Reviewerly/exHarmony huggingface dataset:
Dialogue Act Classification Using RNNs Introducing One-Versus-All and Attention Mechanism

For understanding the contents of a user's utterances, dialogue acts classification is often introduced to chat systems. This paper proposes Enhanced One-versus-All RNN (ENOVA RNN), which is a dialogue act classification model that consists of two RNN layers, one-versus-all layers, and the attention mechanism. The content of past utterances in dialogue is an important feature for dialogue acts classification models. In addition to the fact, classifiers tend to confuse dialogue acts that rarely appear in dialogue with frequent ones because the number of utterances differs greatly in each dialogue act. ENOVA RNN is a classification model to capture some features of dialogue acts that rarely appear after considering the content of past utterances in dialogues. In this study, it was confirmed that ENOVA RNN can classify dialogue acts using contexts not greater than six sentences. Moreover, ENOVA RNN improves rare dialogue acts classification performance keeping the overall quality of the performance by narrow down the dialogue acts using attention weights.
A Parallel Strategy for Generation of Fuzzy Rule Bases in Big Data Problems Using the NSGA-DO

In Fuzzy Rule Based Systems, the process of extracting rule bases from data can lead to problems related to scalability and computational cost. These situations usually occur when considering contexts where data sets have large volume, such as in Big Data problems. Parallel computing approaches with evolutionary computing techniques such as Multi-objective Genetic Algorithms can be used together to enable scalability and reduce the computational cost involved in the extraction process of Rules Bases of the Fuzzy Systems used in Big Data problems. This paper proposes a parallel modeling strategy for the generation of Fuzzy Rules Bases in Big Data problems using the NSGA-DO Multi-objective Genetic Algorithm. The aim of the proposal is to allow the increase of scalability and the reduction of computational cost when maximizing the accuracy (increase the precision) and minimizing the complexity (decrease number of rules and antecedents of the rules) of Fuzzy Systems generate in the context of Big data problems.
A New Pairing-Free Certificateless Signature Scheme for Internet of Things

The Internet of Things (IoT) has been more and more popular in people’s lives because it can bring convenience to our lives. Communication between IoT smart devices is vulnerable to various attacks. Therefore, designing a lightweight and secure cryptographic protocol for IoT applications is an important task. In this paper, we find that Jia et al.’s certificateless signature scheme cannot resist public key replacement attacks. Then, we propose a new certificateless signature scheme (PK-CLS) to resist super type I and type II adversaries. Finally, we prove the security of our PK-CLS scheme in the random oracle model.
A Revised Forensic Process for Aligning the Investigation Process with the Design of Forensic-Enabled Cloud Services

The design and implementation of cloud services, without taking under consideration the forensic requirements and the investigation process, makes the acquisition and examination of data, complex and demanding. The evidence gathered from the cloud may not become acceptable and admissible in the court. A literature gap in supporting software engineers so as to elicit and model forensic-related requirements exists. In order to fill the gap, software engineers should develop cloud services in a forensically sound manner. In this paper, a brief description of the cloud forensic-enabled framework is presented (adding some new elements) so as to understand the role of the design of forensic-enabled cloud services in a cloud forensic investigation. A validation of the forensic requirements is also produced by aligning the stages of cloud forensic investigation process with the framework’s forensic requirements. In this way, on one hand, a strong relationship is built between these two elements and emphasis is given to the role of the forensic requirements and their necessity in supporting the investigation process. On the other hand, the alignment assists towards the identification of the degree of the forensic readiness of a cloud service against a forensic investigation.
Design of Optimal Model for Drainage Capacity of Marine Peripheral Building Drainage System

Liao, L., 2019. Design of optimal model for drainage capacity of marine peripheral building drainage system. In: Hoang, A.T. and Aqeel Ashraf, M. (eds.), Research, Monitoring, and Engineering of Coastal, Port, and Marine Systems. Journal of Coastal Research, Special Issue No. 97, pp. 248–253.In order to improve the drainage ability of the water drainage system of the perimeter of the sea, an optimization model of the drainage capacity of the water drainage system of the marine surroundings based on the dynamic monitoring of the drainage capacity and the evaluation of the hysteretic behavior of the finite element is proposed. The dynamic load monitoring model of the marine peripheral building drainage system is constructed, a large amount of the strain mechanical monitoring data of the marine peripheral building drainage system is accumulated, the strain sensing mechanical characteristic reconstruction of the marine peripheral building drainage system is carried out based on the structural finite element model analysis method, and the statistic characteristic quantity of the drainage capacity optimization of the drainage structure is extracted. in that method, a column-hole expansion stress balance analysis method is adopted to analyze the distribution law of the strain field distribution in the water drainage of the marine peripheral building, the tensile strength of each unit of the drainage pipeline is obtained by adopting a column-hole expansion stress balance analysis method, and the concentrated force and the distributed force model of the water drainage system of the marine peripheral building are obtained, and the design of the drainage capacity optimization model of the water drainage system of the marine peripheral building drainage system is realized. The simulation results show that the method can effectively improve the drainage capacity of the water drainage system of the perimeter of the ocean, optimize the design and deployment of the drainage node, and improve the drainage capacity of the building on the periphery of the ocean.
Anchor User Oriented Accordant Embedding for User Identity Linkage

