Reviewerly/exHarmony huggingface dataset:
Centerless Clustering

Although lots of clustering models have been proposed recently, <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means and the family of spectral clustering methods are both still drawing a lot of attention due to their simplicity and efficacy. We first reviewed the unified framework of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means and graph cut models, and then proposed a clustering method called <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -sums where a <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbor ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -NN) graph is adopted. The main idea of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -sums is to minimize directly the sum of the distances between points in the same cluster. To deal with the situation where the graph is unavailable, we proposed <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -sums-x that takes features as input. The computational and memory overhead of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -sums are both <inline-formula><tex-math notation="LaTeX">$O(nk)$</tex-math></inline-formula> , indicating that it can scale linearly w.r.t. the number of objects to group. Moreover, the costs of computational and memory are Irrelevant to the product of the number of points and clusters. The computational and memory complexity of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -sums-x are both linear w.r.t. the number of points. To validate the advantage of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -sums and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -sums-x on facial datasets, extensive experiments have been conducted on 10 synthetic datasets and 17 benchmark datasets. While having a low time complexity, the performance of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -sums is comparable with several state-of-the-art clustering methods.
Unseen Anomaly Detection on Networks via Multi-Hypersphere Learning

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Unseen Anomaly Detection on Networks via Multi-Hypersphere LearningShuang Zhou, Xiao Huang, Ninghao Liu, Qiaoyu Tan, and Fu-Lai ChungShuang Zhou, Xiao Huang, Ninghao Liu, Qiaoyu Tan, and Fu-Lai Chungpp.262 - 270Chapter DOI:https://doi.org/10.1137/1.9781611977172.30PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Network anomaly detection is a crucial task since a few anomalies can cause huge losses. Semi-supervised anomaly detection methods can effectively leverage a small number of labels as prior knowledge to enhance detection accuracy. But in real-world scenarios, novel types of anomalies (i.e., unseen anomalies) usually exist on networks which may present different characteristics with the seen anomalies and are hard to be identified by prior semi-supervised anomaly detection methods. In this paper, we propose the novel problem of unseen network anomaly detection that aims to identify both seen and unseen anomalies to eliminate potential dangers. Accordingly, we propose a method called Multi-hypersphere Graph Learning (MHGL) to effectively leverage existing labels by learning fine-grained normal patterns to discriminate anomalies. Experiments demonstrate that MHGL outperforms state-of-the-art methods significantly. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737Key words:Anomaly Detection, Attributed Networks, Unseen Anomaly
Learning to Embed Semantic Similarity for Joint Image-Text Retrieval

We present a deep learning approach for learning the joint semantic embeddings of images and captions in a euclidean space, such that the semantic similarity is approximated by the L2 distances in the embedding space. For that, we introduce a metric learning scheme that utilizes multitask learning to learn the embedding of identical semantic concepts using a center loss. By introducing a differentiable quantization scheme into the end-to-end trainable network, we derive a semantic embedding of semantically similar concepts in euclidean space. We also propose a novel metric learning formulation using an adaptive margin hinge loss, that is refined during the training phase. The proposed scheme was applied to the MS-COCO, Flicke30K and Flickr8K datasets, and was shown to compare favorably with contemporary state-of-the-art approaches.
Evaluation of the Convincing Ability through Presentation Skills of Pre-Service Management Wizards Using AI via T2 Linguistic Fuzzy Logic

Convincing ability is one of the most desirable an attribute to be a successful management wizard in any field whether political, business or domestic. They require to present various facts in such convincing style strategically employing verbal as well as non-verbal communication that all being persuaded accede to their proposal and their goal is achieved very smoothly. Till date this presentation evaluation skill has been measured in various ways without emphasizing much on various style enhancing attributes. So in this present paper a communicative approach has been made proposing a model devised centering Interval Type-2 Fuzzy logic for evaluating the convincing ability of the presenter/communicator through the positive response from the affected audience conducting the study on a group of students budding to be masters in business administration. Here it has also been shown the advantage of the present evaluation procedure over the previously suggested methods.
Integrated Machine Learning Models for Enhanced Security of Healthcare Data

