Reviewerly/exHarmony huggingface dataset:
Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

Recent developments in the Internet of Things (IoT) and various communication technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into various medical devices and equipment, leading to the progression of the Internet of Medical Things (IoMT). Therefore, various IoMT-based healthcare applications are deployed and used in the day-to-day scenario. Traditionally, machine learning (ML) models use centralized data compilation and learning that is impractical in pragmatic healthcare frameworks due to rising privacy and data security issues. Federated Learning (FL) has been observed as a developing distributed collective paradigm, the most appropriate for modern healthcare framework, that manages various stakeholders (e.g., patients, hospitals, laboratories, etc.) to carry out training of the models without the actual exchange of sensitive medical data. Consequently, in this work, the authors present an exhaustive survey on the security of FL-based IoMT applications in smart healthcare frameworks. First, the authors introduced IoMT devices, their types, applications, datasets, and the IoMT security framework in detail. Subsequently, the concept of FL, its application domains, and various tools used to develop FL applications are discussed. The significant contribution of FL in deploying secure IoMT systems is presented by focusing on FL-based IoMT applications, patents, real-world FL-based healthcare projects, and datasets. A comparison of FL-based security techniques with other schemes in the smart healthcare ecosystem is also presented. Finally, the authors discussed the challenges faced and potential future research recommendations to deploy secure FL-based IoMT applications in smart healthcare frameworks.
A broad review on class imbalance learning techniques

The imbalanced learning issue is related to the performance of learning algorithms in the presence of asymmetrical class distribution. Due to the complex characteristics of imbalanced datasets, learning from such data need new algorithms and understandings to convert efficient large amounts of initial data into suitable datasets. Although several review papers can be found about imbalanced classification problems, none of them contributed an in-depth review of SVM for imbalanced classification problems. To fill this gap, we present an exhaustive review of existing methods to deal with issues linked with class imbalance learning. The majority of the existing survey addresses only classification tasks. We also describe methods to deal with similar problems in regression tasks. A new taxonomy for class imbalanced learning techniques is proposed and classified into three parts: (1) Data pre-processing, (2) Algorithmic structures, and (3) Hybrid techniques. The advantages and disadvantages of each type of imbalanced learning technique are discussed. Moreover, we explain the main difficulties in distributions of imbalanced datasets and discuss the main approaches that have been proposed to tackle these issues. Finally, to stimulate the next research in this area, we emphasize the main opportunities and challenges, which can be useful in research directions for learning algorithms from imbalanced data.
A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

Task planning can require defining myriad domain knowledge about the world in which a robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to score potential next actions during task planning, and even generate action sequences directly, given an instruction in natural language with no additional domain information. However, such methods either require enumerating all possible next steps for scoring, or generate free-form text that may contain actions not possible on a given robot in its current context. We present a programmatic LLM prompt structure that enables plan generation functional across situated environments, robot capabilities, and tasks. Our key insight is to prompt the LLM with program-like specifications of the available actions and objects in an environment, as well as with example programs that can be executed. We make concrete recommendations about prompt structure and generation constraints through ablation experiments, demonstrate state of the art success rates in VirtualHome household tasks, and deploy our method on a physical robot arm for tabletop tasks. Website at progprompt.github.io
Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks

The high-speed mobile networks offer great potentials to many future intelligent applications, such as autonomous vehicles in smart transportation systems. Such networks provide the possibility to interconnect mobile devices to achieve fast knowledge sharing for efficient collaborative learning and operations, especially with the help of distributed machine learning, e.g., Federated Learning (FL), and modern digital technologies, e.g., Digital Twin (DT) systems. Typically, FL requires a fixed group of participants that have Independent and Identically Distributed (IID) data for accurate and stable model training, which is highly unlikely in real-world mobile network scenarios. In this paper, in order to facilitate the lightweight model training and real-time processing in high-speed mobile networks, we design and introduce an end-edge-cloud structured three-layer Federated Reinforcement Learning (FRL) framework, incorporated with an edge-cloud structured DT system. A dual-Reinforcement Learning (dual-RL) scheme is devised to support optimizations of client node selection and global aggregation frequency during FL via a cooperative decision-making strategy, which is assisted by a two-layer DT system deployed in the edge-cloud for real-time monitoring of mobile devices and environment changes. A model pruning and federated bidirectional distillation (Bi-distillation) mechanism is then developed locally for the lightweight model training, while a model splitting scheme with a lightweight data augmentation mechanism is developed globally to separately optimize the aggregation weights based on a splitted neural network structure (i.e., the encoder and classifier) in a more targeted manner, which can work together to effectively reduce the overall communication cost and improve the non-IID problem. Experiment and evaluation results compared with three baseline methods using two different real-world datasets demonstrate the usefulness and outstanding performance of our proposed FRL model in communication-efficient model training and non-IID issue alleviation for high-speed mobile network scenarios.
Quantum error mitigation

