Reviewerly/exHarmony huggingface dataset:
Reconstruction of 3D Faces Based on Monocular Vision

Deep learning is receiving more and more attention in the reconstruction of three-dimensional (3D) faces. However, most of the reported methods use a single 2D image to reconstruct a 3D face, which looks true but is lack of 3D accuracy and personalization. In order reconstruct 3D faces for applications with dimensional accuracy requirement such as medical treatment, we propose a deep learning based multi-view reconstruction method, which aims to provide sufficient 3D constraints to obtain high-quality 3D face models. We propose a multi-view hybrid loss function to calculate the loss at the image level and the perception level. Weakly supervised learning is implemented to train an end-to-end network, and regressed directly to extract the face reconstruction parameters. The multi-view approach not only improve the reconstruction effect of the face in the front, but also from the side, such as the jaw, which is critical in dentistry. We set the region of interest mask to improve the accuracy of reconstruction in specific regions. We have done comparative experiments with several state-of-the-art reconstruction methods, and the results show that the method in this paper can effectively improve the overall 3D face reconstruction accuracy.
Cooperative Interference Decision Based on Directed Mutation Search Artificial Bee Colony Algorithm

The rapid development of electronic technology and weapons and equipment has promoted the transformation of the war situation. Electronic warfare has become more and more important in modern warfare. Reasonable interference decision-making according to the battlefield environment can achieve better combat results. Therefore, the interference decision-making link is in a very critical position in combat. For different confrontation environments, obtaining appropriate interference allocation schemes can improve the interference effect and overall revenue, and to some extent, reduce the loss of interference resources and equipment. In terms of the static multi-jammer cooperative interference decision algorithm, this paper aims at the current common optimization algorithms that are greatly affected by human factors and the search method is inefficient, and proposes an improved artificial bee colony algorithm based on directed mutation to solve static multi-interference machine coordination interferes decision-making issues. Simulation shows that the optimization ability and convergence speed of the artificial bee colony algorithm based on directed mutation have been improved to a certain extent.
The Impact of Components of Organizational Culture on Components of the Faculty Members᾽ Development

Introduction: Organizational culture is considered as an important element in the body of an organization that has a positive impact on the performance of the every organization, so that the success of any organization depends on culture of the organization, partly. Much research has been done on the role of this factor in promoting organizational productivity and achieving organizational goals. The present study was conducted to determine the impact of Components of Organizational Culture on components of the Faculty Members᾽ development in Islamic Azad Universities of Khorasan Razavi. Methods: This cross-sectional study conducted on 291 faculty members of Islamic Azad Universities of Khorasan Razavi using stratified random sampling in ​​the academic year of 2016-2017. Two questionnaires of organizational culture (derived from Denison's organizational culture model) and improvement (derived from Ijtihad et al., study) were used for measurements .Data were analyzed by Linear regression test, t-test, one-way ANOVA through SPSS. Results: The findings showed that the components of involvement (β = 0.159), adaptability (β = 0.153) and compatibility (β = 0.117) had a significant effect on the educational component of development. Compatibility (β = 0.291) had a significant effect on the research component of development. Involvement (β = 0.187), compatibility (β = 0.384) had a significant effect on the individual component of development. Involvement (β = 0.359), compatibility (β = 0.183) and adaptability (β = 0.119) had a significant effect on the organizational component of development. Involvement (β = 0.213), compatibility (β = 0.267) had a significant effect on the moral component of development. Components of both organizational culture and development were similar in gender and academic rank among the participants. Conclusion: From this study, it was concluded that the components of organizational culture had a significant effect on the components of faculty members᾽ development. When the organizational culture is in the direction of development, faculty members perform their duties with more motivation and enthusiasm in educational, research, personal, organizational and moral dimensions.
Identity Authentication based on User Mouse Behavior

Improving the performance of identity authentication is a difficult issue due to the sparsity of feature space of user mouse behaviors, this paper investigates a new identity authentication method, which improves the sparse representation of user mouse behaviors from a new perspective. Firstly, the feature space of user mouse behaviors can be characterized from both the mouse's overall behavior features and mouse’s trajectory behavior features and also can be effectively reduced according to the validity of features. Secondly, we present a fusion method of multiple classifiers to solve the problem of over-fitting in a single classifier and improve the performance of mouse-behavior based continuous authentication. Finally, comparative experiments are conducted in order to further examine the effectiveness of the proposed method. These findings suggest that the fusion method of multiple classifiers based on Stacking outperforms the traditional single classification method in user mouse identity identification.
Towards Spark-Based Deep Learning Approach for Fraud Detection Analysis

