Reviewerly/exHarmony huggingface dataset:
Using Visual Anomaly Detection for Task Execution Monitoring

Execution monitoring is essential for robots to detect and respond to failures. Since it is impossible to enumerate all failures for a given task, we learn from successful executions of the task to detect visual anomalies during runtime. Our method learns to predict the motions that occur during the nominal execution of a task, including camera and robot body motion. A probabilistic U-Net architecture is used to learn to predict optical flow, and the robot’s kinematics and 3D model are used to model camera and body motion. The errors between the observed and predicted motion are used to calculate an anomaly score. We evaluate our method on a dataset of a robot placing a book on a shelf, which includes anomalies such as falling books, camera occlusions, and robot disturbances. We find that modeling camera and body motion, in addition to the learning-based optical flow prediction, results in an improvement of the area under the receiver operating characteristic curve from 0.752 to 0.804, and the area under the precision-recall curve from 0.467 to 0.549.
Dealing with Randomness and Concept Drift in Large Datasets

Data-driven solutions to societal challenges continue to bring new dimensions to our daily lives. For example, while good-quality education is a well-acknowledged foundation of sustainable development, innovation and creativity, variations in student attainment and general performance remain commonplace. Developing data -driven solutions hinges on two fronts-technical and application. The former relates to the modelling perspective, where two of the major challenges are the impact of data randomness and general variations in definitions, typically referred to as concept drift in machine learning. The latter relates to devising data-driven solutions to address real-life challenges such as identifying potential triggers of pedagogical performance, which aligns with the Sustainable Development Goal (SDG) #4-Quality Education. A total of 3145 pedagogical data points were obtained from the central data collection platform for the United Arab Emirates (UAE) Ministry of Education (MoE). Using simple data visualisation and machine learning techniques via a generic algorithm for sampling, measuring and assessing, the paper highlights research pathways for educationists and data scientists to attain unified goals in an interdisciplinary context. Its novelty derives from embedded capacity to address data randomness and concept drift by minimising modelling variations and yielding consistent results across samples. Results show that intricate relationships among data attributes describe the invariant conditions that practitioners in the two overlapping fields of data science and education must identify.
An Efficient Multi-sensor Positions Human Activity Recognition: Elderly Peoples in Rural Areas in Focus

The increase in the availability of wearable devices offers a prospective solution to the increasing demand for elderly human activity monitoring, in the essence of improving the independent living standard of the growing population of elderly humans. With all the availability of the wearable devices fully embedded with sensors that are being used for human health monitoring, a lot of techniques are been proposed and used in the process. However, most of the publicly available datasets in use today, are collected in a fully controlled or semi-natural settings. Also, elderly peoples from rural areas and transitional activities are mostly not considered, which will cause a lack of generalization of the dreamed HAR models. The purpose of this research is to collect a new dataset from elderly peoples in a rural area and find the best sensor position among ankle and waist by subjecting the newly collected datasets to different machine learning classifiers. Sliding window technique with 50% overlapping was used to segment the sensor data collected from the elderly subjects. Relevant features were extracted, and selected using the wrapper method. From the results obtained, it has shown that the sensor attached at the waist position yield a better result compared to the ankle position on the newly collected elderly data. KNN algorithm has the highest accuracy level in both cases compared to the remaining tested classifiers.
Min/Max Stability and Box Distributions

In representation learning, capturing correlations between the represented elements is paramount. A recent line of work introduces the notion of learning region-based representations, with the objective of being able to better capture these correlations as set interactions. Box models use regions which are products of intervals on $[0,1]$ (i.e., boxes), representing joint probability distributions via Lebesgue measure. To mitigate issues with training, a recent work models the endpoints of these intervals using Gumbel distributions, chosen due to their min/max-stability. In this work we analyze min/max-stability on a bounded domain and provide a specific family of such distributions which, replacing Gumbel, allow for stochastic boxes embedded in a finite measure space. This allows for a latent noise model which is a probability measure. Furthermore, we demonstrate an equivalence between this region-based representation and a density representation, where intersection is given by products of densities. We compare our model to previous region-based probability models, and demonstrate it is capable of being trained effectively to modeling correlations.
Intelligent Clinical Training during the COVID-19 Pandemic

