Reviewerly/exHarmony huggingface dataset:
Chinese Emergency Event Recognition Using Conv-RDBiGRU Model

In view of the weak generalization of traditional event recognition methods, the limitation of dependence on field knowledge of expert, the longer train time of deep neural network, and the problem of gradient dispersion, the neural network joint model, Conv-RDBiGRU, integrated residual structure was proposed. Firstly, text corpus is preprocessed by word segmentation and stop words processing and uses word embedding to form the matrix of word vectors. Then, local semantic features are extracted through convolution operation, and deep context semantic features are extracted through RDBiGRU. Finally, the learned features are activated by softmax function and the recognition results are output. The novelty of work is that we integrate residual structure into recurrent neural network and combine these methods and field of application. The simulation results show that this method improves precision and recall of Chinese emergency event recognition, and the F -value is better than other methods.
Satellite Image Enhancement Using an Ameliorated Balance Contrast Enhancement Technique

The quality of satellite images is frequently degraded by the low-contrast effect.Therefore, different research works have been developed to deal with this undesirable effect.Still, no conclusive verdicts have been reached and the problem remains open for research.Hence, an ameliorated balance contrast enhancement technique using a parabolic function (ABCETP) is proposed in this article to improve the quality in terms of brightness, contrast, and colors.The original BCETP works by determining a parabolic function with three coefficients.However, the proposed ABCETP works by computing a modified parabolic function with one modified and two unmodified coefficients, as well as, it utilizes two additional methods to produce adequate-quality results.The proposed ABCETP is examined with numerous real contrast-distorted images, compared against different contrast enhancement methods and the results are assessed with two specialized quality appraisal metrics.Empirical results obtained from conducting various comparisons and experiments revealed the favorability of ABCETP, wherein it outputted images with better-perceived quality and outperformed the comparative methods in several aspects.
Social risks of e-learning in a digital society

The concepts of the essence of social risks in the digital society, as well as their specifics in the process of e-learning have been clarified in the article materials. The importance of conducting research in this field, which is dynamically developing on the basis of digital technologies, – has been argued. The classification of scientific works in the field of studying the risks of e-learning on the basis of socio-technological, institutional and General social approaches has been given. A justification for the institutional nature of social risks of e-learning, as well as the grounds for classification by forms of manifestation, levels and sources of occurrence, as well as organizational and technological characteristics has been provided. To identify the essence of these risks, the concept of S.A. Kravchenko’s “humanistic theory of a complex society”, which has a nonlinear dynamics of development, – has been proposed. The concept of types and types of social risks in the field of this form of education has been introduced. The characteristic of risk types has been made in relation to such characteristics of modern society as the complexity of self-organization processes, the open nature and non-linearity of transformation processes. The understanding of various types of risks has been explained, that are associated with the problems of implementing the goals of humanistic educational policy; incomplete integration of methodological tools for e-learning; the break in educational traditions; conflicts between subjects of educational relations; the lack of adaptability of digital technologies to the content of social interaction; the complexity of the nature of distance learning technologies in the modern supercomplex socio-global system; impact on the formalization of learning new types of rationality; objectively inherent in modern society’s global social risks; conflicts of social movements with the interests of the subjects realizing the functions of education; with the indivisibility of the security of complex society in different spheres and levels of social interaction. Ways to manage e-learning risks have been described.
Blockchain in Automotive Supply Chain

This paper describes a summary of info flows and technology, allowing the supply chain within the automotive industry. The supply base of many motorized manufacturers is being restructured, with great consequence for the way dealers connect with their automotive partners. Electronic data interchange (EDI) is by far the most common communication technique used in automotive manufacturing, though new approaches are used as well. All these methods and how they support the information flows are explained in more detail. Companies attempt to companies are trying to find ways to recover their elasticity and responsiveness and in turn competitiveness by changing their operations plan, methods and skills that include the implementation of Supply Chain Management (SCM) paradigm. Hence, Blockchain can enhance the agility of SCM. However, blockchain's impact on SCM is not equivalent. In this paper, we specify the areas that are blockchain effects on the automotive supply chain and evaluate it.
Chatterbot implementation using Transfer Learning and LSTM Encoder-Decoder Architecture

The goal of this project is to develop a chatbot using deep learning models.Chatterbot is an existing research area whose main goal is to appear as human as possible and most of the current models(which use RNN and related sequential learning models) are unable to achieve this task to relate over long dependencies.Adding onto that , NLP tasks require a lot of data which can be hard to collect for smaller projects/tasks which inspired to try out sequence to sequence learning model using LSTM.For that we have used a movie dialog corpus of 220,579 conversation exchanges among which about 50,000 conversational exchanges are only used as a training corpus to our model since training on more conversation exchanges requires high computation power than we have.
Prospective Analysis Using a Novel CNN Algorithm to Distinguish Atypical Ductal Hyperplasia From Ductal Carcinoma in Situ in Breast

