Reviewerly/exHarmony huggingface dataset:
Using alternate reality games to find a needle in a haystack: An approach for testing insider threat detection methods

Insider threats are individuals who pose significant security risks but are difficult to identify with traditional methods that rely on passively collected data. Recently, active indicators have been developed as a more active monitoring method designed to evoke differential behaviors in insider threats and benign employees. While these methods have shown promise, it is unclear how well they can work in real-world office settings. In this experiment, we tested three classes of email-based active indicators in an alternate reality game to assess their ability to differentiate insiders from benign employees in a realistic setting. Participants took turns playing the role of a benign employee and an insider threat in an immersive, realistic environment and were exposed to active indicators under both scenarios. The active indicators were designed to elicit the following behaviors from participants acting as insider threats: exploit opportunities to gather information, avoid accidental or inadvertent discovery, or maintain hypervigilant security awareness. The alternate reality game was successful in creating a highly engaging environment with high ecological validity. Active indicators that revealed opportunities to gather desirable information were most effective; participants acting as insider threats were significantly more likely to engage in the characteristic behavior (e.g. apply for an administrative position to get additional access) than participants acting as benign employees for most of the active indicators in this class. Our results suggest that active indicators can be tested with alternate reality games to help estimate their effectiveness in realistic, noisy environments. The finding that some types of active indicators could identify insider threats in a setting where participants had significant latitude for how they could respond suggests promise for using active indicators in real-world work environments.
Analysis of the Architecture of the Mental Health Education System for College Students Based on the Internet of Things and Privacy Security

In recent years, the rapid development of computer and network technology has produced various positive and negative effects on the mental health of college students.This also brings challenges to mental health education in colleges.In order to strengthen the research on the mental health education model under the network environment, this paper proposes the architecture of the college student mental health education system based on the privacy and security of the Internet of Things.First of all, this article combines the 3DES-RC4 hybrid security encryption algorithm based on the Internet of Things.This article uses the C/S architecture, MQTT protocol and SIP protocol based on the Internet of Things structure to design and implement instant messaging IoT security for mental health education Architecture.The extreme learning machine method combined with the differential privacy method is used in this article.By adding noise to the query results and adding an appropriate amount of noise to the analysis results, the protection of private data can be achieved.Finally, the data set experiment proves that compared with the existing algorithms, the algorithm and model proposed in this paper can better balance the level of privacy protection and classification accuracy.
ExplorerTree: A Focus+Context Exploration Approach for 2D Embeddings

In exploratory tasks involving high-dimensional datasets, dimensionality reduction (DR) techniques help analysts to discover patterns and other useful information. Although scatter plot representations of DR results allow for cluster identification and similarity analysis, such a visual metaphor presents problems when the number of instances of the dataset increases, resulting in cluttered visualizations. In this work, we propose a scatter plot-based multilevel approach to display DR results and address clutter-related problems when visualizing large datasets, together with the definition of a methodology to use focus+context interaction on non-hierarchical embeddings. The proposed technique, called ExplorerTree, uses a sampling selection technique on scatter plots to reduce visual clutter and guide users through exploratory tasks. We demonstrate ExplorerTree's effectiveness through a use case, where we visually explore activation images of the convolutional layers of a neural network. Finally, we also conducted a user experiment to evaluate ExplorerTree's ability to convey embedding structures using different sampling strategies.
An ISM Modelling of Success Factors for Blockchain Adoption in a Cyber Secure Supply Chain

The main purpose of this paper is to identify and rank the factors affecting the implementation of blockchain technology as a key component of cyber supply chain risk management (CSCRM). This paper adopted a comprehensive literature review and the opinion of experts with knowledge of blockchain to identify success factors for blockchain adoption in a CSCRM. The seven factors identified were modeled using an “Interpretive Structural Modelling (ISM)” in the structure of a hierarchical model to envision the contextual relationship amongst identified success factors (SFs) for blockchain adoption in a cyber-secure supply chain. Using the MICMAC approach, the influential factors were clustered according to their driving and dependence powers. Findings from the study suggest, “Supporting platforms and tools” and “Interoperability & Standardization” are the two factors at the top level of the hierarchy, implying weak driving power but strongly dependent on the other factors. It is also observed that “Clarity regulatory provisions” and “Organizational culture and top management support” are significant adoption success factors of blockchain in cyber supply chain risk management and were placed at the bottom level of the hierarchy with higher driving power.
Dip-based Deep Embedded Clustering with k-Estimation