User Identity Linkage is to find users belonging to the same real person in different social networks. Besides, anchor users refer to matching users known in advance. However, how to match users only based on network information is still very difficult and existing embedding methods suffer from the challenge of error propagation. Error propagation means the error occurring in learning some users' embeddings may be propagated and amplified to other users along with edges in the network. In this paper, we propose the Anchor UseR ORiented Accordant Embedding (AURORAE) method to learn the vector representation for each user in each social network by capturing useful network information and avoiding error propagation. Specifically, AURORAE learns the potential relations between anchor users and all users, which means each user is directly connected to all anchor users and the error cannot be propagated without paths. Then, AURORAE captures the useful local structure information into final embeddings under the constraint of accordant vector representations between anchor users. Experimental results on real-world datasets demonstrate that our method significantly outperforms other state-of-the-art methods.
DMCM: A Deep Multi-Channel Model for Dynamic Movie Recommendation

Online movie recommender systems aim to address information overload problem in movie perspective. Recently, incorporating knowledge graph into recommender systems as auxiliary information has attracted much attention due to its rich semantic content. In this paper, we propose a deep multi-channel model for dynamic movie recommendation (DMCM), which makes full use of user-item interaction and knowledge graph. First, we learn item embedding, entity embedding and genre embedding from interaction matrix and knowledge graph. Then we design a CNN-based network which can fuse the learnt embeddings and acquire the final movie representation, among which an attention module is applied to better represent the user. Finally, the click-through rate for the user-movie pair is calculated utilizing the obtained user and movie representation. Results of extensive experiments on a real-world dataset show that the proposed DMCM outperforms state-of-art baselines.
A study on speculative task scheduling for apache spark in fog computing realms

Speculative task scheduling is a technique used by most of today's distributed data processing systems to cope with slow running tasks that delay subsequent computations and hamper the overall performance of analytics jobs. Fog computing is now becoming a prominent service paradigm to foster applications with low-latency analytics requirements by moving the analysis closer to the data sources. However, task scheduling in fog realms is not without challenges, as resource heterogeneity and highly volatile network connections have the potential to delay instead of improving the completion time of analytics jobs. In this paper, we present a comprehensive study showcasing the various obstacles that are presented in speculative task scheduling for analytics jobs deployed in fog realms and derive critical insights that must be taken into account when introducing new task scheduling algorithms.
Analysis of Contents of Human Development and Family Area of Middle School Technology-Home Economics Textbooks in Accordance with the 2015 Revised Curriculum: Focused on Core Concept ‘Development’

The purpose of this study was to analysis contents of the middle school Technology-Home Economics textbook dealing with the core concept ‘Development’ based on the 2015 revised curriculum. The subjects were 5 textbooks from the twelve middle school ‘Technology-Home Economics’ textbooks. The results of this study were as follow: First, most textbook used ‘Adolescence development and understanding’ as a chapter name. Second, each unit structure has various picture, graphs and activities to make interesting class to students. Finally, 5 textbooks include goals of self understanding, positive self-identity, relationship building and problem-solving. Also, various activities help students accomplish achievement standard 2015 revised curriculum oriented. And cooperation focused teaching-learning methods were used the most. Based on the findings, this study presents the contents of middle school Technology-Home Economics textbook and propose improvement direction of future textbooks reform. Suggestions and implications for Technology-Home Economics textbook contents analysis were also provided.
Control and Identification in Cognitive Maps with Suppressing Constrained External and Internal Disturbances in Impulse Processes

Dynamics of complex systems of different nature is described by mathematical models of impulse processes in cognitive maps (CM). Dynamics model is decomposed into two interrelated difference equations systems for measurable and unmeasurable CM nodes respectively. In the first equations system of the CM model fluctuations of the unmeasurable coordinates are considered as external disturbances. Structure and weighting coefficients of the CM model is previously identified in impulse process mode under incomplete measurements of the CM nodes coordinates. Variations of the CM weights against the identified values are considered as internal disturbances during further exploitation of the complex system. A closed-loop robust control system of the CM impulse process is designed in the report to suppress these constrained internal and external disturbances.
A Novel Computationally Bounded Oblivious Transfer with Almost Non-trivial Communication