Healthcare practice aims to improve and change each individual’s physical and mental life cycle, helping individuals toward a longer life, as well as avoidance of mental and physical sickness, illness, and infection. Hospitals and clinics are examining for state-of-art Machine learning algorithm approaches that will suit the expansion of information technology and evaluate a large quantity of complicated information, according to a study in the field of Healthcare. The proposed model enhances the patient's data’s security by analyzing the application user's movement and scrolling pattern and classifies it as suspicious or unsuspicious using a Convolutional Neural Network and Random Forest Classification approach. If the outcome indicates suspicious behavior, the user’s profile will be locked and a warning notice will be sent to higher authorities right away. The model also utilizes Time Series Analysis to compute the scope of the clinical equipment used by the patient, and if it is altered within that scope, it will be allowed to change the value; otherwise, it will require authentication from two or more senior doctors to verify there is no harm. The key advantage of utilizing the model is the added high-security function, which protects data better than any other system now available. It can reduce human labor to almost 0% in terms of monitoring security cameras all day, saving organizations money by allowing the algorithm to handle the heavy lifting. As machine learning algorithms gather experience, their accuracy and efficiency improve, allowing them to make better decisions.
Adapting Distilled Knowledge for Few-shot Relation Reasoning over Knowledge Graphs

Knowledge graphs (KGs) are serving as important resources for many applications, such as semantic search, question answering, or dialogue generation. As one of the fundamental tasks, multi-hop KG reasoning aims at generating effective and explainable relation prediction through reasoning paths. The current methods often require sufficient amount of training data (i.e., fact triples) for each query relation, impairing their applicabilities and performances over few-shot relations (with limited data) which are common in KGs. Despite that some few-shot relation reasoning methods have been proposed, their effectiveness and efficiency remain to be improved. To address these challenges, we propose a novel model called ADK-KG for multi-hop few-shot relation reasoning over KGs. In ADK-KG, we introduce a reinforcement learning framework to model the sequential reasoning process. We further develop a text-enhanced heterogeneous graph neural network to encode node embeddings, where entity and relation embeddings are pre-trained using content information. Later, we employ a task-aware meta-learning algorithm to optimize the model parameters that could be fast adapted for few-shot relations. A knowledge distillation module is further designed to make use of unlabeled data for improving model training. Extensive experiments on three benchmark datasets demonstrate that ADK-KG has satisfactory efficiency and outperforms state-of-the-art approaches.MSC codesKnowledge graphsRelation reasoningFew-shot learningGraph neural network
Joint Personalized Search and Recommendation with Hypergraph Convolutional Networks

Traditionally, the search and recommendation tasks are performed separately, by distinct models. Having a unique model for the two tasks is however particularly appealing for platforms that offer search and recommendation services to a shared user base over common items. In this paper, we study this unification scenario denoted as Joint Personalized Search and Recommendation (JPSR). To tackle this problem, we introduce HyperSaR, an hypergraph convolutional approach for search and recommendation. From the interaction data, we first build an hypergraph composed of user, item and query keyword nodes in which recommendation instances form user-item edges and search instances define user-item-query hyperedges. We then propagate user, item and query keyword embeddings using hypergraph convolution, and train HyperSaR with the combination of two complementary losses. The first one amounts to assessing the probability of an interaction, while the second one aims at predicting the query of a search interaction given a (user, item) pair. The proposed method is evaluated on the JPSR task using three datasets: a real-world, industrial dataset, and the public MovieLens and Lastfm datasets, which have been adapted to the task. Our experiments demonstrate the superior effectiveness of HyperSaR over competing approaches.
The recommendation method for distance learning resources of college English under the MOOC education mode

In view of the low efficiency and low accuracy of the traditional recommendation method for college English distance learning resources, a new recommendation method for college English distance learning resources under the MOOC education model is proposed. The metadata set of distance learning resources was extracted, and the metadata set was used as the data support of the learning recommendation model. The review matrix method was used to establish the interest evaluation matrix and the user interest feedback model, and the interest feedback model was used to optimise the recommendation algorithm, so as to realise the recommendation of distance learning resources in the MOOC education model. The experimental results verify that the proposed method is more efficient than the traditional method and has higher recommendation accuracy, which provides a scientific basis for the recommendation method of distance learning resources.
A Searchable Encryption Scheme Based on Lattice for Log Systems in燘lockchain

