Reviewerly/exHarmony huggingface dataset:
Mixed Reality Multimedia Learning to Facilitate Learning Outcomes from Project Based Learning

Effective construction engineering and management education requires hands-on experiences that have not traditionally been offered in classroom settings. Physical building competitions like Solar Decathlon are valuable for providing experiential learning opportunities that may support tacit and explicit knowledge development among students, but they are often not available to all students due to funding and resource limitations. Less resource intensive teaching strategies, such as project based learning, can mimic the benefit of physical experiences by providing context to learning content. This paper reviews project based learning literature to identify trends in reported learning gains from the adoption of this strategy. Additionally, emerging technologies offer the ability to create low cost, immersive multimedia environments that may be able to support the types of learning targeted by physical design and construction experiences. Literature on multimedia learning theory is explored to identify opportunities for multimedia applications to facilitate learnings derived by physical educational contexts, but with the use of increasingly affordable multimedia strategies. This paper resulted in identifying six learning gains that have a theoretical potential to be facilitated using augmented reality and virtual reality technologies. The theoretical potential was deduced based on prior research on teaching strategies that provide real-world context to learning content. The authors of this paper propose using the identified learning gains as targets to specifically design implementation studies to verify this potential. The learning gains identified in the results section can be targeted and measured in future research when empirically validating the use of immersive technologies for construction education. The contribution of this work is in synthesizing the learning gains that future researchers should target based on evidence from prior research in related learning contexts.
Towards Online Handwriting Recognition System Based on Reinforcement Learning Theory

In this work, we formalize the problem of online handwriting recognition according to the reinforcement learning theory. The handwriting trajectory is divided into strokes and we extracted their structural and parametric features based on freeman codes, visual codes and beta-elliptic features respectively. The environments were trained using tabular q-learning algorithm in order to calculate the optimal sate-to-action values for each class of handwriting. The proposed model was evaluated on LMCA database and achieved very promising results for both structural and parametric representations.
No-Reference Quality Assessment Based on Spatial Statistic for Generated Images

In recent years, generative adversarial networks has made remarkable progress in the field of text-to-image synthesis whose task is to obtain high-quality generated images. Current evaluation metrics in this field mainly evaluate the quality distribution of the generated image dataset rather than the quality of single image itself. With the deepening research of text-to-image synthesis, the quality and quantity of generated images will be greatly improved. There will be a higher demand for generated image evaluation. Therefore, this paper proposes a blind generated image evaluator(BGIE) based on BRISQUE model and sparse neighborhood co-occurrence matrix, which is specially used to evaluate the quality of single generated image. Through experiments, BGIE surpasses all no-reference methods proposed in the past. Compared to VSS method, the surpassing ratio: SRCC is 8.8%, PLCC is 8.8%. By the "One-to-Multi" high-score image screening experiment, it is proved that the BGIE model can screen out best image from multiple images.
A classification framework for multivariate compositional data with Dirichlet feature embedding

Compositional data which contain relative or structure information of a whole occur commonly in many disciplines and practical scenarios. Yet relatively few works are available for multivariate compositional data classification with different numbers of parts using machine learning. This is because compositional data is inherently constrained to unit sum, resulting in the existing methods cannot be directly applied. Particularly, the multivariate analysis methods for compositional data variables with unequal sizes of parts are not sufficiently investigated. Moreover, to design a good classification model is indeed a complicated work. Except for the learning algorithm, data quality is also an essential determinant, which is rarely been concerned. In this paper, we propose an effective framework for multivariate compositional data classification. Specifically, the Dirichlet feature embedding is proposed to implement on the original compositional data features with the goal of removing the constraint and obtaining high quality training data, as well as reducing the dimension. Support vector machine is then used to build the classification model. Results of simulation study and real-world dataset show our proposed method can achieve good performances.
Post-selection inference via algorithmic stability