Existing privacy preserving schemes have attainedBhat Radhakrishna maximum privacy level either using the exponential modular operations or the strong intractability assumptions. But they failed to guarantee the following challenges till date namely (1) computationally bounded single database scheme with non-trivial communication (2) inbuilt integrity support (3) only linear encryption operations. We have proposed a single database Oblivious Transfer (OT) or Symmetric Private Information Retrieval (SPIR) schemes using the quadratic residuosity as the underlying cryptographicSunitha N. R. primitive. In this paper, we have constructed a new quadratic residuosity based concurrently executing recursive 2-bit encryption function which fulfill all the above mentioned challenges. This recursive 2-bit encryption functions receive Quadratic Residuosity Assumption (QRA) based queries and produce the reasonable communication bits as the response bits. The greatest advantages of the proposed schemes is that they operate on the plain database and the concurrently executing recursive 2-bit encryption functions involve only linear number of modular multiplications.
Using an Evolutionary Algorithm to Optimize the Hyper-parameters of a Cascading Neural Network

This paper describes the implementation of an evolutionary algorithm to optimize the ability of a Casper neural network to classify the porosity of a reservoir into groups using the linguistic petrographical characteristics of the data. The Casper neural network technique is implemented on the petrographical data gained from a paper from Gedeon et al. [3]. We used an evolutionary algorithm to optimize the hyper-parameters of the network, specifically the learning rates of the different regions in the network and the period of time between the additions of new neurons. Several methods of producing offspring were tested, and each was able to improve the accuracy of the Casper neural network. Future work is suggested to improve the optimization of the evolutionary algorithm, such as testing for more generations, and utilizing other selection methods. Ultimately the results achieved suggest that there is potential for optimizing the hyper-parameter of a cascading neural network using evolutionary algorithms.
Inappropriate Usage Examples in Web API Documentations

Application Programming Interfaces (APIs) are common in software development to reuse other products. Although the documentation allows API consumers to learn about API usages, it can be unreliable. Here, we investigate the characteristics of inappropriate usage examples in web API documentation by extracting and comparing OpenAPI Specifications from usage example-response pairs. About 65.5% of the endpoints have some form of inappropriate usage examples. Furthermore, mismatches are classified into four categories: undocumented keys pattern, dynamic keys pattern, unreturned keys pattern, and type mismatched pattern. Our results suggest that the number of keys in the response is correlated with the number of mismatches. These findings should assist both API providers and consumers who deal with unreliable documentation in web APIs.
과제 수행 중심의 프랑스 언어․문화 통합 교육 평가 방안 연구

The purpose of this study is to suggest a sample of test tool sets for evaluating the task-based performance assessment in integrative teaching of the French language and culture. For this purpose, this study analyzed the characteristics of task-based performance assessment, the communicative competence of Hymes, Canale, Swain, and Bachman, and the cultural competence. In addition, this study investigated the competences of learner/user that Common European Framework of Reference provides. The following results were drawn: 1) assessment criteria: language competence(knowledge of language system, discourse competence, sociolinguistic competence), cultural competence(cultural knowledge, attitude, competence), task performance competence (procedural knowledge, performance competence, strategic competence), 2) subcategory and construct, and 3) self-assessment and peer assessment.
C-Rank: A Concept Linking Approach to Unsupervised Keyphrase Extraction

Keyphrase extraction is the task of identifying a set of phrases that best represent a natural language document. It is a fundamental and challenging task that assists publishers to index and recommend relevant documents to readers. In this article, we introduce C-Rank, a novel unsupervised approach to automatically extract keyphrases from single documents by using concept linking. Our method explores Babelfy to identify candidate keyphrases, which are weighted based on heuristics and their centrality inside a co-occurrence graph where keyphrases appear as vertices. It improves the results obtained by graph-based techniques without training nor background data inserted by users. Evaluations are performed on SemEval and INSPEC datasets, producing competitive results with state-of-the-art tools. Furthermore, C-Rank generates intermediate structures with semantically annotated data that can be used to analyze larger textual compendiums, which might improve domain understatement and enrich textual representation methods.
Hand-Dorsa Vein Recognition Based on Task-Specific Cross-Convolutional-Layer Pooling

Hand-dorsa vein recognition is solved based on the convolutional activations of the pre-trained deep convolutional neural network (DCNN). In specific, a novel task-specific cross-convolutional-layer pooling is proposed to obtain the more representative and discriminative feature representation. Rigorous experiments on the self-established database achieves the state-of-the-art recognition result, which demonstrates the effectiveness of the proposed model.
Selective Affinity Propagation Ensemble Clustering

