Reviewerly/exHarmony huggingface dataset:
Exceptional Survival Model Mining

The development of treatments based on the patient’s individual characteristics has been an emergent medical approach. The objective is to improve individual responses and overall survival. Thus, there is a need for computational tools able to identify and describe subgroups of patients for which the survival response significantly differs from the overall behaviour. However, there are few algorithms that address this matter. The majority of works of literature aim at building predictive models rather than understanding the characteristics that delineate subgroups with unusual survival. The approaches that provide understanding on factors that interfere in the survival behaviour usually resort to the stratification of the data based on previously known variable’s interactions, lacking the ability to shed light into new, possibly unknown, interactions. In contrast to the existent predictive approaches, we propose the use of supervised descriptive pattern mining in order to discover local patterns able to describe subsets of patients that present unusual survival behaviour. In this paper, we present the ESM-AM (Exceptional Survival Model Ant Miner) algorithm, an Exceptional Model Mining approach to the discovery of subgroups with exceptional survival functions that explores the use of ant-colony optimization as search heuristic for the pattern mining task.
Study on Security Analysis and Efficient Imrovement of Certificateless Aggregate Signature Scheme

In the traditional signature, anyone with the public key of the signer can verify the validation of a signature which is not suitable in some scenes where needs keeps the data privacy of the signer. Certificateless aggregate signature (CL-AS) scheme solves the problem with two part private keys from key generation center and the signer. CL-AS can also reduce the signature verification cost due to the aggregation property. In this manuscript, we first give a security analysis on a CL-AS scheme proposed recently. The analysis give a detail describe on two types of attacks on this CL-AS scheme which shows that the scheme is not secure and a malicious enemy can forgeabile an aggregate signature. Finally, we also present an improved CL-AS scheme. The improved scheme not only can overcome the existed security problem but also almost keeps the original efficiency. The scheme needs not bilinear pair operation and the computation cost is low, therefore the scheme can be applied into many fields, such as ad-hoc network, sensor network, and vehicular network.
Research on Strong Tracking UKF Algorithm of Integrated Navigation Based on BP Neural Network

Aiming at vehicles in the dense environment of tunnels, viaducts, mountainous areas, and high-rise buildings, GPS signals often suffer from short-term lock-out. A strong tracking unscented Kalman filter (STUKF) integrated navigation algorithm derived from Back Propagation neural network was proposed. This paper combines the idea of strong tracking filtering with the idea of unscented Kalman filtering, with the assistance of BP-neural network, and applies it to GPS/SINS integrated navigation with complementary advantages. The availability and reliability of the algorithm are tested by experimental simulation. Compared with the influence of BP neural network training before and after training on the accuracy of integrated navigation, the test results that this algorithm not only overcomes the shortcomings of GPS signal unlocking in harsh environment and Kalman filter fluctuates greatly in nonlinear environment, but also immensely improves the positioning precision of integrated navigation system, and provides new ideas for intelligent navigation fields such as unmanned vehicles and drones.
RustyHermit: A Scalable, Rust-Based Virtual Execution Environment

System-level development has been dominated by programming languages such as C/C++ for decades. These languages are inherently unsafe, error-prone, and a major reason for vulnerabilities. High-level programming languages with a secure memory model and strong type system are able to improve the quality of the system software. This paper explores the programming language Rust for development of a scalable, virtual execution environment and presents the integration of a Rust-based IP stack into RustyHermit. RustyHermit is part of the standard Rust toolchain and common Rust applications are able to build on top of RustyHermit.
Research on Chinese Question-Answering for Gaokao Based on Graph

Reading comprehension Question-Answering (QA) for College Entrance Examination (Gaokao in Chinese) is a challenging AI task because it requires effective representation to capture complicated semantic relations between the question and answers. In this paper, a novel method of Chinese Automatic Question-Answering based on a graph is proposed. The method first uses the Chinese FrameNet and discourse topic (paragraph topic sentence and author’s opinion sentence) to construct the affinity matrix between the question and candidate sentences and then employs the algorithm based on the graph to iteratively calculate the importance of each sentence. At last, the top 6 candidate answer sentences are selected based on the ranking scores. The recall on Beijing College Entrance Examination in the recent twelve years is 67.86%, which verifies the effectiveness of the method.
Investigating the Impact of Synthetic Data Distribution on the Performance of Regression Models to Overcome Small Dataset Problems

