Reviewerly/exHarmony huggingface dataset:
Temporal Pattern Recognition in Graph Data Structures

Graph data structures model relations between entities in various domains. Graph processing systems enable scalable distributed computations over large graphs, but are limited to static scenarios in which the structure of the graph does not change. However, many applications are dynamic in nature, and this reflects to graphs that continuously evolve over time. In these contexts, understanding the evolution of graphs is key to enable timely reactions when necessary. We address this problem by proposing a new model to express temporal patterns over graph data structures. The model seamlessly integrates computations over graphs to extract relevant values, and temporal operators that define patterns of interest in the evolution of the graph. We present the syntax and semantics of our model and discuss its concrete implementation in FlowGraph, a middleware for temporal pattern recognition in large scale graphs. FlowGraph presents a level of performance that is comparable to state-of-the-art graph processing tools when processing static graphs. In the presence of temporal patterns, it can further optimize processing by avoiding complex graph computations until strictly necessary for pattern evaluation.
Spear-Phishing campaigns: Link Vulnerability leads to phishing attacks, Spear-Phishing electronic/UAV communication-scam targeted

One of the most important strategies for gaining unauthentic early access to some person/company's computing resources/data is spear phishing. Phishing is, at its core, a sort of social engineering intended to persuade a user to give sensitive information or run a payload that will infect their system. Spear phishing is a type of phishing in which bogus emails are sent to specific businesses with the goal of obtaining confidential information. A successful phishing campaign necessitates the use of a few different resources as well as some setup. Impersonation, inducement, and access- control bypass techniques are among its approaches. In this paper we have studied and found up to date approaches to spear phishing attacks and their preventative measures. The paper also demonstrates the steps to set up and run successful phishing campaign and the results astonishingly shows that even only personality-targeted messaging can alter the response to phishing attacks. As a result of learning the facts, the paper suggests that users should seek to improve their security awareness by becoming familiar with the warning signs of phishing attacks. Moreover, more often in Unmanned Aerial Vehicles (UAV) or drones (which are now being used in various domains including military operations, monitoring, etc.), the resources are deployed as web services which makes them vulnerable to phishing activities. A data mining technique is also suggested as a tool for the detection of phishing attacks in UAVs.
Document similarity

In recent years, development of tools and methods for measuring document similarity has become a thriving field in informatics, computer science, and digital humanities. Historically, questions of document similarity have been (and still are) important or even crucial in a large variety of situations. Typically, similarity is judged by criteria which depend on context. The move from traditional to digital text technology has not only provided new possibilities for discovery and measurement of document similarity, it has also posed new challenges. Some of these challenges are technical, others conceptual. This paper argues that a particular, well-established, traditional way of starting with an arbitrary document and constructing a document similar to it, namely transcription, may fruitfully be brought to bear on questions concerning similarity criteria for digital documents. Some simple similarity measures are presented and their application to marked up documents are discussed. We conclude that when documents are encoded in the same vocabulary, n-grams constructed to include markup can be used to recognize structural similarities between documents.
Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

Available data may differ from true data in many cases due to sensing errors, especially for the Internet of Things (IoT).Although privacy-preserving data mining has been widely studied during the last decade, little attention has been paid to data values containing errors.Differential privacy, which is the de facto standard privacy metric, can be achieved by adding noise to a target value that must be protected.However, if the target value already contains errors, there is no reason to add extra noise.In this paper, a novel privacy model called true-value-based differential privacy (TDP) is proposed.This model applies traditional differential privacy to the ''true value'' unknown by the data owner or anonymizer but not to the ''measured value'' containing errors.Based on TDP, the amount of noise added by differential privacy techniques can be reduced by approximately 20% by our solution.As a result, the error of generated histograms can be reduced by 40.4% and 29.6% on average according to mean square error and Jensen-Shannon divergence, respectively.We validate this result on synthetic and five real data sets.Moreover, we proved that the privacy protection level does not decrease as long as the measurement error is not overestimated.
CTHGAT:Category-aware and Time-aware Next Point-of-Interest via Heterogeneous Graph Attention Network