With the increasing popularity of cloud storage, data security on the cloud has become increasingly visible.Searchable encryption has the ability to realize the privacy protection and security of data in the cloud.However, with the continuous development of quantum computing, the standard Public-key Encryption with Keyword Search (PEKS) scheme cannot resist quantumbased keyword guessing attacks.Further, the credibility of the server also poses a significant threat to the security of the retrieval process.This paper proposes a searchable encryption scheme based on lattice cryptography using blockchain to address the above problems.Firstly, we design a lattice-based encryption primitive to resist quantum keyword guessing attacks.Moreover, blockchain is to decentralize the cloud storage platform's jurisdiction of data.It also ensures that the traceability of keyword retrieval process and maintains the credibility of search result, which malicious platforms are prevented as much as possible from deliberately sending wrong search results.Last but not least, through security analysis, our proposed scheme satisfies the credibility and unforgeability of the keyword ciphertext.The comprehensive performance evaluates that our scheme has certain advantages in terms of efficiency compared with others.
Quantum Blockchain Approach for Security Enhancement in Cyberworld

Quantum technology is an asset for digitisation and the cyberrealm. It aims at designing faster and more advanced solutions to present-day problem statements. Blockchain is a decentralised structure and thus lacks a supervisory authority to monitor it. Hence, it is important to imbibe security in blockchain when we are specifically moving towards quantum development. While dealing with blockchain, quantum technology enables faster transactions and quantum cryptosystems and devices can safeguard security into the blockchain systems as well. Thus, the paper focuses on discussing approaches to implement blockchain within quantum cryptosystems along with quantum cryptography. The paper discusses the attacks compromising the classical or quantum security and mechanisms proposed to counter the attacks. The prime focus of the paper is to discuss security implementations and methods in the quantum domain and contribute to quantum cryptography in the blockchain.
The Impact of Bitcoin in the Financial Market. A Cybernetics Approach

The cryptocurrency market is now a decentralized system used more and more often, based on the concept of blockchain. A blockchain is essentially a growing list of records that are linked and secured using cryptography. This article presents the current situation and predicts possible situations that may arise in the future in the cryptocurrency market, blockchain, and more. Both individuals and legal entities are starting to invest in cryptocurrencies, creating a quick and easy way to make a profit from them. In the near future, states and governments may also use cryptocurrencies for purposes such as storing fractional resources, thus replacing currently used resources such as gold. The main purpose of the paper is to explain this new emergence of cryptocurrencies from a cybernetic approach. Analyzing the cryptocurrency market from a cybernetic perspective means approaching this market as a complex adaptive system from the perspective of system dynamics. We believe that the results and analyses of this article can contribute to the development of existing literature. The topicality of this research is the analysis performed from the cybernetic perspective, but also the construction of a complex adaptive system of the financial market to which we add the cryptocurrency market that interacts with the other markets that define the cybernetics system of the financial market.
Engineering strong beamsplitter interaction between bosonic modes via quantum optimal control theory

In continuous-variable quantum computing with qubits encoded in the infinite-dimensional Hilbert space of bosonic modes, it is a difficult task to realize strong and on-demand interactions between the qubits. One option is to engineer a beamsplitter interaction for photons in two superconducting cavities by driving an intermediate superconducting circuit with two continuous-wave drives, as demonstrated in a recent experiment [Gao et al., Phys. Rev. X 8, 021073 (2018)]. Here we show how quantum optimal control theory (OCT) can be used in a systematic way to improve the beamsplitter interaction between the two cavities. We find that replacing the two-tone protocol by a three-tone protocol accelerates the effective beamsplitter rate between the two cavities. The third tone's amplitude and frequency are determined by gradient-free optimization and make use of cavity-transmon sideband couplings. We show how to further improve the three-tone protocol via gradient-based optimization while keeping the optimized drives experimentally feasible. Our work exemplifies how to use OCT to systematically improve practical protocols in quantum information applications.
Improvement of Population Diversity of Meta-heuristics Algorithm Using Chaotic Map

Particle swarm optimization (PSO) is a global optimization and nature-inspired algorithm known for its good quality and easily applied in various real-world optimization challenges. Nevertheless, PSO has some weaknesses such as slow convergence, converging prematurely and simply gets stuck at local optima. This study aims to solve the problem of deprived population diversity in the search process of PSO which causes premature convergence. Therefore, in this research, a method is brought to PSO to keep away from early stagnation which explains premature convergence. The aim of this research is to propose a chaotic dynamic weight particle swarm optimization (CHPSO) wherein a chaotic logistic map is utilized to enhance the populace diversity within the search technique of PSO with the aid of editing the inertia weight of PSO in an effort to avoid premature convergence. This study additionally investigates the overall performance and feasibility of the proposed CHPSO as a function selection set of rules for fixing problems of optimization. 8 benchmark functions had been used to assess the overall performance and seek accuracy of the proposed (CHPSO) algorithms and as compared with a few other meta-heuristics optimization set of rules. The outcomes of the experiments show that the CHPSO achieves correct consequences in fixing an optimization and has established to be a dependable and green metaheuristics algorithm for selection of features.
Task Scheduling in Cloud Computing Using PSO Algorithm

