Reviewerly/exHarmony huggingface dataset:
A Multi-constraint Deep Semi-supervised Learning Method for Ovarian Cancer Prognosis Prediction

Evaluating ovarian cancer prognosis is important for patients' follow-up treatment. However, the limited sample size tends to lead to overfitting of the supervised evaluation task. Considering to get more useful information from different perspectives, we proposed a semi-supervised deep neural network method called MCAP. MCAP introduced the heterogeneity information of the tumors through unsupervised clustering constraint, to help the model better distinguish the difference in the prognosis of ovarian cancer. Besides, the data recovering constraint is used to ensure learning a high-quality and low-dimensional representation of the genes in the network. For making a comprehensive analysis for ovarian cancer, we applied MCAP to seven gene expression datasets collected from TCGA and GEO databases. The results proved that the MCAP is superior to the other prognosis prediction methods in both 5-fold cross-validation and independent test.
Improving Small and Cluttered Object Detection by Incorporating Instance Level Denoising into Single-shot Alignment Network for Remote Sensing Imagery

Object detection on satellite and aerial images has gained the attraction of the scientific community of computer vision due to its immense value, high difficulty and the development of large scale datasets enough to train deep learning models. The progress is tremendous with the increase in the precision of the fast single-stage object detection models which used to sacrifice precision for speed. Aerial and satellite images are of large sizes which make slow models infeasible for production. Single-shot Alignment Network (S2A-Net) is a fast and competitive single-stage model in terms of precision. However, there is a potential to increase its precision in detecting small and cluttered objects in a complex background. In this paper, a new hybrid approach by incorporating Instance Level Denoising (ILD) module from Small, Cluttered and Rotated Object Detector ++ (SCRDet++) into S2A-Net is proposed. The model is trained and tested on Dota V1.0. The proposed model achieves higher mean average precision (mAP) over S2A-Net to be 79.73% and when it is trained using Kullback–Leibler divergence as a regression loss function, the proposed model can reach as high as 80.39% mAP.
Phishing Susceptibility Across Industries

Research into the human factors of cyber security is becoming increasingly important in helping to understand how human behaviour can be influenced in the modern age of human targeted cyber-attacks. Phishing is one of the most prevalent methods used to socially engineer human targets, and as such it is important to establish which factors may influence susceptibility to phishing emails. The majority of research has thus far been dedicated to individual level and semantic factors of susceptibility, while other important issues such as organisational context have been largely absent. This paper explores whether industry type influences behaviours resulting from phishing simulations. Here we present a large sample of real-world data from phishing simulations deployed to employees from banking, education, healthcare and pharmaceutical organisations and construction. Analyses were conducted across multiple potential responses - opening an email, clicking a link, replying to the email, entering data, and reporting the email as suspicious. The results revealed significant differences in susceptibility to phishing depending on which industry type employees belonged to. Consistent with previous work, the banking industry had the fewest number of employees engaged in opening phishing emails and clicking links. Implications for future work and industry professionals are discussed.
Donkey Anaphora in Non-Monotonic Environments

Abstract Donkey anaphora in quantified sentences is ambiguous between an existential and a universal reading. The extent to which different readings are accessible depends on the quantifier, but how to model this dependence is debated (Kanazawa, 1994; Champollion et al. 2019). This study advances this debate by providing novel experimental data on the interpretation of donkey anaphora in sentences with non-monotonic quantifiers exactly 3 and all but one. We establish that while the existential reading of donkey anaphora is the preferred one with both exactly 3 and all but one, the universal reading is accessed more with all but one than with exactly 3. These results have important implications for both Kanazawa (1994) and Champollion et al. (2019) theories, as both need to be amended to fully capture the empirical picture.
On Next-Generation Sequencing Compression via Multi-GPU

In the last decades, the human genoma analysis for addressing health-care problems, has widely grown. With the high throughput of biological data and, needing of represent them, the Next-Generation Sequencing was introduced. In order to store genomic features without losing information, different data format (such as FAST-A, FAST-Q, SAM, VCF) have been proposed. To overcome the storing process issues of these data, several genomic compressors have been presented. A specific VCF compressor is analyzed. Due to the restricted hardware resources limit of multi-core architecture when input size dimension data are given, large execution times are required. Thanks to the well-known parallel nature of the most recent Graphic Process Units, in this work we present a Multi-GPU based parallel implementation by exploiting CUDA framework. An ad-hoc memory approach combined with a suitable work decomposition strategy are able to give a strong increase in performance. To observe the benefits in terms of performance, tests and experiments complete our work.
Optimizing Energy Usage for an Electric Drone

