Reviewerly/exHarmony huggingface dataset:
“Get a red-hot poker and open up my eyes, it's so boring”1: Employee perceptions of cybersecurity training

Organisations and security professionals design Security Education, Training, and Awareness (SETA) programs to improve cybersecurity behaviour, but they are often poorly received by employees. To understand employee negative perceptions of SETA programs, we conducted in-depth interviews with 20 Australian employees regarding their experiences with both SETA programs and non-cybersecurity related workplace training. As expected, employees had a generally poor view of SETA programs. They reported that the same factors that are important for effective non-cybersecurity training are also important for SETA programs, such as management role modelling and well-designed workplace systems. However, the level of importance of these factors differed across the two contexts. For example, employees indicated that the misbehaviour of their colleagues is a more important factor for their appraisal of a SETA program than it is for a non-cybersecurity workplace training program. Our results suggest that employee perceptions of SETA programs relate to their previously held beliefs about cybersecurity threats, the content and delivery of the training program, the behaviour of others around them, and features of their organisation. From an applied perspective, these findings can explain why employees often do not engage with cybersecurity training material, and how their current beliefs can influence their receptivity for future training.
CL-ME: Efficient Certificateless Matchmaking Encryption for Internet of Things

The Internet of Things (IoT) is gradually stepping out of its infancy into maturity. Its widespread applications cover from tiny wearable devices to large industrial systems. Although many security solutions have been introduced to address data security and privacy problems caused by the unique characteristics of IoT, how to simultaneously achieve data confidentiality, protect the privacy of access control policy, and provide reasonable data source identification has been a challenging problem. Moreover, lacking one of the above properties may result in serious issues (e.g., leakage information and forging identity), and the situation grows steadily worse with the expansion of “things” scale. To address the above issues, we propose a new cryptographic primitive named certificateless matchmaking encryption (CL-ME), which inherits the security properties of certificateless cryptosystem and matchmaking encryption. Meanwhile, we also present two effective concrete constructions with formal security proofs based on the standard hard assumptions. The basic construction is the first instance of CL-ME based on bilinear pairing, and the enhanced construction is a pairing-free lightweight solution. Finally, we implement our proposed schemes using popular cryptography library and compare their performance with existing works. Theoretical analysis and experimental evaluations demonstrate that our proposed schemes are more suitable for IoT environment.
A Brief Survey of Word Embedding and Its Recent Development

Learning effective representations of text words has long been a research focus in natural language processing and other machine learning tasks. In many early tasks, a text word is often represented by a one-hot vector in a discrete manner. Such a solution is not only restricted by the dimension curse, but also unable to reflect the semantic relationships between words. Recent developments focus on the learning of low-dimension and continuous vector representations of text words, known as word embedding, which can be easily applied to downstream tasks such as machine translation, natural language inference, semantic analysis and so on. In this paper, we will introduce the development of word embedding, describe the representative methods, and report its recent research trend. This paper can provide a quick guide for understanding the principle of word embedding and its development.
Preference prediction based on a photo gallery analysis with scene recognition and object detection

In this paper, a user modeling task is examined by processing mobile device gallery of photos and videos. We propose a novel engine for preferences prediction based on scene recognition, object detection and facial analysis. At first, all faces in a gallery are clustered, and all private photos and videos with faces from large clusters are processed on the embedded system in offline mode. Other photos may be sent to the remote server to be analyzed by very deep sophisticated neural networks. The visual features of each photo are obtained from scene recognition and object detection models. These features are aggregated into a single descriptor in the neural attention unit. The proposed pipeline is implemented in mobile Android application. Experimental results for the Photo Event Collection, Web Image Dataset for Event Recognition and Amazon Fashion data demonstrate the possibility to efficiently process images without significant accuracy degradation.
MBTree: Detecting Encryption RATs Communication Using Malicious Behavior Tree

Network trace signature matching is one reliable approach to detect active Remote Control Trojan, (RAT). Compared to statistical-based detection of malicious network traces in the face of known RATs, the signature-based method can achieve more stable performance and thus more reliability. However, with the development of encrypted technologies and disguise tricks, current methods suffer inaccurate signature descriptions and inflexible matching mechanisms. In this paper, we propose to tackle above problems by presenting MBTree, an approach to detect encryption RATs Command and Control (C&C) communication based on host-level network trace behavior. MBTree first models the RAT network behaviors as the malicious set by automatically building the multiple level tree, MLTree from distinctive network traces of each sample. Then, MBTree employs a detection algorithm to detect malicious network traces that are similar to any MLTrees in the malicious set. To illustrate the effectiveness of our proposed method, we adopt theoretical analysis of MBTree from the probability perspective. In addition, we have implemented MBTree to evaluate it on five datasets which are reorganized in a sophisticated manner for comprehensive assessment. The experimental results demonstrate the accurate and robust of MBTree, especially in the face of new emerging benign applications.
Extreme Learning Machine for Accurate Indoor Localization Using RSSI Fingerprints in Multifloor Environments

