Reviewerly/exHarmony huggingface dataset:
Targeting Stop-Words for Quality Assurance of Biomedical Ontologies

ObjectiveWe assess the potential of exploiting stop-words in biomedical concept names to complete the logical definitions of partially defined (PD) concepts.MethodsConcepts containing stop-words are selected from the Disorder hierarchy of SNOMED. Fully defined (FD) concepts are treated as a source of ground-truth to complete the definitions of, lexically and semantically similar, PD concepts. FD and PD concepts are lexically and semantically analysed to create sample sets. Mandatory attribute relationships are calculated by using an intersection set logic for each FD sample set. PD sample sets are audited against this mandatory attribute relationship template to identify inconsistencies in modelling styles and potentially missing attribute relationships.ResultsLexical and semantic patterns around 11 stop-words were analysed. 26 samples sets were extracted for the 11 stop-words. Mandatory attribute relationships were identified for 24 out of the 26 sample sets. More than two-thirds of the PD concepts containing the stop-words due to and in were found to be inconsistent in their modelling style and potentially missing at least one attribute relationship according to the created template.
Detecting Accounting Fraud with Noisy Labels

Partial observability is a prominent feature of accounting fraud. Not only the act of committing fraud is not directly observable, but the detected frauds are often revealed to the public with substantial time delays. In this article, we specify an underlying fraud data generating process that gives rise to noisy labels. It comprises two latent processes: fraud commission and fraud detection. We model fraud probability and detection probability separately, and combine them optimally in an ensemble machine learning algorithm. We show that our new fraud prediction model significantly outperforms various competing models, and generates more accurate fraud prediction.
A Comparison of DBMSs for Mobile Devices

With the growth in the reach and use of the Internet, smartphones and social networks, there is an exponential increase in the volume of data managed, which can be structured, semi-structured or unstructured. In this context, NoSQL databases are emerging, which facilitate the storage of semi-structured or unstructured data. In this context, NoSQL databases emerge, which facilitate the storage of semi-structured or unstructured data. On the other hand, improvements in hardware performance of mobile devices are leading to more information being managed by these, and to the emergence of new database management systems that are installed on these devices. The purpose of this work is to survey the database management systems for mobile devices, and to perform an experimental analysis of the most representative systems of the most widely used database models.
Entanglement

This chapter is entirely dedicated to the phenomenon of entanglement and its role in the quantum information theory. It begins by discussing how Bell states can emerge from controlled operations, and continues by illustrating the violation of Bell’s and CHSH inequalities using basic Q# programs. What follows is the general description of the non-local games, including one based on GHZ states. Finally, the chapter covers the theory and provides runnable Q# examples for the concepts of teleportation, entanglement swapping and superdense coding.
Multi-band Photometric Stereo Using Random Sampling of Channels and Pixels

One of the main problems faced by the photometric stereo method is that several measurements are required, as this method needs illumination from light sources from different directions. A solution to this problem is the color photometric stereo method, which conducts one-shot measurements by simultaneously illuminating lights of different wavelengths. However, the classic color photometric stereo method only allows measurements of white objects, while a surface-normal estimation of a multicolored object using this method is theoretically impossible. This paper estimates the surface normal of a multi-colored object under multi-spectral lighting. This is a difficult problem since the albedo is different for each pixel and each channel. We solve this problem by sampling some pixels randomly. If we randomly sample neighboring pixels, the probability of picking the same albedo pixels may be high. Therefore, if the sampled pixels all have the same albedo, we can determine the surface normal uniquely. To demonstrate the effectiveness of this study, a measurement device with seven colors is used.
A comparison between expected value and realised value of cloud computing: an exploratory study

The purpose of the research is to examine the business benefit of cloud computing by comparing the benefits perceived by respondents whose companies had already adopted cloud computing and the benefits expected by those whose companies had not. The motivation for the study is that while business benefits of cloud computing have been widely touted, a survey on practitioners shows that not all kinds of benefits were achieved to expectations. The study is intended to examine the realisation of claimed business benefits of cloud computing in three aspects: cost reduction, improved capability and enhanced scalability. The study shows that no sufficient evidence supports the existence of difference between realised and expected cost reductions. Furthermore, there is no difference between realised and expected improvement in IT and business capability. Nevertheless, respondents reported that their companies reaped more than expected in the aspect of enhancement of scalability.
Fuzzy Subsets Theory-Based Imprecision Modeling Using Ontology and Applied to Risk Estimation in Project Intelligent Management

