Reviewerly/exHarmony huggingface dataset:
Controller-Based Interactive Proof to Ensure Trust in Multiagent Systems

When multiple agents need to perform tasks that need cooperation, communication is necessary for coordination and also to assess completion. In this case, trust needs to be guaranteed, so agents know if others are following a common strategy. If communication is explicit, other agents, including agents that are not involved directly in the mission, can forge information and lead agents to believe that they are behaving according to the mission plan. Several methods have been proposed in the literature, and in this article, we propose a novel method based on interactive proofs in which trust between two agents is asserted by one agent demonstrating to the other that both follow the same mission plan. We formally represent the problem as a series of Markov decision processes and demonstrate that the formulation is complete and sound. Simulations of multiple agents in a maze problem demonstrate the behavior of the system.
Application of Machine Learning Algorithm Based on Big Data

In order to understand the machine learning algorithm based on big data, this paper will carry out relevant research, mainly discussing the basic concept of big data and the relationship between it and machine learning algorithm, then introducing common machine learning algorithm, finally focusing on the algorithm application, put forward the algorithm application platform design scheme. Through the analysis of this paper, it can give full play to the role of machine learning algorithm with the help of big data, and the algorithm itself has a wide range of applications, can be applied to a number of fields, only need to choose the algorithm according to the actual needs, can solve practical problems.
INFORMATION TECHNOLOGIES AND DEVELOPING A SINGLE PAGE WEB APPLICATION USING THE REACT LIBRARY

At present, the Internet has firmly entered the life of almost every person, has become an integral environment for supporting business processes. Every year technologies develop, and there is a need for web applications that have a fast response to actions in the user interface and work perfectly on all devices (both stationary and mobile). The article discusses the development of the architecture of the client side of a single-page application, its features and implementation using the modern React JavaScript library, and a comparative analysis of the use and performance of libraries for developing the client side of React and AngularJS applications. Also, why would using one of these be more cost effective for a development company than using core-js. Ecommerce has increased trade in services between countries. The sector accounts for 60% of the world's total production. With the advent of modern ICTs, e-commerce facilitates the process of bringing consumers and producers closer together and eliminating geographic distance. The use of e-commerce will eliminate traditional jobs and replace them with new skills, requiring workers to be trained in the use of technology so they can compete and win jobs. This means that e-commerce has a negative impact on manual and unskilled labor, but has a positive impact on technically trained workers who can easily find jobs and help create positions and disciplines that were previously not needed or known.
Design of Automatic Machine Translation System Based on Fuzzy Algorithm

With the rapid development of computer technology, machine translation has also become an important content. Because it is a tool that can be understood, remembered, stored and processed by human beings. This paper introduces how to use fuzzy language to realize automatic translation of translation. Firstly, this paper expounds the definition and characteristics of automatic machine translation, introduces the role of machine translation, and studies the application of fuzzy algorithm in automatic machine translation. Based on this algorithm, an automatic machine translation system is designed, and the performance of the system is tested by performance simulation experiment. Finally, the test results show that the average translation time of the automatic machine translation system based on fuzzy algorithm is about 3 s, the system operation time delay is low, the translation accuracy is basically more than 90%, and the maximum translation volume is 5000 words. This shows that the performance of the system meets the translation requirements.
An Overview of the Research in the Security Issues of Ethereum Ecosystem

Blockchain is a revolutionary technology that enables users to communicate in a trust-less manner. It enables users to store data globally on thousands of computers in an immutable format and empowers users to deploy small pieces of programs known as smart contracts. The blockchain-based smart contract enables auto enforcement of the agreed terms between two untrusted parties. There are several security vulnerabilities in Ethereum blockchain-based smart contracts, due to which sometimes it does not behave as intended. Because a smart contract can hold millions of dollars as cryptocurrency, so these security vulnerabilities can lead to losses. We present a review of the security issues in the Ethereum ecosystem.
Transfer learning and end-to-end self-driving

This chapter mainly introduces the concepts of transfer learning and end-to-end self-driving. To develop new deep learning applications efficiently, transfer learning strategies are often applied to engineering practices. End-to-end selfdriving is an intuitive model of unmanned driving. Three deep learning models, namely, shallow neural network, LeNet-like convolutional neural network, and NVIDIA end-to-end self-driving models, are discussed. In a real unmanned driving environment, relying solely on the road condition images collected by the camera to make driving decisions is unsafe. Therefore, the data collected by multiple sensors need to be comprehensively considered to make joint decisions.
Distributed Non-monotonic Reasoning for Transportation Network Congestion Monitoring: Theoretical Foundation

