Reviewerly/exHarmony huggingface dataset:
Real-time patch-based stylization of portraits using generative adversarial network

We present a learning-based style transfer algorithm for human portraits which significantly outperforms current state-of-the-art in computational overhead while still maintaining comparable visual quality. We show how to design a conditional generative adversarial network capable to reproduce the output of Fiser et al.'s patch-based method [FJS* 17] that is slow to compute but can deliver state-of-the-art visual quality. Since the resulting end-to-end network can be evaluated quickly on current consumer GPUs, our solution enables first real-time high-quality style transfer to facial videos that runs at interactive frame rates. Moreover, in cases when the original algorithmic approach of Fiser et al. fails our network can provide a more visually pleasing result thanks to generalization. We demonstrate the practical utility of our approach on a variety of different styles and target subjects.
BIAS GENDER DALAM BUKU TEKS PELAJARAN BAHASA INDONESIA SEKOLAH MENENGAH ATAS

Abstract: Gender Bias in Indonesian Language Textbook in Senior High School.This research to described gender bias based on the subject-object viewpoint and author-reader viewpoint of Sara Mills theory on textbook. The data of this research are vocabulary, clause, sentence, or paragraph that contain gender perspective and the source of the data of this research is reading text on the Indonesian language textbook for senior high school grade X by Kemendikbud, revised edition of 2017. Technique of data collection are reading and writing. The whole data collected were analyzed in depth and identified based on gender perspective. The results of this research are (1) forms of gender bias of the subject-object viewpoint are subordination and stereotyped to women, (2) forms of gender bias of the author-reader viewpoint, there are two aspects of the findings, namely indirect sweeping carried out by means of mediation and cultural code. Direct sweeping which uses first-person singular pronoun, third-person singular pronoun, and third-person plural pronoun.
The Possibility of Personality Extraction Using Skeletal Information in Hip-Hop Dance by Human or Machine

The same dance can give different impressions depending on the way the dancers convey their own emotions and personality through their interpretation of the dance. Beginner dancers who are teaching themselves often search for dance videos online that match their own personality in order to practice and mimic them, but it is not easy to find a dance that suits their own personality and skill level. In this work, we examined hip-hop dance to determine whether it is possible to identify one’s own dance from skeleton information acquired by Kinect and whether it is possible to mechanically extract information representing the individuality of dance. Experimental results showed that rich experienced dancers could distinguish their own dances by only skeleton information, and it was also possible to distinguish from averaged skeletal information. Furthermore, we generated features from the skeletal information of dance and clarified that individual dance can be distinguished accurately by machine learning.
Deep Predictive Neural Network: Unsupervised Learning for Hand Pose Estimation

The discriminative approaches for hand pose estimation from depth images usually require dense annotated data to train a supervised network.Additionally, generative methods depend on temporal information in generating candidate poses which can be trapped due to local minima during the optimization process.Different from these methods, we propose a hybrid two-stage deep predictive neural network approach that performs predictive coding of image sequences of hand poses in order to capture latent features underlying a given image.Firstly, we train a deep convolutional neural network (CNN) for direct regression of hand joints position.Secondly, we add an unsupervised error term as a part of the recurrent architecture connected with predictive coding portion.An error regression term (ERT) ensures minimal residual errors of the estimated values while the predictive coding portion allows training of the network without the supervision of image sequences, so no dense annotation of data is required.We conduct a complete experiment using two challenging public datasets, ICVL and NYU.Using the ICVL datasets, our approach improved accuracy over the current state of the art methods with an average error joint of 7.5mm.We also achieve 12.2mm average error joint on NYU dataset which is the smallest error to be achieved on all state-of-art approaches.
Simulating the Impact of Annotation Guidelines and Annotated Data on Extracting App Features from App Reviews

The quality of automatic app feature extraction from app reviews depends on various aspects, e.g. the feature extraction method, training and evaluation datasets, evaluation method etc. Annotation guidelines used to guide the annotation of training and evaluation datasets can have a considerable impact to the quality of the whole system but it is one of the aspects that is often overlooked. We conducted a study in which we explore the effects of annotation guidelines to the quality of app feature extraction. We propose several changes to the existing annotation guidelines with the goal of making the extracted app features more useful to app developers. We test the proposed changes via simulating the application of the new annotation guidelines and evaluating the performance of the supervised machine learning models trained on datasets annotated with initial and simulated annotation guidelines. While the overall performance of automatic app feature extraction remains the same as compared to the model trained on the dataset with initial annotations, the features extracted by the model trained on the dataset with simulated new annotations are less noisy and more informative to app developers.
New mission and opportunity for mathematics researchers: cryptography in the quantum era