In most of physics it is normal to obtain information by analysis of noisy data. The paradigm of quantum computing has been a simplified version of this -- one measurement of a two-level system gives one bit of reliable information about the result of a computation. But real-world quantum computers do not work this way: the noisiness of quantum evolution also requires good strategies for extracting information. This review covers many error-mitigation strategies used in present-day quantum processors. These strategies make it much more feasible to obtain useful results before fault tolerance is achieved.
Bayesian Sparse Topical Coding

Sparse topic models (STMs) are widely used for learning a semantically rich latent sparse representation of short texts in large scale, mainly by imposing sparse priors or appropriate regularizers on topic models. However, it is difficult for these STMs to model the sparse structure and pattern of the corpora accurately, since their sparse priors always fail to achieve real sparseness, and their regularizers bypass the prior information of the relevance between sparse coefficients. In this paper, we propose a novel Bayesian hierarchical topic models called Bayesian Sparse Topical Coding with Poisson Distribution (BSTC-P) on the basis of Sparse Topical Coding with Sparse Groups (STCSG). Different from traditional STMs, it focuses on imposing hierarchical sparse prior to leverage the prior information of relevance between sparse coefficients. Furthermore, we propose a sparsity-enhanced BSTC, Bayesian Sparse Topical Coding with Normal Distribution (BSTC-N), via mathematic approximation. We adopt superior hierarchical sparse inducing prior, with the purpose of achieving the sparsest optimal solution. Experimental results on datasets of Newsgroups and Twitter show that both BSTC-P and BSTC-N have better performance on finding clear latent semantic representations. Therefore, they yield better performance than existing works on document classification tasks.
Evaluating Software Defect Prediction Performance: An Updated Benchmarking Study

Accurately predicting faulty software units helps practitioners target faulty units and prioritize their efforts to maintain software quality. Prior studies use machine-learning models to detect faulty software code. We revisit past studies and point out potential improvements. Our new study proposes a revised benchmarking configuration. The configuration considers many new dimensions, such as class distribution sampling, evaluation metrics, and testing procedures. The new study also includes new datasets and models. Our findings suggest that predictive accuracy is generally good. However, predictive power is heavily influenced by the evaluation metrics and testing procedure (frequentist or Bayesian approach). The classifier results depend on the software project. While it is difficult to choose the best classifier, researchers should consider different dimensions to overcome potential bias.
Data replication strategy with satisfaction of availability, performance and tenant budget requirements

We propose a dynamic replication strategy that satisfies simultaneously availability and performance tenant requirements while taking into account the tenant budget and the provider profit. The proposed strategy is based on a cost model that aims to calculate the minimum number of replicas required to maintain a high data availability. A replica creation is triggered only when this number of replicas is not reached or when the response time objective is not satisfied. Then, the replication must be profitable for the provider when creating a new replica. Furthermore, data replication and query scheduling are coupled in order to place these replicas in a load balancing way while dealing with the tenant budget. The experiment results prove that the proposed strategy can significantly improve availability and performance while the tenant budget is taken into account.
Business Intelligence Improved by Data Mining Algorithms and Big Data Systems: An Overview of Different Tools Applied in Industrial Research

