Reviewerly/exHarmony huggingface dataset:
Learning to create multi-stylized Chinese character fonts by generative adversarial networks

Owing to the complex structure of Chinese characters and the huge number of Chinese characters, it is very challenging and time consuming for artists to design a new font of Chinese characters. Therefore, the generation of Chinese characters and the transformation of font styles have become research hotspots. At present, most of the models on Chinese character transformation cannot generate multiple fonts, and they are not doing well in faking fonts. In this paper, we propose a novel method of Chinese character fonts transformation and generation based on Generative Adversarial Networks. Our model is able to generate multiple fonts at once through font style specifying mechanism and it can generate a new font at the same time if we combine the characteristics of existing fonts.
Faster parallel collision detection at high resolution for CNC milling applications

This paper presents a new and more work-efficient parallel method to speed up a class of three-dimensional collision detection (CD) problems, which arise, for instance, in computer numerical control (CNC) milling. Given two objects, one enclosed by a bounding volume and the other represented by a voxel model, we wish to determine all possible orientations of the bounded object around a given point that do not cause collisions. Underlying most CD methods are 3 types of geometrical operations that are bottlenecks: decompositions, rotations, and projections. Our proposed approach, which we call the aggressive inaccessible cone angle (AICA) method, simplifies these operations and, empirically, can prune as much as 99% of the intersection tests that would otherwise be required and improve load balance. We validate our techniques by implementing a parallel version of AICA in SculptPrint, a state-of-the-art computer-aided manufacturing (CAM) application used CNC milling, for GPU platforms. Experimental results using 4 CAM benchmarks show that AICA can be over 23× faster than a baseline method that does not prune projections, and can check collisions for 4096 angle orientations in an object represented by 27 million voxels in less than 18 milliseconds on a GPU.
North Sumatera tourism spots searching system based on semantic web

Abstract North Sumatera Province in Indonesia has many amazing tourism spots to visit. Unfortunately, information about these places is not organized well yet. Not all of these tourism spots have official websites and that made information with specific parameters according to user needs cannot be fulfilled if only depend on a search engine on the internet. The limitation of ordinary search engines cannot generate complex queries that involving relevant background knowledge of these places such as tourist locations, facilities offered, culinary and so on. Therefore in this paper, we offer North Sumatera Tourist sports searching system based on the semantic web. We utilized ontology as a source of knowledge that describes tourism in the province of North Sumatera. We observed and scrapped information about tourism spots in North Sumatera from trusted traveling websites. The next stage is the design of the Resource Description Framework (RDF) and ontology to describe relations between parameters. The result of this study produces a system that can help and understand the desires of tourists to get information about tourism objects in North Sumatera. Test results with various relevant semantic keywords parameter show the success of this system is 85%.
Interactive Narrative Design for Geographically Dispersed Points of Interest

In 2020, the RheijnLand.Xperiences project will connect eight museums along the Dutch-German border region by a continuation network using a story-driven application for mobile devices for an audience between the ages of 14 and 25. The goal of the project is to motivate users to visit other museums in the network. While experiences and stories are individually tailored for each museum, an overarching narrative and experience structure is also designed to establish connections between them. This structure relies heavily on the contemporary narrative and location-based gaming concepts and practices to compensate for the environmental and thematic diversity of each museum. To enrich the overall experience of visiting multiple museums in the network, the design also relies upon shared and regional characteristics. In this chapter, the authors summarize their conceptual approach, findings of the analysis of the distribution of the museums in the region, core elements of the “universal” narrative and experience design, and early results of the development.
Path planning optimization for a mobile manipulator

Nowadays, mobile manipulators are increasing its popularity on modern industries due to their ability to enhance process flexibility and performance. Mobile manipulators are a wide field of research and one of the main directions is trying to control the whole system as a single device. In this context, this paper addresses the problem of path planning of the end-effector of a mobile manipulator. The proposed approach is based on the integration of the kinematic chain of both the manipulator and the omni-directional base. At the end, a collision-free path planner for the mobile manipulator in complex and known environments with obstacles using A * is derived.
Quantitative Analysis of Feature Extraction Techniques for Isolated Word Recognition

