Reviewerly/exHarmony huggingface dataset:
Email Summarization to Assist Users in Phishing Identification

Cyber-phishing attacks recently became more precise, targeted, and tailored by training data to activate only in the presence of specific information or cues. They are adaptable to a much greater extent than traditional phishing detection. Hence, automated detection systems cannot always be 100% accurate, increasing the uncertainty around expected behavior when faced with a potential phishing email. On the other hand, human-centric defence approaches focus extensively on user training but face the difficulty of keeping users up to date with continuously emerging patterns. Therefore, advances in analyzing the content of an email in novel ways along with summarizing the most pertinent content to the recipients of emails is a prospective gateway to furthering how to combat these threats. Addressing this gap, this work leverages transformer-based machine learning to (i) analyze prospective psychological triggers, to (ii) detect possible malicious intent, and to (iii) create representative summaries of emails. We then amalgamate this information and present it to the user to allow them to (i) easily decide whether the email is "phishy" and (ii) self-learn advanced malicious patterns.
Heterogeneous Multi-Blockchain Model-based Intellectual Property Protection in Social Manufacturing Paradigm

[Purpose/meaning] In this paper, a unified scheme based on blockchain technology to realize the three modules of intellectual property confirmation, utilization, and protection of rights at the application layer is constructed, to solve the problem of unbalanced and inadequate resource distribution and development level in the field of industrial intellectual property. [Method/process] Based on the application of the core technology of blockchain in the field of intellectual property, this paper analyzes the pain points in the current field of intellectual property, and selects matching blockchain types according to the protection of intellectual property and the different decisions involved in the transaction process, to build a heterogeneous multi-chain model based on blockchain technology. [Conclusion] The heterogeneous multi-chain model based on Polkadot[1] network is proposed to realize the intellectual property protection scheme of a heterogeneous multi-chain model, to promote collaborative design and product development between regions, and to make up for the shortcomings of technical exchange, and weaken the phenomenon of "information island" in a certain extent. [Limitation/deficiency] The design of smart contracts in the field of intellectual property, the development of cross-chain protocols, and the formulation of national standards for blockchain technology still need to be developed and improved. At the same time, the intellectual property protection model designed in this paper needs to be verified in the application of practical cases.
Research on user satisfaction of mobile short video based on rough set

With the continuous development and upgrade of information technology, the mobile short video has gradually become the source of generating information, entertainment, and online shopping on our smartphones. In the article, we build evaluation indicators based on the consumer satisfaction theory to construct a system of user satisfaction evaluation. Meanwhile, conducting a sample survey on the popular mobile short video users on the Internet to collect data using a random online questionnaire. Then, use the rough set to reduce the system and determine the weight, eliminating the subjectivity of the weight setting of the traditional method. Finally, a fuzzy comprehensive evaluation model is established to calculate the total score of user satisfaction of mobile short videos. It improves the authenticity, reliability and accuracy of the results of this study. Based on the research on mobile short video user satisfaction, we propose practical suggestions for developing mobile Internet to promote the short video based on user needs. Then, increase user stickiness and user scale so that mobile short videos will have better development in the future. The advantage of this model is to further optimize the rough set applied to the complex evaluation system. By simplifying the indexes and determining the weights of each index according to the importance of the influential factors, the evaluation results are generally consistent with the original system, which is not only objective and accurate, but also more feasible and meaningful in many complex index systems.
UTransNet: Transformer within U-Net for Stroke Lesion Segmentation

U-Net[1] framework which containing an encoder- decoder architecture is still a comment choice for semantic segmentation in medical area. However, due to the intrinsic locality of convolution operations, the U-Net framework is not capable of capturing long-range dependency. Transformer[2] which can model long-range dependency because of the insider self-attention mechanism, first proposed in natural language processing domain and got a great success, is introduced to computer vision and has achieved promising results in the downstream tasks such as image classification and segmentation. In this paper, we propose UTransNet to explore a way to fuse transformer into U-Net to take both advantage of the characteristics of convolution layer and transformer to segment medical images. We test our end-to-end network on ATLAS datasets and the results demonstrate that the performance of our method is superior than previous U-Net based methods but with the least parameters.
PassImg: A Secure Password Generation and Management Scheme without Storing

