Reviewerly/exHarmony huggingface dataset:
Buoy Monitoring System in Lombok Strait for The Indonesian Tsunami Coastal Acoustic Tomography (Ina-CAT)

Ina-CAT is an Indonesian Coastal Acoustic Tomography (CAT) system that is applied to the Tsunami Early Warning System (TEWS). Also known as Tsunami Coastal Acoustic Tomography (TCAT), one of these systems was installed in Karangasem-Bali using a buoy platform. TCAT requires power for the electronic system and energy to transmit the acoustic signal through its transducer. The charging system is carried out by installing a solar cell on the surface of the buoy. Thus, a monitoring system is needed to observe the charging process and power consumption. The system also observes the temperature and humidity in the buoy so that the environmental conditions of electronic equipment can be monitored. In addition, the actual position and buoy shift also can be observed. The monitoring system can also serve as early detection of problems when data acquisition does not work properly or the buoy shifts or drifts too far from the initial position. This paper is aimed to discuss the main architecture and sensors utilized in the system. Results indicate that the monitoring system can function properly and be able to monitor power and environmental conditions, yet the charging system needs to be optimized. To improve the accuracy of the monitoring system, sensors that are more precise and calibrated in measurement are required.
The Effects of Spending Rules and Asset Allocation on Non-Profit Endowments

We examine the long-run impact and implications of an endowment’s spending policy and asset allocation decisions. Using a dynamic model, we explore how different endowment spending rules influence the dynamics of an endowment’s size and future spending. We find that different parameters within each spending rule have significant long-term impact on wealth accumulation and spending capacity. Using Merton's (1993) endowment model and compiled asset allocation data, we estimate the intertemporal preferences and risk aversion of several major endowments. We find significant variation across endowments in their propensity to increase portfolio risk in response to increased spending needs. Pre-print is available at: https://dspace.mit.edu/handle/1721.1/143562
MAV-UAV Collaborative Route Planning Based on Intelligent Emotion Mode

The survival probability and operational efficiency of the aircraft can be improved when choosing the right route. In order to fully consider the formation state of MAV (Manned Aerial Vehicle) and UAV (Unmanned Aerial Vehicle) in complex battlefield environment and improve the adaptability of collaborative route planning, a model framework of collaborative route planning based on intelligent emotion mode is proposed. Firstly, an intelligent emotion model of UAV based on cognitive load of MAV operator and dynamic battlefield situation information is established by using DFCM (Dynamic Fuzzy Cognitive Maps); then, the intelligent emotion is introduced into the calculation model of UAV threat avoiding radius, and the route planning evaluation model focusing on the safety of route is established; finally, the intelligent emotion model is applied to the route planning based on artificial potential field method, Astar algorithm and genetic algorithm, and the simulation results are compared to verify the effectiveness and adaptability of the intelligent emotion based route planning methods for MAV and UAV by AHP (Analytic Hierarchy Process).
ATKey.Net: Keypoint Detection by Handcrafted and Learned CNN with Attention

In image matching, it is essential to obtain more stable and effective feature points. This paper proposes Attention Key.net (ATKey.Net) for the keypoint detection task. Handcrafted and Learned CNN filters are used in a shallow multi-scale architecture with an attention module. Handcrafted filters provide anchor structures for learned filters, which localize, score, and rank repeatable features. Learned CNN filters improve the stability and convergence during backpropagation. Shallow multi-scale architecture has fewer parameters and less computational cost. The attention module gives channel importance. The model is trained on ImageNet and evaluated on the HPatches benchmark. The results show that the repeatability and matching performance is better than the experimental detector.
Means of providing temperature regimes for extended transport tunnels of the Baikal-Amur Railway in the cold period of the year

Abstract The paper presents the results of studying the distribution of air temperature along the length of an extended railway tunnel depending on various configurations and locations of tunnel heating and ventilation units. It is shown that the most rational way is the uniform arrangement of heating and ventilation units along the length of the tunnel combined with the installation of air-thermal curtains. This allows ensuring a positive air temperature in all sections along the length of the tunnel.
EMBEDDED FUZZY CONTROLLER FOR APPLICATION IN IRRIGATION SYSTEMS