Introduction We previously developed a convolutional neural networks (CNN)-based algorithm to distinguish atypical ductal hyperplasia (ADH) from ductal carcinoma in situ (DCIS) using a mammographic dataset. The purpose of this study is to further validate our CNN algorithm by prospectively analyzing an unseen new dataset to evaluate the diagnostic performance of our algorithm. Materials and Methods In this institutional review board-approved study, a new dataset composed of 280 unique mammographic images from 140 patients was used to test our CNN algorithm. All patients underwent stereotactic-guided biopsy of calcifications and underwent surgical excision with available final pathology. The ADH group consisted of 122 images from 61 patients with the highest pathology diagnosis of ADH. The DCIS group consisted of 158 images from 79 patients with the highest pathology diagnosis of DCIS. Two standard mammographic magnification views (craniocaudal and mediolateral/lateromedial) of the calcifications were used for analysis. Calcifications were segmented using an open source software platform 3D slicer and resized to fit a 128 × 128 pixel bounding box. Our previously developed CNN algorithm was used. Briefly, a 15 hidden layer topology was used. The network architecture contained 5 residual layers and dropout of 0.25 after each convolution. Diagnostic performance metrics were analyzed including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. The “positive class” was defined as the pure ADH group in this study and thus specificity represents minimizing the amount of falsely labeled pure ADH cases. Results Area under the receiver operating characteristic curve was 0.90 (95% confidence interval, ± 0.04). Diagnostic accuracy, sensitivity, and specificity was 80.7%, 63.9%, and 93.7%, respectively. Conclusion Prospectively tested on new unseen data, our CNN algorithm distinguished pure ADH from DCIS using mammographic images with high specificity.
A Complete Characterization of Projectivity for Statistical Relational Models

A generative probabilistic model for relational data consists of a family of probability distributions for relational structures over domains of different sizes. In most existing statistical relational learning (SRL) frameworks, these models are not projective in the sense that the marginal of the distribution for size-n structures on induced substructures of size k&lt;n is equal to the given distribution for size-k structures. Projectivity is very beneficial in that it directly enables lifted inference and statistically consistent learning from sub-sampled relational structures. In earlier work some simple fragments of SRL languages have been identified that represent projective models. However, no complete characterization of, and representation framework for projective models has been given. In this paper we fill this gap: exploiting representation theorems for infinite exchangeable arrays we introduce a class of directed graphical latent variable models that precisely correspond to the class of projective relational models. As a by-product we also obtain a characterization for when a given distribution over size-k structures is the statistical frequency distribution of size-k substructures in much larger size-n structures. These results shed new light onto the old open problem of how to apply Halpern et al.'s ``random worlds approach'' for probabilistic inference to general relational signatures.
Perceived success of hybrid microorganizations in a contested category

The organizational literature privileges objective performance indicators often selected by researchers. There is scarce research focusing on legitimacy-challenged hybrid and microorganizations and on perceived success under exigent conditions. To address this scarcity, this study, conducted among complementary and alternative medicine (CAM) microorganizations, explores success as a subjective measure originating from managers’ perceptions. For the purpose, it integrates cognitive mapping and multiple criteria decision analysis (MCDA)—a methodological contribution to construct a subjective-success framework that can be helpful for contested hybrid microorganizations. Seven factors emerged, of which human capital is recognized as critical, while external factors are considered unimportant.
Interpretable Deep Gaussian Processes with Moments

Deep Gaussian Processes (DGPs) combine the expressiveness of Deep Neural Networks (DNNs) with quantified uncertainty of Gaussian Processes (GPs). Expressive power and intractable inference both result from the non-Gaussian distribution over composition functions. We propose interpretable DGP based on approximating DGP as a GP by calculating the exact moments, which additionally identify the heavy-tailed nature of some DGP distributions. Consequently, our approach admits interpretation as both NNs with specified activation functions and as a variational approximation to DGP. We identify the expressivity parameter of DGP and find non-local and non-stationary correlation from DGP composition. We provide general recipes for deriving the effective kernels for DGP of two, three, or infinitely many layers, composed of homogeneous or heterogeneous kernels. Results illustrate the expressiveness of our effective kernels through samples from the prior and inference on simulated and real data and demonstrate advantages of interpretability by analysis of analytic forms, and draw relations and equivalences across kernels.
A new geometry-based secondary path planning for automatic parking

