Reviewerly/exHarmony huggingface dataset:
BHF: BERT-based Hierarchical Attention Fusion Network for Cyberbullying Remarks Detection

Cyberbullying remarks based on social media platforms have seriously endangered the physical and mental health of netizens. In order to purify the network environment and create a healthy atmosphere, it needs to be identified. At present, most of the existing studies only analyze the overall features of the text, while ignoring the semantic representation of words at local locations. In order to mine the richer semantic features of text, this paper proposes a BHF model, which uses the BERT pre-training model to extract global semantic feature information, and then uses the Hierarchical Attention Network (HAN) to calculate the two-dimensional space of words and short sentences. Local semantic feature information is obtained by fusing two layers of semantic feature information to obtain three-dimensional semantic feature representation of words, short sentences and full text. The experimental results show that based on the BHF model, the semantic features of the text can be better extracted on the three types of problems of cyberbullying detection, and the accuracy of cyberbullying remarks detection can be greatly improved.
Data locality-aware and QoS-aware dynamic cloud workflow scheduling in Hadoop for heterogeneous environment

Hadoop has become a popular data-parallel computing framework for data-intensive scientific applications in recent years. Most scientific applications employ workflows to portray procedures and dependencies between jobs. However, the current default scheduling policy in Hadoop does not take data locality into account. The movement of data among virtual machines (VMs) produces latency in workflow scheduling. In addition, the heterogeneous and dynamics of cloud resources cannot satisfy the user's demand for quality of service (QoS) in static workflow scheduling. Hence, we propose a data locality-aware and QoS-aware dynamic cloud workflow scheduling algorithm (DQ-DCWS) based on dynamic programming. The algorithm balances data locality and delays by grouping nodes that hold tasks correlated with data blocks. We consider five QoS factors and normalise them as a path optimisation issue to realise maximum QoS. DQ-DCWS is implemented and validated by running Montage workflow on real Hadoop clusters which are deployed on Amazon EC2.
CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)

Recommender systems are a kind of data filtering that guides the user to interesting and valuable resources within an extensive dataset. by providing suggestions of products that are expected to match their preferences. However, due to data overloading, recommender systems struggle to handle large volumes of data reliably and accurately before offering suggestions. The main purpose of this work is to address the recommender system’s data sparsity and accuracy problems by using the matrix factorization algorithm of collaborative filtering based on the dimensional reduction method and, more precisely, the Nonnegative Matrix Factorization (NMF) combined with ontology. We tested the method and compared the results to other classic methods. The findings showed that the implemented approach efficiently reduces the sparsity of CF suggestions, improves their accuracy, and gives more relevant items as recommendations.
A New Characterization of 2-Resilient Rotation Symmetric Boolean Functions

In this paper, the notion of 2-tuples distribution matrices of the rotation symmetric orbits is proposed, by using the properties of the 2-tuples distribution matrix, a new characterization of 2-resilient rotation symmetric Boolean functions is demonstrated. Based on the new characterization of 2-resilient rotation symmetric Boolean functions, constructions of 2-resilient rotation symmetric Boolean functions (RSBFs) are further studied, and new 2-resilient rotation symmetric Boolean functions with prime variables are constructed.
Fixed-Time Sliding Mode Synchronization of Uncertain Fractional-Order Hyperchaotic Systems by Using a Novel Non-Singleton-Interval Type-2 Probabilistic Fuzzy Neural Network

In this study, we proposed a sliding mode control method based on fixed-time sliding mode surface for the synchronization of uncertain fractional-order hyperchaotic systems. In addition, we proposed a novel self-evolving non-singleton-interval type-2 probabilistic fuzzy neural network (SENSIT2PFNN) to estimate the uncertain dynamics of the system. Moreover, an adaptive compensator was designed to eliminate the influences of random uncertainty and fuzzy uncertainty, thereby yielding an asymptotically stable controlled system. Furthermore, an adaptive law was introduced to optimize the consequence parameters of SENSIT2PFNN. The membership layer and rule base of SENSIT2PFNN were optimized using the self-evolving algorithm and whale optimization algorithm, respectively. The simulation results verified the effectiveness of the proposed methods for the synchronization of uncertain fractional-order hyperchaotic systems.
L2RT-FIQA: Face Image Quality Assessment via Learning-to-Rank Transformer

