Reviewerly/exHarmony huggingface dataset:
Reasoning about Non-Deterministic Observability and Hypothetical Action Occurrences in Multi-Agent Domains

This paper proposes a method based on high-level action description languages for reasoning about hypothetical action occurrences in a multi-agent environments. In order to accommodate this type of reasoning, one needs to consider non-deterministic observability of action occurrences of agents. The paper presents an extension of the language mA*, called mA*e, to allow for non-deterministic observability and hypothetical actions. The paper defines the semantics of the new language using edge-conditioned update models and discusses properties of the new language, including differences from approaches that use Dynamic Epistemic Logic. The new definitions are illustrated using the well-known story of two stockbrokers from the literature.
An Approach of Intuitive Learning ERD by Using Auto-generated Failure Examples

For decades, the entity relationship diagram is regarded as an important modeling method for transforming information into well-organized structures in databases. The difficulty is how to observe and extract meaning for rules from the real world in the first place. In this paper, we propose a method by that learners can drag-and-drop components for instant diagrams and corresponding auto-generated text results. By reading the stories and comparing them with the abstract concepts in learners' minds, learners can get motivated by the entertainment from the failure experience, or get subtle guidance for the ER modeling.
Performance Evaluation of Ordering Services and Endorsement Policies in Hyperledger Fabric

Hyperledger Fabric is an open-source project designed to deploy permissioned blockchains. Performance characteristics of Hyperledger Fabric v2.2, such as the impact of ordering services, bottleneck, and scalability) are difficult to understand due to the performance challenges of distributed systems. In this paper, we evaluate the performance of each phase in Hyperledger Fabric's new execute-order-validate architecture. We also assessed the performance of ordering services (Solo, Kafka, and Raft) on the OR and the AND endorsement policies. We found the execution phase was more scalable using the OR endorsement policy than the AND endorsement policy. While all three ordering services (Solo, Raft, and Kafka) performed relatively well, we discuss why Raft may be the best choice for most organizations. Last, we focus on the performance capabilities of theF Raft ordering service using different transaction settings. This evaluation helps to understand some of the trade-offs in Hyperledger Fabric v2.x.
Research on Instance-level Video Object Detection Based on Momentum

Since the targets in video have dynamic features, such as appearance, shape, and size, etc. Therefore, the method of frame by frame detection of static images is directly applied to video target detection, which is difficult to meet the requirements of video target detection for spatio- temporal consistency. The traditional solution is to aggregate the non-key frame features near the key frames, enhance each key frame, and finally transfer the enhanced key frame feature map to the static image target detection method, but such methods lack the utilization of the time-domain information between video frames, resulting in low detection accuracy. To address these shortcomings, this paper proposes a momentum-based instance-level video target detection method to improve the detection accuracy of the model. The method fully considers the time-domain relationship between the instance-level calibration corresponding to each key frame in the time-domain, obtains the video momentum-level calibration features by bonding the pixel-level features and the instance-level calibration features, and fuses the pixel-level calibration with the momentum-level calibration to enhance the model robustness. In this paper, we experimentally validate the ImageNet VID dataset, and the results show the effectiveness of the momentum-based instance-level video target detection algorithm with an mAP accuracy of 81.2%.
Improved Sine Cosine Algorithm for searching global optima

SCA is the most successful population-based meta-heuristic algorithm commonly used to achieve global optimal value. The purpose of this paper is to study an improved version of the sine-cosine algorithm (ISCA) is tested on 13 standard functions and three technical problems like Welded Beam Design(WBD), compression spring design(CSD), pressure vessel design(PVD) and carried out results are compared with recent research articles of meta-heuristicalgorithms.A Wilcoxon rank sum test is performed to statistically validate the results.. The illustrated results prove that the better rendition of improved Sine Cosine Algorithm (ISCA) which indeed more capable to explore the search space, avoidance of local optima provide effective convergence for global optima and exploit search space for efficient optimization.
MultiPathGAN: Structure Preserving Stain Normalization using Unsupervised Multi-domain Adversarial Network with Perception Loss