Abstract Unmanned Aerial Vehicles (UAVs) are rapidly gaining popularity in a wide variety of applications, e.g., agriculture, health care, environmental management, supply chains, law enforcement, surveillance, and photography. Dones are often powered by batteries, making energy a critical resource that must be optimised during the mission of the drone. The duration of a done’s mission depends on the amount of energy required to perform some manoeuvering actions (takeoff, level flight, hovering, and landing), the energy required to power the ICT modules in the drone, the drone’s speed, payload, and the wind. In this paper, we present a model that minimizes the energy consumption of a low power drone and maximizes the time required to completely drain the drone’s battery and ensure the safe landing of the drone.
Towards Lithuanian Grammatical Error Correction

Everyone wants to write beautiful and correct text, yet the lack of language skills, experience, or hasty typing can result in errors. By employing the recent advances in transformer architectures, we construct a grammatical error correction model for Lithuanian, the language rich in archaic features. We compare subword and byte-level approaches and share our best trained model, achieving F $$_{0.5}=0.92$$ , and accompanying code, in an online open-source repository.
Privacy-Preserving Distributed Similarity Retrieval of Large CT Image Sequence Database in Mobile Telemedicine Networks

As computed tomography image(CTI) sequence can be regarded as a medical image set, which contains several neighboring and visually similar CTIs with temporal information, conducting an efficient similarity retrieval of the large CTI sequences poses great challenge. Finding the similar CTI sequences can assist diagnosis and treatment by allowing physicians to quickly locate pathological images of lesion tissues, accurately find the source of disease. The paper proposes a progressive Privacy-preserving similarity Retrieval scheme for the CTI Sequences in the mobile telemedicine network(MTN) called the PRS method. To better facilitate the PRS processing, five enabling techniques are devised: 1) key CTI(KCTI)-based similarity measure, 2) local privacy preserving scheme, 3) zero-distance(ZD)-based distributed storage scheme, 4) uniform distributed index framework, and 5) crowd-assisted verification. Extensive experiments demonstrate that the retrieval efficiency of our proposed PRS method is about 90% higher than that of the existing ones.
Isoga: An Isogeny-Based Quantum-Resist Searchable Encryption Scheme Against Keyword Guessing Attacks

Driven by cloud computing technologies, public-key encryption with keyword search (PEKS) is becoming a common practice from aspects of the Industrial Internet of Things, smart healthcare, vehicular social networks, and so on. However, a dozen years of PEKS development is accompanied by some security and privacy issues in the encrypted data search and access processes. The keyword guessing attack is a typical user privacy threat model, that is, an adversary could guess the user’s retrieval keyword given a search trapdoor. On the other hand, the emergence of quantum computers make traditional PEKS schemes no longer secure. Although scholars put forward some postquantum secure PEKS schemes, these schemes are based on lattice cryptography with a larger key size. To the best of our knowledge, there is no quantum-resist PEKS scheme established on elliptic curve cryptography. This article utilizes PEKS with designated tester primitive and quantum resistance of isogeny. Then, we put forward a postquantum searchable encryption scheme named Isoga, which fights against keyword guessing attacks. We prove Isoga’s searchable ciphertext security and trapdoor indistinguishability under isogeny-related difficult assumptions. Performance evaluation indicates that the Isoga scheme is more practical in the quantum environment, considering seven schemes’ security properties, communication cost, and computing overload among.
Calibration of Few-Shot Classification Tasks: Mitigating Misconfidence From Distribution Mismatch

As many meta-learning algorithms improve performance in solving few-shot classification problems for practical applications, the accurate prediction of uncertainty is considered essential.In metatraining, the algorithm treats all generated tasks equally and updates the model to perform well on training tasks.During the training, some of the tasks may make it difficult for the model to infer the query examples from the support examples, especially when a large mismatch between the support set and the query set exists.The distribution mismatch causes the model to have incorrect confidence, which causes a calibration problem.In this study, we propose a novel meta-training method that measures the distribution mismatch and enables the model to predict with more precise confidence.Moreover, our method is algorithm-agnostic and can be readily expanded to include a range of meta-learning models.Through extensive experimentation, including dataset shift, we show that our training strategy prevents the model from becoming indiscriminately confident, and thereby helps the model to produce calibrated classification results without the loss of accuracy.
Bi-RSTU: Bidirectional Recurrent Upsampling Network for Space-Time Video Super-Resolution