Face recognition (FR) systems are easily constrained by complex environmental situations in the wild. To ensure the accuracy of FR systems, face image quality assessment (FIQA) is applied to reject low-quality face image unsuitable for recognition. Face quality can be defined as the accuracy or confidence of face images being correctly recognized by FR systems, which is desired to be consistent with recognition results. However, current FIQA methods show more or less inconsistency with face recognition due to the following four biases, including implicit constraint, quality labels, regression models, and backbone networks. In order to reduce such biases and enhance the consistency between FR and FIQA, this paper proposes a FIAQ method based on Learning to rank (L2R) algorithm and vision Transformer named L2RT-FIQA. L2RT-FIQA consists of three parts: relative quality labels, L2R framework, and vision Transformer backbone. Specifically, we utilize normalized intra-class and inter-class angular distance to generate relative quality labels; we employ L2R model to focus more on the quality order rather than the absolute quality value; we apply unpretrained vision transformer as our backbone to improve generalization and global information learning. Experimental results show our L2RT-FIQA effectively reduces the aforementioned four kinds of biases and outperforms other state-of-the-art FIQA methods on several challenging benchmarks.
Phishing Website Detection Based on Hybrid Resampling KMeansSMOTENCR and Cost-Sensitive Classification

In many real-world scenarios such as fraud detection, phishing website classification, etc., the training datasets normally have skewed class distribution with majority (e.g., legitimate websites) class samples overwhelming the minority (e.g., phishing websites) class samples. The machine learning algorithms assume balanced class distributions and are biased towards the majority (uninteresting) class ignoring the minority (interesting) class (es). For handling class imbalance, researchers have proposed solutions both at the (i) data-level and (ii) algorithm-level. In this study we propose a dual approach for handling class imbalance in phishing website classification both at the data and algorithm. We propose a novel hybrid resampling approach KMeansSMOTENCR which balances the dataset by first oversampling the minority class using KMeans Synthetic Minority Oversampling Technique (KMeansSMOTE) (Douzas et al. in Inf Sci 465:1–20, 2018 [1]) followed by Neighborhood Clearing Rule (NCR) (Laurikkala in AIME, LNAI 2001. Springer, Berlin, pp 63–66, 2001 [2]) under sampling technique as the data cleaning approach to take care of the possibility of synthetic minority class samples invading the majority class samples. Finally, we employed Cost-Sensitive Random Forest (CS-RF), Cost-Sensitive Extreme Gradient Boosting (CS-XGB), Cost-Sensitive Support Vector Machine (CS-SVM), and Cost-Sensitive Logistic Regression (CS-LR) classifiers as algorithm-level balancing approach. We evaluated the performance of CS-RF, CS-XGBoost, CS-SVM, and CS-LR classifiers on (i) Original-(Imbalanced), (ii) NCR-(Balanced), (iii) KMeansSMOTE-(Balanced), and (iv) KMeansSMOTENCR-(Balanced) datasets. In Sect. 4 Result and Discussion we demonstrate that the highest ROC_AUC, F1 and GMean are obtained from our proposed method which outperforms the other three. To the best of our knowledge and belief our novel hybrid resampling approach ‘KMeansSMOTENCR’ has not been published in the existing studies as of now.
A Variational Algorithm for Quantum Single Layer Perceptron

Hybrid quantum-classical computation represents one of the most promising approaches to deliver novel machine learning models capable of overcoming the limitations imposed by the classical computing paradigm. In this work, we propose a novel variational algorithm for quantum Single Layer Perceptron (qSLP) which allows producing a quantum state equivalent to the output of a classical single-layer neural network. In particular, the proposed qSLP generates an exponentially large number of parametrized linear combinations in superposition that can be learnt using quantum-classical optimization. As a consequence, the number of hidden neurons scales exponentially with the number of qubits and, thanks to the universal approximation theorem, our algorithm opens to the possibility of approximating any function on quantum computers. Thus, the proposed approach produces a model with substantial descriptive power and widens the horizon of potential applications using near-term quantum computation, especially those related to quantum machine learning. Finally, we test the qSLP as a classification model against two different quantum models on two different real-world datasets usually adopted for benchmarking classical algorithms.
Innovative cloud computing object-oriented model to unify heterogeneous data

Nowadays, unifying heterogeneous databases has received a lot of attention. This paper presents a new object-oriented model to unify heterogeneous databases into one highly versatile database based on three phases: preprocessing phase in which the 'materialised view' (MV), is adopted to collect the data from multiple resources. The 'OODB-construction' phase includes a single unified database. The last phase, 'web-based and cloud computing', deploys the required services and disseminates them based on the cloud-hosted environment. In order to evaluate the proposed model, a real practical example was used. The efficiency of MV was analysed and compared against the normal-view. Then, comparative results are conducted to show the behaviour of our proposed model against other conventional database models. Thereafter, the performance of the adopted OODB model was compared against the RDB model. In conclusion, our proposed model achieved the best results with ×2.49 speed-up that minimising the overall execution time.
A priority-reservation queueing-based approach for Blockchain-assisted smart-grid system

