Reviewerly/exHarmony huggingface dataset:
Adversarial Attack with Adaptive Gradient Variance for Deep Fake Fingerprint Detection

In recent years, fingerprint liveness detection methods based on deep neural networks have been widely used in various fingerprint recognition systems, such as fingerprint locks and smart phones, etc. However, recent research has revealed that these devices are vulnerable to the spoofing attack of adversarial examples. That is, adding some invisible perturbations to fingerprint image, the deep fake fingerprint detection network may misclassify the image as other classes. The classical example generation methods usually adopt gradient optimization method, but it is prone to falling into local optimum, resulting in poor adversarial attack performance. Accordingly, this paper proposes a novel adversarial attack method called adaptive gradient variance attack (AGVA) for deep fake fingerprint detection. Firstly, we obtain gradient information through the target network to calculate the gradient variance. Then, to avoid falling into the local optimum, the gradient direction is optimized by the gradient variance. In particular, to improve the attack performance, we propose an adaptive hyper-parameter search algorithm using stochastic gradient ascent to search the value of hyper-parameters in adversarial example generation. Experimental results show that compared to existing methods, our method achieves a better black-box attack success rate and higher robustness on the basis of ensuring the white-box attack performance.
Privacy-Preserving Blockchain-Based EHR Using ZK-Snarks

The electronic health record (EHR) of a patient is a digital record of their health records, progress notes, problems, and medications. As with any online digital format, concerns of breach exist. Similarly, EHR data is susceptible to significant security and privacy concerns these days. This paper has designed a secure decentralized medical blockchain to address privacy and security concerns associated with the exchange of patient health care data across entities. Historically advanced methods for protecting EHRs have often rendered data unavailable to patients. These methods are always attempting to strike a balance between data confidentiality, patient request, and continuous engagement with provider data. The aforementioned difficulties are resolved by blockchain technology, which distributes information in a transactional and decentralised manner. This paper offers a blockchain-based security architecture for efficiently and securely storing and maintaining electronic health records. Moreover, the patients feel insecure about their data being shared so they do not prefer to choose EHR. Maintaining anonymity may facilitate more efficient dialogue between physician and patient, which is critical for ensuring the highest possible quality of treatment. To ensure this, We propose a non-interactive zero-knowledge proof-based authentication method that is lightweight enough to operate on medical heath devices with limited resources. The concept of Zero-Knowledge Proof refers to demonstrating anything without disclosing the facts on which the proof is based. It enables physicians, patients, and insurance agents to get medical information efficiently and securely. The goal of this research is to establish if our architecture complies with doctors’, patients’, and third-party security needs. The simulation results demonstrate that this architecture effectively safeguards EHR data.
A Blockchain Sharing Model of Seafarers Electronic Medical Certificate

In the process of issuance, administration, and verification of traditional seafarers' medical certificate, there are problems such as simple fraud, easy damage, inconvenience to carry, and high management cost. In order to solve the above problems, this paper proposed a blockchain sharing model of Seafarers Electronic Medical Certificate (SEMC). Firstly, a practical data structure of electronic medical certificate storage for seafarers is designed, which realizes that the data related to seafarers' electronic medical certificate is put away within the consortium chain and cannot be tampered with, hence ensuring the traceability and security of electronic medical certificate information of seafarers. Then, the ES search engine is constructed to realize precise and proficient inquiry of indicated watchwords. Through model testing and performance analysis, the effectiveness of the model in realizing SEMC quick sharing among multi-user is verified.
Driving Anomaly Detection Using Contrastive Multiview Coding to Interpret Cause of Anomaly

Modern advanced driver assistant systems (ADAS) rely on various types of sensors to monitor the vehicle status, driver's behaviors and road condition. The multimodal systems in the vehicle include sensors, such as accelerometers, pressure sensors, cameras, lidar and radars. When looking at a given scene with multiple modalities, there should be congruent in-formation among different modalities. Exploring the congruent information across modalities can lead to appealing solutions to create robust multimodal representations. This work proposes an unsupervised approach based on contrastive multiview coding (CMC) to capture the correlations in representations extracted from different modalities, learning a more discriminative rep-resentation space for unsupervised anomaly driving detection. We use CMC to train our model to extract view-invariant factors by maximizing the mutual information between mul-tiple representations from a given view, and increasing the distance of views from unrelated segments. We consider the vehicle driving data, driver's physiological data, and external environment data consisting of distances to nearby pedestrians, bicycles, and vehicles. The experimental results on the driving anomaly dataset (DAD) indicate that the CMC representation is effective for driving anomaly detection. The approach is efficient, scalable and interpretable, where the distances in the contrastive embedding for each view can be used to understand potential causes of the detected anomalies.
Hightlight Video Detection in Figure Skating