Clinical training is one of the most challenging areas for education especially during the COVID-19 pandemic. There are limited access to apprenticeship training in the complex scenarios with corresponding difficulty training in a time-effective manner. Our work on intelligent clinical training systems provides one effective solution to this problem by introducing intelligent clinical training systems that can supplement tutoring sessions by expert clinical instructors. The Bayesian representation techniques and algorithms for generating tutoring feedback in medical problem-based learning group problem solving made important contributions to the field of Intelligent Tutoring Systems. In particular, it was one of the first systems for tutoring groups of students and the first intelligent tutoring systems for medical problem-based learning. The virtual reality simulator we developed is one of the most sophisticated dental simulators. It stands out as the first dental simulator to integrate sophisticated analysis of the surgical procedure. Particularly noteworthy is also the creative way to understand important issues such as differences in expert and novice performance, the effectiveness of virtual pre-operative practice, and the teaching effectiveness of the simulator. The systems have been implemented in undergrad pre-clinical training and postgrad pre-surgical training with strong scientific evidence of their effectiveness.
Security Analysis and Bypass User Authentication Bound to Device of Windows Hello in the Wild

Windows Hello is a Fast IDentity Online- (FIDO-) based new login system for Windows 10, which provides a single sign-on (SSO) service to diverse online applications. Hardware protection is essential for Window Hello’s security. This paper aims to examine the security of Windows Hello on a device where hardware protection is unavailable. We present the first detailed analysis of Windows Hello’s security. The results show that, on a hardware-unsupported device, the authentication data for Windows Hello is not properly protected. We propose a migration attack to compromise Windows Hello’s security. In the proposed attack, an attacker extracts authentication data from a device to impersonate a victim in his or her Microsoft online account. We consider the possibility of such an attack to be serious and harmful to our society and demand immediate attention for remediation.
Modern Restaurants Using QR Code with Android

Nowadays fashionable eating house systems of building business like Ordering System room Order price tag, Billing System, client Relationship Management System(CRM) along. Fashionable eating house this implies rather than waiters, we have a tendency to use androids to put order. Once the client selects the food things by scanning the QR code to put the order, the ordered list is directly sent to room and to the bill counter. This technique will increase quality and speed of service. This technique offers a cost-effective chance to administer your customers a personalized service expertise wherever they are on high of things selecting what they have, after they need it from feeding to ordering to payment and feedback. we have a tendency to area unit implementing this technique mistreatment mechanical man application for pill PC’s. The face are going to be developed mistreatment hypertext mark-up language, CSS and so the backend can work on MySQL information.
Institutional Metamorphosis and Creative Destruction in the Era of Crisis

Research regarding entrepreneurship and crisis has typically focused on how entrepreneurs are affected by catastrophic events, how they respond to external shakeouts, how they learn from failure, and how they build resilience to recover from adversity. However, despite this recent accumulation of knowledge, little is known about external shakeouts re-configure or transform the “rules of the game”. Based on this research gap, we theorize the conditions under which an economic shakeout motivates an institutional metamorphosis. In doing this, we proposed a model based on a retrospective/longitudinal cryptomarket case that emerged in the aftermath of the 2008 Global Financial Crisis. Results suggest that a new digital currency, new transaction-oriented technologies, trust mechanisms, and shared values led to an institutional metamorphosis. By understanding the conditions that have led to institutional metamorphosis, several implications emerged oriented to policymakers, entrepreneurs, and society.
Mass-ratio-variance based Outlier Factor

An outlier of a finite dataset in statistics is defined as a data point that differs significantly from others. It is normally surrounded by a few data points while normal ones are engulfed by others. This behavior leads to the proposed outlier factor called Mass-ratio-variance Outlier Factor (MOF). A score is assigned to a data point from the variance of the mass-ratio distribution from the rest of data points. Within a sphere of an outlier, there will be few data points compared with a normal one. So, the mass-ratio of an outlier will be different from that of a normal data point. The algorithm to generate MOF requires no parameter and embraces the density concept. Experimental results show that top-10 highest scores from MOF could identify all outliers from synthesized datasets similar to those scores from the state-of-the-art outlier scoring methods such as LOF and FastABOD. Moreover, it could retrieve more outliers from three real World datasets.
Discovering Spatial High Utility Itemsets in High-Dimensional Spatiotemporal Databases