In this work, we formulate premises of a non-monotonic and distributed logical theory to model the traffic control actions of urban networks. The distributed intelligent agent transaction mechanisms based on distributed default logic are used. We demonstrate the validity of our approach and the contribution of the revisable reasoning formalism and the knowledge representation of multi-agent systems for the problems of traffic congestion management and supervision according to a route or arterial approach. In distributed and collaborative environments, revisable reasoning processes provide necessary facts to activate default rules, which gives the reactive agent the best use of its local and external knowledge. These mechanisms allow the network’s symbolic control system to operate efficiently in a dynamic and hypothetical environment, where basic facts and observations are often problematic.
Sound-Guided Framing in Cinematic Virtual Reality – an Eye-Tracking Study

When watching films made and displayed with Virtual Reality approaches, the viewers can freely move the visual field, resulting in possible disruptions in the narratives designed by the directors. This phenomenon demands novel narrative strategies in Cinematic Virtual Reality (CVR) to effectively guide a viewer's attention in following important plots. In this study, we evaluate the effect of using sound as a guiding mechanism in CVR. We conduct experiments to analyze the participants' responses to the sound cues outside the field of view with the eye-tracking technique. Statistical methods are then used to infer the significance of the differences among the responses. The experiments are conducted in a virtual scene with low complexity, reducing the possible confounding effects of the variety of visual elements. The results show that the viewer's visual attention can be guided by sounds sourced at the range outside the field of view. More specifically, the viewers react significantly better to sound stimuli varying in horizontal directions than those in vertical directions. Furthermore, different types of sounds also significantly affect the viewers' attention in the virtual scene.
Customer Response Modeling Using Ensemble of Balanced Classifiers: Significance of Web Metrics

In this paper we have proposed a customer response model for extremely imbalanced data. In this case, the class imbalance occurs when it comes to online shoppers, as there is a huge number of website sessions, of which only a small number end up with an order. The influence of web metrics on the model performance was examined, as some previous papers have confirmed the importance of web metrics for various types of analysis in web marketing. However, in this paper, their impact on the predictive performance of customer response models based on undersampling in combination with ensemble techniques was tested for the first time. The obtained results indicate good model performance-AUC of 0.839, sensitivity (positive class of respondents) of 75% and fallout of 21.26%, especially having in mind that the test data set had a response rate of only 0.41%. Additionally, web metrics were found to be more significant predictors than classical predictors of purchasing behavior such as transaction recency and frequency, as well as monetary value, or data on purchased products.
Multicnn-Filterlstm: Resource-Efficient Sensor-Based Human Activity Recognition in Iot Applications

With the recent advances in Internet of Things (IoT) technologies, various human-centered applications have proliferated and improved the quality of users' life. In the meantime, human activity recognition (HAR) has been considered as an essential component of human-centered applications in IoT due to its capability of providing substantial information about the user states. Whereas deep learning-based HAR methods have been recently proposed, there are still rooms to improve the HAR models, particularly from the perspective of IoT, as the models need to be precise but resource efficient.In this paper, to support IoT systems that require a resource efficient model, we thereby propose a deep learning-based HAR model, called MultiCNN-FilterLSTM that combines a multihead convolutional neural network (CNN) with a long short-term memory (LSTM) through a residual connection in which feature vectors are efficiently processed in hierarchical order.Accordingly, a novel approach to using LSTM cells, which we call filterwise LSTM (FilterLSTM), is proposed whereby the HAR model can learn the dependencies among the features at different hierarchical levels.The proposed HAR model has been exhaustively evaluated on a publicly available dataset, wireless sensor data mining (WISDM). The proposed HAR model enhances the classification accuracy by 2.3%-4.5%, while it requires 21%-70% fewer operations than the state-of-the-art models.The experimental results are presented to verify the merits of the proposed HAR model compared to the state-of-art models by exploiting hierarchical relationships and to deliver significant insights on the effectiveness of the proposed FilterLSTM from the perspective of the IoT system.
The Old Man: Reaching for the Light

