Reviewerly/exHarmony huggingface dataset:
Research on discipline development and discipline difference of intelligence science in China

Purpose With the continuous change of research contents and methods of intelligence science, its integration with other disciplines is also deepening. The purpose of this paper is to further explore the interdisciplinary research characteristics of intelligence science in theoretical depth and application value. Design/methodology/approach This paper summarizes and explores in two aspects. The first is a large number of literature review, mainly combined with the historical characteristics of the development of intelligence science researches in China and international comparison. The second is to refine the discipline construction ideas suitable for the development of contemporary intelligence science. Findings From the perspective of the historical development of discipline relevance, the development characteristics and positioning of intelligence science in China are introduced, with the comparison of many disciplines including information technology, library science, information science, data science, management science and other disciplines. In order to better meet the practical needs of intelligence service in the new era, this paper mainly analyzes the construction method of intelligence science research system and the relocation of intelligence science research content. Originality/value This paper summarizes the historical characteristics and international comparison of the development of intelligence science in China. It proposes the development characteristics and orientation of intelligence science in China from the perspective of historical development of discipline relevance. It also proposes the discipline construction ideas suitable for the development of contemporary intelligence science.
Automatic Recognition of Target Words in Infant-Directed Speech

This study assesses the performance of a state-of-the-art automatic speech recognition (ASR) system at extracting target words in two different speech registers: infant-directed speech (IDS) and adult-directed speech (ADS). We used the Kaldi-NL ASR-service, developed by the Dutch Foundation of Open Speech Technology. The results indicate that the accuracy of the tool is much lower in IDS than in ADS. There are differences between IDS and ADS which negatively affect the performance of the existing ASR system. Therefore, new tools need to be developed for the automatic annotation of IDS. Nevertheless, the ASR system can already find more than half of the target words, which is promising.
A Novel Density-Based Performance Evaluation Index for Multipath Components Clustering

The machine learning algorithms applied in multipath components (MPCs) clustering should be evaluated by an appropriate performance index. In this paper, a novel performance evaluation index for MPCs clustering is proposed, which is based on the density of clusters instead of cluster distance. The proposed index improves the traditional S_Dbw validity index by taking into account the intra-cluster density obtained according to the Graham scanning method and Green's formula, which can be applied to MPCs with arbitrary distribution characteristics. The proposed index is used to evaluate the performance of different machine learning algorithms, such as K-means and Gaussian mixture model (GMM) algorithms, which shows a more accurate result than other traditional indexes. In addition, the utility of the proposed index is verified by the measured MPCs data involving both delay and angle information. The evaluation result shows that the variational Bayesian GMM (VB-GMM) algorithm outperforms the K-means and expectation maximization GMM (EM-GMM) algorithms in MPCs clustering.
A Small Sample Face Recognition Method Based on Deep Learning

Face recognition is a hot topic in the field of artificial intelligence in recent years. In many practical applications, face data acquisition is difficult, the accuracy of face recognition based on small samples can't reach a satisfactory accuracy. To solve this problem, a lightweight convolutional neural network (LWCNN) base on deep learning is built for small sample data. In LWCNN, two convolutional blocks are used to extract the features of the input face image. Then the k-fold cross validation is adopted to verify the robustness of the network. The simulation results show that compared with other three methods, our proposed LWCNN achieves better recognition accuracy in small sample space and avoids the over-fitting problem to a certain extent.
Character-level Recurrent Neural Network for Text Classification Applied to Large Scale Chinese News Corpus

At present, most recurrent neural network models used in text classification are shallow models and have limited ability to express texts especially large scale texts. This paper conducts an empirical study on the use of character-level deep recurrent neural network (Char-RNN) for Chinese corpus text classification. Firstly, it uses character-level features as input, and then uses a multilayer recurrent neural network structure to complete feature extraction. The evaluations on THUCNews dataset that is large scale Chinese news corpus showed that our proposed model is able to reach 94.4% accuracy, which performs better than the traditional models such as LibSVM(A Library for Support Vector Machines),CBOW(Continuous Bag-of-Words),CWE(char-acter enhanced word embedding) and deep learning models such as recurrent neural network on large-scale Chinese text classification mission.
Transforming Natural Language Specifications to Logical Forms for Hardware Verification