Location-based recommendation has become a significant method to help people locate fascinating and appealing points of interest (POIs) with the rapid popularity of smart mobile devices and the prevalence of location-based social networks (LBSN). However, the sparsity of the user-POI matrix and the cold-start issue have generated serious challenges, resulting in a substantial decrease in collaborative filtering methods&#x2019; recommendation results. In reality, location-based recommendation demands spatiotemporal context awareness. In order to overcome these challenges, we develop an embedding model based on the heterogeneous graph attention network. Geographic influence, social relation and historical check-in influence are captured in a unified way by constructing a user-POI heterogeneous graph. Subsequently, we use the LSTM-based model to learn the category weight of the next POI to select. We are developing a score function to recommend the next POI for users by integrating category weights, user preferences and time impact. We conduct experiments on existing large-scale datasets to evaluate the performance of our model. The results demonstrate our proposal is superior to other rivals. Additionally, our method has been significantly improved compared with other competitive approaches in terms of recommending cold-start POI.
Deep Non-Rigid Structure From Motion With Missing Data

Non-rigid structure from motion (NRSfM) refers to the problem of reconstructing cameras and the 3D point cloud of a non-rigid object from an ensemble of images with 2D correspondences. Current NRSfM algorithms are limited from two perspectives: (i) the number of images, and (ii) the type of shape variability they can handle. These difficulties stem from the inherent conflict between the condition of the system and the degrees of freedom needing to be modeled - which has hampered its practical utility for many applications within vision. In this paper we propose a novel hierarchical sparse coding model for NRSFM which can overcome (i) and (ii) to such an extent, that NRSFM can be applied to problems in vision previously thought too ill posed. Our approach is realized in practice as the training of an unsupervised deep neural network (DNN) auto-encoder with a unique architecture that is able to disentangle pose from 3D structure. Using modern deep learning computational platforms allows us to solve NRSfM problems at an unprecedented scale and shape complexity. Our approach has no 3D supervision, relying solely on 2D point correspondences. Further, our approach is also able to handle missing/occluded 2D points without the need for matrix completion. Extensive experiments demonstrate the impressive performance of our approach where we exhibit superior precision and robustness against all available state-of-the-art works in some instances by an order of magnitude. We further propose a new quality measure (based on the network weights) which circumvents the need for 3D ground-truth to ascertain the confidence we have in the reconstructability. We believe our work to be a significant advance over state-of-the-art in NRSFM.
Lane Mark Detection with Pre-Aligned Spatial-Temporal Attention

Lane mark detection plays an important role in autonomous driving under structural environments. Many deep learning-based lane mark detection methods have been put forward in recent years. However, most of current methods limit their solutions within one single image and do not make use of the de facto successive image input during the driving scene, which may lead to inferior performance in some challenging scenarios such as occlusion, shadows, and lane mark degradation. To address the issue, we propose a novel lane mark detection network which takes pre-aligned multiple successive frames as inputs to produce more stable predictions. A Spatial-Temporal Attention Module (STAM) is designed in the network to adaptively aggregate the feature information of history frames to the current frame. Various structure of the STAM is also studied to ensure the best performance. Experiments on Tusimple and ApolloScape datasets show that our method can effectively improve lane mark detection and achieve state-of-the-art performance.
A resource allocation and scheduling model for hierarchical distributed services in cloud environment using game theory

Summary In this paper, a game theoretic‐based method has been proposed to solve the problem of allocating computing resources in a hierarchical cloud structure. The proposed method has two phases: “topology construction” and “game‐theoretic‐based resource allocation.” In the first phase, a hierarchical structure will be established to determine a reliable communication infrastructure between cloud computing resources. In the second phase, a game theoretic model is used to allocate cloud resources and schedule executing of tasks in the constructed hierarchical structure of cloud. In the proposed method, end user's tasks are collected through leaf resources in hierarchical tree structure. Then each computing resource will consider managing collected tasks using a game‐based model. The efficiency of the proposed method is evaluated in a simulated environment and the results are compared with some of the best previous studies. The results show that our proposed method reduces energy consumption and computational cost. Also, using the proposed game‐theoretic method, more computational tasks can be processed using cloud resources, so this method can be used more efficiently for real‐world scenarios.
Confidence-Based Simple Graph Convolutional Networks for Face Clustering