Finding spatial high utility itemsets (SHUIs) in a quantitative spatiotemporal database is a challenging problem of great importance in many real-world applications. Most previous studies have focused on the efficient discovery of SHUIs in a low-dimensional database. This paper advances the state-of-the-art by introducing an efficient algorithm to find SHUIs in a high-dimensional spatiotemporal database. Two novel concepts, ‘spatially closed utilities of an itemset’ and ‘spatially non-closed utilities of an itemset,’ were explored to tackle the predicament caused by the violation of downward closure property of SHUIs. The performance study shows that our algorithm is efficient and is about an order of magnitude faster than the state-of-the-art algorithm.
Role of creativity and resilience in contemporary and entrepreneurial knowledge work

In the contemporary economy, knowledge work is entrepreneurial whether it takes place in organizations or in any form of entrepreneurship. In this paper, we investigated individual creativity in contemporary knowledge work among organisational and platform knowledge workers. Specifically, we investigated what role creativity and resilience play in knowledge work and how creativity and resilience are related. We conducted a mixed-method study, collecting qualitative data (N=15) from platform knowledge workers to build understanding regarding the nature of contemporary knowledge work. Based on the findings of the qualitative study, we created a research model in which we linked creativity and resilience and tested the proposed model with the data collected from 419 organisational knowledge workers. We found that knowledge worker creativity and resilience are bidirectionally linked in the context of organizational knowledge work. We discuss the implications of our study for research on knowledge work and creativity in knowledge work as well as for practitioners in the field of contemporary knowledge work.
Dependent Multi-Task Learning with Causal Intervention for Image Captioning

Recent work for image captioning mainly followed an extract-then-generate paradigm, pre-extracting a sequence of object-based features and then formulating image captioning as a single sequence-to-sequence task. Although promising, we observed two problems in generated captions: 1) content inconsistency where models would generate contradicting facts; 2) not informative enough where models would miss parts of important information. From a causal perspective, the reason is that models have captured spurious statistical correlations between visual features and certain expressions (e.g., visual features of "long hair" and "woman"). In this paper, we propose a dependent multi-task learning framework with the causal intervention (DMTCI). Firstly, we involve an intermediate task, bag-of-categories generation, before the final task, image captioning. The intermediate task would help the model better understand the visual features and thus alleviate the content inconsistency problem. Secondly, we apply Pearl's do-calculus on the model, cutting off the link between the visual features and possible confounders and thus letting models focus on the causal visual features. Specifically, the high-frequency concept set is considered as the proxy confounders where the real confounders are inferred in the continuous space. Finally, we use a multi-agent reinforcement learning (MARL) strategy to enable end-to-end training and reduce the inter-task error accumulations. The extensive experiments show that our model outperforms the baseline models and achieves competitive performance with state-of-the-art models.
On the Union Closed Fragment of Existential Second-Order Logic and Logics with Team Semantics

We present syntactic characterisations for the union closed fragments of existential second-order logic and of logics with team semantics. Since union closure is a semantical and undecidable property, the normal form we introduce enables the handling and provides a better understanding of this fragment. We also introduce inclusion-exclusion games that turn out to be precisely the corresponding model-checking games. These games are not only interesting in their own right, but they also are a key factor towards building a bridge between the semantic and syntactic fragments. On the level of logics with team semantics we additionally present restrictions of inclusion-exclusion logic to capture the union closed fragment. Moreover, we define a team based atom that when adding it to first-order logic also precisely captures the union closed fragment of existential second-order logic which answers an open question by Galliani and Hella.
Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain

In this paper, we focus on machine reading comprehension in social media. In this domain, one normally posts a message on the assumption that the readers have specific background knowledge. Therefore, those messages are usually short and lacking in background information, which is different from the text in the other domain. Thus, it is difficult for a machine to understand the messages comprehensively. Fortunately, a key nature of social media is clustering. A group of people tend to express their opinion or report news around one topic. Having realized this, we propose a novel method that utilizes the topic knowledge implied by the clustered messages to aid in the comprehension of those short messages. The experiments on TweetQA datasets demonstrate the effectiveness of our method.
An Investigation into the Impact Covid-19 Has Had on the Cyber Threat Landscape and Remote Working for UK Organizations