The author opens the chronicle with a reflection on the process of aging and its effects on his body and mind. His thoughts flow freely into considerations of parallels between childhood and old age. It is the realm of dreams and imagination which intrigues him most and, in fact, we learn that he and his two young neighbors have begun recounting each other their dreams, on an almost daily basis. The children, by their nature, dream of the future (the “not yet”), while the old man dreams of the past. In keeping with the book’s ‘fil rouge,’ still, his vivid dream possesses strong futuristic content: his dream concerns the “Gruppo Futuro” Project in Naples. This ‘story’ allows the author to describe several participatory design approaches with children and provides an opportunity to present additional evidence of the catalytic power of children’s imagining the future to affect/change reality. He also reminds the reader that imagination is a fundamental factor (cause and effect) in all children’s free play (and in imagining the future).
CHAMP: Channel Merging Process for Cost-Efficient Highly-Pruned CNN Acceleration

This paper presents an advanced offline scheduling scheme to improve the accelerator efficiency, especially for the highly-pruned convolutional neural networks (HP-CNNs). Based on the existing outlier-aware accelerator design, we demonstrate the efficiency drop of HP-CNN processing for the first time, and element-wise channel merging is proposed to make a dense processing sequence even for the highly-pruned model. The dedicated hardware architecture is also presented to process the merged channels with the minimum hardware-level overheads, improving the energy efficiency for handling HP-CNNs by preserving the hardware utilization. We further investigate the optimal size of accumulator and multiplexer, in addition to the number of merged channels, exploiting the attractive energy-performance trade-offs. As a result, unlike the practical HP-CNNs for the on-device solutions, the proposed method enhances the overall efficiency by up to 33% compared to the state-of-the-art schemes.
Saliency Detection From Complex Background with Attention-Based Boundary-Aware Pyramid Pooling Network

Deep convolution network have been widely adopted in saliency detection in recent years and achieved state-of-the-art performance. A central issue for previous methods is the limited capability of detecting objects that are overwhelmed by complex background. Aim to address this problem, we propose a novel attention-based boundary-aware pyramid pooling network, namely ABAPNet, which consists of feature aggregation module, pyramid pooling module and cascading dual attention module. The proposed ABAPNet method transits high-level information to shallow layers to keep transferring important features between layers and obtains more semantic features. Hybrid loss by fusing binary cross entropy (BCE), structural similarity (SSIM) and intersection-over-union (IOU) is employed to reduce the gradient of mixed loss of network output features, which guides the network to better segment boundaries from complex background. Experimental results on 5 public datasets show that the proposed ABAPNet outperforms the competitors.
Leveraging a Bilingual Corpus to Resolve Date–Duration Ambiguity in Japanese Numeric Day Expressions

In Japanese, time expressions are often unaccompanied by explicit temporal markers, and thus their temporal types are not always obvious. One of the most representative cases is date–duration ambiguity arising from the commonly used time expression, “** 日 [** nichi].” To build a supervised classifier for this ambiguity while minimizing the annotation burden, we introduce an automatic label generation method using a bilingual corpus. Inspired by an annotation projection technique, we associate Japanese time expressions with their corresponding English words. Ambiguity in Japanese time expressions is comparatively easily resolved using their associated English words. We prepared several simple rules to determine temporal type labels from sentence pairs, and automatically created a training set for this task. Through a human evaluation, we verified that 98.7% of the sampled labels match the hand-crafted labels. We then developed a classification model on these training examples and compared our automatically created examples with existing manually annotated data. Experimental results show that the produced examples improve classification models by up to 14.0% accuracy points. Hence, our label generation method not only minimized the annotation task but is also sufficiently reliable for building temporal type classifiers.
LACNNER: Lexicon-Aware Character Representation for Chinese Nested Named Entity Recognition

Named Entity Recognition (NER) is one of fundamental researches in natural language processing. Chinese nested-NER is even more challenging. Recently, studies on NER have generally focused on the extraction of flat structures by sequence annotation strategy while ignoring nested structures. In this paper, we propose a novel model, named LACNNER, that utilizing lexicon-aware character representation for Chinese nested NER. We select the typical character-level framework to overcome error propagation problem caused by incorrect word separation. Considering the situation that Chinese words always contain much richer semantic information than single characters do, it firstly obtains more significant matching words through external lexicon in our LACNNER model, and then generates lexicon-aware character representations that make full use of word-level knowledge for nested named entity. We also evaluate the effectiveness of LACNNER by taking ACE-2005-Zh dataset as a benchmark. The experimental results fully verified the positive effect of incorporating lexicon-aware character-representation in recognition of Chinese nested entity structure.
Yerkes’ Neuropsychological Test Using Leap Motion