Machine learning is widely used in various fields, its ability to study data without having to determine the functional relationships that govern a system. However, small datasets often make it difficult for learning algorithms to make accurate predictions. To overcome this, an oversampling technique is needed. However, for the regression learning model this is not easy to do, because in regression to place synthesis data in a certain feature space must be accompanied by an appropriate target value, usually represented by an estimate function. Therefore in this paper oversampling is done by distributing synthetic data according to the Bus, Star, and Mesh topology, using the SMOTE (Synthetic Minority Over-sampling Technique) method. In the experiment, one of the ISE (Istanbul Stock Exchange) public datasets and one of the CF (Color Filter) real datasets were tested to measure the performance of the proposed oversampling technique. Besides, the results of experiments conducted on the same dataset using the MPV, FCM, and MMPV methods were used as a comparison. The results show that oversampling using the Bus, Star, or Mesh distribution results in better performance than without using oversampling. The ISE dataset tested using the proposed method has an average RMSE value smaller than the MPV, FCM, and MMPV methods. For CF datasets, the proposed method has an average RMSE value smaller than the MPV, FCM, and MMPV methods when the amount of training data is smaller than the amount of testing data.
Epistemic Logic in Ensemble Specification

Different logics are used to specify the structure, life cycle and interaction of dynamically forming ensembles. Specified aspects include the construction and finalization of an ensemble, joining and leaving of collaborators, acceptable and forbidden behaviors of the ensemble, local and global goals and boundaries, and the permission to access resources. As ensembles are dynamically formed from heterogeneous agents, it is reasonable to assume an evolving information asymmetry between its collaborators. Epistemic logic explicitly considers the concepts of knowledge, as held and developed by the different agents in a scope. In this paper, we explore the idea of applying epistemic logic in the specification of different aspects of an ensemble.
Syntax-aware Neural Semantic Role Labeling for Morphologically Rich Languages

Semantic Role Labeling (SRL) is one of the most challenging tasks in Natural Language Processing (NLP). SRL is a task that consists of predicate identification, argument identification and argument classification. In this article we present novel approach for argument classification that is based on deep neural network architecture. Traditional discrete based SRL relies heavily on feature engineering that uses syntactic structures in contrast to deep learning approaches that encode whole sentences without taking into account syntactic features. We present an approach that uses combination of syntactic features and external word representations from FastText. The advantages of using FastText embeddings is the generation of better vector representations for rare words and FastText gives better results for words that are not within the dictionary. These attributes for vector representations give good results for morphologically rich languages. Most of the SRL approaches today are trained on resource rich languages. In this article we present novel neural architecture for SRL that is suitable for resource poor morphology rich languages. Experiments on hr500k corpus shows that our syntax-aware approach shows competitive results for argument classification. We present architecture for argument classification that is based on Bidirectional Long-Short Term Memory (Bi-LSTM) and Conditional Random Field (CRF) decoding for finding optimal sequence. Our approach showed results that are very close to benchmark results with F1 score of 72%.
Blockchain for Increased Cyber-Resiliency of Industrial Edge Environments

The advent of the Internet of Things (IoT) together with its spread in industrial environments have changed pro-duction lines, by dramatically fostering the dynamicity of data sharing and the openness of machines. However, the increased flexibility and openness of the industrial environment (also pushed by the adoption of Edge devices) must not negatively affect the security and safety of production lines and its opera-tional processes. In fact, opening industrial environments towards the Internet and increasing interactions among machines may represent a security threat, if not properly managed. The paper originally proposes the adoption of the Blockchain to securely store in distributed ledgers topology information and access rules, with the primary goal of maximizing the cyber-resiliency of industrial networks. In this manner, it is possible to store and query topology information and security access rules in a completely distributed manner, ensuring data availability even in case a centralized control point is temporarily down or the network partitioned. Moreover, Blockchain consensus algorithms can be used to foster a participative validation of topology information, to reciprocally ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a non-repudiable manner.
Zero-Shot User Intent Detection via Augmented Conditional Variational Autoencoders

User intent detection plays a critical role in modern information retrieval and dialog question answering systems. It can provide intelligent retrieval results through understanding user query intent and serve as the semantic understanding in the human-machine conversation process. However, intent labeling is a time-consuming and labor-intensive work, and how to handle diverse and novel intentions is becoming an urgent and challenging task. In this paper, we propose a cvae-based architecture for the user intent detection which tries to make the latent vectors of training far away from the most similar misclassified intent labels in order to improve the detection accuracy. The cvae-based architecture includes two parts. Intent-AugCVAE extracts semantic features from utterance and is used to discriminate existing intents, while Intent-AugCVAE-ZSL transfers the learning ability of Intent-AugCVAE to discriminate emerging user intentions. Experimental results show that our method can not only discriminate existing intents, but also be able to discriminate emerging intents effectively when no labeled utterances exist.
Guide Circle-based Improved Ant Colony Algorithm