An ensemble clustering algorithm based on affinity propagation clustering (APC) algorithm is proposed in the paper. Different base clustering results are obtained by using APC algorithm under the selected preference parameter. However, some base clusters have little effect on the final clustering result, the “good” base clusters should be selected from all base clusters. Then the diversity and coverage ratio are applied to the judgement of the homogeneity of generated base clusters. Finally, the evidence accumulation clustering (EAC) algorithm is used for obtaining the co-association matrix from the selected base clusters, and generating final the clustering result. Experimental results on two data sets demonstrate that the clustering ensemble based on the refined co-association matrix outperforms some state-of-the-art clustering ensemble schemes.
Demonstrating Customisation Markup in the Siren Songs Sonification System

Live and retrospective analysis of network traffic is a challenging task for IT Departments and Network Operating Centres. Examining graphs and log files is time-consuming, and alerts can be missed. Network sonification provides an 'at-a-listen' state of the network that, during normal operation, is easy to ignore and non-fatiguing for listeners, and during attacks surfaces the type of attack with a unique sound signature. Prior research into network sonification lacks a formal grammar and aesthetic considerations. This demo paper presents the technical details of Siren Songs, a network sonification system which translates network traffic and attacks into a customisable MIDI output that can feed into any MIDI instrument or Digital Audio Workstation (DAW). The related research provoked interesting questions about the aural bandwidth of listeners, and the system as presented enables bespoke sonifications for future investigation of aural bandwidth in network monitoring.
Deep Learning Based Gesture Recognition System for Immersive Broadcasting Production

In this paper, we implement a system that provides the convenience of personal broadcasting production by recognizing the user’s operation using the sensor tag and implementing the function corresponding to the operation. The system can acquire sensor data and learn the data through deep learning to distinguish the user’s gesture. In this paper, we study the process of recognition of data through the data acquisition process and the deep learning process using the sensor tag and propose a method to perform the function using it.
The Image Matching Algorithm Basing on Homographic Constraints for Marine Surveying, Mapping and 3D Reconstruction

Chen, Y.; Le, Y.; Xie, Z.; Qiu, Z; Zhang, C.; Cao, B., and Fang, S., 2019. The image matching algorithm basing on homographic constraints for marine surveying, mapping and 3D reconstruction. In: Hoang, A.T. and Aqeel Ashraf, M. (eds.), Research, Monitoring, and Engineering of Coastal, Port, and Marine Systems. Journal of Coastal Research, Special Issue No. 97, pp. 184–190.In the marine surveying and mapping, high-accuracy image matching is of crucial importance to improve the surveying and mapping accuracy. For the low texture area, coastal zone, island and reef, the accuracy of the mapping and 3D reconstruction would be impacted by the number of matching points and the matching accuracy, using the conventional matching algorithms basing on the imaging feature, such as scale-invariant feature transform (SIFT), speed-up robust features (SURF) and oriented brief (ORB) et al.. To overcome and reduce the problem, this paper proposes a new image matching algorithm, the homographic constraint SURF algorithm, which combines the homographic constraint condition and SURF algorithm in order to increase the number of the high-accuracy matching points and improve the accuracy of the mapping and 3D reconstruction for the marine surveying and mapping. Through experiments performance, HCSURF can extract the more number of the matching points and obtain the higher matching accuracy for island and reef images, comparing with the SIFT, SURF and ORB algorithms. In addition, the characteristics and application categories of the HCSURF are analyzed and discussed basing on the experimental results. Using the new proposed matching algorithm can improve and ensure the accuracy of the marine surveying, mapping and 3D reconstruction.
Opportunistic Synthesis in Reactive Games under Information Asymmetry

Reactive synthesis [1]is used to synthesize a provably-correct strategy (controller) with respect to a given Linear Temporal Logic (LTL) specification in a dynamic, potentially adversarial environment. The core idea of reactive synthesis is to model the interaction between a controlled agent (called the robot) and its dynamic and uncontrollable environment as a two-player turn-based zero-sum game. In particular, players' Boolean payoffs are specified by temporal logic formulas [2]-a rich language that can expressive properties such as safety, liveness, stability, and temporally extended goals.
Applying Matrix Factorization for Predicting Click Through Rate on Advertizing in Apps on Mobile Devices

The number of smart phone and smartphone users has increased rapidly, boosting the growth of mobile advertising. Predicting the percentage of users who click on an advertisement when displayed on an application (CTR: Click Through Rate) plays an important role in choosing an advertising push strategy for each application. However, because the field of mobile advertising is still new, the study of solutions to these problems is only for Web advertising. This paper presents a new predictive method based on Matrix Factorization (MF) algorithm. The paper also proposes a method of integrating the reliability of data into MF algorithms. The method was tested on actual data provided by Amobi advertising company with 1.7 million records including 300 advertisements and 400 mobile device applications. The AUC (Area Under The Curve) value obtained on the test set is 0.9037 with MF and 0.9411 when integrating reliability into the MF algorithm. Thus, the results show that the predictive quality is significantly improved when integrating data reliability and evaluation into MF algorithms.