Isolated word recognition has been a subject of research since the 1940s when speech recognition technology was in a nascent stage. Recurrent neural networks and deep feed-forward networks are currently being explored by researchers to increase the efficiency of the speech recognition systems. However, probabilistic techniques like Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) have been the state-of-the-art since long. This paper performs a quantitative analysis of feature extraction techniques for isolated word recognition to provide better insights in improving the efficiency of the system. In this regard, a basic architecture of the word recognizer system has been modelled and it has been observed that Mel Frequency Cepstrum Coefficients (MFCC) in combination with Delta and Delta-Delta parameters have 92.4% accuracy for a sufficiently large dataset. Also MFCC features, appended with Delta parameters have 87.0% accuracy which is 36.4% higher than that of Short Time Fourier Transform (STFT) features. The feature extraction techniques have been classified by a Gaussian Mixture Model- Hidden Markov Model (GMM-HMM) classifier. This paper also studies the effect of varying data size and number of features on the recognition model towards efficient word recognition. These recognizers may then be used as a building block for mispronunciation detection in a Computer Aided Pronunciation Training (CAPT) system.
Toward the Analysis of Distributed Code Injection in Post-mortem Forensics

Distributed code injection is a new type of malicious code injection technique. It makes existing forensics techniques for injected code detection infeasible by splitting a malicious code into several code snippets, injecting them into multiple running processes, and executing them in each process spaces. In spite of the impact of it on practical forensics fields, there was no discussion on countermeasures against this threat. In this paper, we present a memory forensics method for finding all code snippets distributively injected into multiple processes to defeat distributed code injection attacks. Our method is designed on the following observation for distributed code injection attacks. Even though malicious code is split and distributed in multiple processes, the split code snippets have to synchronize each other at runtime to maintain the order of the execution of the original malicious code. We exploit this characteristic of distributed code injection attacks with our method. The experimental results showed that our method successfully found all distributed code snippets and assisted to reconstruct the original code from them. We believe that we are the first to present a countermeasure against distributed code injection attacks. We also believe that our method is able to improve the efficiency of forensics especially for a host compromised with distributed code injection attacks.
Construction and Evaluation of Examination Conditions for Complex Electromagnetic Environment Based on Subject and Object

Based on the discussion of subject-object relationship in electronic information and countermeasure system, this paper sums up different kinds of influence factors by analyzing the vulnerability mechanism of various radio receivers. A new method of constructing complex electromagnetic environment based on the influence factors of subject and object is proposed, which solves the technical problems of spectrum game simulation, such as dynamic spectrum timing, intensive signal spectrum, fluctuation of signal intensity and changeable signal pattern. The evaluation model of multi-excitation and multi-response electromagnetic environment effect based on the relationship between subject and object is established, which solves the objectivity and image evaluation problem of the subjects, and provides a useful reference for the identification of complex electromagnetic environment adaptability of electronic information systems.
Designing, Together and Apart

Designing is a conversational Conversation activity. This chapter draws on second-order cybernetic concepts, particularly Pask’s Pask, Gordon Conversation Theory Conversation theory (CT) and subsequent work inspired and derived from it, to propose a conceptual basis for articulating various modalities of designing-with-others. This approach unifies different ways in which people and artifacts are linked in processes of designing Design(ing) process , from the simple designer-client relationship to people’s interactions Interaction with designed things, to the ripples of effects of these things, beyond the control Control and intentions Intention of the designer, once they are out into the world at large. After introducing the concept of conversation Conversation as applied to the processes of design Design(ing) process , the chapter explores the detailed anatomy of such conversations Conversation with reference to a model Model of conversations Conversation by Dubberly and Pangaro, drawing on Pask’s Pask, Gordon concept of the conversation Conversation . Other cybernetic scholars, notably Krippendorff, are cited to demonstrate that the “statements” exchanged in the course of design conversations Conversation consist of both verbal and written utterances and the diverse range of artifacts produced in the course of the design process Design(ing) process . The chapter then applies these concepts and perspectives to discuss different levels of design conversations Conversation , from those that take place within teams of designers Designers working on a project, to those that occur in the wider communities formed by various designers Designers and non-designers who cooperate in the realization of designed things, to those that permeate broad societies of people affected in one way or another by designed things. Implications are proposed for the role of designers Designers in society, in light of this cybernetic framing of design processes Design(ing) process . The chapter concludes by synthesizing these insights to demonstrate the potential of cybernetic perspectives in articulating the intrinsically political nature of design.
Image Segmentation Using Electromagnetic Field Optimization (EFO) in E-Commerce Applications