We propose a framework for extracting natural language assertions from hardware design specification documents. The entire parse tree of each input sentence in a design spec is viewed as a network of words connected to facilitate the creation of semantic frames. We employ a lexicalized grammar that associates words with both semantic and syntactic relations that assist in filling the slots in the semantic frames. At the same time, the accuracy of the extracted semantics is ensured by the incremental understanding algorithm that is guided by both syntactic and semantic rules of the hardware verification domain. We evaluated the framework by writing assertions taken from specification documents of the Memory controller, UART, and the AMBA ACE protocol. System Verilog Assertions (SVA) were automatically generated from logical expressions. Since accuracy is of paramount importance, whenever a complex sentence cannot be understood. we identify and report to the user.
Brightness Preserving Generalized Histogram Equalization

Histogram equalization (HE) is the one of the simplest and most effective methods for contrast enhancement. It can automatically define the gray-level transformation function based on the distribution of gray-level included in the image. However, HE fails to produce satisfactory results for broad range of low-contrast images because the HE does not use a spatial feature included in the input image. The differential gray-level histogram (DH) which is contained edge information of the input image, were defined and the differential gray-level histogram equalization (DHE) has been proposed. The DHE shows better enhancement results compared to the HE result for many kinds of images. In this paper, we propose a generalized histogram equalization (GHE) method including the HE and the DHE. In GHE, the histograms is created using power of gradient including a special feature. In the HE, the mean brightness of the enhancement image cannot be controlled. On the other hand, the GHE can control the mean brightness of the enhancement image by changing the power, thus, the mean brightness of the input image is perfectly preserved.
Comparing users' perception of different chatbot interaction paradigms

In this work, we used the results of applying the Semiotic Inspection Method (SIM) to popular chatbots[12, 13], which yielded eleven strategies for conveying features to users, as well as six sign classes used for designing their interaction. We conducted user studies comparing two prototype chatbots with same features and different approaches to interaction: one using Natural Language Processing, and other using the sign classes and strategies to guide the user interaction. After that, we interviewed the participants, asking about their preferred aspects of each chatbot and their opinions regarding some of these aspects, and, later, analyzed the results. These point to the effectiveness of the strategies and sign classes. Then, we discuss users' perceptions of different ways of interacting with chatbots and their communicative strategies.
Special Session: Potentially Leaky Controller: Examining Cache Side-Channel Attacks in Programmable Logic Controllers

Industrial Control Systems (ICS) have evolved during the last decade following a new paradigm in the fourth industrial revolution, defined by the flexibility provided from open-source software such as Embedded Linux. In the same time-span, cache side-channel attacks have been identified as a prominent threat to any kind of computer system, facilitating highly stealthy confidentiality compromising attacks that enable information exfiltration. Given their crucial position in the systems they facilitate, ICS can be exploited to extract highly sensitive information that can compromise the system itself and, by extension, the infrastructure they belong to. Thus, in this paper we will examine the threat cache side-channel attacks pose in modern ICS. We will introduce the current landscape of side-channel threats that can target processors found in ICS, the potentially vulnerable points for information extraction across an ICS system stack as well as the nature of the information itself, and discuss viable countermeasures.
Research on Intelligent Detection Method of Composite Insulator Hydrophobicity Based on Water Spraying of Drone

Composite insulators are widely used in power systems due to their good hydrophobicity and migration of hydrophobicity, excellent pollution flashover resistance, light weight, and high mechanical strength. However, with the increase of operating life, the aging phenomenon of the composite insulator is very serious. The degradation of the electrical and mechanical properties of composite insulators poses a potential threat to the reliability of the power supply. In order to improve the safety and stability of transmission lines, the hydrophobicity of composite insulators needs to be tested regularly. This paper presents a method for intelligently charged hydrophobic detection of composite insulators based on drone technology. The detection method is composed of a drone, a camera device, a water spray device and an embedded artificial intelligence algorithm module. The water spray device can adjust the fineness of water drops and the length of the water spray gun. The drone equipped with a camera device and a water spray device flying to the composite insulator to be measured, the water spray device sprays water on the composite insulator, and the camera device takes a photo after spraying water. The intelligent algorithm module grades the shape of water drops on the insulator from the photo, and finally outputs the result of the aging degree. This method can realize the efficiency and intelligence of the charged hydrophobic inspection of composite insulators, which is of great significance for ensuring the safe operation of composite insulators, and has certain innovation and good application prospects.
Extensional realizability for intuitionistic set theory

