Reviewerly/exHarmony huggingface dataset:
Smart Contracts for Certified and Sustainable Safety-Critical Continuous Monitoring Applications

Monitoring applications are increasingly important to enable predictive maintenance and real-time anomaly detection in industrial and civil safety-critical infrastructures. Typical monitoring pipelines consist of a sensor network that collects and streams IoT data toward a cloud infrastructure that provides storage, visualisation and data analytic capabilities. However, since critical data generated must be often retained for regulatory and tracking purposes, cloud storage requirements become poorly sustainable when dealing with critical infrastructures that have to remain operative for decades while supporting lifelong continuous monitoring. While policies can be applied to remove redundant or outdated information, anti-tamper mechanisms are required to guarantee that data modifications are not driven by malicious intents to alter recorded data. This work presents a blockchain-based framework for continuous monitoring applications enabling certified removal of IoT data in safety-critical databases. The framework allows for the deployment of data-evaluation policies to identify redundant/outdated measurements flowing in the database and, therefore, mark them as eligible for removal. The novelty of our approach stands in the implementation of the data-evaluation policy as a smart contract. Furthermore, the use of a blockchain ensures that critical database operations (like removal) are tamper-proof and compliant with the guideline determined by system stakeholders. We demonstrate the effectiveness of the proposed framework in a real case study using accelerometer data of a bridge monitoring application, and we characterise the overhead of transactions to the blockchain.
Explainable, Interpretable, Trustworthy, Responsible, Ethical, Fair, Verifiable AI... What’s Next?

Artificial Intelligence plays an increasingly important role in many knowledge fields: computer science, technology, and other sciences such as health care, one of its most compelling applications. Artificial Intelligence has impacted arts, linguistics, law, sociology, society, and everyday lives. We are demanding many properties from the products of Artificial Intelligence: users of their application fields need trust and ask for fairness, accountability, and privacy. We overview the desired properties and recall the technology that enables Artificial Intelligence to satisfy them.
Towards Prescriptive Analyses of Querying Large Knowledge Graphs

Leveraging relational Big Data (BD) processing frameworks to process large-scale (RDF) graphs yields a great interest in optimizing query performance. Modern BD systems are yet complicated data systems, where the configurations notably affect the performance. Benchmarking different frameworks and configurations provides the community with best practices for better performance. However, most of these benchmarking efforts are classified as descriptive and diagnostic analytics. Moreover, there is no standard for comparing these benchmarks based on quantitative ranking techniques. In this paper, we discuss how our work fills this timely research gap. Particularly, we investigate how to enable prescriptive analytics via ranking functions (called "BenchRank"). We present a research plan that builds on the state-of-the-art benchmarking efforts in the area of querying large RDF graphs. Finally, we present our research results of the proposed plan.
Developing and Aligning a Detailed Controlled Vocabulary for Artwork

Controlled vocabularies have proved to be critical for data interoperability and accessibility. In the cultural heritage (CH) domain, description of artworks are often given as free text, thus making filtering and searching burdensome (e.g. listing all artworks of a specific type). Despite being multi-language and quite detailed, the Getty's Art & Architecture Thesaurus –a de facto standard for describing artworks– has a low coverage for languages different than English and sometimes does not reach the required degree of granularity to describe specific niche artworks. We build upon the Italian Vocabulary of Artworks, developed by the Italian Ministry of Cultural Heritage (MIC) and a set of free text descriptions from ArCO, the knowledge graph of the Italian CH, to propose an extension of the Vocabulary of Artworks and align it to the Getty's thesaurus. Our framework relies on text matching and natural language processing tools for suggesting candidate alignments between free text and terms and between cross-vocabulary terms, with a human in the loop for validation and refinement. We produce 1.166 new terms (31% more w.r.t. the original vocabulary) and 1.330 links to the Getty's thesaurus, with estimated coverage of 21%.
Comparision of Models Built Using AutoML and Data Fusion