The proposed paper shows different tools adopted in an industry project oriented on business intelligence (BI) improvement.The research outputs concern mainly data mining algorithms able to predict sales, logistic algorithms useful for the management of the products dislocation in the whole marketing network constituted by different stores, and web mining algorithms suitable for social trend analyses.For the predictive data mining and web mining algorithms have been applied Weka, Rapid Miner and KNIME tools, besides for the logistic ones have been adopted mainly Dijkstra's and Floyd-Warshall's algorithms.The proposed algorithms are suitable for an upgrade of the information infrastructure of an industry oriented on strategic marketing.All the facilities are enabled to transfer data into a Cassandra big data system behaving as a collector of massive data useful for BI.The goals of the BI outputs are the real time planning of the warehouse assortment and the formulation of strategic marketing actions.Finally is presented an innovative model oriented on E-commerce sales neural network forecasting based on multi-attribute processing.This model can process data of the other data mining outputs supporting logistic actions.This model proves how it is possible to embed many data mining algorithms into a unique prototypal information system connected to a big data, and how it can work on real business intelligence.The goal of the proposed paper is to show how different data mining tools can be adopted into a unique industry information system.
An Efficient Knowledge-Graph-Based Web Service Recommendation Algorithm

Using semantic information can help to accurately find suitable services from a variety of available (different semantics) services, and the semantic information of Web services can be described in detail in a Web service knowledge graph. In this paper, a Web service recommendation algorithm based on knowledge graph representation learning (kg-WSR) is proposed. The algorithm embeds the entities and relationships of the knowledge graph into the low-dimensional vector space. By calculating the distance between service entities in low-dimensional space, the relationship information of services which is not considered in recommendation approaches using a collaborative filtering algorithm is incorporated into the recommendation algorithm to enhance the accurateness of the result. The experimental results show that this algorithm can not only effectively improve the accuracy rate, recall rate, and coverage rate of recommendation but also solve the cold start problem to some extent.
Integration of teamwork and critical thinking skills in the process of teaching students

Reforms taking place in the educational process of higher professional school impose new requirements on the quality of training students. Today, graduates of higher educational institutions are required to have new professional thinking, high mobility, competence, tolerance and focus on intra-group activities carried out in teams. With the team form of the organisation of training, an environment is created that allows students to master not only the skills and abilities of business interaction and cooperation, but also to form their ability to think critically, which includes the ability to think creatively, use new information and apply the knowledge in practice. Theoretical justification jointly with quantitative and qualitative data obtained as a result of the experimental work carried out by the authors confirms the efficiency of team learning, which contributes to developing critical thinking skills in higher education students.&#x0D; Keywords: Students, education, formation, interaction, critical thinking, team, skills, competences
Research ethics for mobile sensing device use by vulnerable populations

Devices equipped with sensors to track mobility, such as through Global Position Systems (GPS) and accelerometery, are increasingly being used for research. Following Canadian, US, and International guidelines there is a need to give special consideration when conducting research with vulnerable populations. This paper examines specific ethical concerns for conducting research with mobile sensing devices for use by vulnerable populations, considering aspects of both research design and research process. Drawing on insights from feminist design and aligned fields, such as participatory design and action research, we contend that any research design and process for working with vulnerable populations must be developed in collaboration with the particular groups and communities who are part of the research. As part of this process of collaborative research, we discuss risks in terms of the lack of control over data associated with choosing commercial devices, as well as practicality and obtrusiveness of devices for the wearer. We also discuss the significance of informed consent and refusal and issues relating to security and safety during research. As part of the collaborative research design and process, we argue that participants should be given as much control over their data as possible. Based on this discussion, we provide recommendations for researchers to consider, which are broadly relevant for research using mobile sensing devices but particularly significant in relation to vulnerable populations.
A Deception Model Robust to Eavesdropping Over Communication for Social Network Systems