Abstract In generic realizability for set theories, realizers treat unbounded quantifiers generically. To this form of realizability, we add another layer of extensionality by requiring that realizers ought to act extensionally on realizers, giving rise to a realizability universe $\mathrm{V_{ex}}(A)$ in which the axiom of choice in all finite types, ${\textsf{AC}}_{{\textsf{FT}}}$, is realized, where $A$ stands for an arbitrary partial combinatory algebra. This construction furnishes ‘inner models’ of many set theories that additionally validate ${\textsf{AC}}_{{\textsf{FT}}}$, in particular it provides a self-validating semantics for ${\textsf{CZF}}$ (constructive Zermelo–Fraenkel set theory) and ${\textsf{IZF}}$ (intuitionistic Zermelo–Fraenkel set theory). One can also add large set axioms and many other principles.
Adaptive MIMO Fuzzy Controller for Double Coupled Tank System optimizing by Jaya Algorithm

This paper proposes an adaptive MIMO fuzzy controller optimized with Jaya optimization technique used to robust control of uncertain coupled tank system. Firstly, the parameters of MIMO fuzzy controller are optimally identified using Jaya algorithm. Then a fuzzy sliding surface is implemented to ensure that the coupled tank system is asymptotically stable based on Lyapunov concept. The proposed algorithm is applied to control the fluid level of double tank systems. The comparison results with PID and traditional fuzzy controller are presented to confirm that the new Adaptive MIMO Fuzzy control method proves a robust and simple approach to effectively control highly nonlinear uncertain systems.
A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

With the rapid development of mobile internet technology, mobile applications (apps) have been rapidly popularized. To facilitate users' choice of apps, app recommendation is becoming a research hotspot in academia and industry. Although traditional app recommendation approaches have achieved certain results, these methods only mechanically consider the user's current context information, ignoring the impact of the user's previous related context on the user's current selection of apps. We believe this has hindered the further improvement of the recommendation effect. Based on this fact, this paper proposes a novel context-aware mobile application recommendation approach based on user behavior trajectories. We named this approach CMARA, which is the initials acronym of the proposed approach. Specifically, 1) CMARA integrates the heterogeneous information of the target users such as the user's app, time, and location, into users behavior trajectories to model the users' app usage preferences; 2) CMARA constructs the context Voronoi diagram using the users' contextual point and leverages the context Voronoi diagram to build a novel user similarity model; 3) CMARA uses the target user's current contextual information to generate an app recommendation list that meets the user's preferences. Through experiments on large-scale real-world data, we verified the effectiveness of CMARA.
Intelligent English classroom video clarity improvement based on motion compensation and grid flow

For the English classroom teaching video denoising algorithm, it is not only necessary to consider whether the noise removal of the output video is thorough, but also to consider the actual operating efficiency and robustness of the algorithm. In the process of the thesis research, after reading a large number of internal and external documents on video denoising algorithms and analyzing the pros and cons of various denoising algorithms, this paper proposes a new video denoising algorithm, which uses the recently proposed grid flow motion model based on camera motion compensation to generate denoised video. Compared with the current advanced video denoising schemes, our method processes noisy frames faster and has good robustness. In addition, this article improves the algorithm framework so that the algorithm can not only deal with offline video denoising, but also deal with online video denoising.
Exploiting Satellite Data for Solar Performance Modeling