With Covid-19 creating an unexpected worldwide situation that has brought about changes to the way that people have had to go about their everyday lives, organizations have also had to change their business practices to comply with social distancing guidance, meaning the adoption of remote work solutions where possible. There has also been an observed increase in Cyber attacks, that risk taking advantage of these sudden changes. This paper analyzes the effect of Covid-19 on remote working practices and the Cybersecurity threat landscape. The research has shown an indication that Cybercriminals and Advanced Persistent Threat actors have adapted common attacks such as phishing and teleconferencing vulnerabilities, to take advantage of the confusion and fear surrounding Covid-19. There is a suggestion that the larger or more technically focused organizations within the UK were best prepared and least affected by the move to remote work yet also had a general lack of policies and plans in the event of pandemics and natural disasters that might affect security operations. Based on these observations, it is recommended that organizations collaborate more and aim to explore the possibility of creating regional cyber first responder groups to help organizations in future situations like Covid-19. It can also be recommended that organizations introduce more tailored cyber security awareness training based around threats of remote working. Also to introduce pandemic related policies and plans into business continuity strategies, as standard. This should also contain plans covering who is responsible for what action or roles in an emergency and what to do if security teams are reduced in size.
Blockchain-Enabled Secure and Smart Healthcare System

Technology has developed over the years, making our lives easier. The healthcare sector has benefited from the advancement in technology, leading to an increase in the average life expectancy of human beings. However, there are several problems with the way the sector functions. There is a lack of transparency in the healthcare system, which results in inherent trust problems between patients and hospitals. There is no guarantee of getting the proper treatment from the hospital for the fee charged. Blockchain integrated with the smart contract is a well-known disruptive technology that builds trust by providing transparency to the system. In this paper, we propose a blockchain-enabled Secure and Smart HealthCare System. Fairness of the two entities, patient and hospital, involved in the system are guaranteed if they behave honestly. Privacy and security of patients’ medical data are ensured as well. We have implemented the prototype in the Ethereum platform and Ropsten test network and have included the analysis as well.
Video Object Detection Using Event-Aware Convolutional Lstm and Object Relation Networks

Common video-based object detectors exploit temporal contextual information to improve the performance of object detection. However, detecting objects under challenging conditions has not been thoroughly studied yet. In this paper, we focus on improving the detection performance for challenging events such as aspect ratio change, occlusion, or large motion. To this end, we propose a video object detection network using event-aware ConvLSTM and object relation networks. Our proposed event-aware ConvLSTM is able to highlight the area where those challenging events take place. Compared with traditional ConvLSTM, with the proposed method it is easier to exploit temporal contextual information to support video-based object detectors under challenging events. To further improve the detection performance, an object relation module using supporting frame selection is applied to enhance the pooled features for target ROI. It effectively selects the features of the same object from one of the reference frames rather than all of them. Experimental results on ImageNet VID dataset show that the proposed method achieves mAP of 81.0% without any post processing and can handle challenging events efficiently in video object detection.
Infectious Disease Relational Data Analysis Using String Grammar Non-Euclidean Relational Fuzzy C-Means

Statistical analysis in infectious diseases is becoming more important, especially in prevention policy development. To achieve that, the epidemiology, a study of the relationship between the occurrence and who/when/where, is needed. In this paper, we develop the string grammar non-Euclidean relational fuzzy C-means (sgNERF-CM) algorithm to determine a relationship inside the data from the age, career, and month viewpoint for all provinces in Thailand for the dengue fever, influenza, and Hepatitis B virus (HBV) infection. The Dunn's index is used to select the best models because of its ability to identify the compact and well-separated clusters. We compare the results of the sgNERF-CM algorithm with the string grammar relational hard C-means (sgRHCM) algorithm. In addition, their numerical counterparts, i.e., relational hard C-means (RHCM) and non-Euclidean relational fuzzy C-means (NERF-CM) algorithms are also applied in the comparison. We found that the sgNERF-CM algorithm is far better than the numerical counterparts and better than the sgRHCM algorithm in most cases. From the results, we found that the month-based dataset does not help in relationship-finding since the diseases tend to happen all year round. People from different age ranges in different regions in Thailand have different numbers of dengue fever infections. The occupations that have a higher chance to have dengue fever are student and teacher groups from the central, north-east, north, and south regions. Additionally, students in all regions, except the central region, have a high risk of dengue infection. For the influenza dataset, we found that a group of people with the age of more than 1 year to 64 years old has higher number of influenza infections in every province. Most occupations in all regions have a higher risk of infecting the influenza. For the HBV dataset, people in all regions with an age between 10 to 65 years old have a high risk in infecting the disease. In addition, only farmer and general contractor groups in all regions have high chance of infecting HBV as well.