When the target of statistical inference is chosen in a data-driven manner, the guarantees provided by classical theories vanish. We propose a solution to the problem of inference after selection by building on the framework of algorithmic stability, in particular its branch with origins in the field of differential privacy. Stability is achieved via randomization of selection and it serves as a quantitative measure that is sufficient to obtain nontrivial post-selection corrections for classical confidence intervals. Importantly, the underpinnings of algorithmic stability translate directly into computational efficiency—our method computes simple corrections for selective inference without recourse to Markov chain Monte Carlo sampling.
Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine

The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries, including the transportation sector.The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features.One of these tasks is to ensure that vehicles are autonomous, intelligent and able to grow their repository of information.Machine learning has recently been implemented in wireless networks, as a major artificial intelligence branch, to solve historically challenging problems through a data-driven approach.In this article, we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging field.Deep Extreme Learning Machine (DELM) framework is introduced in this article to be incorporated in vehicles so they can take human-like assessments.The present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain conditions.It leads to the concept of vehicle controller making self-decisions.The proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route observations.This overcomes inadequacy of the current in-vehicle route-finding technology and its support.All the relevant route-related information for the ride will be provided to the user based on its availability.Using the DELM method, a high degree of precision in smart decision taking with a minimal error rate is obtained.During investigation, it has been observed that proposed framework has the highest accuracy rate with 70% of training (1435 samples) and 30% of validation (612 samples).Simulation results validate the intelligent prediction of the proposed method with 98.88%, 98.2% accuracy during training and validation respectively.
Learning attention for object tracking with adversarial learning network

Abstract Artificial intelligence has been widely studied on solving intelligent surveillance analysis and security problems in recent years. Although many multimedia security approaches have been proposed by using deep learning network model, there are still some challenges on their performances which deserve in-depth research. On the one hand, high computational complexity of current deep learning methods makes it hard to be applied to real-time scenario. On the other hand, it is difficult to obtain the specific features of a video by fine-tuning the network online with the object state of the first frame, which fails to capture rich appearance variations of the object. To solve above two issues, in this paper, an effective object tracking method with learning attention is proposed to achieve the object localization and reduce the training time in adversarial learning framework. First, a prediction network is designed to track the object in video sequences. The object positions of the first ten frames are employed to fine-tune prediction network, which can fully mine a specific features of an object. Second, the prediction network is integrated into the generative adversarial network framework, which randomly generates masks to capture object appearance variations via adaptively dropout input features. Third, we present a spatial attention mechanism to improve the tracking performance. The proposed network can identify the mask that maintains the most robust features of the objects over a long temporal span. Extensive experiments on two large-scale benchmarks demonstrate that the proposed algorithm performs favorably against state-of-the-art methods.
Research on the Method of Determining Highway Truck Load Limit Based on Image Processing

Over-limit transportation is the leading cause of highway pavement diseases and traffic accidents. The Ministry of Transport of China has determined the weight limit for different trucks entering the highway based on the number of truck axles and the coupling method. However, because determining the truck weight limit is more complicated, the current work is mainly done manually by the toll station staff at the expressway entrance, and the recognition speed is slow Cand the recognition result is inaccurate. In response to this problem, we first analyzed and established the relationship between the number of axles and the position distribution, and the truck weight limit. Based on the above, we developed a circle detection method with an improved Hough and CURE algorithm as the core to identify truck wheel axles. We collect 100 test images at the expressway entrance and use 50 of them to perform detection experiments to determine the radius step, angle step, and the maximum and minimum circle values during the detection process. After using the entire data set, comparing the hough algorithm, the improved Hough algorithm, and our proposed method, the results show that our proposed method has higher accuracy and efficiency.
Design and Implementation of SM9 Identity Based Cryptograph Algorithm

With the rapid development of Internet technology, the spread of information and the arrival of the big data era, data security is becoming more and more important, in the process of data transmission is to solve the first problem of identification and data privacy of both sides, these problems have to rely on cryptography to solve, but with the explosive growth of Internet users in recent years, the traditional PKI/CA public key password system due to the management of certificates cumbersome and complex use, gradually exposed its shortcomings. In a password system that uses an identity password algorithm, the consumer has a meaningful and unique identity, which is itself the public key of the entity. Without the need to pre-negotiate keys or exchange certificates, the time it takes to apply and validate in a traditional certificate system can be significantly reduced and easy to use. This paper is mainly based on the Description of the SM9 digital signature algorithm based on the sM9 logo password national standard “GM/T 0044 SM9 logo password algorithm”, and uses C language programming to realize the complete process of SM9 digital signature algorithm. The implementation process is divided into parameter settings, key generation, digital signature generation, signature verification four core steps. This design mainly provides the function of selecting its own identity key generation, digital signing of message, signature verification, and has the advantages of simple operation and high efficiency.
Automated Mortality Prediction in Critically-ill Patients with Thrombosis using Machine Learning