Most IoT-enabled smart grids are centralized. Blockchain-based decentralized systems are secure, private, transparent, stable, and auditable. Since Blockchain transactions are immutable, energy trade and tracking are more straightforward. Scalable connection and access control management are implemented in these networks through the queueing concept. By keeping this in mind, the present article proposes a priorityreservation queueing-based analytical model for Blockchain-assisted smart grid systems to facilitate easy accessibility between consumers and distributors. We also present various numerical findings to illustrate the effectiveness of the proposed system.
OBI: a multi-path oblivious RAM for forward-and-backward-secure searchable encryption

Dynamic searchable encryption (DSE) is a usercloud protocol for searching over outsourced encrypted data.Many current DSE schemes resort to oblivious RAMs (ORAM) to achieve forward privacy and backward privacy, which is a concept to describe security levels of the protocol.We show that, however, most prior ORAM-based DSE suffers from a new problem: it is inefficient to fetch/insert a large set of data blocks.We call this the large-stash eviction problem.To address the problem, we present OBI, a multi-path Oblivious RAM, which accesses multiple tree paths per query for handling a large set of data blocks.We classify traditional tree-based ORAMs as singlepath ORAMs if they access a single path per query.OBI has two new high-throughput multi-path eviction algorithms that are several orders of magnitude more efficient than the wellknown PATH-ORAM eviction algorithm when the stash is large.We prove that the proposed multi-path ORAM outperforms the traditional single-path ORAM in terms of local stash size and insertion efficiency.Security analysis shows that OBI is secure under the strong forward and backward security model.OBI can protect the well-known DSE leakage, such as the search pattern and the size pattern.We also show that OBI can be applied to oblivious file systems and oblivious conjunctive-query DSE schemes.We conduct experiments on the Enron dataset.The experimental results demonstrate that OBI is far more efficient than the state-of-the-art ORAM-based DSE schemes.
SoundLock: A Novel User Authentication Scheme for VR Devices Using Auditory-Pupillary Response

Virtual Reality (VR) has shown promising potential in many applications, such as e-business, healthcare, and social networking.Rich information regarding users' activities and online accounts is stored in VR devices.If they are carelessly unattended, adversarial access will cause data breaches and other critical consequences.Practical user authentication schemes for VR devices are in dire need.Current solutions, including passwords, digital PINs, and pattern locks, mostly follow conventional approaches for general personal devices.They have been criticized for deficits in both security and usability.In this work, we propose SoundLock, a novel user authentication scheme for VR devices using auditory-pupillary response as biometrics.During authentication, auditory stimuli are presented to the user via the VR headset.The corresponding pupillary response is captured by the integrated eye tracker.User's legitimacy is then determined by comparing the response with the template generated during the enrollment stage.To strike a balance between security and usability in the scheme design, an optimization problem is formulated.Due to its nonlinearity, a two-stage heuristic algorithm is proposed to solve it efficiently.The solution provides necessary guidance for selecting effective auditory stimuli and determining their corresponding lengths.We demonstrate through extensive in-field experiments that SoundLock outperforms state-of-the-art biometric solutions with FAR (FRR) as low as 0.76% (0.91%) and is well received among participants in the user study.
On the Anonymity of Peer-To-Peer Network Anonymity Schemes Used by Cryptocurrencies

Cryptocurrency systems can be subject to deanonymization attacks by exploiting the network-level communication on their peer-to-peer network.Adversaries who control a set of colluding node(s) within the peer-to-peer network can observe transactions being exchanged and infer the parties involved.Thus, various network anonymity schemes have been proposed to mitigate this problem, with some solutions providing theoretical anonymity guarantees.In this work, we model such peer-to-peer network anonymity solutions and evaluate their anonymity guarantees.To do so, we propose a novel framework that uses Bayesian inference to obtain the probability distributions linking transactions to their possible originators.We characterize transaction anonymity with those distributions, using entropy as metric of adversarial uncertainty on the originator's identity.In particular, we model Dandelion, Dandelion++ and Lightning Network.We study different configurations and demonstrate that none of them offers acceptable anonymity to their users.For instance, our analysis reveals that in the widely deployed Lightning Network, with 1% strategically chosen colluding nodes, the adversary can uniquely determine the originator for ≈ 50% of the total transactions in the network.In Dandelion, an adversary that controls 15% of the nodes has, on average, uncertainty among only 8 possible originators.Moreover, we observe that due to the way Dandelion and Dandelion++ are designed, increasing the network size does not correspond to an increase in the anonymity set of potential originators.Alarmingly, our longitudinal analysis of the Lightning Network reveals rather an inverse trend-with the growth of the network, the overall anonymity decreases.
Cascaded Graph Convolution Approach for Nuclei Detection in Histopathology Images