Histopathology relies on the analysis of microscopic tissue images to diagnose disease. A crucial part of tissue preparation is staining whereby a dye is used to make the salient tissue components more distinguishable. However, differences in laboratory protocols and scanning devices result in significant confounding appearance variation in the corresponding images. This variation increases both human error and the inter-rater variability, as well as hinders the performance of automatic or semi-automatic methods. In the present paper we introduce an unsupervised adversarial network to translate (and hence normalize) whole slide images across multiple data acquisition domains. Our key contributions are: (i) an adversarial architecture which learns across multiple domains with a single generator-discriminator network using an information flow branch which optimizes for perceptual loss, and (ii) the inclusion of an additional feature extraction network during training which guides the transformation network to keep all the structural features in the tissue image intact. We: (i) demonstrate the effectiveness of the proposed method firstly on H&E slides of 120 cases of kidney cancer, as well as (ii) show the benefits of the approach on more general problems, such as flexible illumination based natural image enhancement and light source adaptation.
Cyber Security and Online Safety Education for Schools in the UK: Looking through the Lens of Twitter Data

In recent years, digital technologies have grown in many ways. As a result, many school-aged children have been exposed to the digital world a lot. Children are using more digital technologies, so schools need to teach kids more about cyber security and online safety. Because of this, there are now more school programmes and projects that teach students about cyber security and online safety and help them learn and improve their skills. Still, despite many programmes and projects, there is not much proof of how many schools have taken part and helped spread the word about them. This work shows how we can learn about the size and scope of cyber security and online safety education in schools in the UK, a country with a very active and advanced cyber security education profile, using nearly 200k public tweets from over 15k schools. By using simple techniques like descriptive statistics and visualisation as well as advanced natural language processing (NLP) techniques like sentiment analysis and topic modelling, we show some new findings and insights about how UK schools as a sector have been doing on Twitter with their cyber security and online safety education activities. Our work has led to a range of large-scale and real-world evidence that can help inform people and organisations interested in cyber security and teaching online safety in schools.
Student Research Abstract: SplitChain: Blockchain with fully decentralized dynamic sharding resilient to fast adaptive adversaries

Over the past few years, blockchains have captured the public's interest with the promise of pseudo-anonymous decentralized exchange infrastructures. However, their potential is hindered by various technical issues, such as their ability to scale with problematic storage and communication costs and a fairly low transaction throughput. In this paper, we present SplitChain, a protocol intended to support the creation of scalable account-based blockchains without undermining decentralization and security. This is achieved by using sharding, i.e. by splitting the blockchain into several lighter chains managed by their own disjoint sets of validators called shards. These shards balance the load by processing disjoint sets of transactions in parallel. SplitChain distinguishes itself from other sharded blockchains by minimizing the synchronization constraints among shards while maintaining security guarantees. Finally, the protocol is designed to dynamically adapt the number of shards to the system load, to avoid over-dimensioning issues of most of the existing sharding-based solutions where the number of shards is static.
Model‐based offline reinforcement learning for sustainable fishery management

Abstract Fisheries, as indispensable natural resources for human, need to be managed with both short‐term economical benefits and long‐term sustainability in consideration. This has remained a challenge, because the population and catch dynamics of the fisheries are complex and noisy, while the data available is often scarce and only provides partial information on the dynamics. To address these challenges, we formulate the population and catch dynamics as a Partially Observable Markov Decision Process (POMDP), and propose a model‐based offline reinforcement learning approach to learn an optimal management policy. Our approach allows learning fishery management policies from possibly incomplete fishery data generated by a stochastic fishery system. This involves first learning a POMDP fishery model using a novel least squares approach, and then computing the optimal policy for the learned POMDP. The learned fishery dynamics model is useful for explaining the resulting policy's performance. We perform systematic and comprehensive simulation study to quantify the effects of stochasticity in fishery dynamics, proliferation rates, missing values in fishery data, dynamics model misspecification, and variability of effort (e.g., the number of boat days). When the effort is sufficiently variable and the noise is moderate, our method can produce a competitive policy that achieves 85% of the optimal value, even for the hardest case of noisy incomplete data and a misspecified model. Interestingly, the learned policies seem to be robust in the presence of model learning errors. However, non‐identifiability kicks in if there is insufficient variability in the effort level and the fishery system is stochastic. This often results in poor policies, highlighting the need for sufficiently informative data. We also provide a theoretical analysis on model misspecification and discuss the tendency of a Schaefer model to overfit compared with a Beverton–Holt model.
Student Research Abstract: Why and Where Software Developers are (not) using Project Data in Agile Retrospectives