One-stage space-time video super-resolution (STVSR) aims to directly reconstruct high-resolution (HR) and high frame rate (HFR) video from its low-resolution (LR) and low frame rate (LFR) counterpart. Due to the wide application, one-stage STVSR has drawn much attention recently. However, existing one-stage methods suffer from ineffective exploration of the auxiliary information from adjacent time steps that may be useful to STVSR at the current time step. To address this issue, we propose a novel Bidirectional Recurrent Space-Time Upsampling network called Bi-RSTU for one-stage STVSR to utilize auxiliary information at various time steps. Specifically, an efficient channel attention feature interpolation (ECAFI) module is devised to synthesize the intermediate frame’s LR feature by exploiting its two neighboring LR video frame features. Subsequently, we fuse the information from the previous time step into these intermediate and neighboring features. Finally, second-order attention spindle (SOAS) blocks are stacked to form the feature reconstruction module that learns a mapping from LR fused feature space to HR feature space. Experimental results on public datasets demonstrate that our Bi-RSTU shows competitive performance compared with current two-stage and one-stage state-of-the-art STVSR methods.
Scene Change Captioning in Real Scenarios

This paper discusses the scene change captioning task that describes scene changes using natural language for real scenarios. Most current three-dimensional understanding tasks focus on recognizing static scenes. Despite its importance in a variety of real environment applications, scene change understanding remains less discussed. Existing change understanding methods discussed in robotics focus on change detection and lack the ability to perform detailed recognition of scene changes. Most previous experiments on change captioning methods were conducted on simulation datasets with limited visual complexity, limiting their availability for real scenarios. To solve the above issues, we propose a scene change captioning dataset with scenes photographed using RGB-D cameras. We also propose an automatic simulation dataset generation process, aiming for training models transferring to real scenarios. We conducted experiments with various input modalities and proposed a method that integrates different input modalities using an attention mechanism over modalities and dynamic attention to select related information during the sentence generation process. The experimental results show that models trained on the proposed simulation dataset obtained promising results on real scenario dataset, indicating the proposed dataset generation process’s practicality in real scenarios. The proposed multimodality integrating method can generate change captions with high change type and object attribute accuracy while showing robustness in real scenarios. We hope our work can open a door for future research on scene change understanding in real scenarios.
Data Anonymization Through Multi-modular Clustering

A growing interest in data privacy protectionData privacy protection is mostly motivated by countries and organizations’ desire to demonstrate democracy and practice guidelines by opening their data. Anonymization of data refers to the process of removing sensitive data’s identifiers while keeping their structure and also the information type [35]. A fundamental challenge with data anonymizationData anonymization is achieving a balance between the data’s worth and the degree of disclosure. The k-anonymity framework is a well-known framework for data anonymizationData anonymization. It states that a dataset is anonymous if and only if it contains at least $$k-1$$ similar items. In this chapter, we introduce two ways for attaining k-anonymity: k-CMVM and Constrained-CMVM. All methods rely on collaborative topological unsupervised learning [27, 32] to generate k-anonymous content. The k-CMVM method selects the k layers automatically, but the contrainted version does it through exploration. Additionally, we boosted the performance of these two strategies by including the pLVQ2 weighted vector quantization method. These techniques were evaluated on the basis of two metrics: separability and structural utility. The obtained experimental results indicate very good performance.
BFAC-CS: A Blockchain-Based Fine-Grained Access Control Scheme for Complex Scenarios

Blockchain technology is becoming increasingly mature and has a great deal of research in a variety of industries. Because of its characteristics such as unalterable, decentralized and unforgeable, it has become a general trend that blockchain technology becomes the infrastructure of various technologies. Therefore, there are many studies on the application of blockchain combined with access control technology. Role-based access control (RBAC), as an important tool of system information management, has become a security solution recognized by many enterprises, but in many complex scenarios, the roles in the system will be more redundant, and the permissions between roles are likely to conflict. In order to solve the problems of RBAC, which is difficult to control the roles in complex scenarios and the role explosion caused by frequent creation and revocation of roles, and to manage the access permissions more finely without losing the comprehensibility of the system, we propose BFAC-CS, a blockchain-based access control scheme combining RBAC and ABAC, using smart contracts as a means to implement its entire framework. We designed three contracts to manage user’s role, attributes and permission. In this scheme, attributes are appended to user's role, so that in some complex scenarios, the creation and deletion of role will not be so frequent. Modifying the corresponding attributes of the role can achieve the purpose of changing the user's access permission, and the management of user’s permission is more detailed. We used Solidity to write these smart contracts and deployed them on test network for testing, and evaluated the cost and security of the whole scheme.
Towards Development with Multi-version Models: Detecting Merge Conflicts and Checking Well-Formedness

