Reviewerly/exHarmony huggingface dataset:
Chapter 4. “Tu masssss ❤ te amo”

Complimenting behavior within a group of Ecuadorian teenage girls on Instagram was examined by Placencia (2019). Using the same corpus, in the present study we consider the responses to compliments offered. These responses, while short, are remarkably complex, containing both text and images (emojis) and indeed textual stand-ins for paralinguistic cues such as laughter. Responses were grouped according to Holmes’s (1986) taxonomy and the data shows a distinct preference for accepting compliments given (97.7%), with fewer sub-strategies used compared to other studies. These results are considered in the context of the type of photos examined and the overall function that Instagram appears to fulfil for the group of teenagers in this study. Beyond what might be termed the basic strategies of accepting or rejecting compliments, we also look at the rich and varied sub-strategies used by the girls in this Instagram community, which they employ to express solidarity and show modesty when accepting compliments.
Web User Grouping Based on Navigation Patterns Through Pair Wise Sequence Alignment and Breadth First Search

User grouping is one of the domains that have gained more interest among the web researchers so that users can be provided a customized and better environment. This paper attempts on finding user groups through their web page navigation pattern. The methodology primarily incorporates five different ways of interpreting the input navigation sequence and investigates if there is any influence in the obtained results. For each interpretation, pair-wise sequence alignment is carried out to determine the number of aligned pages. Subsequently, a similarity matrix is formulated based on the number of aligned pages and the unique number of pages between the users. Then, the matrix is thresholded based on the maximum value at each column as column thresholding. After that, breadth first search is performed to identify the user groups. MSNBC dataset, a publicly available data is used for assess the proposed methodology. Jaccard similarity co-efficient is computed to calculate the inter-group similarity. The values exhibits that the sorted input navigation sequence without redundancy provides effective clustering.
Development of Integrated Geotechnical Safety Monitoring System

This paper proposes a geological disaster monitoring system based on data fusion algorithm. In a monitoring system consisting of multiple sensors, the target is monitored at the same sampling rate at the same time, and the collected measured values are expanded and then combined with the direct measured values to form a new system. The Kalman filtering algorithm and the extended Kalman filtering algorithm can obtain the centralized fusion and fusion estimation algorithm of the system. Using this algorithm, the state value of the current time can be combined with the measured value of the current time to obtain a state estimate for the next time.
Comprehensive Facial Expression Synthesis Using Human-Interpretable Language

Recent advances in facial expression synthesis have shown promising results using diverse expression representations including facial action units. Facial action units for an elaborate facial expression synthesis need to be intuitively represented for human comprehension, not a numeric categorization of facial action units. To address this issue, we utilize human-friendly approach: use of natural language where language helps human grasp conceptual contexts. In this paper, therefore, we propose a new facial expression synthesis model from language-based facial expression description. Our method can synthesize the facial image with detailed expressions. In addition, effectively embedding language features on facial features, our method can control individual word to handle each part of facial movement. Extensive qualitative and quantitative evaluations were conducted to verify the effectiveness of the natural language.
Trajectory Anonymity Based on Quadratic Anonymity

Due to the leakage of privacy information in the sensitive region of trajectory anonymity publishing, which is resulted by the attack, this paper aims at the trajectory anonymity algorithm of division of region. According to the start stop time of the trajectory, the current sensitive region is found with the k-anonymity set on the synchronous trajectory. If the distance between the divided sub-region and the adjacent anonymous area is not greater than the threshold d, the area will be combined. Otherwise, with the guidance of location mapping, the forged location is added to the sub-region according to the original location so that the divided sub-region can meet the principle of k-anonymity. While the forged location retains the relative position of each point in the sensitive region, making that the divided sub-region and the original Regional anonymity are consistent. Experiments show that compared with the existing trajectory anonymous algorithm and the synchronous trajectory data set with the same privacy, the algorithm is highly effective in both privacy protection and validity of data quality.
3D Reconstruction from A Single Image

3D reconstruction is a field of computer vision. The last decade has seen great interest in multi-images 3D reconstruction, and proposed some classic algorithms such as SFM(surface from motion)[1]. But 3D reconstruction based on a single image is a long-standing and difficult problem due to great information loss from 3D to 2D. With the rise of neural networks and deep learning, people began to realize that neural networks could be trained to learn the three-dimensional structure of objects in a single image. 3D reconstruction is divided into scene reconstruction, and object reconstruction, this paper mainly discusses the reconstruction of objects. The purpose of this paper is to briefly summarize the major challenges in this field, research progress and potential research directions.
A Formal Approach for the Modeling of High-Level Architectures Aligned with System Requirements