This paper describes the modeling, implementation, and evaluation of a control system based on an embedded fuzzy controller for application in irrigation systems. The motivation for the development of this system comes from the need to offer farmers resources that provide a reduction in water and electricity consumption in irrigation, which contributes to reducing production costs and preserving natural resources. The proposed control system aims to keep the water flow constant, as close as possible to the desired value and independent of load variations. A single-board computer, with the fuzzy controller software, a frequency inverter to control the speed of the irrigation motor pump system, and a flow sensor to measure the water flow, was used to implement the system. The proposed control system was evaluated in laboratory and field experiments to simulate real operating conditions. The results showed that the system presents satisfactory performance, representing a viable alternative for application in general irrigation systems.
Mkgqa: Multi-Turn Question Answering System Based on Medical Knowledge Graph

The development of question answering for knowledge graph (KGQA) and the emergence of the graph database such as NEO4J allow to increase performance on domain-specific question answering system (QAs). However, it is hard to understand questions especially in the step of semantic analysis. This research is focused on the multi-turn QAs in the medical field. Our contribution in this paper is twofold: (1) we introduce a pre-trained language model for classification questions intentions. This model attempt to divides questions raised by users into several categories, and (2) we present a neural model for named entity recognition (NER). The aim of this model is to identify the common vocabulary in the medical field. An additional advantage of above two models can support multi-turn question answering (QA) by recycling the model results. From this study, we conclude that the proposed models work well and give accurate estimates in KGQA.
Intelligent Transaction Model of Agricultural Supply Chain Based on Blockchain

The traditional rural supply chain transaction model based on public service platform of cloud architecture has disadvantages such as vulnerability to attack and poor trust. In view of these problems, combining with the advantages of decentralization and imtamability of blockchain, an intelligent transaction model of agricultural supply chain based on blockchain is constructed to ensure the authenticity of transaction information data. Write and test smart contracts for trading and smart contracts for storing, realize the automatic execution of transactions, and the transaction details can be traced; Through the improvement of authority proof consensus mechanism, the transaction reliability is improved. Finally, experiments show that the proposed scheme realizes the integration of dispersed resources and adaptive matching transactions, and effectively ensures the security and reliability of transactions.
Virtual Learning Environment to Strengthen STEM Competencies in Preschool Children

Intervention programs in early childhood education are aimed at cognitive, physical, linguistic, and socioemotional development, which can be strengthened with the incorporation of technological resources, so the objective was to develop a virtual learning environment; the first stage includes aspects that strengthen the areas of Science, Technology, Engineering, and Mathematics (STEM) and present the information of reinforcement and integration of exercises to identify the elements they observe in nature, strengthen their fine and gross motor skills, design things with different materials and solve problems through counting. Three stages are identified (i) General aspects, the study participants were selected, the competencies and cognitive process, topics to be addressed, the materials to be developed, the instruments to be applied were selected to determine the quality of the application, (ii) The virtual learning environment is composed of four sections: tutors, students, teachers, and an application for mobile devices that includes augmented reality resources, (iii) Functionality and usability testing. The results are encouraging to verify the premise of contributing to the education of children from 3 to 6 years of age in the eastern zone of Mexico.
Dense Optical Flow and Residual Network-Based Human Activity Recognition

Sultana, Negar Mridula, Tasaouf Sheikh, Zahid Iffath, Fariha Shopon, Md Nowadays Human activity recognition takes a fascinating part in miscellaneous fields of computer vision like medical care, video surveillance, human-computer interface. Recently optical flow were shown to be an efficient feature for action recognition and attained state-of-the-art accuracy on different datasets. In this work, we have taken a deeper look at the combination of action recognition and optical flow. This paper proposes a novel human activity recognition technique based on the 3D dense optical flow from video sequences. Transfer learning and 3D dense optical flow was used in a two-stream neural network architecture. For transfer learning, ResNet152 pre-trained architecture was used. ResNet152 extracted several fine grained features from the dense optical flow. The proposed method was tested with UCF-101 dataset and outperformed the existing state-of-the art methods in terms of accuracy.
A Chinese Document-level Event Extraction Method based on ERNIE

Event extraction is a key technology in natural language processing and has strong application prospects for extracting knowledge from unstructured data. The current event extraction technique is mainly based on sentence-based event extraction, which has the disadvantages of incomplete coverage of extracted events and ambiguity in event classification. In this paper, we propose the ERNIE-BiGRU-CRF model for chapter-level event extraction, which encodes the semantic enhancement of paragraph text by ERNIE pretrained language model, inputs a bidirectional gated neural network for feature extraction, and finally obtains the annotated sequence by CRF layer. In this paper, we perform event extraction on the Baidu financial domain documen-level event extraction dataset using the sequence-labeled trigger extraction model and the sequence-labeled event element extraction model, and the results show that the final model's F1 value is 5.45 percentage points higher than the baseline model.
Network Accounting Model in Intelligent Internet of Things Network Computing and Multimedia Environment