Developing complex software requires that multiple views and versions of the software can be developed in parallel and merged as supported by views and managed by version control systems. In this context, this paper considers permanent monitoring of merging and related consistency problems at the level of models and abstract syntax. The presented approach introduces multi-version models based on typed graphs that permit to store changes and multiple versions in one graph in a compact form and allow (1) to study well-formedness for all versions without the need to extract each version individually, (2) to report all possible merge conflicts without the need to merge all pairs of versions, and (3) to report all violations of well-formedness conditions that will result for merges of any two versions independent of any merge decisions without the need to merge all pairs of versions. Thereby, the approach aims to permit early and frequent conflict detection while developing in parallel. The paper defines the related concepts and algorithms operating on multi-version models, proves their correctness w.r.t. the usually employed three-way-merge, and reports on preliminary experiments concerning the scalability.
A Data-Driven Approach for Online Pre-impact Fall Detection with Wearable Devices

The implementation of wearable airbags to prevent fall injuries depends on accurate pre-impact fall detection and a clear distinction between activities of daily living (ADL) and them. We propose a novel pre-impact fall detection algorithm that is robust against ambiguous falling activities. We present a data-driven approach to estimate the fall risk from acceleration and angular velocity features and use thresholding techniques to robustly detect a fall before impact. In the experiment, we collect simulated fall data from subjects wearing an inertial sensor on their waist. As a result, we succeeded in significantly improving the accuracy of fall detection from 50.00 to 96.88%, the recall from 18.75 to 93.75%, and the specificity 81.25 to 100.00% over the baseline method.
Median Filtering Detection Using Ensemble Methods

Passive image forgery techniques are aimed at detecting image tampering without any a prior knowledge about the images. One of the major challenges in the field of image forensics is detection of basic operators such as application of median filtering. Median filter is one of the basic operators that is being used for malicious intent of hiding history of operations applied on an image. The studies related to detection of median filtering are becoming very popular in the field of digital image forensics. A large number of methods have been proposed for detection of median filtering. The paper presents improved percentage streak area based on streak effect in median filtered images. An ensemble classifier is used for detection of median filtered images by performing a multi class classification using bagged method with decision tree as weak learner. The results show that the classifier performs better than state-of-the-art methods for multiclass classification between original, median filtered and postJPEG compressed median filtered images.
Numerical and Topological Conditions for Suboptimal Distributed Kalman Filtering

This article considers the problem of distributed state estimation of a linear time-invariant system by a network of sensors in a discrete-time setting. Specifically, we consider a consensus-based distributed Kalman filter where each sensor updates its estimate in two steps: 1) a consensus step dictated by a weighted and 2) a directed communication graph followed by a local Luenberger filtering step. For a given network, we show that the suboptimal filtering gains that minimize an upper bound of a quadratic filtering cost can be computed only by exchanging state estimates and their covariance matrices among agents. The resulting dynamics for the network’s covariance matrices are represented by a set of coupled algebraic Riccati equations (CAREs) that can be analyzed to ensure stability. Next, we connect some of the results from the Markovian jump linear systems literature regarding the underlying CARE dynamics to the distributed estimation problem, and provide separate necessary and sufficient conditions for successful estimation. We show that the notion of strong detectability plays an important role in the stability of the filter matrices and ensures that the noise-free error dynamics exponentially converge to zero. We then utilize the notion of weak detectability to provide necessary conditions in terms of the network topology and self-weights of the communication graph. We further demonstrate how these notions of detectability can be combined to search for feasible network topologies and weights that ensure this suboptimal performance. Numerical examples are given to illustrate these results.
Introduction to machine learning

Machine learning (ML) is the process of using different algorithms to make predictions regarding observations. In this chapter, we review some of the basic concepts used in ML, such as data structure, size of the data set, dimensionality reduction, and feature selection, in addition to indices of the model’s performance.
Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

An obviously challenging problem in named entity recognition is the construction of the kind data set of entities.Although some research has been conducted on entity database construction, the majority of them are directed at Wikipedia or the minority at structured entities such as people, locations and organizational nouns in the news.This paper focuses on the identification of scientific entities in carbonate platforms in English literature, using the example of carbonate platforms in sedimentology.Firstly, based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts, this paper designs a literature content extraction method that allows dealing with complex text structures.Secondly, based on the literature extraction content, we formalize the entity extraction task (lexicon and lexical-based entity extraction) for entity extraction.Furthermore, for testing the accuracy of entity extraction, three currently popular recognition methods are chosen to perform entity detection in this paper.Experiments show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition task.This study presents a pilot study of entity extraction, which involves the use of a complex structure and specialized literature on carbonate platforms in English.