Text password is an important authentication method for computer supported cooperative systems and is usually required to be memorable, strong, and non-reusable. Some password managers can help users manage site-specific passwords locally or on the cloud. However, existing solutions still have a single point of failure risk if the password management scheme is breached. To address the risks identified in current password managers, we proposed a password generation and management scheme called PassImg that can generate consistent passwords through hashing a master password with a user-specified salt image. In this scheme, a weak master password is also allowed while not affecting the security of the scheme. We’ve also proposed a solution for offline parameters synchronization, which would decrease online attack vectors. The user-recognizable image as a parameter can be configured on different devices and synchronized through a QR code. PassImg can ensure password usability and security through an offline managing process without storing any data on the server.
Image Based Measurement of Individual Fiber Lengths for Randomly Oriented Short Fiber Composites

Abstract Among a wide range of fiber-reinforced composites, those with randomly oriented short fibers, which are also known as random-chopped fiber-reinforced composites (RaFCs), are the most common composites owing to its ease of manufacturing, flexibility of composite shapes, and good material properties, including light weight and high stiffness. These properties of RaFCs are involved with the lengths and distributions of fibers inside the composites. However, inspecting the fiber lengths and distribution remains a challenging problem, particularly when the lengths and locations of individual fibers need to be distinguished using only X-ray transmission images. The main difficulty arises from the variety of fiber widths and their frequent intersections. To address this problem, this paper proposes a comprehensive software system to localize fibers and measure their lengths. Our system is inspired by a previous work for tracing human hair strands. To adopt the previous method for RaFCs, our system extends classic Gabor filter to explore the locally best parameter sets to suit different fiber shapes. With this adaptive filter, we can extract the locations and orientations of local fibers more robustly for RaFCs. Then individual fibers are traced by solving an initial value problem of an ordinary differential equation. To avoid erroneous tracing which typically occurs at intersections, our method traces only the non-intersecting parts of the fibers initially. After that, we connect the fiber segments using the proximity of their endpoints and the orientations. Through experimental validations on different fiber samples, we demonstrate the stability of the fiber tracing and the robustness of the fiber length calculation. Our system works properly even for X-ray radiographic images of heavily tangled fibers in carbon-fiber-reinforced thermoplastic laminates taken by X-ray Talbot–Lau interferometer.
Enhancement of direct waves based on the probability density function of seismic wave amplitudes

SUMMARY The enhancement of direct waves in seismic records is desirable in various seismological situations, where the accurate picking of seismic phases is necessary for hypocentre location, spectral inversion, waveform inversion and seismic tomography. In this study, we developed a new method to enhance direct waves in seismic waveforms based on the statistical distribution (probability density function, PDF) of the amplitudes of waveforms. The seismic waveform changes from a direct wave to a coda wave as time proceeds, which also causes the PDF of its amplitude to vary with time. The PDF becomes similar to a Gaussian or Rayleigh distribution with time, and hence, direct waves can be enhanced using the dissimilarity between the PDF of the amplitude of the seismic waveform and the Gaussian or Rayleigh distribution. In the proposed method, the Kullback–Leibler divergence is used to measure dissimilarity and it is applied as a weight. We confirmed that the method can effectively enhance direct P and S waves by applying it to seismic records of the 2016 Kumamoto earthquake, synthetic waveforms with noise and volcanic events. The method was also applied to waveforms of the 2008 Iwate–Miyagi Nairiku earthquake, which revealed that direct waves were enhanced, and an overview of the wave field and rupture process was obtained. The proposed method can detect direct waves even from subevents, thereby producing detailed information on the earthquake rupture process. We detected two significant pulses and determined their source location and rupture times. The estimated pulse sources were consistent with parts of the rupture scenario proposed by the previous studies. In addition, for local and teleseismic event records, the proposed method was compared with other methods to study its merits and demerits. The present method can enhance seismic phases on records of local and teleseismic events. Our method detects or enhances direct waves based neither on their amplitudes nor on their temporal variations but on the statistical distribution of their amplitudes, which has both advantages and disadvantages. Although polarization filtering is a powerful method that can enhance seismic phases, it requires three-component waveforms. The proposed method can be applied even to a single component record, which is a significant advantage.
Pedestrian Angle Recognition Based on JDE Multi-object Tracking Algorithm