The use of eHealth tools allow more people to have access to health services. In particular, neuropsychological treatments can benefit from the use of this type of tools. These tests can be applied using computer devices such as desktops, tablets or smartphones. Yerkes’ test allows an oculomotor evaluation and is related to spatial intelligence. Due to the oculomotor skills that the user must perform in the test, it is necessary to use a device, such as Leap Motion, to capture the hand movement. In this work, the design of the virtual reality Yerkes’ tests for a desktop application using Unity and Leap Motion for interaction is presented. Finally, the definition of user gestures is included to perform some main tasks through Leap Motion.
Multi-attribute ordered fuzzy numbers approach to transport trolley control

The paper is a case study of the application of a multi-attribute decision-making (MADM) method with Ordered Fuzzy Number (OFNs) formalism to a transport trolley control in a production system. The issue of a particular control problem and a proposition of its solution are described. The aim of the control is the optimal order of handling the station for transport trolley, which is situated between production lines. For this purpose, the OFNs are used as a description of the parameters values uncertainty. The use of the OFNs formalism additionally allows taking into account the trends in the value of key factors – it undoubtedly increases the flexibility of production. The time efficiency and equal workload are the main factors of evaluation during multi-attribute control, taking into account the MADM method. The approach can be implemented as a decision algorithm located in a Petri net model.
Groundwater sustainability in a digital world

Focused on connectivity and enabled by smart technologies, the fourth industrial revolution is creating a digital transformation that is advancing and prompting sustainable groundwater (GW) management practices. Enabled by artificial intelligence, big data, blockchain technology, cloud computing, cyber-physical systems, and the Internet of Things, this digital transformation is creating a paradigm shift in how we manage our GW resources. The next generation GW management tools are emerging with web-based GW management platforms following FAIR Data Principles—Findability, Accessibility, Interoperability, and Reusability. These web-based platforms can improve participation, cooperation, and collective action, and enable alternative forms of GW management such as commons-based peer management of GW resources. These web-based platforms not only provide comprehensive and timely information about the GW system with simulation and gaming tools, but also enable agents to work cooperatively. In this chapter, we discuss GW sustainability in a digital world using examples from both developing and developed countries. We show how these web-based platforms can provide historical and future-scenario information for strategic planning and development, and real-time information for daily decision-making. These web-based platforms can additionally facilitate cooperation among GW users through a blockchain-based system for GW credit, GW trading, and GW policy incentives, as an example. Furthermore, these we-based platforms do not only inform on GW systems, but also can be useful for other smart technologies such as smart agriculture, and smart city. Insights regarding the prospects of digital GW and the anticipated transformation of GW management practices are important for researchers, GW regulators, GW managers, GW users, and technology providers to plan for tomorrow.
Power Theft Detection in Residential Distribution Grids Using Dirichlet Process Mixture Modelling

In developing nations like India, distribution companies lose billions of dollars annually due to electricity theft. Conventional manual inspection is time-consuming and inefficient. Hence, advanced methods need to be implemented into distribution networks to substantially detect electricity theft. The technique proposed in this work involves irregular-scenario classification, feature extraction and clustering to identify suspicious customers. A monitoring system at the distribution transformer for a residential grid and Smart Meters installed at customer households in that grid have the information that is used for identifying any dishonest customers. Bayesian nonparametric Dirichlet Process Mixture modelling (BNP-DPMM) is an advanced clustering algorithm that eliminates the problem of having a fixed number of clusters, allowing flexible analysis of data. The proposed technique successfully grouped irregular and regular customers in very distinct clusters in an analysis on actual Smart meter data provided by UK Power Networks. Comparative evaluation is carried out against similar techniques and DPMM shows much better performance.
Timing of Deposit Formation

To understand how gold deposits form it is useful to know the relationship between the time of deposit formation and that of sedimentation, volcanism, metamorphism, igneous intrusion, and deformation. Timing is the fourth characteristic that needs to be accommodated when understanding how gold deposits may form.
On Regulation of Over-Registration of Copyright

China enforces a system of voluntary copyright registration, and only carries out formal examination of works. Now in China, over-registration of copyright is very common. Through empirical research, it can be divided into two types of over-registration of the copyright of other' works and over-registration of the copyright of his own works. Over-registration of copyright not only destroys the demonstration effect of copyright registration, but also reduces the public credibility of copyright registration institutions and the evidence effect of copyright registration. Therefore, it should be regulated by a series of measures. Through theoretical and empirical analysis, it is suggested to regulate over-registration of copyright by improving the registration clause of Copyright Law, formulating supporting registration measures and unifying registration review standards.