Fraud activity is a major concern in the telecommunication business domain. Thus, it’s very important to analyze the huge amount of data available in the network in order to detect early potential fraud behaviors and take countermeasures accordingly. In this paper, we used a novel approach using deep learning techniques to analyze datasets from a real mobile communication career and to extract and analyze features from labeled/unlabeled data. Our dataset was extracted from the business support system (BSS) containing 2.5 million call details records (CDR) of active subscribers. The proposed method integrates deep learning techniques with Apache Spark framework for parallel training execution. We found that our approach performed better than traditional machine learning with an F1 score of 91% and enhance the training speed of our deep learning model significantly when using the spark platform. Thus, the use of this proposed model can remarkably decrease the cost related to the unauthorized use of telecommunication services.
Implementation of reliability buildings based on health principles

Heritage Buildings Cultural heritage, which is generally more than 50 years old, is a material cultural heritage. This cultural heritage building has essential value for history, science, education, religion, and culture, so that its existence needs to be preserved. Preservation of cultural heritage buildings, of course, must pay attention to health requirements because health is the right of every person guaranteed by law. Requirements for cultural heritage buildings include ventilation systems, lighting, and sanitation, and the use of materials thereof. This paper aims to describe and analyze the implementation of cultural heritage buildings’ reliability based on health principles, using research methods with an empirical juridical approach with qualitative analysis. The research location is in the Kota Lama area in the city of Semarang. Data sources consist of primary and secondary data. Based on the findings and analysis, there are many buildings in the Kota Lama area in Semarang that have been revitalized for their preservation. However, because many buildings in Kota Lama are still owned individually, for that with principles of health, it is necessary to conduct periodic reviews, care for and maintain as an effort to preserve cultural heritage buildings as regulated in Articles 8 paragraph 3 of PUPR Ministerial Regulation No: 01/PRT/M/2015 concerning the maintained Cultural Heritage Building. This is necessary so that the preservation of cultural heritage buildings can be achieved and beneficial to the community.
Towards Defensive Autonomous Driving: Collecting and Probing Driving Demonstrations of Mixed Qualities

Designing or learning an autonomous driving policy is undoubtedly a challenging task as the policy has to maintain its safety in all corner cases. In order to secure safety in autonomous driving, the ability to detect hazardous situations, which can be seen as an out-of-distribution (OOD) detection problem, becomes crucial. However, conventional datasets often only contain expert driving demonstrations, although some non-expert or uncommon driving behavior data are needed to implement a safety guaranteed autonomous driving platform. To this end, we present a dataset called the R3 Driving Dataset, composed of driving data with different qualities. The dataset categorizes abnormal driving behaviors into eight categories and 369 different detailed situations. The situations include dangerous lane changes and near-collision situations. To further enlighten how these abnormal driving behaviors can be detected, we utilize different uncertainty estimation and anomaly detection methods for the proposed dataset. From the results of the proposed experiment, it can be inferred that by using both uncertainty estimation and anomaly detection, most of the abnormal cases in the proposed dataset can be discriminated. https://rllab-snu.github.io/projects/R3-Driving-Dataset/doc.html
Intention prediction of UAVs based on improved DDQN

Abstract Intention prediction plays an indispensable role in future informationized air combat, which will help the command and control center to make a more correct decision. In this paper, an intelligent intention prediction approach based on an improved double deep Q network (DDQN) is developed to generate real-time intention flight paths for unmanned aerial vehicles (UAVs) in complex air combat environment. Initially, by introducing an actual topographic map, the different threats of UAVs in different terrains on the map are analyzed, based on which, a terrain environment reward function is constructed. Secondly, by splitting a complete maneuver action into six basic maneuver units and determining the probability value of each unit, a maneuver reward function is obtained. Further, to improve the real-time performance and accuracy of the standard DDQN algorithm, an improved DDQN algorithm is proposed by using temporal-difference (TD) method and binary tree data structure. Finally, the simulation results verify show that the proposed algorithm has achieved better results under complex terrain conditions.
Research on WebGIS Application Based on Edge Computing