Automated machine learning (AutoML) has made life easier for data analysts or scientists by providing quick insights into data by building machine learning (ML) models. AutoML techniques are applied to vast areas from image processing, speech recognition, natural language processing reinforcement learning, and more. However, there is still room for many improvements. AutoML techniques focus only on problems related to predictive modeling, and most of them are designed to work with structured data. AutoML techniques are also time-consuming as they require time to select the appropriate ML pipeline. This paper presents an alternative time-efficient approach for mixed data (both categorical and numerical features obtained from UCI and Kaggle repository) using a data fusion process, which provides high macro average accuracy in less time as compared to AutoML. The AutoML tool considered here is autoscikit-learn (auto-sklearn). This specific library is built in Python using scikit-learn. The implementation of data fusion is also done in Python using scikit-learn. We conclude from the experimental analysis that the pipeline constructed provides better results than the auto-sklearn. This obtained conclusion is supported by a statistical test (Wilcoxon signed ranks test) based on macro average accuracy obtained for both approaches.
Megalithism Representation in CIDOC-CRM

The definition of Megalithism is found in specialized and academic works, on Archaeology and Prehistory, as well as in generalist dictionaries, glossaries and encyclopedias. However we are not aware of formal definitions specifically to this particular domain. This paper presents an under development proposal of Megalithism Knowledge Representation that relies on CIDOC CRM to represent the monuments and concepts of European Megalithism. It includes the granularity required to represent this form of architecture: from composite parts to single elements, such as standing stones. A structured way for representing such definitions is required in order to guide future knowledge extraction from Megalithism reports.
Research on Indoor Spatial Behavior Perception IoT Smart System for Solitary Elderly at Home

This research aims at contributing to a seamless, integrated technology intelligent living system for solitary older adults at home. The capacitive intimate sensing module, that can be easily pasted to the existing home space element surfaces, daily objects, or home furniture, such as a wall, door, stairs, a chair, cabinet, table, sofa, etc, is developed in this research. This 30 × 30 cm sensing module can actively sense people’s physical behaviors and body movements in spaces. The signals acquired from the sensing modules in indoor spaces will then integrate into the controller system through the IoT application and logically define the behavior classification. From the preliminary analysis of observing the 80-year-old elderly subject’s daily activities, the movement trajectory of the ‘Move–Stop’ pattern is found. There will be a touch (T) and a touchless (TL) relationship between the body and the space elements or objects. The touchless or non-contact intimate relationship also can be divided into two types: 1. the body ‘Passes by’ (P) the spatial elements or objects, and 2. the body ‘Stays’ (S) in front of the object and performs activities. This research pasted eight sensing modules on nine objects in six spaces. Finally, the specific actions and life pattern can be recognized and analyzed through the developed IoT spatial behavior smart system and provide the customized intelligent application function for the elderly.
Addressing ‘Wicked Problems’ Using Visual Analysis

Phenomena studied within the field of professional learning and development are often highly complicated as well as highly variable. In general, we accept that (professional) learning has many facets, overlapping elements and interconnected aspects. Yet, when empirically examining these phenomena, we tend to reduce the complex reality to a set of tangible variables and actors that can be explained by (at most a combination of) theoretical frameworks. As such, we treat professional learning ‘problems’ as if they were ‘tame’ problems that are well-defined, stable, and prone to control and prediction. Starting from the notion of wicked problems (Rittel HWJ, Webber MM. Policy Sci 4:155–169, 1973), the current state of the art of visual analysis as a methodology for approaching research questions in the field of professional learning is presented. Visual analysis complements and extends insights from traditional analysis as it enables researchers to discover hidden patterns and deal with big data. After giving a general introduction to what visual analysis is, this chapter presents an empirical example of how it was used to examine the implications of missingness in longitudinal multilevel data on teachers’ engagement.
Object Tracking in Satellite Videos Based on Improved Kernel Correlation Filter Assisted by Road Information

Video satellites can stare at target areas on the Earth’s surface to obtain high-temporal-resolution remote sensing videos, which make it possible to track objects in satellite videos. However, it should be noted that the object size in satellite videos is usually small and has less textural property, and the moving objects in satellite videos are easily occluded, which puts forward higher requirements for the tracker. In order to solve the above problems, consider that the remote sensing image contains rich road information, which can be used to constrain the trajectory of the object in a satellite video, this paper proposes an improved Kernel Correlation Filter (KCF) assisted by road information to track small objects, especially when the object is occluded. Specifically, the contributions of this paper are as follows: First, the tracking confidence module is reconstructed, which integrates the peak response and the average peak correlation energy of the response map to more accurately judge whether the object is occluded. Then, an adaptive Kalman filter is designed to adaptively adjust the parameters of the Kalman filter according to the motion state of the object, which improves the robustness of tracking and reduces the tracking drift after the object is occluded. Last but not least, an object tracking strategy assisted by road information is recommended, which searches for objects with road information as constraints, to locate objects more accurately. After the above improvements, compared with the KCF tracker, our method improves the tracking precision by 35.9% and the tracking success rate by 18.1% with the tracking rate at a speed of 300 frames per second, which meets the real-time requirements.
SGSNet: A Lightweight Depth Completion Network Based on Secondary Guidance and Spatial Fusion