We often execute multi-object tracking algorithms and pedestrian angle recognition algorithms independently when we solve complex problems that need to track multiple pedestrians and identify their angles simultaneously. Firstly, the multi-target tracking algorithm is used to locate the target and determine the target&#x2019;s identity. Then the pedestrian angle recognition model is used to recognize the pedestrian angle. This strategy is complicated and inefficient since the two models repeatedly extract target features. This paper realizes the combination of multi-object tracking algorithm and pedestrian angle recognition algorithm in response to this problem. Specifically, we added a new classification branch based on the JDE multi-object tracking algorithm. It enables the model to perform pedestrian angle recognition while detecting and embedding feature extraction. Finally, experiments show that the improved algorithm can perform pedestrian tracking and pedestrian angle recognition simultaneously. The algorithm&#x2019;s ability to track large and medium targets is the same as the JDE multi-object tracking algorithm. Compared with the Hydraplus model, the improved algorithm has a higher recognition accuracy. Besides, the algorithm can achieve real-time performance.
Critical factors affecting the decision to adopt cloud computing in Saudi health care organizations

Abstract This research aims to identify factors that affect cloud computing (CC) adoption decisions among Saudi health care organizations. More precisely, a research model has been developed and tested to present factors that can facilitate or inhibit the decision to adopt CC in such organizations. In addition to its theoretical contributions, this model would help to formulate some recommendations that can aid IT/IS managers in Saudi health care organizations in making the right decisions when adopting cloud solutions. These managerial contributions are very interesting since the health care sector is very advanced and the number of health care organizations searching for new and effective IT solutions is growing quickly in Saudi Arabia. A quantitative type of research was conducted using a self‐administered survey methodology. Data collected from 123 Saudi health care organizations are used to test the research hypotheses. They are analyzed by using XLStat by structural equation modeling. The findings revealed that top managers' support and adequate resources, competitive pressure, and security and compatibility, representing organizational, environmental and technology factors, respectively, are perceived as significant factors for adopting a CC system. Considering the direct and indirect effects of the main variables on CC decisions regarding adoption, we propose some recommendations that would benefit decision‐makers and scholars in this area.
Cleaning ground truth data in software task assignment

In the context of collaborative software development, there are many application areas of task assignment such as assigning a developer to fix a bug, or assigning a code reviewer to a pull request. Most task assignment techniques in the literature build and evaluate their models based on datasets collected from real projects. The techniques invariably presume that these datasets reliably represent the “ground truth”. In a project dataset used to build an automated task assignment system, the recommended assignee for the task is usually assumed to be the best assignee for that task. However, in practice, the task assignee may not be the best possible task assignee, or even a sufficiently qualified one. We aim to clean up the ground truth by removing the samples that are potentially problematic or suspect with the assumption that removing such samples would reduce any systematic labeling bias in the dataset and lead to performance improvements. We devised a debiasing method to detect potentially problematic samples in task assignment datasets. We then evaluated the method’s impact on the performance of seven task assignment techniques by comparing the Mean Reciprocal Rank (MRR) scores before and after debiasing. We used two different task assignment applications for this purpose: Code Reviewer Recommendation (CRR) and Bug Assignment (BA). In the CRR application, we achieved an average MRR improvement of 18.17% for the three learning-based techniques tested on two datasets. No significant improvements were observed for the two optimization-based techniques tested on the same datasets. In the BA application, we achieved a similar average MRR improvement of 18.40% for the two learning-based techniques tested on four different datasets. Debiasing the ground truth data by removing suspect samples can help improve the performance of learning-based techniques in software task assignment applications. • Devised a debiasing method to clean task assignment datasets. • Conducted experiments in two task assignment applications. • Debiasing the ground truth data improves learning-based techniques’ performance.
A dynamic penalty approach to state constraint handling in deep reinforcement learning

Deep reinforcement learning (RL) has emerged as a promising approach to solving sequential optimization problems that involve high dimensional state/action space and stochastic uncertainties. Unfortunately, many such problems, especially those related to process control, involve state constraints (typically expressed as inequality constraints) that are difficult to handle. Most RL application studies have incorporated inequality constraints into the training by adding penalty terms for violating the constraints to the reward function. However, while training neural networks to learn the value (or Q) function, one can run into numerical difficulties caused by sharp changes in the value function (VF) at the constraint boundary. This problem can lead to an array of issues including getting stuck in local minima and slow convergence during training which ultimately manifest as poor closed-loop performance when training samples are limited. In this paper, we first examine the effect of the penalty function form on the neural network training performance in deep RL algorithms. To address the slow convergence, we propose a dynamic penalty (DP) approach where the penalty factor is gradually and systematically increased as the iteration episodes proceed during training. The agents trained by a Deep Q-Learning algorithm with the proposed approach were compared with agents trained with other constant penalty functions in a vehicle control problem and in a battery management control problem. Results show that the DP approach can help improve the accuracy of the VF approximation, leading to superior results in the constraint satisfaction and in the degree of violation.
Spectral response feature bands extracted from near standard soil samples for estimating soil Pb in a mining area