Figure skating is an ornamental competitive sport with fancy technical moves. In particular, highlight videos in figure skating, which contain these elegant moves, have always been a favorite part of the vast audience. However, research in highlight video detection has not yielded much success. Previous researches mainly focus on detecting the falling action in the video rather than numerous technical moves. Therefore, we propose a segmentation method for the whole video to use Tube self-Attention for highlights detection. In particular, we have added a new module outside the existing network, which enables the editing and integration of the highlight moments of athletes in a single competition to produce highlight videos. Additionally, since few datasets have explored highlight actions in figure skating, we design a new dataset HS-FS (Highlight Shot in Figure Skating), which can be used to train the Tube Self-Attention model to satisfy highlight detection. Experiments show that the training accuracy obtained on the new dataset is 99.35% during training. Visualizations have demonstrated that our proposed methods could identify the highlight moment in figure skating videos.
Iterative Methods for Signal Reconstruction on Product Graphs with Arbitrary Missing Data

In this paper, we address the problem of signal reconstruction on Cartesian product graphs, where an arbitrary subset of the input observations is corrupted or missing. A typical approach to Graph Signal Reconstruction (GSR) is to define the problem in terms of Bayesian inference of an underlying signal that is assumed to be smooth with respect to the graph topology. However, in the case of a general Cartesian product graph, the linear system that results from this model specification is often intractable to solve directly. To overcome this, we derive two alternative iterative procedures for obtaining the posterior mean that are computationally efficient and avoid the need to utilise Markov chain-based sampling solutions. The first is a stationary iterative method based on matrix splitting and the second is a conjugate gradient method with a graph-spectral preconditioner. By comparing the convergence behaviour of these two algorithms theoretically and numerically, we show that the optimal choice depends on the model hyperparameters. Next, we demonstrate the link between GSR on product graphs and Kernel Graph Regression (KGR). This allows us to generalise the KGR model to include arbitrary missing data in the input signals. Finally, we propose two models for accurately and efficiently estimating the marginal uncertainty of the posterior mean. Whilst direct computation of the exact posterior covariance is possible, it is typically prohibitively expensive. Our proposed model, by contrast, achieves an $R$-squared statistic of over 0.9 whilst maintaining realistic time-complexity for large graphs. Applications of the techniques studied throughout this paper include large sensor and social network studies, where node-level data may be corrupted by equipment failure or difficult to reliably collect.
Visual Localization Through Virtual Views

This paper addresses the problem of camera localization, i.e. 6 DoF pose estimation, with respect to a given 3D reconstruction. Current methods often use a coarse-to-fine image registration framework, which integrates image retrieval and visual keypoint matching. However, the localization accuracy is restricted by the limited invariance of feature descriptors. For example, when the query image has been acquired at the illumination (day/night) not consistent with the model image time, or from a position not covered by the model images, retrieval and feature matching may fail, leading to false pose estimation. In this paper, we propose to increase the diversity of model images, namely new viewpoints and new visual appearances, by synthesizing novel images with neural rendering methods. Specifically, we build the 3D model on Neural Radiance Fields (NeRF), and use appearance embeddings to encode variation of illuminations. Then we propose an efficient strategy to interpolate appearance embeddings and place virtual cameras in the scene to generate virtual model images. In order to facilitate the model image management, the appearance embeddings are associated with image acquisition conditions, such as daytime, season, and weather. Query image pose is estimated through similar conditional virtual views using the conventional hierarchical localization framework. We demonstrate the approach by conducting single smartphone image localization in a large-scale 3D urban model, showing the improvement in the accuracy of pose estimation.
Probability Based Bone Marrow Cell Detection

Bone marrow cell images are widely used to di-agnose leukaemia. Accurate detection of cells has great impact on the sound effects of diagnosis. In this paper, we propose a new framework to detect bone marrow cells and to improve the detection results of some specific cell types. We first use detection network to get preliminary bounding boxes and corre-sponding cell types, then we analyze the probability distribution for each bounding box, finding that those easily misclassified bounding boxes' probability distribution obey some specific rules. Therefore, we chose those bounding boxes that satisfied specific probability distribution as easily confused cell pairs, and then use a feature extraction network to classify these cell pairs. We evaluate this framework on the bone marrow cell dataset and compare with the traditional object detection framework.
Document Summarization Model Using Modified Pagerank Algorithm