The increasing complexity and dynamism of construction projects has imposed substantial uncertainties and subjectivities into the risk analysis process. Effective risk management involves a process that includes risk identification, risk assessment, risk response and risk monitoring. Risk assessment is already solved using fuzzy expert systems, entropic weighting or fuzzy linguistic multiple attribute decision making. Given the methods or algorithms absence for project risk management, we opt to solve this kind of problem through a fuzzy ontology where we were able to welcome the expertise of a few executives and projects managers in the project management domain. So, the objective of this paper is to develop a fuzzy ontology-based risk assessment model in intelligent project management compared to our expert system-based previous work which have used a translation scheme and contrary to the previous works in this domain and which focus on either fuzzy language-based works or fuzzy expert-based solution. To my knowledge, there has been no intelligent project management risk estimation study using both a fuzzy ontology and a syntax-driven translation scheme. For simplification reasons, we present in this paper only predicate fuzziness case of a simple fuzzy query. The first results are encouraging although the work is still in its early stages. To illustrate this solution, we use again the Cox work, based on the Metus System group managers’ directives to determine the project risks.
A Tool for Syntactic Dependency Analysis on the Web Stack

One of the most common errors developers make is to provide incorrect string identifiers across the HTML5-JavaScript-CSS3 stack. The existing literature shows that a significant percentage of defects observed in real-world codebases belong to this category. Existing work focuses on semantic static analysis, while this paper attempts to tackle challenges that can be solved using syntactic static analysis. While semantic state analysis is more powerful, it creates a greater computational burden on tool processing while simple static analysis may be computed faster, allowing for better integration in inline syntax-highlighting marker in a user interface or a quick pass through large codebases. This paper proposes a tool for quickly identifying defects at the time of injection due to dependencies between HTML5, JavaScript, and CSS3, specifically in syntactic errors in string identifiers. The proposed solution reduces the delta (time) between defect injection and discovery with the use of a dedicated just-in-time syntactic string identifier resolution tool. The solution focuses on modeling the nature of syntactic dependencies across the stack, and providing a tool that helps developers discover such dependencies. This tool was validated against a set of real-world codebases to analyze the significance of these defects.
An Approach to Sri Lankan Sign Language Recognition Using Deep Learning with MediaPipe

Currently, there are millions of people around the world with speech and hearing impairments, and according to the latest census, there are nearly 70,000 people who use Sri Lankan Sign Language. Sign language is a visual language, and it is the main medium of communication in their daily conversations. But they face obstacles when communicating with people who do not know sign language. There are communication barriers in different contexts, such as in work environments, knowledge exchange, and message sharing. Therefore, technology should play a major role in helping people with these hearing and speech impairments to improve their quality of life. This research aims to suggest models that use Google MediaPipe Hand Pose landmarks to identify Sri Lankan Sign Language. Moreover, this article compares vision-based approaches with convolutional neural network (CNN) and recurrent neural network (RNN). We also considered activation functions (such as ReLU, Linear, and Softmax), loss functions (mean squared error (MSE) and Categorical_crossentropy), and optimizations (Adam and Stochastic Gradient Descent (SGD)). The result showed that most algorithms built with Long Short-Term Memory (LSTM), CNN, and CNN-LSTM achieved an accuracy greater than 95%, both with the training dataset and the test dataset. In particular, models with MSE as the loss function and Adam as the optimizer showed higher accuracy.
Usage Pattern of Social Media among Higher Secondary School Students of Haryana

Social media is most modernistic form of media and acquire many features and characteristics. It has many facilities like as texting, communicating, images sharing, audio and video sharing, fast publishing, gaming, blogging, linking with all over world, direct connecting. Social media provides an opportunity for students to improve social networking and learning processes, which promotes knowledge in society. The study was conducted in year 2017-18 between rural and urban areas of Hisar district of Haryana. Thus total of 160 students were selected randomly. Results highlighted that the students keep themselves updated with latest know and how through newspaper (30.00%), television (83.70%), mobile phones (62.50%), and mobile with internet connections (43.70%). Majority of the students were aware and utilizing social media such as Facebook (90.00%), WhatsApp (95.00%), YouTube (75.00%), Messenger (47.50%) and Wikipedia (30.00%) respectively. The social media use was positively correlated with age, sex and stream of study and negatively associated with the academic performances of the students.
Explanation of Fui Value by Virtual Simulation Technology

FUI is a type of conceptual UI design that is capable for cross-field academic researches. Since FUI can both exists in fiction and reality, its values are different from other types of UI. It contains specific academic, publicity, and economic values that are beneficial for both theoretical and practical fields. With the FUI in Crew Dragon Demon-2 as a key example, it’ll help to reveal the value of FUI.
Conclusion