Software project data holds knowledge of the development process enacted in teams. However, it is rarely used for process improvement approaches as part of agile retrospective meetings. We argue that this integration shows promise: Project data is evidence of what occurred in the last development iteration(s). It complements the perceptions of individuals that are usually exclusively relied upon in retrospectives. In this paper, we present the results of semi-structured interviews with agile practitioners, which revealed missing knowledge of the capabilities, implementation, and application of project data analyses as a key reason for the lack of usage. Looking forward, we provide an initial look at a structured approach to the usage of software project data in agile retrospectives.
Abstractive Text Summarisation Using Keywords with Transformers Model

The application of linguistics to machine learning models has changed text summarization techniques This abstractive method is used to give the best possible summary of huge data without losing the important key points in it. We will be using the BERT encoder for encoding the input text and collecting the vector of the important sentence in the paragraph and that vector will be sent to Transformers for decoding into a text. This text will be created abstractedly using the transform decoder model. For a long time, automatic text summarization has been the focus of natural language processing (NLP) research. The goal of abstractive text summarising is to produce a summary of a given document that only includes the most crucial and pertinent information. Among deep learning models, BERT and transformer models have dominated abstractive text summarization in recent years. This work offers a thorough analysis of transformer-based and BERT abstractive text summarization. We also go through how these models are developed and trained, as well as the many methods for bringing context and subject-matter expertise into the summarising procedure.
Object Tracking in Intelligent Video Surveillance System Based on Artificial System

Object moving tracking is one of the furthermost attractive and critical chores in computer science and computer vision research domain. Object moving tracking is underpinned for various higher-level automated applications in different research domains such as surveillance of video, motion capture and Augmented Reality (AR) in computer vision. Object tracking is the significant section of an intelligent video surveillance system (IVS) which can be further used for finding many other apprehensive or suspicious activities. Through the independent and exhaustive research of the current object motion and moving tracking technology, collective with hands- on application of IVS (intelligent video surveillance). In computer science many different algorithms available for object path tracking, object tracking and object motion detection, but mean shift algorithm (MSA) is the most popular and basic approach. We use mean shift algorithm (MSA) for our research work. The proposed work in this paper with following objectives, a) Video series frame production for tracking of a moving body, b) Design of experiments for object detection, c) Formulation and implementation of an algorithm to detect object tracking.The algorithm was applied on an image frame of a recorded video. Based on multiple input images, we were able to detect the object and track its successive image frames. The realistic execution may want to obtain the object body tracking without losing any input frame and could magnificently coat bounding field. After completion of the mean shift algorithm (MSA) it would be able to produce an image sequence. The maneuver and precision of the algorithm was tested by successfully overlay function in the identified object and user defined object.
On the Effect of Low-Ranked Documents: A New Sampling Function for Selective Gradient Boosting

Learning to Rank is the task of learning a ranking function from a set of query-documents pairs. Generally, documents within a query are thousands but not all documents are informative for the learning phase. Different strategies were designed to select the most informative documents from the training set. However, most of them focused on reducing the size of the training set to speed up the learning phase, sacrificing effectiveness. A first attempt in this direction was achieved by Selective Gradient Boosting a learning algorithm that makes use of customisable sampling strategy to train effective ranking models. In this work, we propose a new sampling strategy called High_Low_Sampl for selecting negative examples applicable to Selective Gradient Boosting, without compromising model effectiveness. The proposed sampling strategy allows Selective Gradient Boosting to compose a new training set by selecting from the original one three document classes: the positive examples, high-ranked negative examples and low-ranked negative examples. The resulting dataset aims at minimizing the mis-ranking risk, i.e., enhancing the discriminative power of the learned model and maintaining generalisation to unseen instances. We demonstrated through an extensive experimental analysis on publicly available datasets, that the proposed selection algorithm is able to make the most of the negative examples within the training set and leads to models capable of obtaining statistically significant improvements in terms of NDCG, compared to the state of the art.
The Role of Blockchain Technology: COVID-19 Pandemic Point of View