Document summarization refers to the writing of huge documents in a precise and short manner without losing the vital information. Automatic summarization of documents is important due to the fact that it consumes less time, less biased compared with human summarizations and the selection process can be made easier using these document summarization methods. Advanced natural language processing techniques are utilized to interpret the text and providing summarizations. In this research, a pagerank-based document summarization model is implemented to effectively summarize the document. Initially, the documents are collected and all the documents present in various formats are converted into text format and summarized using the modified pagerank algorithm. The experimentation results are analyzed using Rouge-N and Rouge-L with F1 measure, precision and recall and the results shows that the proposed model works more efficient than the existing methods with an f1 measure of 76.85%.
Mixed Reality for Mechanical Design and Assembly Planning

Design for Manufacturing and Assembly (DFMA) is a crucial design stage within the heavy vehicle manufacturing process that involves optimising the order and feasibility of the parts assembly process to reduce manufacturing complexity and overall cost. Existing work has focused on conducting DFMA within virtual environments to reduce manufacturing costs, but users are less able to relate and compare physical characteristics of a virtual component with real physical objects. Therefore, a Mixed Reality (MR) application is developed for engineers to visualise and manipulate assembly parts virtually, conduct and plan out an assembly within its intended physical environment. Two pilot evaluations were conducted with both engineering professionals and non-engineers to assess effectiveness of the software for assembly planning. Usability results suggest that the application is overall usable (M=56.1, SD=7.89), and participants felt a sense of involvement in the activity (M=13.1, SD=3.3). Engineering professionals see the application as a useful and cost-effective tool for optimising their mechanical assembly designs.
Binding Dancers Into Attractors

To effectively perceive and process observations in our environment, feature binding and perspective taking are crucial cognitive abilities. Feature binding combines observed features into one entity, called a Gestalt. Perspective taking transfers the percept into a canonical, observer-centered frame of reference. Here we propose a recurrent neural network model that solves both challenges. We first train an LSTM to predict 3D motion dynamics from a canonical perspective. We then present similar motion dynamics with novel viewpoints and feature arrangements. Retrospective inference enables the deduction of the canonical perspective. Combined with a robust mutual-exclusive softmax selection scheme, random feature arrangements are reordered and precisely bound into known Gestalt percepts. To corroborate evidence for the architecture’s cognitive validity, we examine its behavior on the silhouette illusion, which elicits two competitive Gestalt interpretations of a rotating dancer. Our system flexibly binds the information of the rotating Figure into the alternative attractors resolving the illusion’s ambiguity and imagining the respective depth interpretation and the corresponding direction of rotation. We finally discuss the potential universality of the proposed mechanisms.
Statistically Significant Pattern Mining With Ordinal Utility

Statistically significant pattern mining (SSPM), which evaluates each pattern via a hypothesis test, is an essential and challenging data mining task for knowledge discovery. We introduce a preference relation between patterns and aim to discover the most preferred patterns under the constraint of statistical significance, which has never been considered in existing SSPM problems. We propose an iterative multiple testing procedure that can alternately reject a hypothesis and safely ignore the less useful hypotheses than the rejected one. By filtering out patterns with low utility, we can avoid the significance budget consumption of rejecting useless (uninteresting) patterns and focus the significance budget on more useful patterns, leading to more useful discoveries. We show that the proposed method can control the familywise error rate (FWER) under certain assumptions, which can be satisfied by a realistic problem class in SSPM. We also show that the proposed method always discovers equally or more useful patterns than Tarone-Bonferroni and Subfamily-wise Multiple Testing (SMT). Finally, we conducted several experiments with both synthetic and real-world data to evaluate the performance of our method. The proposed method discovered many more useful patterns in the experiments with real-world datasets than the existing method for all five conducted tasks.
Abstains from Prediction: Towards Robust Relation Extraction in Real World

Supervised learning is a classic paradigm of relation extraction (RE). However, a well-performing model can still confidently make arbitrarily wrong predictions when exposed to samples of unseen relations. In this work, we propose a relation extraction method with rejection option to improve robustness to unseen relations. To enable the classifier to reject unseen relations, we introduce contrastive learning techniques and carefully design a set of class-preserving transformations to improve the discriminability between known and unseen relations. Based on the learned representation, inputs of unseen relations are assigned a low confidence score and rejected. Off-the-shelf open relation extraction (OpenRE) methods can be adopted to discover the potential relations in these rejected inputs. In addition, we find that the rejection can be further improved via readily available distantly supervised data. Experiments on two public datasets prove the effectiveness of our method capturing discriminative representations for unseen relation rejection.
PROBLEMS OF DEVELOPMENT OF THE SECONDARY VOCATIONAL EDUCATION SYSTEM IN THE BASHKORTOSTAN REPUBLIC

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Event-Based Obstacle Sensing and Avoidance for an UAV Through Deep Reinforcement Learning