Image recognition plays a vital role in image-based product searches and false logo identification on e-commerce sites. For the efficient recognition of images, image segmentation is a very important and is an essential phase. This article presents a physics-inspired electromagnetic field optimization (EFO)-based image segmentation method which works using an automatic clustering concept. The proposed approach is a physics-inspired population-based metaheuristic that exploits the behavior of electromagnets and results into a faster convergence and a more accurate segmentation of images. EFO maintains a balance of exploration and exploitation using the nature-inspired golden ratio between attraction and repulsion forces and converges fast towards a globally optimal solution. Fixed length real encoding schemes are used to represent particles in the population. The performance of the proposed method is compared with recent state of the art metaheuristic algorithms for image segmentation. The proposed method is applied to the BSDS 500 image data set. The experimental results indicate better performance in terms of accuracy and convergence speed over the compared algorithms.
FAID: Feature Aftermath for Irony Discernment

This paper deals with the impediment of identifying sarcasm in social media text which can be used to improve sentiment analysis technique. After thorough analysis, some features were identified which could help in recognition of sarcasm. In state of art, features have been extracted from the data set which embraced standalone sentences. Proposed algorithm analyzes the impact of these features and a combination of them on the review data set in which reviews had three or more sentences, so that context of sentence is also taken into consideration by the machine before classifying a review.
Cognitive Style and Teaching Method: The Effect on Students’ Writing Performance

Teaching method and cognitive style are considered to involve in learners' academic performance.This paper investigates the effect of cognitive style and teaching method on students' writing performance.This experimental study involved 44 participants administered a Group Embedded Figures Test (GEFT) before applying the treatment.The GEFT scores was used to group them into experimental and control classes.The experimental and control classes were taught respectively metaphorming and discovery methods.After 10 meetings, the two groups were administered an essay writing post-test organized in 100 minutes.The data were analyzed statistically using two-way ANOVA at the significant level 0.05.The findings showed that the experimental group performed better in writing English essay than the control group for fieldindependent students.It implies that teaching methods can benefit students when aligning with their cognitive style dealing with essay writing skill.
Ellipsis in Dependency Grammar

Abstract This contribution considers how dependency grammars (DGs) view ellipsis phenomena. For the most part, comprehensive explorations of particular ellipsis phenomena are rare in the tradition of DG, but when ellipsis is examined, there is a tendency to posit the existence of null material/nodes to accommodate aspects of ellipsis. One recent development in the DG tradition that is particularly relevant to the analysis of ellipsis is the catena unit. A catena is a word or combination of words that are linked together by dependencies. Most ellipsis phenomena are eliding catena, whereby these catenae are often not constituents. Given the catena unit, a more economical approach to ellipsis has become possible, one that does not need to resort to movement to vacate the remnant from the encompassing constituent before that encompassing constituent is elided.
SOS: Save Our Social Network Accounts

Social media platforms such as Facebook and Twitter have become universally prevalent for connecting people, sharing information, or spreading messages and ideas. However, the social dominance of these platforms have also made them attractive targets for malicious intruders. Successful attacks against personal social media accounts can cause serious harm to their users, due to the possibility of revealing intimate private information. Discovering these hacking attempts can be done by detecting anomalous (out of the normal patterns) behavior over the user's account. However, personal accounts show inconsistent behavioral patterns over time, making it highly challenging to extract definitive features for each user, and accordingly to detect anomalies. In this paper, we propose a novel methodology based on subjective logic and machine learning techniques to detect and extract the most highly defining features of each personal account. These features are then used to build a behavioral profile for each user, which can be used to detect anomalies. We conducted 2 studies, collected data from 47 Facebook users and 616 Twitter users. We extracted features from the collected data and assigned each feature a weight as a scale of importance according to the user's deviation in this feature. We simulated 2,209 different attacks in the first study and 379,456 different attacks in the second study. Our results show a detection accuracy of 94.9% and 87.54% for the first and second studies, respectively.
Research on online user comments in artificial intelligence times

In this era of artificial intelligence, the online user comment in the e-commerce platform contains a lot of valuable information. Mining and analyzing these online comments can help enterprises and organizations better understand the clients' needs, attitudes and concerns. Based on studies on word of mouth, automatic tag generation, and deep learning technologies, this paper uses Long Short-Term Memory (LSTM) as a hidden layer neuron, introduces the attention mechanism, captures information in text sequences, understands user comment text, and builds a model of automatic tags generation. The model can be used to help consumers make smarter shopping decisions and help merchants make better business decisions.
Knowledge Management for Problem Solving Using Semistructured Contradiction Matrix Based on Physical Quantity Description

