 1 for Al, Fe, Mn, Mg, K, Na, P, Ni, Co, Zn, Pb, Cd, Cu, Ba, and V, and Qi < 1 for Si; Qi = 1 for Ca. The hierarchical cluster analysis shows two groups: the first one includes the samples from the central and western parts, while the second one includes those from the eastern and southern parts of the lake. The water column is subject to oxic conditions, while the sediments are anoxic. The rapid consumption of oxygen is due to organic mineralization, which is the main diagenesis observed in the lake. This phenomenon is more accentuated in the western part of the lake.
A comparison of quaternion neural network backpropagation algorithms

This research paper focuses on quaternion neural networks (QNNs) - a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. Previous studies have shown that QNNs outperform real-valued neural networks in basic tasks and have potential in high-dimensional problem spaces. However, research on QNNs has been fragmented, with contributions from different mathematical and engineering domains leading to unintentional overlap in QNN literature. This work aims to unify existing research by evaluating four distinct QNN backpropagation algorithms, including the novel GHR-calculus backpropagation algorithm, and providing concise, scalable implementations of each algorithm using a modern compiled programming language. Additionally, the authors apply a robust Design of Experiments (DoE) methodology to compare the accuracy and runtime of each algorithm. The experiments demonstrate that the Clifford Multilayer Perceptron (CMLP) learning algorithm results in statistically significant improvements in network test set accuracy while maintaining comparable runtime performance to the other three algorithms in four distinct regression tasks. By unifying existing research and comparing different QNN training algorithms, this work develops a state-of-the-art baseline and provides important insights into the potential of QNNs for solving high-dimensional problems.
A data-driven emergency plan evaluation method based on improved RIMER

Implementing suitable emergency plan can greatly reduce the damage and risk caused by emergencies. However, the impact of emergency plan on the response effect to emergency is complex, which makes emergency plan evaluation difficult. Rule-base inference methodology using the evidential reasoning (RIMER) can simulate real complex systems, and adopting RIMER to evaluate emergency plans would rely on less subjective information, so RIMER can be regarded as a competitive potential evaluation tool. In conventional RIMER, the antecedent attributes are usually selected subjectively. Unfortunately, subjectively selecting the antecedent attributes that affect rescue results is difficult due to the complexity of emergencies. Therefore, the joint optimization model for antecedent attributes and belief rule base parameters is constructed in this paper. However, using meta-heuristic algorithm to solve the joint optimization model will face encoding and optimization difficulties. So the joint optimization is simplified into an optimization problem for antecedent attributes and their weights, and a data-driven scheme is designed to determine belief rule base parameters. Finally, a case study on a wildland fire rescue dataset is carried out to verify the effectiveness of the proposed method. Compared with other relative methods, the proposed method shows competitive performance in both predictive accuracy and interpretability.
Task scheduling in real-time industrial scenarios

Task scheduling for microservice-oriented industrial software is a complex process. It is a real-time process where multiple task attributes should be considered and different tasks should be processed in parallel. To address this problem, a scheduling model including a task model and resource model applicable to industrial scenarios is established. Then a task importance function for evaluating the importance of tasks is designed. After that, a Dynamic Importance-aware Online Scheduling Algorithm (DIOS) is proposed, which ranks and schedules all the tasks in decreasing order of importance. To further improve the efficiency and adaptability of the DIOS algorithm, resource reservation, preemptive scheduling and online adaptive tuning methods are established. Finally, simulation results in two typical industrial software scenarios show that the proposed algorithm performs significantly better than the widely used algorithms such as the First Come First Serve Algorithm (FCFS), the Earliest Deadline First Algorithm (EDF), the Least Laxity First Algorithm (LLF) and the Fixed Priority Scheduling Algorithm (FPS).
Continuous authentication with feature-level fusion of touch gestures and keystroke dynamics to solve security and usability issues

Behavioral Biometrics (BB) Continuous Authentication (CA) systems monitor user behavior and continuously re-authenticate user identity alongside the initial login process. Most studies use single behavioral modality systems to authenticate users. However, the behaviors of genuine users may change, and systems fail when significant changes occur. This results in either usability or security issues. In the literature, the fusion of biometrics is used to solve this problem and achieves improved results. This paper presents our research on the design and evaluation of new approaches to CA using fusion of touch gestures and keystroke dynamics. To collect the biometric data from mobile device users we have developed the BioGames App which follows an innovative approach based on the gamification paradigm. We examine each modality separately and investigate if we can improve the performance results with a feature-level fusion. For this reason, a new appropriate feature set is developed that combines touch gestures and keystroke dynamics. We used the Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) and compared their performance. We have shown that feature-level fusion of touch gestures and keystroke dynamics improves the performance of systems and solves security and usability issues. We found that the MLP is superior to LSTM in this context. The MLP achieved Accuracy 98.3% (increased 21.1%), EER 1% (error reduction by 23.7%), TAR 99.4% (increased 46%), TRR 97.4% (increased 10%), FAR 2.6% (reduced by 10.5%), and FRR 0.6% (reduced by 46%).
Evaluating organizational phishing awareness training on an enterprise scale

Employees are often the victims of phishing attacks, posing a threat to both themselves and their organizations. In response, organizations are dedicating resources, time, and employee effort to train staff to identify simulated phishing attacks. However, the real-world effectiveness of these training efforts in large enterprises remains largely unexplored. To address this, we carried out a controlled experiment in an Israeli financial institution with approximately 5,000 employees. The experiment included three simulated phishing emails, and we examined how different factors influence the phishing Click-Through Rate (CTR). Our findings suggest that employees are more likely to engage with phishing simulation emails that use personalized phrasing. We also found that phishing CTR varies between business units, and that the timing of training before the simulated email did not significantly affect phishing CTR. Furthermore, it became clear that training prior to phishing simulations and adopting a data-driven approach that includes process, variable and measure analysis, can enhance organizational awareness of phishing. Although advanced technologies can mitigate some phishing attacks, our research indicates that employee awareness and proactive behavior will continue to play a critical role in the foreseeable future. The paper concludes by providing guidelines to information security officers on establishing effective organizational awareness to prevent phishing attacks.