Face clustering is an effective method for taking advantage of unlabeled face data. Recent studies use graph convolutional networks (GCNs) to learn feature embeddings from the neighborhood information between face images. However, most of the face clustering methods require numerous overlapping subgraphs to characterize the local structure around the nodes, which causes significant redundancy. Moreover, the nonlinearity of the GCN itself increases the calculation complexity, which further reduces the model’s training efficiency. In this study, we propose a lightweight clustering framework, the confidence-based simple graph convolutional network (CSGCN), for face clustering, which achieves more accurate clustering results and significantly improves the efficiency of GCN-based face clustering. Specifically, CSGCN does not construct any subgraphs but convolves the entire graph as a whole and also removes the nonlinearity of the convolution in the graph convolution module, which further reduces the computational complexity. Subsequently, an effective new confidence score is constructed to better characterize the embedded features and to ensure that the subsequent clustering still maintains a high accuracy rate under the aforementioned model simplification. In addition, while most of the existing GCN-based methods are actually supervised, we construct an unsupervised confidence to make it more suitable for clustering tasks. Extensive experiments with MS-Celeb-1M, YouTube-Faces and DeepFashion datasets show that our method not only improves the clustering accuracy but also significantly reduces the execution time, whether in supervised or unsupervised models.
Trajectory planning using Mixed Reality: an experimental validation

Trajectory planning is at the very core of robotics. Nonetheless, it is not trivial to plan trajectories for industrial or cooperative robots in an intuitive way by non roboticists. Visualizing in 3D a trajectory consisting of a sequence of positions of the end-effector and its orientations is difficult, and becomes even more challenging when kinematics and dynamics constraints should be taken into account. In this work we propose a novel approach, exploiting Mixed Reality, to improve and make easier the planning phase by allowing the user to &#x0027;dropping&#x2019; way points and leaving to the system the computation of the trajectory, its visualization in augmented reality and, finally, send it to the real robot controller. The method has been experimentally validated on a Panda robot by Franka Emika.
Network Multimedia Table Tennis Teaching Design Based on Embedded Microprocessor

Multimedia technology and network technology have been rapidly developed. Embedded Micro Processor Unit (MPU) is a processor with a data width of more than 32 bits, which has relatively high performance and high price. Different from the traditional general-purpose computer, due to the limitation of system size and power consumption, the embedded microprocessor only retains related functions. Embedded processors currently mainly include Am186/88, PowerPC, MIPS, ARM, and other mainstream series, among which ARM occupies the main market share. Computer-Aided Instruction (CAI) is a variety of teaching activities carried out under the assistance of computers, discussing the teaching content. The implementation of computer-assisted teaching has followed the pace of China’s education system reform. As far as teaching is concerned, it has not only caused changes in the environment and teaching methods, but most importantly, it has brought about the renewal of educational thoughts and concepts, especially a series of changes in the educational system, methods, and content. This article is based on educational theory, educational planning theory, and educational psychology theory, starting from the integration of network multimedia and table tennis course learning, grasping the teaching characteristics of table tennis course, doing detailed demand analysis and system design, and making full use of advanced information technology that has created a table tennis multimedia network teaching platform. This article discusses the design, development, and application research of the network multimedia table tennis teaching system based on the embedded microprocessor through teaching experiments. Based on the network multimedia table tennis, the network multimedia teaching, extension of the system to other physical education teaching provides suggestions and references and explores the future development direction of the network multimedia teaching system through teaching methods. The experiment proves that each test score of the experimental group is tested by a pair of samples, indicating that the application of the platform increases the communication channels and knowledge acquisition; the method facilitates the management of teachers in teaching and improves the degree of informatization of course teaching.
Investigation of the Blockchain Structure for Hydroxyapatite-Based Scaffolds

Regenerative biomechanics provides exciting technologies for developing functional substitutes, intending to restore and regenerate damaged tissues and organs. Scaffolds are in great demand. However, there are risks of biocompatibility when using scaffolds. Each bone substitute has its chemical composition, and other characteristics have advantages and disadvantages. Reproducibility, data sharing, privacy concerns, and patient participation in clinical trials are significant problems in modern clinical trials. In the era of the Internet, data is collected constantly. Today we need applications that ensure the privacy of users’ data. Blockchain technology helps to compensate for severe data management problems (e.g., patient recruitment, ongoing monitoring) in clinical trials (CT). The article examines the principles of blockchain operation and approaches to bone substitutes’ design. Based on this data, a blockchain model for biomaterial surgery has been created, facilitating interaction between the parties and reducing errors.
A Transformer-Based Model for Low-Resource Event Detection