Abstract A Contradiction Matrix of TRIZ that classifies problems to solve as contradictions of features is an effective framework of knowledge management for problem solving. The features, however, may have a problem of completeness because they may not cover contradictions about all physical phenomena. In addition, rigidly structured Contradiction Matrix may have a problem of searchability because a relevant contradiction may not be properly searched if a recorder and a retriever describe it differently. To solve these problems, this paper proposes a semistructured contradiction matrix using not TRIZ features but physical quantities in SI unit. To enable not only exact match but also partial match in searching for relevant contradictions, dimensional similarity and qualitative value similarity of physical quantity and similarity between contradictions are defined. The proposed method is implemented as software in Python and contradictions are described in XML and stored in a semistructured matrix. From the result of similarity calculation between stored contradictions, possible effectiveness of the proposed method is confirmed.
Lattice LSTM Model for Function Point Based Software Cost Measurement

One of the core problems in the field of software engineering is to measure the software development cost, which helps to ensure the software quality and avoid the waste of investment. Traditionally, the software cost measurement is implemented by experts who are strictly trained, which is relatively expensive and inefficient. In this paper, a neural network based machine learning method is proposed to perform software cost measurement task, which decreases the time and manpower costs. The proposed method takes advantage of the long short term memory (LSTM) neural network and conditional random field to identify different types of function points in software requirements document, where the function points is the key to measure to software cost. To solve the incorrect word segmentation problem in open domain such as software development cost field, lattice LSTM model is constructed to utilize both Chinese characters and words information. Experiments show the proposed method significantly reduces the human labor and ensure the quality of software cost measurement.
Framework for Hiding Information in Audio Sub-signals by Using Singular Spectrum Analysis with Psychoacoustic Model

This paper proposes a method for hiding information in audio signals by using singular spectrum analysis (SSA) with a psychoacoustic model to enhance the sound quality of a watermarked signal from our previous work. In this framework, an original signal is decomposed into many sub-signals by applying the combination of the SSA-based method and the psychoacoustic model. The sub-signals to be embedded are chosen using signal analysis based on human perception. The watermark bits are embedded into selected sub-signals by modifying some parts of their singular spectra according to an embedding rule. Then, the accuracy of extracted watermark bits and the sound quality of the watermarked signal, compared with that of the original one, are used to verify the performance of our framework. The experimental results show that the proposed framework satisfies the evaluation criteria in terms of inaudibility and robustness. However, it has a significant trade-off between them.
Improving the Speaking Ability through Role Playing Model in Learning Indonesian Language

This research is motivated by the students' low speaking ability in Indonesian language learning, which aims to improve students' speaking skills through the application of the Role Playing Model. This research is a Classroom Action Research (CAR) carried out in cycles II using a model from Kemmis and MC. Taggart, each action in each cycle consists of four stages, namely planning, action, observation, reflection. This research was conducted at elementary school of Rancagoong, District of Cilaku, Cianjur Regency. The subjects of this study were fourth-grade students, amounting to 24 students, consisting of 13 female students and 11 male students. Data collection techniques used are process tests, observations, interviews, and documentation. The data obtained are in the form of test results as primary data as well as observations, while the interviews and documentation as supporting data. The data analysis technique used is descriptive quantitative analysis. The results of the study show that the use of the Role Playing Model can improve students' speaking ability. This is indicated by an increase in the average value from before the action is taken until the implementation of the cycle II. The average value obtained in cycle 1 after being given action reached 66.88% with a graduation percentage of 54.17%. Then in the cycle II the average value obtained increased to 77.40% with a graduation percentage of 83.33%. In general, it can be concluded that the application of the Role Playing Model can improve students' speaking ability in learning the Indonesian Language in grade IV at Elementary school of Rancagoong, District of Cilaku, Cianjur Regency.
Does 5-Minute RV Outperform Other Realized Measures in the Bitcoin Market?

After the seminal work of Liu et al. (2015) finds that the realized volatility (RV) using 5-minute intervals performs well, economists tend to use this simple measure in applications. Existing literature in the cryptocurrency already relies on 5-minute RV, but no paper has evaluated whether 5-minute RV performs well compared with other realized measures. Following Liu et al. (2015), we show that the 5-minute RV of Bitcoin performs well compared to other realized measures, meaning that this result justifies the existing literature that already uses this simple measure. This paper also indicates that realized measures with longer intervals such as 120-minute RV could provide inaccurate estimates.