A new extreme learning machine (ELM) localization technique that uses received signal strength indicator fingerprints only is proposed for multifloor environments. This structured scheme forms multiple individual ELMs for the floors as well as for the geographically formed data clusters of each floor. Multifloor environments often have huge amount of training and online measurement data. To maximize efficiency, we develop a data preprocessing algorithm, aiming to: 1) efficiently extract out only the essential information from the vast amount of data sets and reduce the data dimension and 2) transform the floor-level data sets and positioning data sets of each floor into a proper structure that is suitable for the proposed ensemble ELM technique. The proposed solution is unique in that its offline phase exploits multiple individual ELMs for all floors to generate a set of floor-level classification functions with the preprocessed training data sets, and for each floor, it exploits multiple ELMs for the data clusters to generate a set of position regression functions. The online phase executes a coarse localization step to estimate the floor by using the floor-level classification functions and a refined step to estimate the position on the floor by using the position regression functions. The proposed algorithm and several existing algorithms are implemented to perform localization using the same measured datasets in a multistory building. For both floor estimation and localization on the floor, it outperforms existing schemes. For most cases, the performance gap is substantial.
Phishing website detection using novel machine learning fusion approach

The Phishing is a sort of social designing assault regularly used to take client information, including login accreditations and credit card numbers. With the enhancements in internet technology, websites are the major resource for the cyber-attacks. There are several counter measures available for avoiding phishing attacks, but phishers are changing their attacking methods from time to time. One of the most widely used techniques for solving cybersecurity issues is machine learning. From last several years, Machine Learning and Deep Learning Techniques are suitable for solving security related issues. Machine Learning is most suitable for detecting phishing attacks because most of the phishing attacks have common characteristics. This paper has applied several machine learning techniques for detecting the phishing attacks. Here, two prioritybased algorithms are proposed. Based on the results of these algorithms, the final fusion classifier is decided. We used a dataset from UCI and applied a novel fusion classifier and achieved an accuracy of 97%. We used Python for implementing our experiments.
The relationship between postural stability and cybersickness: It's complicated – An experimental trial assessing practical implications of cybersickness etiology

Virtual reality (VR) is known to cause cybersickness, and studies report that deteriorations of postural stability coincides with the onset cybersickness. It is unclear whether these deteriorations are the cause or a consequence of cybersickness. Thus, it is also unclear whether measures of postural stability may either predict susceptibility (cause) or objectively measure (consequence) the malaise. To examine whether deteriorations of postural stability can either predict or objectively measure cybersickness, healthy active adults (n = 50) were exposed to one of two different 10 min 360˚ VR videos. Postural stability was assessed, using a force platform, before exposure with eyes open (baseline) and eyes closed, during the first and last minute of exposure, and approximately 10 min after exposure. The deterioration of postural stability from baseline to the first minute of exposure was larger in participants who reported cybersickness, compared to those who did not, for both total trace length (p = 0.017) and standard deviation velocity (p = 0.008). However, there was substantial individual variation and overlap between sick and well participants. Deteriorations of postural stability from baseline to the eyes closed condition did not differ between sick and well participants (trace length, p = 0.270; standard deviation velocity, p = 0.112). There was a significant correlation between the severity of cybersickness and the change of postural stability from the first to the last minute of VR exposure for trace length (rs=0.32, p = 0.027), but not standard deviation velocity (rs=0.20, p = 0.187). The deteriorations had returned to baseline levels 10 min after exposure. These findings suggest that deteriorations of postural stability was both a predictor and objective measure at a group level. However, the large individual variation, substantial overlap between sick and well participants, and the limited strength of correlations suggest that deterioration of postural stability has limited practical value as both a predictor and objective measure. These findings emphasize the complicated nature of the relationship between cybersickness and postural stability.
One-Stage Anchor-Free 3D Vehicle Detection from LiDAR Sensors