Nuclei detection in histopathology images of cancerous tissue stained with conventional hematoxylin and eosin stain is a challenging task due to the complexity and diversity of cell data. Deep learning techniques have produced encouraging results in the field of nuclei detection, where the main emphasis is on classification and regressionbased methods. Recent research has demonstrated that regression-based techniques outperform classification. In this paper, we propose a classification model based on graph convolutions to classify nuclei, and similar models to detect nuclei using cascaded architecture. With nearly 29,000 annotated nuclei in a large dataset of cancer histology images, we evaluated the Convolutional Neural Network (CNN) and Graph Convolutional Networks (GCN) based approaches. Our findings demonstrate that graph convolutions perform better with a cascaded GCN architecture and are more stable than centre-of-pixel approach. We have compared our twofold evaluation quantitative results with CNN-based models such as Spatial Constrained Convolutional Neural Network (SC-CNN) and Centre-of-Pixel Convolutional Neural Network (CP-CNN). We used two different loss functions, binary cross-entropy and focal loss function, and also investigated the behaviour of CP-CNN and GCN models to observe the effectiveness of CNN and GCN operators. The compared quantitative F1 score of cascaded-GCN shows an improvement of 6% compared to state-of-the-art methods.
Domain-Aware Dual Attention for Generalized Medical Image Segmentation on Unseen Domains

Recently, there has been significant progress in medical image segmentation utilizing deep learning techniques. However, these achievements largely rely on the supposition that the source and target domain data are identically distributed, and the direct application of related methods without addressing the distribution shift results in dramatic degradation in realistic clinical environments. Current approaches concerning the distribution shift either require the target domain data in advance for adaptation, or focus only on the distribution shift across domains while ignoring the intra-domain data variation. This paper proposes a domain-aware dual attention network for the generalized medical image segmentation task on unseen target domains. To alleviate the severe distribution shift between the source and target domains, an Extrinsic Attention (EA) module is designed to learn image features with knowledge originating from multi-source domains. Moreover, an Intrinsic Attention (IA) module is also proposed to handle the intra-domain variation by individually modeling the pixel-region relations derived from an image. The EA and IA modules complement each other well in terms of modeling the extrinsic and intrinsic domain relationships, respectively. To validate the model effectiveness, comprehensive experiments are conducted on various benchmark datasets, including the prostate segmentation in magnetic resonance imaging (MRI) scans and the optic cup/disc segmentation in fundus images. The experimental results demonstrate that our proposed model effectively generalizes to unseen domains and exceeds the existing advanced approaches.
Accelerating Finite Field Arithmetic for Homomorphic Encryption on GPUs

Fully Homomorphic Encryption (FHE) is a rapidly developing technology that enables computation directly on encrypted data, making it a compelling solution for security in cloud-based systems. In addition, modern FHE schemes are believed to be resistant to quantum attacks. Although FHE offers unprecedented potential for security, current implementations suffer from prohibitively high latency. Finite field arithmetic operations, particularly the multiplication of high-degree polynomials, are key computational bottlenecks. The parallel processing capabilities provided by modern Graphical Processing Units (GPUs) make them compelling candidates to target these highly parallelizable workloads. In this article, we discuss methods to accelerate polynomial multiplication with GPUs, with the goal of making FHE practical.
Database reconstruction does compromise confidentiality

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...
Text2Storyline: Generating Enriched Storylines from Text

In recent years, the amount of information generated, consumed and stored has grown at an astonishing rate, making it difficult for those seeking information to extract knowledge in good time. This has become even more important, as the average reader is not as willing to spare more time out of their already busy schedule as in the past, thus prioritizing news in a summarized format, which are faster to digest. On top of that, people tend to increasingly rely on strong visual components to help them understand the focal point of news articles in a less tiresome manner. This growing demand, focused on exploring information through visual aspects, urges the need for the emergence of alternative approaches concerned with text understanding and narrative exploration. This motivated us to propose Text2Storyline, a platform for generating and exploring enriched storylines from an input text, a URL or a user query. The latter is to be issued on the Portuguese Web Archive (Arquivo.pt), therefore giving users the chance to expand their knowledge and build up on information collected from web sources of the past. To fulfill this objective, we propose a system that makes use of the Time-Matters algorithm to filter out non-relevant dates and organize relevant content by means of different displays: 'Annotated Text', 'Entities', 'Storyline', 'Temporal Clustering' and 'Word Cloud'. To extend the users' knowledge, we rely on entity linking to connect persons, events, locations and concepts found in the text to Wikipedia pages, a process also known as Wikification. Each of the entities is then illustrated by means of an image collected from the Arquivo.pt.
Review of issues on knowledge management system and country’s digital security: a study of Nigeria

The digital world has exposed the whole world into danger and vulnerability of information security and the presence of new ways of compromising information security has moved the attention to a more holistic approach to information security management comprising technological, organizational, and social components. This remains a challenge for the policy maker and practitioners to more concerted effort into creating secure knowledge management and making the appropriate knowledge accessible to the appropriate individuals at the appropriate time. Knowledge is known as a country intellectual infrastructural and significant to overall development of all sectors.