The depth completion task aims to generate a dense depth map from a sparse depth map and the corresponding RGB image. As a data preprocessing task, obtaining denser depth maps without affecting the real-time performance of downstream tasks is the challenge. In this paper, we propose a lightweight depth completion network based on secondary guidance and spatial fusion named SGSNet. We design the image feature extraction module to better extract features from different scales between and within layers in parallel and to generate guidance features. Then, SGSNet uses the secondary guidance to complete the depth completion. The first guidance uses the lightweight guidance module to quickly guide LiDAR feature extraction with the texture features of RGB images. The second guidance uses the depth information completion module for sparse depth map feature completion and inputs it into the DA-CSPN++ module to complete the dense depth map re-guidance. By using a lightweight bootstrap module, the overall network runs ten times faster than the baseline. The overall network is relatively lightweight, up to thirty frames, which is sufficient to meet the speed needs of large SLAM and three-dimensional reconstruction for sensor data extraction. At the time of submission, the accuracy of the algorithm in SGSNet ranked first in the KITTI ranking of lightweight depth completion methods. It was 37.5% faster than the top published algorithms in the rank and was second in the full ranking.
Draw it, Check it, Change it: Reviving Lewin's Topology to Facilitate Organizational Change Theory and Practice

The purpose of this paper is to explain and update Kurt Lewin's concept of topological psychology and show how it can improve how we think about and undertake organizational change. To illustrate the use of this concept, we have created an imaginary dialogue in which Lewin helps an organization to move from fixed- to hot-desking. In this dialogue, we draw on Lewin's writings to show how he might have responded to criticisms of his work (e.g., that his diagnostic approach is too mechanistic to work in a socially constructed world). Based on our discussion of topological psychology, we offer three recommendations to better connect theory building with managerial thinking: (1) use plain language to explain your concepts; (2) use drawings to illustrate and explain the change situation; and (3) focus on the exceptions and not the rules in order to generate a better understanding of the nature of organizational life.
Reformulating Optical Flow to Solve Image-Based Inverse Problems and Quantify Uncertainty

From meteorology to medical imaging and cell mechanics, many scientific domains use inverse problems (IPs) to extract physical measurements from image movement. To this end, motion estimation methods such as optical flow (OF) pre-process images into motion data to feed the IP, which then inverts for the measurements through a physical model. However, this combined OFIP pipeline exacerbates the ill-posedness inherent to each technique, propagating errors and preventing uncertainty quantification. We introduce a Bayesian PDE-constrained framework that transforms visual information directly into physical measurements in the context of probability distributions. The posterior mean is a constrained IP that tracks brightness while satisfying the physical model, thereby translating the aperture problem from the motion to the underlying physics; whereas the posterior covariance derives measurement error out of image noise. As we illustrate with traction force microscopy, our approach offers several advantages: more accurate reconstructions; unprecedented flexibility in experiment design (e.g., arbitrary boundary conditions); and the exclusivity of measurement error, central to empirical science, yet still unavailable under the OFIP strategy.
Disrupting drive-by download networks on Twitter

This paper tests disruption strategies in Twitter networks containing malicious URLs used in drive-by download attacks. Cybercriminals use popular events that attract a large number of Twitter users to infect and propagate malware by using trending hashtags and creating misleading tweets to lure users to malicious webpages. Due to Twitter's 280 character restriction and automatic shortening of URLs, it is particularly susceptible to the propagation of malware involved in drive-by download attacks. Considering the number of online users and the network formed by retweeting a tweet, a cybercriminal can infect millions of users in a short period. Policymakers and researchers have struggled to develop an efficient network disruption strategy to stop malware propagation effectively. We define an efficient strategy as one that considers network topology and dependency on network resilience, where resilience is the ability of the network to continue to disseminate information even when users are removed from it. One of the challenges faced while curbing malware propagation on online social platforms is understanding the cybercriminal network spreading the malware. Combining computational modelling and social network analysis, we identify the most effective strategy for disrupting networks of malicious URLs. Our results emphasise the importance of specific network disruption parameters such as network and emotion features, which have proved to be more effective in disrupting malicious networks compared to random strategies. In conclusion, disruption strategies force cybercriminal networks to become more vulnerable by strategically removing malicious users, which causes successful network disruption to become a long-term effort.
A high‐speed unsupervised hardware architecture for rapid diagnosis of COVID‐19