IRT Railenium ( http://railenium.eu/fr/ ) is a test and applied research center for the rail industry in France. One of its three R&D and innovation programs aims in particular to provide the technological tools and bricks necessary for the development of the Autonomous Train.
Natural Language Agents in a Smart Environment

There have been significant achievements in the area of human–computer interaction using natural language (i.e., speech), applied in various domains. As a result, users can intuitively interact with various smart devices and systems. Unfortunately, the full extent of capabilities is almost exclusively related to the English language. This is expected, as most advanced solutions and achievements in the field come from tech giants originating from English-speaking countries. English is also considered the global lingua franca, so this is definitely the most viable language from the economic perspective. However, in the case of minority languages, such as Croatian, there are many challenges ahead in order to achieve comparable results. In this paper, we present a model which intends to enable spoken notifications in the Croatian language. The presented model defines cognitive and linguistic processes which enable generation and reproduction of understandable, human-friendly notifications in smart environments.
Remote Sensing of Ground Objects 2: Multispectral Chemical Characteristics of Surface Polarization Reflection

In this chapter, multi-spectral chemical characteristics, which are the second most important feature of polarization remote sensing, are introduced in terms of surface polarization reflection. In the formulation the various new concepts and equations we shall not aim at the maximum generality possible but limit ourselves, rather, by the situations which the problems recounted in this chapter actually need. In view of this, we shall devote this chapter to the study of multi-spectral chemical characteristics.
Canonical Correlation Analysis of Principal Component Scores for Multiple-set Random Vectors

Canonical correlation analysis (CCA) is often used to analyze correlations between the variables of two random vectors. As an extension of CCA, multiple-set canonical correlation analysis (MCCA) was proposed to analyze correlations between multiple-set random vectors. However, sometimes interpreting MCCA results may not be as straightforward as interpreting CCA results. Principal CCA (PCCA), which uses CCA between two sets of principal component (PC) scores, was proposed to address these difficulties in CCA. We propose multiple-set PCCA (MPCCA) by applying the idea to multiple-set of PC scores. PCs are ranked in descending order according to the amount of information they contain. Therefore, it is enough to use only a few PC scores from the top instead of using all PC scores. Decreasing the number of PC makes it easy to interpret the result. We confirmed the effectiveness of MPCCA using simulation studies and a practical example.
Intelligent Substation Communication Network Traffic Predicting Based on Improved Elman Neural Network

Due to the introduction of a large number of auxiliary monitoring equipment in smart substation, higher requirements are put forward for the performance of substation communication network. At present, there is a lack of effective methods for monitoring and predicting the traffic of substation communication network. In this paper, a communication Network traffic predicting method based on improved Elman neural network has been proposed, according to the characteristics of network communication in intelligent substation. An improved PSO algorithm is proposed to optimize the learning process of Elman neural network. The experimental results show that this method has better convergence and stability than the standard Elman network model, and can obtain higher prediction accuracy. The method proposed in this paper can effectively predict the traffic of substation communication network.
Kyaragobi:Enhancing the identity of a <i>Kyara</i> on Japanese fiction in the digital era

There are plenty of kyara[s] created by Japanese authors in fiction. Due to the many kyara, many ways are used by authors of Japanese fiction. One of the ways is by putting a catch phrase at the end of a dialog in a kyara. This kind of catch phrase is called kyaragobi. The method that is used in this research is qualitative method combined with descriptive analysis. The purpose of the research is to prove that kyaragobi to be more than just a catch phrase, but instead as a more significant supporting element to an identity of a kyaragobi's distinctive character, especially in digital era. Based on the analysed questionnaires, kyaragobi gives a different representation related to the catch phrase of a kyara. By using a kyaragobi, an identity of a kyara becomes more distinctive compared to other similar kyaras.
Facilitating Media Distribution with Monetary Incentives

Community media forums such as CGNet Swara and Mobile Vaani leverage interactive voice response (IVR) technology to enable feature phone owners in underprivileged regions of the world to call a toll-free phone number and listen to and report local stories. This excludes potential users living in areas without any mobile network. In this paper, we describe the deployment of an app that both facilitates a new means of media dissemination using a novel incentive scheme, and enables content collection, from no-network areas. In partnership with CGNet Swara, we deployed our app in the rural Indian states of Chhattisgarh and Telangana. In one month, 20,955 stories were transferred through Bluetooth to 2,443 unique phones, for which a total of $930 in mobile airtime credits was disbursed to 307 of the 680 users that installed the app. In an 81-day period, 537 local stories were reported using the app from 117 unique users. We present a quantitative and qualitative analysis of user behavior.
Handwritten Numeral Superposition to Printed Form Using Convolutional Auto-Encoder and Recognition Using Convolutional Neural Network