Communication security deals with attributes such as confidentiality, integrity, and availability. The current strategies used to achieve covertness of communication employs encryption. Encryption techniques minimize eavesdropping on the conversation between the conversing parties by transforming the message into an unreadable form. However, it does not prevent or discourage eavesdroppers from stealing and attempting to decrypt the encrypted messages using a brute-force attack or by randomly guessing the key. The probability of the eavesdropper acquiring the key and recovering the message is high as he/she can distinguish a correct key from incorrect keys based on the output of the decryption. This is because a message has some structure-texts, pictures, and videos. Thus, an attempt at decrypting with a wrong key yields random gibberish that does not comply with the expected structure. Furthermore, the consistent increase in computational power implies that stolen encrypted data may gradually debilitate to a brute-force attack. Thus, causing the eavesdropper to learn the content of the message. To this end, the objective of this research is to reinforce the current encryption measures with a decoy-based deception model where the eavesdropper is discouraged from stealing encrypted message by confounding his resources and time. Our proposed model leverages its foundation from decoys, deception, and artificial intelligence. An instant messaging application was developed and integrated with the proposed model as a proof of concept. Further details regarding the design, analysis, and implementation of the proposed model are substantiated. The result shows that the proposed model reinforces state-of-the-art encryption schemes and will serve as an effective component for discouraging eavesdropping and curtailing brute-force attack on encrypted messages.
On the Security of an Efficient and Robust Certificateless Signature Scheme for IIoT Environments

As the progress of digitization in industrial society, large amount of production data are outsourced to the cloud server in order to reduce data management costs. Nevertheless, how to ensure the outsourced data integrality, validity, and availability is a challenging research topic. Recently, Zhang et al. (IEEE Trans. Industrial Informatic, doi:10.1109/TII.2019.2894108) presented an efficient and robust certificateless signature scheme to achieve the data authenticity for industrial Internet of Things (IIoT) environments. However, we found that their scheme is insecure. In this paper, we show that an attacker with replacing public key ability can easily impersonate other legitimate users to upload some false messages by forging the target users' valid signatures on these messages. Therefore, their certificateless signature scheme has not solved the IIoT data authenticity issue pointed out by them. Meanwhile, we also demonstrate that their security proof is not sound because the ability of an adversary cannot be applied to solve the difficult problem that they expect.
Playing FPS Games With Environment-Aware Hierarchical Reinforcement Learning

Learning rational behaviors in First-person-shooter (FPS) games is a challenging task for Reinforcement Learning (RL) with the primary difficulties of huge action space and insufficient exploration. To address this, we propose a hierarchical agent based on combined options with intrinsic rewards to drive exploration. Specifically, we present a hierarchical model that works in a manager-worker fashion over two levels of hierarchy. The high-level manager learns a policy over options, and the low-level workers, motivated by intrinsic reward, learn to execute the options. Performance is further improved with environmental signals appropriately harnessed. Extensive experiments demonstrate that our trained bot significantly outperforms the alternative RL-based models on FPS games requiring maze solving and combat skills, etc. Notably, we achieved first place in VDAIC 2018 Track(1).
Detection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra

Hyperspectral imaging (HSI) provides more detailed information than red-green-blue (RGB) imaging, and therefore has potential applications in computer-aided pathological diagnosis. This study aimed to develop a pattern recognition method based on HSI, called hyperspectral analysis of pathological slides based on stain spectrum (HAPSS), to detect cancers in hematoxylin and eosin-stained pathological slides of pancreatic tumors. The samples, comprising hyperspectral cubes of 420-750 nm, were harvested for HSI and tissue microarray (TMA) analysis. As a result of conducting HAPSS experiments with a support vector machine (SVM) classifier, we obtained maximal accuracy of 94%, a 14% improvement over the widely used RGB images. Thus, HAPSS is a suitable method to automatically detect tumors in pathological slides of the pancreas.
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update

We propose Episodic Backward Update (EBU) – a novel deep reinforcement learning algorithm with a direct value propagation. In contrast to the conventional use of the experience replay with uniform random sampling, our agent samples a whole episode and successively propagates the value of a state to its previous states. Our computationally efficient recursive algorithm allows sparse and delayed rewards to propagate directly through all transitions of the sampled episode. We theoretically prove the convergence of the EBU method and experimentally demonstrate its performance in both deterministic and stochastic environments. Especially in 49 games of Atari 2600 domain, EBU achieves the same mean and median human normalized performance of DQN by using only 5% and 10% of samples, respectively.