Venous thromboembolism (VTE) is the third most common cardiovascular condition. Some high risk patients diagnosed with VTE need immediate treatment and monitoring in intensive care units (ICU) as the mortality rate is high. Most of the published predictive models for ICU mortality give information on in-hospital mortality using data recorded in the first day of ICU admission. The purpose of the current study is to predict in-hospital and after-discharge mortality in patients with VTE admitted to ICU using a machine learning (ML) framework. We studied 2,468 patients from the Medical Information Mart for Intensive Care (MIMIC-III) database, admitted to ICU with a diagnosis of VTE. We formed ML classification tasks for early and late mortality prediction. In total, 1,471 features were extracted for each patient, grouped in seven categories each representing a different type of medical assessment. We used an automated ML platform, JADBIO, as well as a class balancing combined with a Random Forest classifier, in order to evaluate the importance of class imbalance. Both methods showed significant ability in prediction of early mortality (AUC =0.92). Nevertheless, the task of predicting late mortality was less efficient (AUC =0.82). To the best of our knowledge, this is the first study in which ML is used to predict short-term and long-term mortality for ICU patients with VTE based on a multitude of clinical features collected over time.
Stochastic Block Models are a Discrete Surface Tension

Networks, which represent agents and interactions between them, arise in myriad applications throughout the sciences, engineering, and even the humanities. To understand large-scale structure in a network, a common task is to cluster a network's nodes into sets called "communities", such that there are dense connections within communities but sparse connections between them. A popular and statistically principled method to perform such clustering is to use a family of generative models known as stochastic block models (SBMs). In this paper, we show that maximum likelihood estimation in an SBM is a network analog of a well-known continuum surface-tension problem that arises from an application in metallurgy. To illustrate the utility of this relationship, we implement network analogs of three surface-tension algorithms, with which we successfully recover planted community structure in synthetic networks and which yield fascinating insights on empirical networks that we construct from hyperspectral videos.
Comparison of Maintainability Index Measurement from Microsoft CodeLens and Line of Code

Higher software quality demands are in line with software quality assurance that can be implemented in every step of the software development process. Maintainability Index is a calculation used to review the level of maintenance of the software. MI has a close relationship with software quality parameters based on Halstead Volume (HV), Cyclomatic Complexity McCabe (CC), and Line of Code (LOC). MI calculations can be carried out automatically with the help of a framework that has been introduced in the industrial world, such as Microsoft Visual Studio 2015 in the form of Code Matric Analysis and an additional software named Microsoft CodeLens Code Health Indicator. Previous research explained the close relationships between LOC and HV, and LOC and CC. New equations can be acquired to calculate the MI with the LOC approach. The LOC Parameter is physically shaped in a software program so that the developer can understand it easily and quickly. The aim of this research is to automate the MI calculation process based on the component classification method of modules in a rule-based C # program file. These rules are based on the error of MI calculations that occur from the platform, and the estimation of MI with LOC classification rules generates an error rate of less than 20% (19.75 %) of the data, both of which have the same accuracy.
From women for women: The role of social media in online nonprofit activities during Wuhan lockdown