The explosive growth of geospatial data makes the application services based on WebGIS face the challenges of computing intensive and concurrency intensive. This paper proposes to apply the edge cloud computing platform to WebGIS services to solve the problems of insufficient bandwidth and computing delay in GIS applications. In this paper, KubeEdge platform is used as the basic architecture support, and it is oriented to WebGIS based oilfield production management system to realize the edge computing of convective spatial big data and relieve the intensive computing pressure of underlying data in GIS system.
Research on the SPOC Blended Teaching Mode of Public Physical Education in Colleges and Universities : --Take the badminton course as an example

As the "Internet Plus" model develops in a deep-going way, Chinese universities work hard to carry out reforms on teaching and practice, which leads to the exploration of various forms of online learning and blended teaching. This paper first introduces online learning and blended learning respectively, and then takes the "badminton course" as an example to conduct a front-end analysis of blended teaching. According to this point of view, the research builds a multi-dimensional and interactive SPOC blended teaching mode based on the "Rain Classroom" platform and the correlation theory. After the practice of this mode conducted, the research draws a conclusion that this mode focuses on the organic combination of learning online and offline as well as in- and after-class, cultivates students to study autonomously and consciously, and makes up for the insufficient time of teaching in class. It also achieves a multi-dimensional interaction of learning between in-class and after-class, acquiring a relatively ideal teaching effect.
API2CAN: a dataset &amp; service for canonical utterance generation for REST APIs

Abstract Objectives Recently natural language interfaces (e.g., chatbots) have gained enormous attention. Such interfaces execute underlying application programming interfaces (APIs) based on the user's utterances to perform tasks (e.g., reporting weather). Supervised approaches for building such interfaces rely upon a large set of user utterances paired with APIs. Collecting such pairs is typically starts with obtaining initial utterances for a given API method. Generating initial utterances can be considered as a machine translation task in which an API method is translated into an utterance. However, the key challenge is the lack of training samples for training domain-independent translation models. In this paper, we propose a dataset for training supervised models to generate initial utterances for APIs. Data description The dataset contains 14,370 pairs of API methods and utterances. It is built automatically by converting method descriptions of a large number of APIs to user utterances; and it is cleaned manually to ensure quality. The dataset is also accompanied with a set of microservices (e.g., translating API methods to utterances) which can facilitate the process of collecting training samples for building natural language interfaces.
Research on the development strategy of sports venues in China under the background of mobile Internet

"Xiguan" epidemic has brought great influence to the society. Relying on the mobile Internet, Hongshan Stadium and other sports venues have been transformed into "square cabin hospital", which has played a great role in controlling the epidemic. Based on the analysis of the current situation of the development of sports venues in China, this paper objectively elaborated the impact of the epidemic on the development of sports venues, and put forward that under the background of mobile Internet, the development of sports venues in China should improve the ability of planning and design of sports venues, build intelligent stadiums, and establish the emergency function management system of sports venues.
Reduction of Training Samples in Solar Insolation Prediction Under Weather and Climatic Changes

The paper considers the problem of forecasting solar insolation. Due to a large number of factors that are difficult to predict, this problem is complex and difficult like other problems where parameters depend on weather or climate. Despite such factors do not significantly affect the parameters under study, they create an essential bias. The problem is also characterized by a significant amount of information, which should be processed to obtain a reliable forecast. The paper discusses relevant machine learning methods and analyzes methods for reducing the training sample to make quality and reliable predictions of solar insolation under the weather and climatic changes. Reducing the size of the training sample allowed testing a significant number of models and optimizing the hyperparameters of these models, which made it possible to identify the most accurate model, which graph is close to ideal. The effect of reducing the size of the training sample has been measured related to the speed and accuracy of machine learning algorithms. It is shown that the use of clustered data sampling for models that prone to overfitting can improve the accuracy of these models. It is shown that using the method of clustering to reduce data samples can also reduce variance in the training dataset. The reduced data sample helps to perform optimization of hyperparameters because the accuracy of different models is preserved over a wide range of input data. The results obtained can be successfully applied in control systems for large objects of solar energy generation under conditions of fast and frequent weather changes as well as slow climatic changes.
Problems and analysis of network mixed teaching under the background of innovative education