Summary In the diagnosis of COVID‐19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time‐consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood‐count‐test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID‐19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID‐19. This is achieved by combining three algorithms: the K‐means algorithm, a novel statistical analysis technique‐HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID‐19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high‐speed and low‐cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation.
Design, Analysis and Development of an Autonomous Modular Disinfectant Robot

The rate at which harmful microorganisms have been amplifying has rapidly increased in recent years, affecting our daily lives. Ultraviolet radiation has proved to significantly reduce the spread of these microorganisms. This process can be automated as some of these radiations might be harmful to anyone in the near vicinity of the radiation. As a result, an autonomous disinfectant robot that can sanitize and disinfect the floor surface by killing these harmful microorganisms without any physical intervention is becoming an unavoidable requirement. This paper describes the development of an autonomous, modular mobile robot that can be used for disinfection in hospitals, restaurants, corporate, academic institutions and housing societies. It can be reconfigured for different purposes by replacing the top plate of the robot chassis with application-based add on.
Recognising the English Language using Context Free Grammar with PyFormlang

Natural language recognition is a sub-field of Natural Language Processing (NLP), a popular research playground that lies in the intersection of multiple areas of linguistics, computer science, artificial intelligence and machine learning. The purpose of NLP is to define a computer that can "understand" the contents of documents, including the language's contextual nuances. The first step, however, would be to pre-process and parse a particular statement to check if it is a legitimate sentence of the language or not. This is where Automata theory comes into the picture. Using the Python PyFormlang and nltk libraries, we develop an English language recognizer based on Context free grammar (CFG) representations of the English Language, with parts-of-speech (POS) tags making up the constituencies of the CFG. Syntactically accurate sentences are accepted if parsed without errors, else they are deemed invalid. We also layout the set of productions for the English language, which has proven to work well with most sentences including simple and complex, with an accuracy of 84.90%.
Performance Analysis of Real and Synthetic Data using Supervised ML Algorithms for Prediction of Chronic Kidney Disease

The introduction of Electronic Health Records (EHRs) is causing fast transformation in healthcare. EHR contains the patient private information and health history in digital form. Hence, EHR data cannot be shared due to privacy concerns to the Machine Learning(ML) research community, through which we can make the healthcare system smarter and provide quality healthcare services to the patients. As a result, synthetic data is utilised as a backup when real-world data (such as EHR data) is unavailable. Synthetic data can be shared without revealing any private information of the patient. This paper focuses on generating synthetic data from the real dataset. As a use case, we have selected Chronic Kidney Disease(CKD) dataset (real) and generated three datasets – real, synthetic, and a combination of real + synthetic. To test the accuracy of the synthetic data, we ran six supervised machine learning algorithms on these three datasets with all characteristics and reduced features to see if the patient had CKD or not. Supervised ML algorithms on the three datasets are assessed based on the following performance metrics - Confusion Matrix, Accuracy, Recall, Precision, and F1-Score. According to the results, XGBoost surpasses with 100 percent accuracy on all three datasets with full features and a 100 percent accuracy on the mix of real and synthetic datasets with feature reduction.
Multi Robot Path Planning Using Priority Based Algorithm

In this paper we propose a novel algorithm to plan paths for multiple robots with multiple goal points. The algorithm utilizes D* Algorithm as a backbone with a priority order optimizer. For testing the algorithm, we propose an architecture to direct multiple robots in a centralized manner using Robot Operating System (ROS). Further, four maps have been defined to gauge the performance of the system. The results show a significant advantage over decoupled and coupled path planning algorithm in terms of computation time while still retaining coupled approach’s characteristic of avoiding deadlocking in single corridor cases.