Recent one-stage 3D detection methods generate anchor boxes with various sizes and orientations in the ground plane, then determine whether these anchor boxes contain any region of interest and adjust the edges of them for accurate object bounding boxes. The anchor-based algorithm calculates the classification and regression label for each anchor box during the training process, which is inefficient and complicated. We propose a one-stage, anchor-free 3D vehicle detection algorithm based on LiDAR point clouds. The object position is encoded as a set of keypoints in the bird’s-eye view (BEV) of point clouds. We apply the voxel/pillar feature extractor and convolutional blocks to map an unstructured point cloud to a single-channel 2D heatmap. The vehicle’s Z-axis position, dimension, and orientation angle are regressed as additional attributes of the keypoints. Our method combines SmoothL1 loss and IoU (Intersection over Union) loss, and we apply (cosθ,sinθ) as angle regression labels, which achieve high average orientation similarity (AOS) without any direction classification tricks. During the target assignment and bounding box decoding process, our framework completely avoids any calculations related to anchor boxes. Our framework is end-to-end training and stands at the same performance level as the other one-stage anchor-based detectors.
ReXPlug: Explainable Recommendation using Plug-and-Play Language Model

Explainable Recommendations provide the reasons behind why an item is recommended to a user, which often leads to increased user satisfaction and persuasiveness. An intuitive way to explain recommendations is by generating a synthetic personalized natural language review for a user-item pair. Although there exist some approaches in the literature that explain recommendations by generating reviews, the quality of the reviews is questionable. Besides, these methods usually take considerable time to train the underlying language model responsible for generating the text. In this work, we propose ReXPlug, an end-to-end framework with a plug and play way of explaining recommendations. ReXPlug predicts accurate ratings as well as exploits Plug and Play Language Model to generate high-quality reviews. We train a simple sentiment classifier for controlling a pre-trained language model for the generation, bypassing the language model's training from scratch again. Such a simple and neat model is much easier to implement and train, and hence, very efficient for generating reviews. We personalize the reviews by leveraging a special jointly-trained cross attention network. Our detailed experiments show that ReXPlug outperforms many recent models across various datasets on rating prediction by utilizing textual reviews as a regularizer. Quantitative analysis shows that the reviews generated by ReXPlug are semantically close to the ground truth reviews, while the qualitative analysis demonstrates the high quality of the generated reviews, both from empirical and analytical viewpoints. Our implementation is available online.
Type-2 Fuzzy Broad Learning System

The broad learning system (BLS) has been identified as an important research topic in machine learning. However, the typical BLS suffers from poor robustness for uncertainties because of its characteristic of the deterministic representation. To overcome this problem, a type-2 fuzzy BLS (FBLS) is designed and analyzed in this article. First, a group of interval type-2 fuzzy neurons was used to replace the feature neurons of BLS. Then, the representation of BLS can be improved to obtain good robustness. Second, a fuzzy pseudoinverse learning algorithm was designed to adjust the parameter of type-2 FBLS. Then, the proposed type-2 FBLS was able to maintain the fast computational nature of BLS. Third, a theoretical analysis on the convergence of type-2 FBLS was given to show the computational efficiency. Finally, some benchmark and practical problems were used to test the merits of type-2 FBLS. The experimental results indicated that the proposed type-2 FBLS can achieve outstanding performance.
Four best practices for measuring news sentiment using ‘off-the-shelf’ dictionaries: a large-scale p-hacking experiment

We examined the validity of 37 sentiment scores based on dictionary-based methods using a large news corpus and demonstrated the risk of generating a spectrum of results with different levels of statistical significance by presenting an analysis of relationships between news sentiment and U.S. presidential approval.We summarize our findings into four best practices: 1) use a suitable sentiment dictionary; 2) do not assume that the validity and reliability of the dictionary is 'built-in'; 3) check for the influence of content length and 4) do not use multiple dictionaries to test the same statistical hypothesis.
One-On-One Foreign Language Speaking Practice in High-Immersion Virtual Reality

This autoethnographic study investigated the dynamics of five one-on-one tutoring sessions conducted in high-immersion virtual reality (VR). A lower-intermediate, adult learner of German practiced her foreign language speaking skills in VR with a tutor guiding and assisting her. Both the learner and the tutor visited various VR locations, which offered opportunities for situated learning. A qualitative analysis of the tutor-learner interactions confirmed that social VR applications can offer pedagogically valuable settings for the development of foreign language speaking skills. With a pedagogical mindset, we address affordances of social VR applications, including the authenticity of the settings, and practical implications of using VR as a medium for one-on-one instruction. We also discuss the limitations and technical impediments of using VR, including the lack of facial expression tracking and other nonvocal paralinguistic cues for foreign language instruction.
Tumor classification in automated breast ultrasound (ABUS) based on a modified extracting feature network