Developing accurate solar performance models, which infer solar power output in real time based on the current environmental conditions, are an important prerequisite for many advanced energy analytics. Recent work has developed sophisticated data-driven techniques that generate customized models for complex rooftop solar sites by combining well-known physical models with both system and public weather station data. However, inferring solar generation from public weather station data has two drawbacks: not all solar sites are near a public weather station, and public weather data generally quantifies cloud cover-the most significant weather metric that affects solar-using highly coarse and imprecise measurements.In this paper, we develop and evaluate solar performance models that use satellite-based estimates of downward shortwave (solar) radiation (DSR) at the Earth's surface, which NOAA began publicly releasing after the launch of the GOES-R geostationary satellites in 2017. Unlike public weather data, DSR estimates are available for every 0.5km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area. As we show, the accuracy of solar performance modeling using satellite data and public weather station data depends on the cloud conditions, with DSR-based modeling being more accurate under clear skies and station-based modeling being more accurate under overcast skies. Surprisingly, our results show that, overall, pure satellite-based modeling yields similar accuracy as pure station-based modeling, although the relationship is a function of conditions and the local climate. We also show that a hybrid approach that combines the best of both approaches can also modestly improve accuracy.
Educational Games for Cybersecurity Awareness

This paper presents a project for building cybersecurity awareness for Thai youth and children. The project consisted of video-based learning material, five educational games, assessment systems, and the retrieval system of cybercrime news. In this paper, the five educational games are emphasized and described in their design and efficiency. The games were designed and developed in different game types: trivia, matching, shooting, and runner. The user evaluation was conducted in a domain of youth users (15-24 years old) and all of them were Thai second-year undergraduate students in the game and interactive program.
Product recommender system using neural collaborative filtering for marketplace in indonesia

Abstract Marketplace has the potential growth in Indonesia indicated by the continued increase in the number of customers. However, the marketplace has some limitations to deliver personalized purchasing experience. Recommender system can support marketplace to overcome that limitations so that customer can find items or services based on their preferences. This study propose to develop product recommender system based on Neural Collaborative Filtering (NCF) algorithm. The product recommender system to be built is using implicit feedback data in the form of customer purchase data. Implicit feedback is reliable data for building recommendation system. The results have shown that NCF achieve the best performance and outperforms over the other collaborative filtering methods.
A contextual ontology for distributed urban data management

The evolution of information and communication technology in the construction domain has yielded a variety of heterogeneous data sources. While bespoke approaches have been developed to explore data merging for a variety of purposes, few have explored how to develop a multipurpose information organisation that can be reconfigured on a per-project basis. This paper describes an approach that, using a lightweight central server, is used to investigate the effectiveness of loose federations of information sources that together serve the information needs of a project. The central server provides both a common context through which relationships between the information sources can be expressed and a data register to enable information discovery. The paper describes the creation of an ontology to capture this context and a software architecture to support its use. The efficacy of the approach is illustrated through describing the use of the server for marshalling data used in a renovation project.
Interaction of Structure and Information on Tor

Tor is the most popular dark network in the world. It provides anonymous communications using unique application layer protocols and authorization schemes. Noble uses of Tor, including as a platform for censorship circumvention, free speech, and information dissemination make it an important socio-technical system. Past studies on Tor present exclusive investigation over its information or structure. However, activities in socio-technical systems, including Tor, need to be driven by considering both structure and information. This work attempts to address the present gap in our understanding of Tor by scrutinizing the interaction between structural identity of Tor domains and their type of information. We conduct a micro-level investigation on the neighborhood structure of Tor domains using struc2vec and classify the extracted structural identities by hierarchical clustering. Our findings reveal that the structural identity of Tor services can be categorized into eight distinct groups. One group belongs to only Dream market services where neighborhood structure is almost fully connected and thus, robust against node removal or targeted attack. Domains with different types of services form the other clusters based on if they have links to Dream market or to the domains with low/high out-degree centrality. Results indicate that the structural identity created by linking to services with significant out-degree centrality is the dominant structural identity for Tor services.
CNN-Based Speaker Verification and Speech Recognition in Tibetan

Abstract In recent years, there have a little studies on speaker and speech recognition in Tibetan, which are mainly based on traditional methods of probability statistics. With the development of deep learning, neural networks have been widely used in speaker and automatic speech recognition, which have achieved remarkable results. In this paper, we utilize end-to-end model to study speaker verification and speech recognition in Tibetan. This article uses the ResCNN network for Tibetan speaker verification. In speech recognition, we adopt the DFCNN-CTC structure, where connectionist temporal classification (CTC) directly outputs the probability of sequence prediction without external post-processing. We have made some improvements to the two models. Experiments show that the improved model reduces EER by 3% and WER by 18% in speaker verification and speech recognition, respectively.