Abstract The article examines the role of social media in mitigating information asymmetry and coordination problems during COVID‐19 epidemic crisis. We use “Sisters‐Fight‐Epidemic” online volunteering project during the outbreak of COVID‐19 in Wuhan, China, as a case to demonstrate how social media plays a role as a mechanism in linking multiple stakeholders and shaping their actions during the epidemic response. We show that social media facilitates the self‐organizing processes of volunteers and develops the emergency information networks, therefore enabling a relatively efficient relief responses to the needs of epidemic victims particularly female medical workers. This article also identifies spontaneous online volunteering project as a new form of nonprofit organization and as a new emergent response group that can leverage the strengths of social media in disaster responses to enable effective coordination, initiate advocacy, and improve transparency of relief efforts.
DiffTech: a tool for differencing similar technologies from question-and-answer discussions

Developers can use different technologies for different software development tasks in their work. However, when faced with several technologies with comparable functionalities, it can be challenging for developers to select the most appropriate one, as trial and error comparisons among such technologies are time-consuming. Instead, developers resort to expert articles, read official documents or ask questions in Q&A sites for technology comparison. However, it is still very opportunistic whether they will get a comprehensive comparison, as online information is often fragmented, contradictory and biased. To overcome these limitations, we propose the DiffTech system that exploits the crowd sourced discussions from Stack Overflow, and assists technology comparison with an informative summary of different comparison aspects. We found 19,118 comparative sentences from 2,410 pairs of comparable technologies. We released our DiffTech website for public use. Our website attracts over 1800 users and we also receive some positive comments on social media. A walkthrough video of the tool demo: https://www.youtube.com/watch?v=ixX41DXRNsI Website link: https://difftech.herokuapp.com/
Classifying heuristic textual practices in academic discourse

Abstract In this paper, we investigate how deep learning techniques can be applied to discourse pragmatics. As a testcase we analyse heuristic textual practices, defined as linguistic implementations of decision routines in research processes in academic discourse. We develop a complex annotation scheme of pragmalinguistic categories on different levels of granularity and manually annotate a corpus of texts across various scientific disciplines. This is the basis for training recurrent neural networks to classify heuristic textual practices. Our experiments show that the annotation categories are robust enough to be recognised by our models which learn similarities of the sentence-surfaces represented as word embeddings. Our study aims at an iterative human-in-the-loop process in which manual-hermeneutic and algorithmic procedures mutually advance the insight process. It underlines the fact that the interaction between manual and automated methods opens up a promising field for further research, allowing interpretative analyses of complex pragmatic phenomena in large corpora.
Real-time continuous calibration method for an ultraviolet camera

The accuracy of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">S</mml:mi> <mml:mi mathvariant="normal">O</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> cameras is significantly determined by the ability to obtain an accurate calibration. This work presents a real-time continuous calibration method for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">S</mml:mi> <mml:mi mathvariant="normal">O</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> cameras with a moderate resolution spectrometer by taking realistic radiative transfer into account. The effectiveness and accuracy of the proposed method have been verified through simulations and experiments. The calibration error can be reduced by about 20–80% compared with the commonly used cell calibration, especially for situations of long distance, poor visibility, or optically thick plumes.
On machine vision and photographic imagination

Abstract In this article we introduce the concept of implied optical perspective in deep learning computer vision systems. Taking the BBC's experimental television programme “Made by Machine: When AI met the Archive” (2018) as a case study, we trace a conceptual and material link between the system used to automatically “watch” the television archive and a specific type of photographic practice. From a computational aesthetics perspective, we show how deep learning machine vision relies on photography, its technical regimes and epistemic advantages, and we propose a novel way to identify the latent camera through which the BBC archive was seen by machine.
Efficient Big-Data Access: Taxonomy and a Comprehensive Survey

The emerging systems are not only generating huge amounts of data but also expect this data to be analyzed expeditiously to drive online decision-making and control. Thus, identifying the most relevant data and making it available close to the computation becomes a central challenge in driving the big data revolution. Storage systems play a crucial role in enabling efficient access to the stored data and intelligent storage management techniques are thus central to addressing the problem. Generally, as the data volume increases, the marginal utility of an “average” data item tends to decline, which requires greater effort in identifying the most valuable data items and making them available with minimal overhead and latency. Data driven mechanisms have a big role to play in solving this needle-in-the-haystack problem. In this paper we propose a taxonomy to provide a structure for understanding the common issues surrounding these techniques. We discuss these techniques and articulate many research challenges and opportunities.