People can get consistent Automated Breast Ultrasound (ABUS) images due to the imaging mechanism of scanning. Therefore, it has unique advantages in breast tumor classification using artificial intelligence technology. This paper proposes a method for classifying benign and malignant breast tumors using ABUS sequence based on deep learning. First, Images of Interest (IOI) will be extracted and Region of Interest (ROI) will be cropped in ABUS sequence by two preprocessing deep learning models, Extracting-IOI model and Cropping-ROI model. Then, we propose a Shallowly Dilated Convolutional Branch Network (SDCB-Net). We combine this network with the VGG16 transfer learning network to construct a brand-new Shared Extracting Feature Network (SEF-Net) to extract ROI sequence features. Finally, the correlation features of ABUS images are extracted and integrated by using GRU Classified Network (GRUC-Net) to achieve the accurate breast tumors classification. The final results show that the accuracy of the test set for classifying benign and malignant ABUS sequence is 92.86 %. This method not only has high accuracy but also greatly improves the speed and efficiency of breast tumor classification. It has high clinical application significance that more women can discover breast tumors timely.
Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography

This paper proposes a method to extract the position and pose of vehicles in the 3D world from a single traffic camera. Most previous monocular 3D vehicle detection algorithms focused on cameras on vehicles from the perspective of a driver, and assumed known intrinsic and extrinsic calibration. On the contrary, this paper focuses on the same task using uncalibrated monocular traffic cameras. We observe that the homography between the road plane and the image plane is essential to 3D vehicle detection and the data synthesis for this task, and the homography can be estimated without the camera intrinsics and extrinsics. We conduct 3D vehicle detection by estimating the rotated bounding boxes (r-boxes) in the bird’s eye view (BEV) images generated from inverse perspective mapping. We propose a new regression target called tailed r-box and a dual-view network architecture which boosts the detection accuracy on warped BEV images. Experiments show that the proposed method can generalize to new camera and environment setups despite not seeing imaged from them during training.
Rank Consistency Induced Multiview Subspace Clustering via Low-Rank Matrix Factorization

Multiview subspace clustering has been demonstrated to achieve excellent performance in practice by exploiting multiview complementary information. One of the strategies used in most existing methods is to learn a shared self-expressiveness coefficient matrix for all the view data. Different from such a strategy, this article proposes a rank consistency induced multiview subspace clustering model to pursue a consistent low-rank structure among view-specific self-expressiveness coefficient matrices. To facilitate a practical model, we parameterize the low-rank structure on all self-expressiveness coefficient matrices through the tri-factorization along with orthogonal constraints. This specification ensures that self-expressiveness coefficient matrices of different views have the same rank to effectively promote structural consistency across multiviews. Such a model can learn a consistent subspace structure and fully exploit the complementary information from the view-specific self-expressiveness coefficient matrices, simultaneously. The proposed model is formulated as a nonconvex optimization problem. An efficient optimization algorithm with guaranteed convergence under mild conditions is proposed. Extensive experiments on several benchmark databases demonstrate the advantage of the proposed model over the state-of-the-art multiview clustering approaches.
DevOps: A Historical Review and Future Works

DevOps is an emerging practice to be followed in the Software Development life cycle. The name DevOps indicates that it's an integration of the Development and Operations team. It is followed to integrate the various stages of the development lifecycle. DevOps is an extended version of the existing Agile method. DevOps aims at Continuous Integration, Continuous Delivery, Continuous Improvement, faster Feedback and Security. This paper reviews the building blocks of DevOps, challenges in adopting DevOps, Models to improve DevOps practices and Future works on DevOps.
Runoff Prediction and Analysis Based on Improved CEEMDAN-OS-QR-ELM

To solve the problems of low prediction accuracy, poor stability, and low calculation efficiency in runoff forecasting, this study develops an extreme learning machine (ELM) model based on improved complete ensemble empirical mode decomposition adaptive noise (CEEMDAN). The model first uses orthogonal triangular decomposition (QR) to redefine the ELM hidden layer output to construct the orthogonal triangular ELM (QR-ELM). It then introduces the online sequence mechanism (OS) into the QR-ELM model to construct online sequence OR-ELM (OS-QR-ELM), which can effectively improve the efficiency of the ELM models. Then, it uses the correlation wave extension method to extend both ends of the original signal to solve the end effect of CEEMDAN. Finally, it integrates CEEMDAN and OS-QR-ELM combined with parallel computing ideas to construct a parallel CEEMDAN-OS-QR-ELM runoff prediction method. The experimental results show that compared with the support vector regression method combined with empirical mode decomposition, the accuracy and reliability of the parallel CEEMDAN-OS-QR-ELM model was 1.09% and 8% higher, respectively. Compared with the ELM model, the efficiency of OS-QR-ELM was 5%.