Abstract In order to solve the path planning problem of autonomous mobile robots in an unknown environment, the traditional ant colony algorithm and the guide circle algorithm were combined, and an improved ant colony algorithm based on the guide circle was proposed. In terms of local path planning, the guide circle algorithm is used to avoid obstacles, which effectively solved the problem of unstable path planning ability in narrow spaces. In the aspect of global path planning, the concept of safety index circle is introduced according to the length between robot and obstacle, and partial heuristic information of ant colony algorithm is improved, so as to avoid the traditional ant colony algorithm falling into local optimal. It is verified by simulation experiments that the improved algorithm has a stronger global search capability, faster convergence speed, and enhanced path planning capability.
Vim: A Tangible Energy Story

Vim is a tangible narrative about energy futures. It is designed to illustrate how a story about a public issue could be designed for physical interaction and whether that interaction can encourage participation in that issue. To date, much research into tangible narrative has focused on fiction and childhood education. Energy, as a wicked problem, provides a challenging context for exploring how tangible elements could be used to tell public interest stories. In this paper, we offer an overview of key energy issues in Australia and the role of design in addressing community concerns. We describe Vim and outline how our design research approach informed decisions about form and interaction. An initial critique of our prototype suggests that public interest stories have qualities that diverge from fictional tangible narrative. In particular, factors such as where a story exists and the role of the reader need to take account of the real-world context and role of ‘characters’ and ‘settings’ outside of the story.
An Enhanced Cryptographic Algorithm in Securing Healthcare Medical Records

E-health is used in a variety of digital technologies. The internet, for example, enables users of e-health to connect to health workers via e-mail, access medical records, examine health data and exchange useful information on an individual basis. As ehealth deals with highly sensitive information, it is important to implement robust cryptographic algorithms to enhance its security. Cryptography is a vital component of all information security systems to protect private information from unauthorized access, ensuring confidentiality, data integrity, authentication and other tasks. This paper proposes a modified version of the hill cipher by implementing a double encryption and decryption process with integration of non-linear operations such as transposition, substitution, circular bit shifting, and exclusive-OR operation to strengthen the key security used during the encryption of healthcare data content and to boost the unpredictability of the ciphertext, which enhances data protection across unsecured networks. The results of experiments conducted on 336 medical records validate the enhanced method’s favorable performance as it improved in key security with an avalanche effect average of 52.45% and the unpredictability of the ciphertext which also achieved an average entropy index of 7.01%.
Target Tracking Algorithm for Template Transformation based on Template Matching

This paper proposes a template transformation matching algorithm. Based on the traditional template matching method, by controlling the scaling ratio of the template to compare with the video frame, the best result of the matching is used as the template to be processed for the next matching. Updated the template size and template content to improve the accuracy of target tracking. Applying this method to target tracking, the tracking recognition rate is increased to more than 99.5%, and the algorithm delay is controlled within 70ms. The experimental results show that the template transformation method proposed in this paper can effectively improve the accuracy and real-time of recognition.
Application of reflectance transformation imaging in subtle traces detection of metal surface

Reflectance Transformation Imaging (RTI) is a computational photography technology that allows interactive manipulation of virtual lighting within two-dimensional images to enhance object surface information. RTI is based on the reflection characteristics of the surface of the object characterized by a specific parabolic function, of which the most classic is Polynomial Texture Mapping (PTM) based on biquadratic polynomial. Therefore, RTI is ideal for diffusely reflecting objects, and metal materials with specular reflection are nearly out of consideration before. In this article, several metal samples with different surface topography were selected to explore the potential of RTI for subtle traces on metal surfaces. The results show that, with the cooperation of different image enhancement algorithms, especially Specular Enhancement and Diffuse Gain, RTI can be well applied to the detection of metal, even metal with relatively strong specular reflection. And the No-Reference Structural Sharpness (NRSS) assessment is consistent with subjective perception, confirming that RTI not only helps to explore the 2D or 3D geometric texture of the surface of metal materials, but also plays an important role in understanding and spreading the cultural phenomena implied in the content on metal surfaces. Promisingly, RTI is expected to be extended to a wider range of fields.
Study of the temporal phase-shifting interferometry measurement under the Tokamak related vibration environment