This book presents a concise introduction to recent research on human-aware decision-making, particularly ones focused on the generation of behavior that a human would find explainable or deceptive. Human-aware AI or HAAI techniques are characterized by the acknowledgment that for automated agents to successfully interact with humans, they need to explicitly take into account the human’s expectations about the agent. In particular, we look at how for the robot to successfully work with humans, it needs to not only take into account its model MR, which encodes the robot’s beliefs about the task and their capabilities but also take into account the human’s expectation of the robot model \(M_h^R\) which captures what the human believes the task to be and what the robot is capable of. It is the model \(M_h^R\) that determines what the human would expect the robot to do and as such if the robot expects to adhere to or influence the human expectations, it needs to take into account this model. Additionally, this book also introduces three classes of interpretability measures, which capture certain desirable properties of robot behavior. Specifically, we introduce the measures Explicability, Legibility, and Predictability.
Context-Adaptive Content-Based Filtering Recommender System Based on Weighted Implicit Rating Approach

Recommender systems’ job is to churn and filter out desired relevant information from an unorganized pile of data available. Ratings score data are the key parameter to recommender engine computations. Rating score data can be either explicit where users directly give the preference score or implicit where user behaviors are to be captured to compute the score. In many applications where user's explicit rating is not possible, the implicit rating is computed. Computing implicit rating needs to be carefully modeled to achieve the best relevant recommendations. A context-aware feature is needed in some applications to tune the filtering model to extract relevant details to address the context. This paper proposes a recommendation application to address the mapping of call for research projects published by various funding agencies to the aspiring researchers who wait and look for applying to such call for proposals relevant to their domain interest. This paper proposes a context-adaptive content-based filtering recommendation system supported by service-oriented on-demand cloud-based architecture. The proposed recommendation system algorithm is adaptive to address the different call for proposals published to achieve the level of mapping of relevant research paper abstracts by allowing it to tune appropriate weight parameters.
Introducing AV1 Codec-Level Video Steganography

Steganography is the ancient art of concealing messages into data. High research interest has grown over the last years, however, techniques in literature are only focused on standard and in some way legacy multimedia formats (e.g., H.264). Moreover, most video steganography techniques are based on concealing data into contents of each frame employing many strategies. In this paper, a codec-level video steganography technique is presented on the novel AV1: a royalty-free video compression format proposed by the Alliance for Open Media (AOM). The proposed method is based on the alteration of intra-prediction angles and, differently from other solutions, it works along the compression process, by allowing the encoder to reduce possible distortions caused by the messages to be hidden. The effectiveness of the technique was demonstrated by hiding up to 1024 characters into an highly compressed video of 40 s maintaining an average Peak Signal-to-Noise Ratio value of 37.53 dB.
Cycle Consistency Based Method for Learning Disentangled Representation for Stochastic Video Prediction

Video frame prediction is an interesting computer vision problem of predicting the future frames of a video sequence from a given set of context frames. Video prediction models have found wide-scale perspective applications in autonomous navigation, representation learning, and healthcare. However, predicting future frames is challenging due to the high dimensional and stochastic nature of video data. This work proposes a novel cycle consistency loss to disentangle video representation into a low dimensional time-dependent pose and time-independent content latent factors in two different VAE based video prediction models. The key motivation behind cycle consistency loss is that future frame predictions are more plausible and realistic if they reconstruct the previous frames. The proposed cycle consistency loss is also generic because it can be applied to other VAE-based stochastic video prediction architectures with slight architectural modifications. We validate our disentanglement hypothesis and the quality of long-range predictions on standard synthetic and challenging real-world datasets such as Stochastic Moving MNIST and BAIR.
Modelling the Progression of the Symptoms of Parkinsons Disease Using a Nonlinear Decomposition of 123I FP-CIT SPECT Images

Parkinson’s Disease (PD) is one of the most relevant neurodegenerative disorder. It is mainly caused by a loss of dopamine neurons leading to a reduction in the neurotransmitter dopamine, which is essential in the control of movement. While the diagnosis of PD is mainly clinical, new markers are being used with high accuracy in the later stages of the disease, where symptoms are clear. However, the early stages of the disease, when symptoms start to evolve and treatments could potentially be more effective, are yet to be explored. In this work we explore the low-dimensional latent space of the Parkinson’s Progression Markers Initiative (PPMI) DaTSCAN imaging dataset, with a twofold objective: to perform an early diagnosis of PD, and to link the low-dimensional representation of the images to symptomatology. Different unsupervised methods have been used to extract the features (ISOMAP and PCA), and the resulting space is evaluated by means of binary or multiclass classification, and linear regression, using Support Vector Machines (SVM). We obtained a diagnosis of PD with an Area Under the ROC Curve (AUC) above 0.94 for three different variables, and a relevant link between the Unified Parkinson’s Disease Rating Scale (UPDRS) and the imaging composite features with $$R^2>0.2$$ even for a simple linear model. These results pave the way to explore latent representations in PD and study the progression of the disease and its symptomatology.
Introducing Microsoft Orleans