The performance of the cloud is largely determined by resource allocation strategies. In cloud computing, service providers have to supply their services to a huge number of customers. As a result, one of the most difficult aspects of cloud computing is assigning cloudlets/tasks to appropriate virtual machines. Different approaches can be used to allocate tasks onto virtual machines. In this paper, we offer a method based on the particle swarm optimization technique for assigning tasks to virtual machines. The primary goal is to reduce the total time of tasks. The proposed method is tested using the cloud simulator Cloudsim-3.03. The existing scheduling methods, First Come First Serve (FCFS) scheduling, and the Round Robin, Min-Min scheduling policy were used for comparison of simulated results from the proposed algorithm on scientific workflows. Overall execution time and makespan time (the time it takes for the last cloudlet to finish) of the proposed approach outperforms the other algorithms.
Bound Photonic Pairs in 2D Waveguide Quantum Electrodynamics

We theoretically predict the formation of two-photon bound states in a two-dimensional waveguide network hosting a lattice of two-level atoms. The properties of these bound pairs and the exclusive domains of the parameter space where they emerge due to the interplay between the on-site photon blockade and peculiar shape of polariton dispersion resulting from the long-range radiative couplings between the qubits are investigated in detail. In addition, we analyze the effect of the finite-size system on localization characteristics of these excitations.
Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation

Deep neural networks have enabled major progresses in semantic segmentation. However, even the most advanced neural architectures suffer from important limitations. First, they are vulnerable to catastrophic forgetting, i.e., they perform poorly when they are required to incrementally update their model as new classes are available. Second, they rely on large amount of pixel-level annotations to produce accurate segmentation maps. To tackle these issues, we introduce a novel incremental class learning approach for semantic segmentation taking into account a peculiar aspect of this task: since each training step provides annotation only for a subset of all possible classes, pixels of the background class exhibit a semantic shift. Therefore, we revisit the traditional distillation paradigm by designing novel loss terms which explicitly account for the background shift. Additionally, we introduce a novel strategy to initialize classifier's parameters at each step in order to prevent biased predictions toward the background class. Finally, we demonstrate that our approach can be extended to point- and scribble-based weakly supervised segmentation, modeling the partial annotations to create priors for unlabeled pixels. We demonstrate the effectiveness of our approach with an extensive evaluation on the Pascal-VOC, ADE20K, and Cityscapes datasets, significantly outperforming state-of-the-art methods.
Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation

Allogenic hematopoietic stem-cell transplant (HCT) is a curative therapy for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Relapse post-HCT is the most common cause of treatment failure and is associated with a poor prognosis. Pathologist-based visual assessment of aspirate images and the manual myeloblast counting have shown to be predictive of relapse post-HCT. However, this approach is time-intensive and subjective. The premise of this study was to explore whether computer-extracted morphology and texture features from myeloblasts' chromatin patterns could help predict relapse and prognosticate relapse-free survival (RFS) after HCT.In this study, Wright-Giemsa-stained post-HCT aspirate images were collected from 92 patients with AML/MDS who were randomly assigned into a training set (St = 52) and a validation set (Sv = 40). First, a deep learning-based model was developed to segment myeloblasts. A total of 214 texture and shape descriptors were then extracted from the segmented myeloblasts on aspirate slide images. A risk score on the basis of texture features of myeloblast chromatin patterns was generated by using the least absolute shrinkage and selection operator with a Cox regression model.The risk score was associated with RFS in St (hazard ratio = 2.38; 95% CI, 1.4 to 3.95; P = .0008) and Sv (hazard ratio = 1.57; 95% CI, 1.01 to 2.45; P = .044). We also demonstrate that this resulting signature was predictive of AML relapse with an area under the receiver operating characteristic curve of 0.71 within Sv. All the relevant code is available at GitHub.The texture features extracted from chromatin patterns of myeloblasts can predict post-HCT relapse and prognosticate RFS of patients with AML/MDS.