This article focuses on the network charging model in the intelligent Internet of Things network computing and multimedia environment. The current human world has entered a century of economic development. The greatest feature of this century is knowledge economy. With the development of society, the importance of accounting has gradually increased. If accounting is better developed, education should be the basis. How to make students more systematically grasp the basic knowledge of accounting and the corresponding accounting methods is the focus of accounting education nowadays. In this paper, a new teaching model of network accounting under multimedia environment is proposed. On the basis of traditional teaching, multimedia technology is introduced. Through network technology, the platform of network accounting education under multimedia environment is designed to better realize the teaching of accounting course. Firstly, according to the specific teaching needs of students, the relevant functions are designed; secondly, B/S architecture mode is used in combination with database development technology to achieve the teaching system; finally, according to the user survey, the rationality and practicability of the system are analyzed. This article starts with the research status of science and technology and accounting informatization, analyzes the impact of the development of IoT technology on accounting theory and accounting work, and then discusses the promotion of these technologies to accounting informatization. The model proposed in this paper realizes the informatization of accounting and is bound to promote the informatization of accounting and realize the third leap of accounting informatization.
Real-Time Slip Detection and Control Using Machine Learning

The handling and gripping of objects by a prosthesis depend on the precise applied force control and the slip detection of the grasped object. These two features combined allow for the adjustment of the minimum grip force required to prevent slipping. Based on this statement, a system was developed to control the slip of objects, composed of a grip controller, for which the objective was to hold the object, and through the signal of a tactile sensor, slip is detected. An artificial neural network was used to identify the slip event for different types of objects. If the response from the classifier is positive, indicating slip, the system sends a signal to the grip controller, so that it increases the grip force performed on the object, aiming at minimizing slippage. In the end, the performance of the system for different objects was analyzed; the result encountered was that the system efficiency is proportional to the mass and the rigidity of the grasped object.
Research on Intelligent elderly care system based on Neural Network

The Internet plus nursing home has formed a new type of "virtual nursing home". At the same time, there are many problems in this mode. For example, it is necessary to provide timely help for the elderly, such as physical discomfort and accidents. To solve these problems, a more intelligent service system is needed to create a better environment for the elderly. Based on artificial intelligence technology, this paper analyzes the problems existing in the traditional human posture recognition algorithm. The traditional algorithm processes the set blur in the image and the results appear in many cases. Based on the neural network, the original VGG-19 module is optimized and replaced by the latest depth level separable convolution network MobileNetv3. Part of the original networks are combined to increase hole convolution, expand receptive field and reduce parameters so that the network is lighter under the condition of ensuring accuracy and speed. The research content of this paper can promote the rapid development of artificial intelligence. Further research can improve the humanization of artificial intelligence and fully achieve artificial intelligence.
A Deep Neural Network Based Financial Statement Fraud Detection Model: Evidence from China

The decision-making of financial report information users largely depends on the financial data disclosed by listed companies. However, in recent years, numerous financial fraud incidents have been exposed, causing investors and stakeholders to suffer huge losses. With fraud methods of listed companies getting more and more sophisticated, the traditional financial report analysis methods have been unable to perform the detection task well. In this study, deep learning was introduced into financial statement fraud detection for the first time. Combined with 82 financial indicators, the rate of change of financial indicators and non-financial indicators, a three-layer fully connected neural network model was used to discriminate financial statement fraud of Chinese listed companies, providing a new idea for the regulatory authorities to combat fraud precisely.
Estimation of bias in numerical values of normalized sixth-order cumulants’ structures for various signal constellations

Automatic modulation classification (AMC) represents technique for the process of modulation format recognition of signals considered to be a priori unknown, which finds wide usage in a numerous wireless systems and applications. Due to the low algorithm complexity and other practical aspects, AMC algorithms based on higher-order statistics are very popular. In this paper, properties of AMC algorithms based on sixth-order cumulants were explored in context of real and complex signals, i.e. different Pulse Amplitude Modulation (PAM) and Quadrature Amplitude Modulation (QAM) signal constellations. This was done through Monte Carlo simulations in a propagation conditions of Additive White Gaussian Noise (AWGN), but also in the channel with multipath propagation, with main focus on numerical quantification of the presence of bias – an interesting effect reported in standard cumulant structures. One new, recently published, approach in AMC, where unbiased nature of classification features is reported, was tested in the same context. Detailed numerical estimations for both standard and unbiased AMC algorithms are presented, confirming that bias issues can be resolved with new sixth-order cumulants formula and thus consequently better performances in classification of real and complex signals can be ensured, in all considered propagation scenarios.
Generalized Zero-Shot Text Classification via Inter-Class Relationship