Blockchain technology is a distributed ledger technology. This technology is based on creating a peer-to-peer network (P2P). As natural disasters and global pandemics expose severe functional vulnerabilities in these industries, key features backed by BCT such as decentralization, consensus mechanism, immutability, and smart contracts offer data integrity, traceability, and structured authorization through proof of work (POW), tokens, distributed ledgers, and encryption and can embolden to route some crucial problems—poor network management, loop in chains, frauds, misinformed structure, and biased authorization. BCT-supported start-ups with promising growth reports are examples to support the argument of blockchain establishing a sustainable economy that is both effective and resilient. Thus, the paper provides insights to the role of blockchain technology with respect to COVID-19 pandemic point of view.
Automatic Extraction of Effective Relations in Knowledge Graph for a Recommendation Explanation System

A knowledge graph represents a network of real-world entities (i.e., objects, events, or concepts) and illustrates the relationship between entities. A recommender system can improve its reasoning and explainability using the knowledge graph. In this paper, we propose a hybrid and modular approach that combines path ranking with graph embedding; it can automatically eliminate the ineffective relations among entities and generate a better relation set for the explanation system. We conducted a user survey for performance evaluation and proved that our proposed approach provided the same quality of explanations for recommended items as our previous approach, which manually selected relations from the knowledge graph.
Anticipation of Method Execution in Mixed Consistency Systems

Distributed applications often deal with data with different consistency requirements: while a post in a social network only requires weak consistency, the user balance in turn has strong correctness requirements, demanding mutations to be synchronised. To deal efficiently with sequences of operations on different replicas of the distributed application, it is useful to know which operations commute with others and thus, when can an operation not requiring synchronisation be anticipated wrt other requiring it, thus avoiding unnecessary waits.
Analyze the Lack of Accuracy in Loan Prediction using Logistic Regression Compared with Random Forest to Improve Accuracy

The major purpose of this research is to evaluate the effectiveness of the Logistic Regression method in contrast to the Random Forest approach in terms of improving the accuracy of loan prediction. The results of the present research, a confidence interval of 95%, a threshold of 0.05 percent, and the mean and standard deviation were used to compile the data, which was obtained from a variety of sources. The G-power tool is scaled up by eighty percent, and two distinct kinds of algorithms are used in order to classify the data based on a sample size of n equaling ten. In contrast to the Random Forest approach, which has an accuracy of 75.6 percent, the Logistic Regression method has an accuracy that is very high at 81.30 percent. A difference of less than 0.05, or 0.001, represents a statistically significant gap between the two groups. It demonstrates that when calculating the amount of a customer's loan, the technique known as Logistic Regression seems to be more accurate than the alternative known as Random Forest.
Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor

Abstract Malignant growth in liver results in liver tumor. The most common types of liver cancer are primary liver disease and secondary liver disease. Most malignant growths are benign tumors, and the condition they cause, essential liver disease, is the end result. Cancer of the liver is a potentially fatal disease that can only be cured by combining a number of different treatments. Machine learning, feature selection and image processing have the capability to provide a framework for the accurate detection of liver diseases. The processing of images is one of the components that come together to form this group. When utilized for the purpose of reviewing previously recorded visual information, the instrument performs at its highest level of effectiveness. The importance of feature selection on machine learning algorithms for the early and accurate diagnosis of liver tumors is discussed in this article. The input consists of images from a CT scan of the liver. These images are preprocessed by discrete wavelet transform. Discrete wavelet transforms increase resolution by compressing the images. Images are segmented in parts to identify region of interest by K Means algorithm. Features are selected by grey wolf optimization technique. Classification is performed by Gradient boosting, support vector machine and random forest. GWO Gradient boosting is performing better in accurate classification and prediction of liver cancer.
A Semantic Evidence-based Approach to Continuous Cloud Service Certification

Continuous certification of cloud services requires a high degree of automation in collecting and evaluating evidences. Prior approaches to this topic are often specific to a cloud provider or a certain certification catalog. This makes it costly and complex to achieve conformance to multiple certification schemes and covering multi-cloud solutions. In this paper, we present a novel approach to continuous certification which is scheme- and vendor-independent. Leveraging an ontology of cloud resources and their security features, we generalize vendor- and scheme-specific terminology into a new model of so-called semantic evidence. In combination with generalized metrics that we elicited out of requirements from the EUCS and the CCMv4, we present a framework for the collection and assessment of such semantic evidence across multiple cloud providers. This allows to conduct continuous cloud certification while achieving re-usability of metrics and evidences in multiple certification schemes. The performance benchmark of the framework's prototype implementation shows that up to 200,000 evidences can be processed in less than a minute, making it suitable for short time intervals used in continuous certification.