Heavy metal contamination has been a critical environmental issue in mining areas, while accurate environmental remediation still faces a great challenge because of the complex feature of historical left-over soil pollution and its unclear spatial distribution patterns. Hyperspectral-based remote sensing techniques became a hot issue in interdisciplinary research on remote sensing and soil contamination, for it enables economical and efficient contamination detection. However, the unclear spectral response feature of soil heavy metal challenges the widespread of its application. In this study, near standard soil samples (NSS) were employed to extract spectral response feature bands of soil Pb. First, NSS was produced by artificially adding heavy metal ion solution in the background soil. Next, based on the proximal hyperspectral data of NSS, the spectral response feature bands were precisely identified by the Monte Carlo Uninformative Variables Elimination (MC-UVE) method as it achieved the highest predicting accuracy of the Partial Least Squares Regression (PLSR) model. Finally, to verify the reliability of the above extracted spectral response feature bands, 46 naturally contaminated soil samples (NCS) were collected synchronously with field and laboratory spectra in a mining area of Hengyang city, China. Enhanced by the corresponding feature bands of NSS, the NCS predicted models for soil Pb achieved the highest accuracy both for laboratory spectra (Rp2 and RPD were improved from 0.45 and 1.36 to 0.71 and 1.87, respectively) and Direct standardization (DS)-converted field spectra (Rp2 and RPD were improved from 0.38 and 1.28 to 0.62 and 1.64, respectively). In conclusion, NSS could provide an effective way to clarify the spectral response feature bands from hyperspectral data. And when combined with MC-UVE, it could serve as a promising approach to inverse the concentration of regional soil heavy metal Pb. HIGHLIGHTSNear standard soil samples were used to extract Pb spectral response feature band.Spectral response feature bands of NSS were effective for the modeling of NCS.Spectral response feature bands of soil Pb are 570-760nm, 1710-2100 nm et al.MC-UVE was a relatively optimal method to extract spectral response feature bands.
Feedback beyond accuracy: Using eye‐tracking to detect comprehensibility and interest during reading

Knowing what information a user wants is a paramount challenge to information science and technology. Implicit feedback is key to solving this challenge, as it allows information systems to learn about a user's needs and preferences. The available feedback, however, tends to be limited and its interpretation shows to be difficult. To tackle this challenge, we present a user study that explores whether tracking the eyes can unpack part of the complexity inherent to relevance and relevance decisions. The eye behavior of 30 participants reading 18 news articles was compared with their subjectively appraised comprehensibility and interest at a discourse level. Using linear regression models, the eye-tracking signal explained 49.93% (comprehensibility) and 30.41% (interest) of variance (p < .001). We conclude that eye behavior provides implicit feedback beyond accuracy that enables new forms of adaptation and interaction support for personalized information systems.
Research on Residual Learning of Deep CNN for Image Denoising

Image denoising is a classical but still popular research topic. Removing noise from corrupted images is an indispensable step for many practical applications. Deep Learning for image denoising has shown favorable performance. Residual Learning of Deep CNN (DnCNN) is proposed for image denoising, and shows desired performance. In the paper, based on DnCNN, some hyperparameters are adjusted for better performance. In addition, a validation step is added during the training process, which allows us to observe the training process to avoid overfitting. With the validation step during the training process, a novel method of learning rate adjustment is introduced to help train the best model for the network. The results show the adjusted network has a better performance compared to the baseline of DnCNN.
Fake online review recognition algorithm and optimisation research based on deep learning

Abstract With the rapid development of the e-commerce industry, online reviews of goods are a great help for consumers to make decisions. With the sharp increase in online order for goods and the explosion of product reviews, some merchants began to hire consumers to make fake purchases for profit, which led to the problem of identifying fake reviews. In this paper, we propose a method that uses feature engineering to eliminate the comments of false reviewers and combines convolutional neural network and recurrent neural network to classify and recognise reviews from the perspective of text. Traditional neural network models such as CNN, LSTM and BILSTM are compared with the hybrid model proposed by the text. The model is optimised by pre-training on the Baidu Baike commodity review database instead of the initial randomising word vector. The experimental results show that the combination of convolutional neural network and recurrent neural network can better extract the global and local features of false comments, and the model has a good effect. The updating of the pre-trained word vector makes the recognition effect of each model better.