Event detection is an important task in natural language processing, which identifies event trigger words in a given sentence. Previous work use traditional RNN/CNN based text encoders, failing to remember long-range dependencies within the sentence. This paper proposes a Transformer-based event detection model, utilizing the self-attention technique of a Transformer encoder to capture long-range dependencies. However, existing labelled event detection datasets such as ACE2005 are in small scale, being insufficient to train a deep neural model. This paper expands the scale of training data with an event trigger-aware back-translation based data augmentation technique. In addition, a multi-instance learning strategy is adopted to alleviate noises in the generated data in training. Experiments on two event detection datasets in different languages verify the effectiveness of the proposed model (The code is released at https://github.com/gitofding/event-detection).
A Character-Word Graph Attention Networks for Chinese Text Classification

Text classification is an important task in natural language processing. Different from English, Chinese text owns two representations, character-level and word-level. The former has abundant connotations and the latter owns specific meanings. Current researches often simply concatenated two-level features with little processing and failed to explore the affiliation relation-ship between Chinese characters and words. In this paper, we proposed a character-word graph attention network (CW-GAT) to explore the interactive information between characters and words for Chinese text classification. A graph attention network is adopted to capture the context of sentences and the interaction between characters and words. Extensive experiments on six real Chinese text datasets show that the proposed model outperforms the latest baseline methods.
Systematic Regulation of Personal Information Rights in the Era of Big Data

Big data has an important impact on people’s production and life. The existing legal and judicial protection, sanctions, and mechanisms for the enforcement of information rights have proved insufficient to stem the serious consequences of rampant leakage and illegal activity. Based on Information Full Life Cycle Theory, this article combines qualitative analysis with quantitative analysis, uses data from the Survey Report on App Personal Information Leakage released by China Consumers Association as an example, and finds that illegal access, illegal provisions, and illegal transactions have become important sources of personal information leakage. The main reasons for this problem include limitations of the technologies used, the falsification of informed consent, the lag of legislative protections, and a lack of administrative supervision. Systematic regulation of the right to protect personal information should include a variety of initiatives. First, it should be used to identify who to protect and how to protect them. Second, there needs to be a shift from identifiable subject regulations to risk control. Third, legislation needs to be comprehensive, entailing a shift from fragmented to systemic reforms. Fourth, protection efforts should include supervision, self-regulation, and management. Finally, the jurisdiction of legislation should extend across cyberspace and physical reality as a means to achieve a balance between effective protection and the reasonable use of personal information.
A Semantically Rich Framework to Automate Cloud Service Level Agreements

Consumers evaluate and choose cloud-based services based on the Service Level Agreements (SLA).These agreements list the service terms and metrics to be agreed upon by the service providers and the customers.Current cloud SLAs are text documents that require significant manual effort to parse and determine if providers meet the SLAs.Moreover, due to the lack of standardization, providers differ in the way they define the terms and metrics, making it more difficult to compare different provider SLAs.We have developed a novel framework to significantly automate the process of extracting knowledge embedded in cloud SLAs and representing it in a semantically rich knowledge graph helping the user to make a calculated decision in choosing a provider.Our framework captures the key terms, measures, and deontic rules, in the form of obligations and permissions present in the cloud SLAs.In this paper, we discuss our framework, technique, and challenges in automating the cloud services agreement.We also describe our results and their validation against well-established standards.
Reuse‐oriented data publishing: How to make the shared research data friendlier for researchers

Abstract Data repositories are an essential component of data infrastructure. This article explores their functionalities with accompanying examples with a focus on open, quantitative data sets. Based on researcher needs, the principle of data friendliness is presented, which involves data context (data characteristics), research context (individual circumstances and research settings) and the context of reuse paths (conditions allowing access to, understanding of and use of data). An examination of data context suggests that metadata and data enrichment are required to provide successful data context. Enrichment is also required for orientation of the research context to provide greater insights across data sets. Finally the article considers the reuse paths, and the say that data paths intersect human interaction and how these are being addressed by different repositories. The review concludes that data repositories need to focus on how to present data that is fully interoperable and allows for reuse as well as access.