The combination of clustering with Deep Learning has gained much attention in recent years. Unsupervised neural networks like autoencoders can autonomously learn the essential structures in a data set. This idea can be combined with clustering objectives to learn relevant features automatically. Unfortunately, they are often based on a k-means framework, from which they inherit various assumptions, like spherical-shaped clusters. Another assumption, also found in approaches outside the k-means-family, is knowing the number of clusters a-priori. In this paper, we present the novel clustering algorithm DipDECK, which can estimate the number of clusters simultaneously to improving a Deep Learning-based clustering objective. Additionally, we can cluster complex data sets without assuming only spherically shaped clusters. Our algorithm works by heavily overestimating the number of clusters in the embedded space of an autoencoder and, based on Hartigan's Dip-test - a statistical test for unimodality - analyses the resulting micro-clusters to determine which to merge. We show in extensive experiments the various benefits of our method: (1) we achieve competitive results while learning the clustering-friendly representation and number of clusters simultaneously; (2) our method is robust regarding parameters, stable in performance, and allows for more flexibility in the cluster shape; (3) we outperform relevant competitors in the estimation of the number of clusters.
Anonymous Blockchain-based System for Consortium

Blockchain records transactions with various protection techniques against tampering. To meet the requirements on cooperation and anonymity of companies and organizations, researchers have developed a few solutions. Ring signature-based schemes allow multiple participants cooperatively to manage while preserving their individuals’ privacy. However, the solutions cannot work properly due to the increased computing complexity along with the expanded group size. In this article, we propose a Multi-center Anonymous Blockchain-based (MAB) system, with joint management for the consortium and privacy protection for the participants. To achieve that, we formalize the syntax used by the MAB system and present a general construction based on a modular design. By applying cryptographic primitives to each module, we instantiate our scheme with anonymity and decentralization. Furthermore, we carry out a comprehensive formal analysis of our exemplified scheme. A proof of concept simulation is provided to show the feasibility. The results demonstrate security and efficiency from both theoretical perspectives and practical perspectives.
Attention-based domain adaptation for single-stage detectors

Abstract While domain adaptation has been used to improve the performance of object detectors when the training and test data follow different distributions, previous work has mostly focused on two-stage detectors. This is because their use of region proposals makes it possible to perform local adaptation, which has been shown to significantly improve the adaptation effectiveness. Here, by contrast, we target single-stage architectures, which are better suited to resource-constrained detection than two-stage ones but do not provide region proposals. To nonetheless benefit from the strength of local adaptation, we introduce an attention mechanism that lets us identify the important regions on which adaptation should focus. Our method gradually adapts the features from global, image level to local, instance level. Our approach is generic and can be integrated into any Single-Shot Detector. We demonstrate this on standard benchmark datasets by applying it to both the single-shot detector (SSD) and a recent variant of the You Only Look Once detector (YOLOv5). Furthermore, for equivalent single-stage architectures, our method outperforms the state-of-the-art domain adaptation techniques even though they were designed for specific detectors.
Deep learning frequent pattern mining on static semi structured data streams for improving fast speed and complex data streams

This paper illustrated deep learning Semi-structured Tree Miner for Data Stream's algorithm focused on frequent pattern mining used in data streams on semi-structured data. In the target time, the algorithm obtains a frequent pattern. The Semistructured Tree Miner for Data Streams algorithm can handle sliding windows use the Time attenuation model to minimise historical data on the mining process relative to traditional algorithms, following the concept of inclusion and exclusion conservation at the same time and full real-time mining operations. And it is effective.
Early-Anomaly Prediction in Surveillance Cameras for Security Applications

In the last decade, the number of surveillance cameras has increased significantly, with much research conducted to automate the process of surveillance, as humans cannot manage to monitor all these cameras individually, which may cause errors in public safety or abnormal situations. Also, humans may overlook key details in such abnormal behaviours in surveillance cameras. The proposed approach predicts abnormal behaviour using generative adversarial networks (GANs). GANs are trained using different datasets that contain various behaviours to predict future frames. These future frames are transmitted to a deep learning neural network to classify them as normal or abnormal activities, and future anomalies can be detected before they happen. Our initial results show that depending on the future frames extracted by the GAN model is possible, as these extracted frames either improve the accuracy of the detection model or do not affect it, but they can also be further enhanced to detect more frames at a longer duration and predict anomalies before they happen. Anomalies in surveillance will not only be detected but also predicted before they happen, which will result in the prevention of crimes, reductions in surveillance costs and a safer environment overall.
Footprints of ore fluid pathway and implications to mineral exploration in the Shihu Gold Deposit, North China: Evidence from short wave infrared spectroscopy of illitic alteration rocks