This paper presents a novel HNR system based on the hypothesis of human learning of writing. Superposition of different shaped, sized and oriented handwritten numerals into same-sized printed form will make recognition task easy because classifier has to classify a small set of fixed patterned printed images leading to improvement of recognition accuracy. A modified version of convolutional auto-encoder (CAE) has been utilized as superposition method to transform images of handwritten numeral into printed numeral, while convolutional neural network (CNN) is used as a classifier to recognize the printed numeral. The efficiency of the proposed system is tested on Bengali numerals owing to achieve fair recognition performance because the recognition accuracy of Bengali HNR is still relatively low and remains an open research challenge.
A Lightweight Architecture Analysis of a Monolithic Messaging Gateway

Background: The Enterprise Messaging Gateway (EMG) from Infoflex Connect (ICAB) is a monolithic system used to deliver mobile text messages (SMS) world-wide. The companies using it have diverse requirements on both functionality and quality attributes and would thus benefit from more versatile customizations, e.g. regarding authorization and data replication. Objective: ICAB needed help in assessing the current architecture of EMG in order to find candidates for architectural changes as well as fulfilling the needs of variability in meeting the wide range of customer requirements. Method: We analysed EMG using a lightweight version of ATAM (Architectural Trade-off Analysis Method) to get a better understanding of how different architectural decisions would affect the trade-offs between the quality requirements from the identified stakeholders. Result: Using the results of this structured approach, it was easy for ICAB to identify the functionality that needed to be improved. It also became clear that the selected component should be converted into a set of microservices, each one optimized for a specific set of customers. Limitation: The stakeholder requirements were gathered intermittently during a long period of continuous engagement, but there is a chance some of their requirements were still not communicated to us. Conclusion: Even though this ATAM study was performed internally at ICAB without direct involvement from any external stakeholders, documenting elicited quality attribute requirements and relating them to the EMG architecture provided new, unexpected, and valuable understandings of the system with a rather small effort.
Geochemical features of soils from the Mid-Ural ore deposit

This paper deals with the contemporary state of soil cover around the Mid-Ural iron ore deposit, focusing on geochemical features. Sample plots were laid based on the technogenic impact in the study area. Soil samples were taken in the zone of the iron ore deposit, in the zones of overburden and gold mining waste dumps influence, and in the rock blasting site. A total of 64 sample plots were laid. Soil samples were sampled incrementally by an envelope method from a depth of 0.00–0.03 m. Soil texture, physicochemical features, and total heavy metal content (V, Mn, Ni, Cu, Zn, As, Cd, and Pb) were defined for the soil samples. As a result, the evaluation of heavy metal soil pollution under the influence of iron ore deposit mining is given. Chemical contamination of soil was assessed by a cumulative chemical contamination index. Geochemical series of elements formed under the influence of iron ore deposit mining, as well as overburden and gold mining are given. Here, geochemical patterns of heavy metal distribution in the soil of iron ore deposit and under the gold mining influence are considered. The analysis of gold mining influence on the elementary composition of soil revealed the anomalous content of Mn and Ni.
Trajectory Planning of Wafer Handling Robot Based on ADAMS Simulation

In view of the need to set different trajectories for wafer handling robot to adapt to the production process, proposed to use computer simulation to run the trajectory of wafer handling robot. The author analysis the kinematics of the robot and established the corresponding mathematical model. The trajectory of the robot is planned for a specific production link. The trajectory is run in ADAMS, and analyzed the result of simulation.
An Improved Non-local Means Denoising Algorithm and Its Application in Denoising Analysis of Metal Fracture

The analysis of metal fracture image plays an important role in metal intelligent diagnosis. Precise analysis of material properties of metal fractures requires denoising preprocessing of images. This paper presents a non-local means denoising algorithm based on improved neural network to denoise metal fracture images. Specifically, the global connection in the non-local means denoising algorithm is used to extract the non-local data in the noise image as the input to train neural network, and the noise image is iteratively filtered by the trained model. The experimental results show that the denoising performance of the proposed algorithm has higher signal-to-noise ratio than the traditional algorithms, and it is more complete in the preservation of metal fracture boundary information.
Integrating Multiple Receptive Fields Through Grouped Active Convolution