Orleans helps developers to easily create cloud-native, elastic, highly available applications. We can take advantage of the actor framework without having to be an expert in distributed systems but, take advantage of them by being elastic, concurrent, and highly-available. Services such as IoT, Stock exchanges, and email systems. What is truly amazing about it is that a single dev has the ability to write an application themselves by using a few NuGet packages, common failure practices, and even extending streams.
A Digitization Pipeline for Mixed-Typed Documents Using Machine Learning and Optical Character Recognition

Although digitization is advancing rapidly, a large amount of data processed by companies is in printed format. Technologies such as Optical Character Recognition (OCR) support the transformation of printed text into machine-readable content. However, OCR struggles when data on documents is highly unstructured and includes non-text objects. This, e.g., applies to documents such as medical prescriptions. Leveraging Design Science Research (DSR), we propose a flexible processing pipeline that can deal with character recognition on the one hand and object detection on the other hand. To do so, we derive Design Requirements (DR) in cooperation with a practitioner doing prescription billing in the healthcare domain. We then developed a prototype blueprint that is applicable to similar problem formulations. Overall, we contribute to research and practice in multiple ways. First, we provide evidence for selected OCR methods provided by previous research. Second, we design a machine-learning-based digitization pipeline for printed documents containing both text and non-text objects in the context of medical prescriptions. Third, we derive a nascent design pattern for this type of document digitization. These patterns are the foundation for further research and can support the development of innovative information systems leading to more efficient decision making and thus to economic resource usage.
Coarse-to-Fine Visual Question Answering by Iterative, Conditional Refinement

Visual Question Answering (VQA) is a very interesting technique to answer natural language questions about an image. Recent methods have focused on incorporating knowledge into an improved VQA model, by augmenting the training set, representing scene graphs, or including reasoning. We also leverage knowledge to make VQA more robust. Yet we take a different route: we take the VQA model as-is and extend it with a novel algorithm called Guided-VQA that guides the questioning by leveraging knowledge to obtain better answers. This enables knowledge-extended VQA while not having to retrain the VQA model. This is beneficial when computing resources and/or time to adapt to new knowledge are limited. We start with the observation that VQA has difficulties with answering compositional and finegrained questions. We propose to solve this by a coarse-to-fine scheme of posing questions. The proposed Guided-VQA algorithm is an iterative, conditional refinement that decomposes a compositional, finegrained question into a sequence of coarse-to-fine questions by leveraging taxonomic knowledge about the involved objects. On Visual Genome, we show that it improves the answers significantly over standard VQA. This is relevant for robust deployment of VQA where resources or adaptation time are limited.
An Efficient Algorithm for Traffic Congestion Control

Recently, car parking space is one of the hard tasks in modern-day traits. Area operation and supervision of vehicles’ area is now a required area of study. Searchings’ for an unfilled parking’ vacancy in overfilled visitors is a time-taking manner. The prevailing to be had parking area popularity techniques are not robust or worldwide for photos took from one-of-a-kind photographic digicam lookouts. Final results of a right parking space in a hectic town is simply a stimulating trouble and public are facing this elaborate on an everyday base. The most reason of this observe is to extensively speak the previous research of vehicle parking area recognition and equivalence them from modified factors. To conquer these problems, we suggest an aircraft-primarily based method which adopts an operational three-D parking lot characteristic such as abundant planar aspects. The aircraft-based totally three-D segment model performs a key element in conduct inter-item occlusion and point of view alteration. However, to relieve the interference of unpredictable illumination deviations and sun shades, we endorse an aircraft-based class system. Furthermore, by using supplying a Bayesian categorized framework to participate the three-D prototypical with the aircraft-based organization technique, we methodically infer the space popularity. Final, to triumph over the insufficient lighting fixtures within the night-time, we additionally gift a preprocessing step to improve picture excellence. The investigational consequences expression that the deliberate framework can comprehend strong locating of car parking places in each sunlight hours and night-time.