In auto-parking systems, a certain degree of error in the path tracking algorithm is inevitable. This is caused by actuator error, tire slipping, or other factors relevant to and included in the parking process. In such situations, the parking path needs to be updated to finish parking successfully which is referred to as secondary path planning. Herein, a new geometry-based method is proposed to deal with this issue, which can be called the pattern-based method. In this method, a predefined path pattern set consisting of 24 multi-segment patterns is developed first. These patterns are composed of straight lines and arcs and account for constraints due to motion and the immediate environment. Then, a traversal policy is adopted to select the path pattern from the set, and the sequential quadratic programming algorithm is used to determine the optimal parameters that fine-tune the pattern to meet the current constraints. In the simulation section, the effectiveness of the proposed method is demonstrated. Moreover, compared to the search-based method represented by a variation of rapidly exploring random tree*, the proposed method has a higher planning performance.
A Secure Multimedia Processing through Blockchain in Smart Healthcare Systems

The rapidly growing, complex and key area of multimedia processing is medical area where the huge amount of patient?s data is disregarded. Further, medial fields used to interrelate with multimedia formats because of their daily real communication. Healthcare associations are renovating themselves into added efficient, coordinated and user-centered methods through several upcoming techniques. Though, the management of large information such as patient information, medical reports escorts to augments the human labors and security hazards. Therefore, to conquer these problems, IoT-healthcare came into existence that enhances the patients care by reducing the cost of resources in an effective way. Along with lot of IoT benefits, associations are afraid to use them because of its compromise initiated by several intruders. For benefiting their own purposes, intruders hack the patient?s online record for selling to third party for doing the researchers analysis. Further, doctors also make the money for their concession by forcing the patients to buy the medicines from their recommended firms. So as to avert these problems, Blockchain technology has been came across in healthcare systems that may keep track of every activity of the entities. The patients and other entities are recognized through bio metric methods so that even if hackers try to compromise the devices or to access the stored information; the patients may easily identify the intruders or alteration in stored record. In this paper, we have projected a secure IoT healthcare mechanism using Blockchain technique that initially identify the patients and other entities using bio metric passwords and further can analyze the modifications in their records. The proposed scheme results have been validated against traditional method by offering 88% success rate over certain parametric scenarios such as grey hole attack, falsification attack and probabilistic authentication scenarios.
Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control

With the development of micro-grids including PV production and storage, the need for efficient energy management strategies arises. One of their key components is the forecast of the energy production from very short to long term. The forecast time-step is an important parameter affecting not only its accuracy but also the optimal control time discretization, hence its efficiency and computational burden. To quantify this trade-off, four machine learning forecast models are tested on two geographical locations for time-steps varying from 2 to 60 min and horizons from 10 min to 6 h, on global irradiance horizontal and tilted when data was available. The results are similar for all the models and indicate that the error metric can be reduced up to 0.8% per minute on the time-step for forecasts below one hour and up to 1.7% per ten minutes for forecasts between one and six hours. In addition, it is shown that for short term horizons, it may be advantageous to forecast with a high resolution then average the results at the time-step needed by the energy management system.
Sturgeon: Preference-aware Co-location for Improving Utilization of Power Constrained Computers

Large-scale datacenters often host latency-sensitive services that have stringent Quality-of-Service requirement and experience diurnal load pattern. Co-locating best-effort applications that have no QoS requirement with latency-sensitive services has been widely used to improve the resource utilization with careful shared resource management. However, existing co-location techniques tend to result in the power overload problem on power constrained computers due to the ignorance of the power consumption. To this end, we propose Sturgeon, a runtime system proactively manages resources between colocated applications in a power constrained environment, to ensure the QoS of latency-sensitive services while maximizing the resource utilization. Our investigation shows that, at a given load, there are multiple feasible resource configurations to meet both QoS requirement and power budget, while one of them yields the maximum throughput of best-effort applications. To find such a configuration, we establish models to accurately predict the performance and power consumption of the colocated applications. Sturgeon monitors the QoS periodically in order to eliminate the potential QoS violation caused by the unpredictable interference. The experimental results show that Sturgeon improves the throughput of best-effort applications by 24.96% compared to the state-of-the-art technique, while guaranteeing the 95%-ile latency within the QoS target.
The Influence Factors of Psychological Understanding and Behavior Choice for Legal Industry Entrepreneurs Based on Artificial Intelligence Technology