Temporal phase-shifting interferometry technique (TPSI) has high accuracy in surface topography metrology and has been designing to diagnose the surface variation of plasma-facing materials (PFMs) in Tokamak. But the mechanical vibration of the Tokamak device will lead to the decrease of the measurement accuracy or even incorrect result. In order to solve the problem more, it is much desired to study the influence of the vibration on the topographic measurement in the broad parameter scope. Series experiments in the different vibration frequencies, amplitudes, modes and different deformation amounts and detection wavelengths were carried out in this work.
Principal Feature Visualisation in Convolutional Neural Networks

We introduce a new visualisation technique for CNNs called Principal Feature Visualisation (PFV). It uses a single forward pass of the original network to map principal features from the final convolutional layer to the original image space as RGB channels. By working on a batch of images we can extract contrasting features, not just the most dominant ones with respect to the classification. This allows us to differentiate between several features in one image in an unsupervised manner. This enables us to assess the feasibility of transfer learning and to debug a pre-trained classifier by localising misleading or missing features.
Perceptual coding scheme for ultra-high definition video based on perceptual noise channel model

Perceptual video coding (PVC) can eliminate visual redundancy and effectively improve the compression performance of ultra-high definition video. The conventional PVC schemes, involving the perceptual characteristics related to the video features, do not account for interference in the human visual system caused by external environment changes. Therefore, this paper presents a PVC scheme based on a perceptual noise channel model. In this approach, visual dither is utilized to imitate the adjustment process of human eyes while environment changes. Considering reliable external visual stimulus decoding in the perceptual noise channel and visual characteristics, dynamic estimation of visual dither quantization is performed based on a message passing algorithm on a factor graph. Furthermore, a weight factor based on the perceptual noise channel capacity is incorporated into the rate distortion optimization process. The experimental results show that the proposed PVC scheme achieves significant bitrate reduction with better subjective video quality than competitive PVC schemes.
Deep Interest-Shifting Network with Meta-Embeddings for Fresh Item Recommendation

Nowadays, people have an increasing interest in fresh products such as new shoes and cosmetics. To this end, an E-commerce platform Taobao launched a fresh-item hub page on the recommender system, with which customers can freely and exclusively explore and purchase fresh items, namely, the New Tendency page. In this work, we make a first attempt to tackle the fresh-item recommendation task with two major challenges. First, a fresh-item recommendation scenario usually faces the challenge that the training data are highly deficient due to low page views. In this paper, we propose a deep interest-shifting network (DisNet), which transfers knowledge from a huge number of auxiliary data and then shifts user interests with contextual information. Furthermore, three interpretable interest-shifting operators are introduced. Second, since the items are fresh, many of them have never been exposed to users, leading to a severe cold-start problem. Though this problem can be alleviated by knowledge transfer, we further babysit these fully cold-start items by a relational meta-Id-embedding generator (RM-IdEG). Specifically, it trains the item id embeddings in a learning-to-learn manner and integrates relational information for better embedding performance. We conducted comprehensive experiments on both synthetic datasets as well as a real-world dataset. Both DisNet and RM-IdEG significantly outperform state-of-the-art approaches, respectively. Empirical results clearly verify the effectiveness of the proposed techniques, which are arguably promising and scalable in real-world applications.
Research on industrial robot reducer state monitoring cloud platform

In this paper we proposed a method of monitoring industrial robot reducer online cloud platform. Based on the theory of cyber physical model, a cloud state monitoring system is designed due to the problems of complex objects, massive failure information, high stable demand and poor real-time control ability in the modern industrial robot maintain. It includes intelligent sensor and edge computing to network cloud technology. With data model, the system uses deep learning and over-limit learning algorithm on the cloud management center server. The deep learning method trains and predicts the collected fault data, and can predict the malfunction of vector reducer. Further, the intelligent optimization scheduling algorithm is used in the cloud to obtain real-time fault in industrial robot reducer. The problem of distribution and the dynamic running performance of cloud system is monitored and improved.
Failure Node Reduction Algorithm to Enhance Fault Tolerance Capability of Cloud Nodes

The growth of cloud computing is rapid and consumers expect additional resources and improved results, so the load balance of cloud computing has come to be very well known. Load balancing is essential in the distributed environment for an efficient operation. It helps achieve a high level of customer loyalty and utilization of resources by certifying that all computer resources are efficiently and unbiasedly distributed. This paper discusses several algorithms to present competent methods for increasing Cloud presentation in its entirety, providing the customer with a more appropriate and competent environment.