Convolutional networks have achieved great success in various vision tasks. This is mainly due to a considerable amount of research on network structure. In this study, instead of focusing on architectures, we focused on the convolution unit itself. The existing convolution unit has a fixed shape and is limited to observing restricted receptive fields. In earlier work, we proposed the active convolution unit (ACU), which can freely define its shape and learn by itself. In this paper, we provide a detailed analysis of the previously proposed unit and show that it is an efficient representation of a sparse weight convolution. Furthermore, we extend an ACU to a grouped ACU, which can observe multiple receptive fields in one layer. We found that the performance of a naive grouped convolution is degraded by increasing the number of groups; however, the proposed unit retains the accuracy even though the number of parameters decreases. Based on this result, we suggest a depthwise ACU (DACU), and various experiments have shown that our unit is efficient and can replace the existing convolutions.
Misinformation and User-Generated content: Applying participatory journalism practices in fact-checking

In the evolving news media landscape, the proliferation of user-generated content in online news outlets and social media platforms has triggered changes in traditional processes and relationships. However, the coexistence of professional and amateur content raises a wide range of matters. Misinformation is one of the main problems faced by media organizations during the exploitation of huge amounts of data. In order to ensure the quality of the content, journalists use control methods and perform fact-checking not only on their own, but also by engaging users. By offering an examination of key issues arising from UGC research, this article seeks to focus on the application of participatory practices in fact-checking. In addition to more traditional methods, the web-based platform of Truly Media, which supports collaborative verification, is used as a case-study.
Case Reasoning Research of Emergency Network Opinion Based on Ontology

This paper introduces the construction process of the emergency network opinion case library. It uses ontology as the basic rule of knowledge representation and knowledge reasoning, it analyzes the field of emergency network opinion, and obtains the ontology model of emergency network opinion case. This paper discusses the process of case reasoning and the main method of case retrieval. It obtains the weighted sum of attribute similarity according to the categories of attributes, and realizes the application test of case reasoning of the emergency network opinion.
Research on Deep Learning Based Code Generation from Natural Language Description

With significant advances in deep learning, the code generation from natural language description has become a prevailing research. Existing researches demonstrate that these methods have achieved high BLEU values. However, the data sets used in the existing researches lack diversity, and they usually use BLEU as the only evaluation metric. To overcome these limitations, in this paper, we crawled a data set that is more suitable for code generation from the online judge system, and re-run the existing code generation models on this data set. We evaluate the generated code from five aspects: lexical similarity, tree similarity, syntactic legality, semantic legality, and functional correctness. This study provides a deeper analysis of the performance of existing code generation methods.
Character Recognition for Automotive Parts Coding Based on Convolutional Neural Network

The recognition of coding characters is vital in automobile manufacturing and assembling, since it guarantees the circulation of essential information on production line. However, due to the fact in real world industrial applications, parts are involved in complex working situation such as reflection, smudge, abrasion and etc. Degradation of the performance of recognition is extremely serious. In this paper, a convolutional neural network (CNN) based method, including localization, segmentation and recognition is proposed to address the problem. Firstly, in order to meet the complex working situation as well as improving the stability and accuracy of character region localization, this method proposes an improved Maximally Stable Extremal Regions (MSER) algorithm introducing Gaussian distribution probability judgment and gray threshold judgment. Next, the combination of statistics and projection algorithm achieves single character segmentation which does not need any prior knowledge. Finally, a convolutional neural network is constructed to recognize character. The proposed method is evaluated through experiments on a platform. Experimental results and comprehensive comparison analysis with respect to traditional recognition method have demonstrated the superiority of the proposed method.
Synthesis of Self-Adaptable Software for Multicore Embedded Systems

This paper presents a method of synthesis of realtime software for self-adaptive multicore systems. The method assumes that the system specification is given as a task graph. Then, tasks are scheduled on multicore architecture consisting of low-power and high-performance cores. We apply the developmental genetic programming to generate the self-adaptive scheduler and the initial schedule. The initial schedule is optimized taking into consideration the power consumption, the real-time constraints as well as the self-adaptivity. The scheduler modifies the schedule, during the system execution, whenever execution time of the recently finished task occurred other than assumed during initial scheduling. We propose two models of self-adaptivity: self-optimization of power consumption and self-adaptivity of real-time scheduling. We present some experimental results for standard benchmarks, showing the advantages of our method in comparison with the worst case design used in existing approaches.