Most hydrothermal ore deposits have experienced multiple stages of hydrothermal activities and the overprinting makes the footprints of ore fluid pathway difficult to identify, which prevents us from targeting the mineral resource effectively. In this paper, the altered wall-rocks from all accessible underground tunnels of No.101 lode in the Shihu Gold Deposit, North China were investigated by the combined methods of petrographic microscopy and short wave infrared spectroscopy (SWIR). The results show that there are three types of alteration in the Shihu Gold Deposit, i.e., the proximal illitic alteration, the transitional chloritic alteration and the distal propylitic alteration. The wavelength (wvl) of the 2200 nm absorption features (2200wvl) of the illitic alteration rocks around ore bodies are shorter than those from the barren areas, suggestive of the compositional variation of illites derived from the multiple stages of hydrothermal fluids. The domains with higher absorption depth at 1400 nm, 1900 nm and 2200 nm (1400D, 1900D and 2200D) are spatially coincident with the economic ore bodies, which is well in accordance with the field truth that the gold mineralization in the Shihu mining district is intimately related to illitic alteration. The band ratio of 2200D/1900D, which is regarded as the proxy to illite crystallinity, is positively related to the temperature of fluid inclusions. The coincidence between low crystallinity and gold mineralization reflects the fact that fluid temperature during the main mineralization stage was relatively low compared to those of previous stages. Detailed investigation indicated that the economic ore bodies mainly occurred in the structural dilation domains, i.e., the widened portions of the ore-controlling structure were more suitable for the fluids migration and probably acted as the fluid up-flow zones. Therefore, SWIR spectroscopy of illite is a powerful tool to trace the ore fluid pathway, which is crucial to the understanding and exploration of gold mineralization. Based on the geological significance of the SWIR signatures and the investigation of the ore-forming conditions, it can be concluded that there are two fluid up-flow zones in No.101 lode, one of which lies on the north of No.13 cross section and centered at No.7 cross section, whereas the other on the south of No.17 cross section and centered at No.27 cross section. As a result, two steeply dipped ore bodies were formed along the up-flow zones. According to this ore-forming analysis, the location centered at No.47 cross section with elevation below 150 m would be an ideal site for the formation of a third ore body. This has recently been verified by underground drillings in the Shihu Gold Deposit (Yougang Zhang, pers. commun. 2020).
High-Resolution Multi-View Stereo with Dynamic Depth Edge Flow

Multi-view stereo based on deep learning is mostly dedicated to improving the accuracy of point clouds, whlile complex scenes, occlusion, and other factors limit their reconstruction completeness, especially in the area with drastic changes in depth direction. In this paper, we propose a multi-view stereo network based on depth edge flow (DEF-MVSNet), using the reference image as a guide to dynamically infer the edge coordinates to improve reconstruction completeness. First, we ignore the boundaries in the depth prediction stage to generate better initial depth inference results. Then, we use a EdgeDetect module to extract the obviously features of the reference image and predict the pixel offset of the depth map. Finally, the EdgeFlow module modifies the initial depth map coordinates according to the offset and uses multiple iterations to dynamically update the depth map. The experimental results prove that our method has a great improvement in the completeness of reconstruction compared with MVSNet and R-MVSNet without increasing the memory and time overhead. Code and models are publicly available at https://github.com/linkuizzZ/EF-MVSNet.
Fact and Fiction: Challenging the Honest Majority Assumption of Permissionless Blockchains

Honest majority is the key security assumption of Proof-of-Work (PoW) based blockchains. However, the recent 51% attacks render this assumption unrealistic in practice. In this paper, we challenge this assumption against rational miners in the PoW-based blockchains in reality. In particular, we show that the current incentive mechanism may encourage rational miners to launch 51% attacks in two cases. In the first case, we consider a miner of a stronger blockchain launches 51% attacks on a weaker blockchain, where the two blockchains share the same mining algorithm. In the second case, we consider a miner rents mining power from cloud mining services to launch 51% attacks. As 51% attacks lead to double-spending, the miner can profit from these two attacks. If such double-spending is more profitable than mining, miners are more intended to launch 51% attacks rather than mine honestly.
Deep Supervised Image Retargeting