Event-based cameras can provide asynchronous measurements of changes in per-pixel brightness at the microsecond level, thereby achieving a dramatically higher operation speed than conventional frame-based cameras. This is an appealing choice for unmanned aerial vehicles (UAVs) to realize high-speed obstacle sensing and avoidance. In this paper, we present a sense and avoid (SAA) method for UAVs based on event variational auto-encoder and deep reinforcement learning. Different from most of the existing solutions, the proposed method operates directly on every single event instead of accumulating them as an event frame during a short time. Besides, an avoidance control method based on deep reinforcement learning with continuous action space is proposed. Through simulation experiments based on AirSim, we show that the proposed method is qualified for real-time tasks and can achieve a higher success rate of obstacle avoidance than the baseline method. Furthermore, we open source our proposed method as well as the datasets.
Low-Resource Emotional Speech Synthesis: Transfer Learning and Data Requirements

Recently, a number of solutions were proposed that improved on ways of adding an emotional aspect to speech synthesis. Combined with core neural text-to-speech architectures that reach high naturalness scores, these models are capable of producing natural human-like speech with well discernible emotions and even model their intensities. To successfully synthesize emotions the models are trained on hours of emotional data. In practice however, it is often difficult and rather expensive to collect a lot of emotional speech data per speaker. In this article, we inquire upon the minimal data requirements of expressive text-to-speech solutions to be applied in practical scenarios and also find an optimal architecture for low-resource training. In particular, a different number of training speakers and a different amount of data per emotion are considered. Frequently occurring situations are considered when there is a large multi-speaker dataset with neutral records and a large single-speaker emotional dataset, but there is little emotional data for the remaining speakers. On top of that we study the effect of several architecture modifications and training procedures (namely adversarial training and transfer learning from speaker verification) on the quality of the models as well as their data avidity. Our results show that transfer learning may lower data requirements from 15 min per speaker per emotion to just 2.5–7 min maintaining non-significant changes in voice naturalness and giving high emotion recognition rates. We also show how the data requirements change from one emotion to another. A demo page illustrating the main findings of this work is available at: https://diparty.github.io/projects/tts/emo/nat .
Exploring Cues and Signaling to Improve Cross-Reality Interruptions

In this paper, we report on initial work exploring the potential value of technology-mediated cues and signals to improve cross-reality interruptions. We investigated the use of color-coded visual cues (LED lights) to help a person decide when to interrupt a virtual reality (VR) user, and a gesture-based mechanism (waving at the user) to signal their desire to do so. To assess the potential value of these mechanisms we conducted a preliminary <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\times 3$</tex> within-subjects experimental design user study <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(N=10)$</tex> where the participants acted in the role of the interrupter. While we found that our visual cues improved participants' experiences, our gesture-based signaling mechanism did not, as users did not trust it nor consider it as intuitive as a speech-based mechanism might be. Our preliminary findings motivate further investigation of interruption cues and signaling mechanisms to inform future VR head-worn display system designs.
An Inverted-ITL Algorithm for Mining Partial Periodic-Frequent Patterns

In this paper, we propose an Inverted-ITL algorithm for mining partial periodic-frequent patterns. Although the GPF-growth algorithm has been proposed to achieve the same goal, it needs to construct the prefix tree which needs too much storage space and processing time. Moreover, the GPF-growth algorithm will scan the database twice and sort each transactional list. To avoid those disadvantages, in this paper, we propose the inverted-ITL algorithm. Our algorithm is more efficient than the GPF-growth algorithm. We will use one data structure, the ITL-tree, to store the items which appear in the database. Therefore, our algorithm can need shorter processing time than the GPF-growth algorithm.
Lifting the Curse: Exploring Dimensionality Reduction on Text Clustering Applications

Nowadays, huge amounts of text are being generated on the Web by a vast number of applications. Examples of such applications include instant messengers, social networks, e-mail clients, news portals, blog communities, commercial platforms, and so forth. The requirement for effectively identifying documents of similar content in these services rendered text clustering one of the most emerging problems of the machine learning discipline. Nevertheless, the high dimensionality and the natural sparseness of text introduce significant challenges that threat the feasibility of even the most successful algorithms. Consequently, the role of dimensionality reduction techniques becomes crucial for this particular problem. Motivated by these challenges, in this article we investigate the impact of dimensionality reduction on the performance of text clustering algorithms. More specifically, we experimentally analyze its effects in the effectiveness and running times of eight clustering algorithms by employing six high-dimensional text datasets. The results indicate that, in most cases, dimensionality reduction may significantly improve the algorithm execution times, by sacrificing only small amounts of clustering quality.