Generalized zero-shot text classification (GZSTC) aims to classify textual instances from both previously seen classes and novel classes which are totally unseen during training. However, previous supervised metric learning methods cause severe domain bias problem. To tackle this problem, we propose a GZSTC method to reduce the gap from the fully trained seen domain and unaware unseen domain using relationship. Concretely, the proposed model gains beneficial experiences through multiple mimic GZSTC tasks during training. In every mimic GZSTC task, the model explicitly takes advantage of the relationship between the mimetic seen classes and unseen classes, which generalizes well on the real testing unseen classes. We extensively evaluate the performance on two GZSTC datasets. The results show that our method can alleviate the domain bias problem and outperform the state-of-the-arts by a large margin.
Modeling Semantic Inspired by Quantum Composite System

In the field of Natural Language Processing (NLP), especially for many specific tasks, representing and modeling semantic of text plays essential role. Generally speaking, by extracting feature of words, we can find the internal relationship between words and model text semantics. Further more, there have been achievements in using quantum concepts to construct language models, which suggests the effectiveness of applying quantum theory to semantic learning and representation. However, in the current research of quantum-inspired language model, semantic of the text is often directly modeled by quantum concept (e.g., density matrix), which blurs the original meaning of the word itself. In this study, we propose the composite semantic system (CSS) hypothesis and construct a quantum-inspired semantic representation network (QSRN). We apply QSRN to four different text classification tasks, the experimental results show the effectiveness of the algorithm and verify the hypothesis we proposed.
The Unified Authority Platform based on High Performance Blockchain

Along with the popularization of enterprise management information system (MIS), more and more business application systems are integrated on the MIS platform, and the need for user identity and authentication management is increasing quickly, which puts forward higher requirements for the security, reliability and disaster recovery of the unified identity authentication service of the unified authority platform. Therefore, there is an urgent need for a new technology architecture to meet the business management needs of unified identity authentication and continuously improve the operation guarantee ability of information communication. Blockchain technology has developed rapidly and has been successfully applied in many fields, especially in data security, with good results. This paper proposes a unified authority authentication mechanism for complex systems based on blockchain, and realizes accurate authority management by dynamically defining user roles, accessible resources and access methods. At the same time, data compression storage technology and parallel access method are used to realize rapid access to blockchain permissions, and improve access efficiency on the premise of ensuring security. Experiments show that the proposed method can realize the dynamic unified control of complex permissions. Compared with ordinary blockchain systems, it has higher access efficiency, and the average authentication time is reduced by more than 39%.
Poster: An Exploration Into Tracking the Use of Consumer Goods with RFID

Smart consumer goods have the potential to positively impact consumers by providing support in areas of great concern, such as helping to reduce waste and promote healthy eating, so tracking their use is essential for the development of their enhanced versions. To explore how we could track the use of these products, we tested a prototype of a radio-frequency identification system (RFID) that detects interactions by recording obstructions to the signals of sensors attached to consumer goods. We tested the system using items associated with the preparation of a cup of tea. Our system was highly reliable in detecting interactions; however, it was imprecise in estimating their durations. We discuss how further development of the proposed RFID system, including improvements in the collection and classification of the signal, could lead to be a viable method for tracking the use of consumer goods and help incorporate a layer of ‘smartness’.
Pedagogical conditions for civil identity formation

Features of civil identity formation in the educational process depend on the pedagogical conditions. Scientific interest in defining the specifics of approaches and methods can be traced both at the school level and at the secondary special and university level. The researchers paid special attention to such subjects as history and social studies. At the present stage of the development of pedagogy and teaching methods of the designated subjects, the formulation of new research problems will help to highlight in more detail the meaning and role of existing approaches to civil identity formation and substantiate new approaches that correspond to modern state and pedagogical tasks. The posing of new topics and questions within the framework of this vast topic will help to approach the process of civil identity formation more substantively and purposefully for both students and teachers at the level of general secondary education and at the level of higher and further education. The trends and patterns discovered in the course of the study will help to create both a new model and a new concept of Russian civil identity formation in the educational process. In addition, the analysis of approaches and models to civil identity formation will help to determine the relationship of scientific developments with the practice of manipulating history, historical memory, images of the past. An integrated approach to the study of the pedagogical conditions for civil identity formation will help to create new concepts and approaches to teaching history.