Recent learning-based image retargeting methods have achieved significant improvement. However, two main is-sues remain in this challenging task: (i) it is difficult to build ground truth datasets for supervised learning; (ii) most methods are based on a certain operator, not suitable for various images with different target sizes. In this paper, for the first time, we address these issues by providing a deep supervised image retargeting solution. We introduce a new dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> of 6, 576 pairs generated by multiple operators using Image Re-targeting Quality Assessment (IRQA) algorithm. We then develop a mult-operator image retargeting model named MR-GAN, which learns the deformation process of retargeted images using multiple methods and conducts retargeting operations in feature space. Experimental results validate the effectiveness as well as its superiority against state-of-the-art alternatives of the proposed approach.
Generating Caption for Image using Beam Search and Analyzation with Unsupervised Image Captioning Algorithm

In today's world of social media, almost everyone is a part of social platform and actively interacting with each other through internet. People on social media upload many pictures on their social media accounts with different captions. Thinking about the appropriate caption is a tedious process. Caption is important to effectively describe the content and meaning of a picture. Caption describes the image in meaningful sentences. A model for image caption generator can be built which is used to generate caption for images of different types and resolutions. Image captioning model which is used to generate caption in language that is understandable by a human being for the input images. CNN(convolution neural network) and RNN(recurrent neural network) is used using the concept of encoder-decoder to build this model. As CNN is used for image feature extraction purpose where only the important features or the important pixels, if the image is considered in the form of matrix of pixels, which are extracted from the resultant image, instead of CNN model, other pre-trained imagenet models which have higher accuracy will be used and their results are then compared by using BLEU score metric for comparison. For the prediction of captions, beam search method and argmax method is used and compared. The above discussed supervised image caption model is also comparedwith the built unsupervised image captioning model. The flickr8k dataset and then MSCOCO dataset are used to train and test the model. This model if implemented with the mobile application, which can be very useful for differently abled people, who completely rely on the assistance of text-to-speech feature.
Segment as Points for Efficient and Effective Online Multi-Object Tracking and Segmentation

Current multi-object tracking and segmentation (MOTS) methods follow the tracking-by-detection paradigm and adopt 2D or 3D convolutions to extract instance embeddings for instance association. However, due to the large receptive field of deep convolutional neural networks, the foreground areas of the current instance and the surrounding areas containing the nearby instances or environments are usually mixed up in the learned instance embeddings, resulting in ambiguities in tracking. In this paper, we propose a highly effective method for learning instance embeddings based on segments by converting the compact image representation to un-ordered 2D point cloud representation. In this way, the non-overlapping nature of instance segments can be fully exploited by strictly separating the foreground point cloud and the background point cloud. Moreover, multiple informative data modalities are formulated as point-wise representations to enrich point-wise features. For each instance, the embedding is learned on the foreground 2D point cloud, the environment 2D point cloud, and the smallest circumscribed bounding box. Then, similarities between instance embeddings are measured for the inter-frame association. In addition, to enable the practical utility of MOTS, we modify the one-stage instance segmentation method SpatialEmbedding for instance segmentation. The resulting efficient and effective framework, named PointTrackV2, outperforms all the state-of-the-art methods including 3D tracking methods by large margins (4.8 percent higher sMOTSA for pedestrians over MOTSFusion) with the near real-time speed (20 FPS evaluated on a single 2080Ti). Extensive evaluations on three datasets demonstrate both the effectiveness and efficiency of our method. Furthermore, as crowded scenes for cars are insufficient in current MOTS datasets, we provide a more challenging dataset named APOLLO MOTS with a much higher instance density.
Enhancing Cybersecurity via Artificial Intelligence: Risks, Rewards, and Frameworks

Recent advances in artificial intelligence challenge classical models of productivity by increasing the scale, complexity, and range of tasks that can be meaningfully automated, including those associated with cybersecurity.
A Combinatorial Approach to Explaining Image Classifiers

Machine Learning (ML) models, a core component to artificial intelligence systems, often come as a black box to the user, leading to the problem of interpretability. Explainable Artificial Intelligence (XAI) is key to providing confidence and trustworthiness for machine learning-based software systems. We observe a fundamental connection between XAI and software fault localization. In this paper, we present an approach that uses BEN, a combinatorial testing-based software fault localization approach, to produce explanations for decisions made by ML models.