The spirit of modern education is that information technology should be used as an aided learning tool in all learning areas to expand learning in various areas and improve students’ ability to solve problems. Through the Internet, teachers and students in different locations can effectively interact with each other, thereby enhancing the quality of teaching. Modern education emphasizes that the study area should take into account the principles of curriculum planning and collaborative teaching, which shows that collaborative teaching is one of the important core topics of teaching promotion. Collaborative teaching can enable students to obtain complete knowledge and life experience. If Internet technology can be used to promote collaborative teaching, it is expected that it will be easier to integrate teachers with different specialties to conduct teaching activities, so that students can obtain a complete learning experience.
Towards a Universal Human-Computer Interaction Model for Multimodal Interactions

Models in HCI describe and provide insights into how humans use interactive technology. They are used by engineers, designers, and developers to understand and formalize the interaction process. At the same time, novel interaction paradigms arise constantly introducing new ways of how interactive technology can support humans. In this work, we look into how these paradigms can be described using the classical HCI model introduced by Schomaker in 1995. We extend this model by presenting new relations that would provide a better understanding of them. For this, we revisit the existing interaction paradigms and try to describe their interaction using this model. The goal of this work is to highlight the need to adapt the models to new interaction paradigms and spark discussion in the HCI community on this topic.
Field observation, photosimulation and video simulation of target detection in maritime environments: update

Laboratory target detection experiments are widely used to assess camouflage techniques for effectiveness in the field. There has been some research to suggest that, in maritime environments, target detection in the laboratory (photosimulation) differs from field observations. This difference suggested that the dynamic nature of real world search tasks, not represented in still images, could be a critical element in field detection. To explore the effect of dynamic elements for inclusion in laboratory experiments, we have initiated a series of studies including videosimulation. In this paper, we extend our previous work, exploring the link between field observations, photosimulation and videosimulation using data obtained at a field trial conducted in Darwin (Australia) with small boat targets. Both laboratory-based experiments (photo- and video-simulation) presented the stimuli on an EIZO colour calibrated monitor, and a Tobii eye tracker was used to record eye movements. Comparing probability of detection (Pd) from the field observations and videosimulation experiment yielded a Pearson correlation coefficient (PCC) of 0.43 and mean absolute error (MAE) of 0.23 from the identity function, whereas comparing the field observations to photosimulation yielded a PCC of 0.45 and MAE 0.20. These new results show the opposite trend to that reported in Culpepper et al 2015. That is, the new results show the laboratory experiments to be mostly easier than the field observations, whereas our 2015 results showed that field observations were easier than photosimulation.
An Improved Outsourcing Algorithm to Solve Quadratic Congruence Equations in Internet of Things

Solving quadratic congruence equations is an expensive operation widely employed in cryptographic constructions for secure Internet of Things applications. Recently, two outsourcing algorithms were proposed by Zhang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> to solve quadratic congruence equations by employing Cippolla’s algorithm. It was claimed that all the inputs and outputs can be obscured in these two algorithms. However, we present two passive attacks in this article to show that all the inputs and outputs can be recovered efficiently by just a curious server, which implies the two outsourcing algorithms are insecure. To fix them, we further propose an improved outsourcing algorithm to solve quadratic congruence equations, which is more efficient and the privacy of actual inputs and outputs can be protected very well.
Integrating Argument-Level Attention with Multi-Level Scores to Predict What Happen Next

Script event prediction is a significant task to extract event evolution rules from text. It requires to capture the intra-rationality and inter-relationship of events. The previous methods have made great progress, but they can not fully capture the inner features of events. This paper proposes an event representation layer with argument-level attention to capture the relationship between predicate and arguments, which benefit to assess the rationality of the event. Besides, we integrate the enhanced event representation with the multi-level event similarity scores to obtain more robust performance. The multi-level similarity consists of scene similarity and semantic consistency. We utilize the similarity between candidates and individual events to evaluate the scene similarity. Semantic consistency is evaluated by the hidden state of Gate Recurrent Unit (GRU). Unlike the previous work, we only use the last hidden state of GRU instead of all hidden states to reduce training time. In addition, we adopt adversarial training to improve the robustness and generalization of the model. The experimental results verify the effectiveness of our model compared to these baseline methods. This paper also demonstrates what the attention mechanism has learned to enhance the interpretability of our model.