This study is aimed to promote China’s support for innovation and entrepreneurship in the context of the rapid development of the artificial intelligence industry. Considering the advance law-based governance implemented in this country, the relationship between the psychological understanding and behavior choice of entrepreneurs in the law industry and artificial intelligence industry was explored. The investigation was conducted in the form of questionnaire survey, and the data was collected by sending emails. Also, the model was established to make the investigation direction clearer. The reliability analysis of the questionnaire was carried out by means of Cronbach α reliability coefficient. The validity analysis of the questionnaire was carried out by KMO (Kaiser-Meyer-Olkin) coefficient. The statistical software was used to analyze the correlation between the factors in the investigation. The results show that there is a positive correlation between behavior speed in the psychological understanding of entrepreneurs in the legal industry as well as the entrepreneurial experience and entrepreneur behavior. The concentration of entrepreneurs’ behavior is negatively correlated with the entrepreneur’s industrial experience. The behavior speed and concentration of entrepreneurs in the artificial intelligence industry are negatively correlated with the age of entrepreneurs, but their entrepreneurial behavior and psychological understanding do not show correlation. The behavior speed and concentration of entrepreneurs in the artificial intelligence industry are positively correlated with product innovation, technological innovation, and market innovation. Therefore, the behavior choice of entrepreneurs in the legal industry are highly correlated with psychological understanding. The entrepreneurs’ psychological understanding and behavior choice in the field of artificial intelligence do not show a direct correlation, but indirect correlation through indirect factor innovation opportunities. The investigation on entrepreneurs’ psychological understanding and behavior choice can provide important theoretical support for entrepreneurs in China’s legal industry and artificial intelligence industry.
Hybrid Ant Colony Optimization and Genetic Algorithm for Rule Induction

In this study, a hybrid rule-based classifier namely, ant colony optimization/genetic algorithm ACO/GA is introduced to improve the classification accuracy of Ant-Miner classifier by using GA.The Ant-Miner classifier is efficient, useful and commonly used for solving rulebased classification problems in data mining.Ant-Miner, which is an ACO variant, suffers from local optimization problem which affects its performance.In our proposed hybrid ACO/GA algorithm, the ACO is responsible for generating classification rules and the GA improves the classification rules iteratively using the principles of multi-neighborhood structure (i.e., mutation and crossover) procedures to overcome the local optima problem.The performance of the proposed classifier was tested against other existing hybrid ant-mining classification algorithms namely, ACO/SA and ACO/PSO2 using classification accuracy, the number of discovered rules and model complexity.For the experiment, the 10-fold cross-validation procedure was used on 12 benchmark datasets from the University California Irwine machine learning repository.Experimental results show that the proposed hybridization was able to produce impressive results in all evaluation criteria.
Hyper resolution image mosaics with unbounded vertical field of view

This paper presents the framework for the creation of hyper resolution image mosaics (HRIM) with a full horizontal and unbounded vertical field of view. Additionally, the hyper-resolution images are mapped to a format compatible with a geo referencing frame. In this manner the images can be viewed using standard mapping services. The measurement device consists of eight cameras covering the full 360 degree horizontal field. The unlimited vertical field of view is obtained by moving the device, e.g., by sinking it down a mine shaft. The cameras are synchronized so that with each frame the full horizontal field of view is captured. The camera sensors are calibrated to an onboard inertial measurement unit (IMU) which allows the reconstruction of the position of each sensor in a world coordinate frame for each captured image using rigid transformations. Perspective projection onto an open manifold (cylinder) allows for the integration of all images into an HRIM. Constrained tensor-polynomial approximations are used to obtain geometric maps, i.e., a distortion lattice, from the image coordinate system onto an open cylindrical manifold. From this lattice binary masks are derived for each projected image. A multi band blending algorithm is implemented for the final composition. The HRIM are then tiled and stored on a GIS mapping server from where the HRIM can be viewed on any device running an HTML browser. It should be noted that this approach provides a significant advantage since viewing the large HRIM is not computationally intensive and can even be done on tablets. The framework has been applied to the remote inspection of deep vertical shafts.
Machine Learning Optimization of Quantum Circuit Layouts

The quantum circuit layout (QCL) problem involves mapping out a quantum circuit such that the constraints of the device are satisfied. We introduce a quantum circuit mapping heuristic, QXX, and its machine learning version, QXX-MLP. The latter automatically infers the optimal QXX parameter values such that the laid out circuit has a reduced depth. In order to speed up circuit compilation, before laying the circuits out, we use a Gaussian function to estimate the depth of the compiled circuits. This Gaussian also informs the compiler about the circuit region that influences most the resulting circuit’s depth. We present empiric evidence for the feasibility of learning the layout method using approximation. QXX and QXX-MLP open the path to feasible large-scale QCL methods.