This article introduces the NIST post-quantum cryptography standardization process. We highlight the challenges, discuss the mathematical problems in the proposed post-quantum cryptographic algorithms and the opportunities for mathematics researchers to contribute.
A Max-Min Ant System Approach to Autonomous Navigation

Multi-goal navigation of an autonomous mobile robot seeks to reach multiple goals with a planned shortest trajectory in some real-world applications such as search and rescue robots, agricultural harvesting robots, resource prospecting robots and service robots, etc. An improved max-min ant system (MMAS) approach is proposed in this paper for real-time concurrent multi-goal navigation and map building. A global trajectory is planned by the proposed MMAS approach, in which a foraging-enabled trail is generated to guide the robot to the multiple goals. A histogram-based local navigator is utilized to guide the robot to follow the trail planned by the global path planner, with obstacle avoidance. In this paper, simulation results demonstrate that the real-time mapping and multi-goal navigation of an autonomous robot is successfully carried out under unknown environments.
eDiscovery Challenges in Healthcare

In todayâ€™s digital age, medical litigation often involves Healthcare data and medical devices. Electronic discovery is a process involving legal parties on a case to preserve, collect, review, and exchange electronic information for the purpose of using it as evidence in the case. With the growth of information in electronic format across the globe, litigations involving Healthcare can get challenging especially with the advent of smart Healthcare devices. In this paper, the authors explore challenges faced in the eDiscovery process involving Healthcare data, systems and devices. The authors propose incorporating industry best practices towards effective management of the eDiscovery process in the Healthcare Industry.
Test-related factors and post-release defects: an empirical study

Testing is a very important activity whose purpose is to ensure software quality. Recent studies have studied the effects of test-related factors (e.g., code coverage) on software code quality, showing that they have good predictive power on post-release defects. Despite these studies demonstrated the existence of a relation between test-related factors and software code quality, they considered different factors separately. That led us to conduct an additional empirical study in which we considered these factors all together. The key findings of the study show that, while post-release defects are strongly related to process and code metrics of the production classes, test-related factors have a limited prediction impact.
Experiences on applying SPL Engineering Techniques to Design a (Re) usable Ontology in the Energy Domain

Global ontologies must provide a balance of reusabilityusability to minimize the ontology reuse effort in different applications.To achieve this balance, ontology design methods focus on designing layered ontologies that classify into abstraction layers the common domain knowledge (reused by most applications) and the variant domain knowledge (reused by specific application types).This classification is performed from scratch by domain experts and ontology engineers.Hence, the design of reusable and usable ontologies that represent complex domains takes a lot of effort.Considering how common and variant software features are classified when designing Software Product Lines (SPLs), we argue that SPL engineering techniques can facilitate the domain knowledge classification taking as reference existing ontologies.In this paper, we show the experiences of applying SPL and ontology design techniques in combination to design a reusable and usable global ontology for the energy domain.Domain experts and ontology engineers evaluated the proposed method.The results show that SPL engineering techniques enable a systematic and accurate domain knowledge classification, thus saving ontology design effort.
ECDSA-compatible Delegable Undeniable Signature

We present the first ECDSA-compatible delegable undeniable signature. Undeniable signature was first introduced by Chaum and Antwerpen. Such signatures cannot be verified without running a zero-knowledge protocol with the signer. Delegable undeniable signature extends this by allowing the signer to delegate the verification ability to a third party. An example use case for delegable undeniable signature is that a trusted party verifies a user’s personal information, signs a message and then passes the signature back to the user. If a verifier needs to know that personal information (e.g. an online merchant selling alcohol needs to verify the user’s age), the user can run the verification protocol as a delegate to prove the trusted party (e.g. the government) signed that personal information. The verifier will be convinced the signature is genuine, but will not be able to convince others. Our signature scheme is based on standard ECDSA, which is the most common signature scheme in blockchain technology. It is easy to construct (it involves two standard ECDSA signatures) and easy to verify (a simple two-round zero-knowledge protocol). We believe our signature scheme is useful especially in Self-Sovereign Digital Identity.
Multirelation Association Rule Mining on Datasets of the Web of Data

The Web of Data is an extremely rich source that contains information of different types, distributed and organized in the form of graphs in interconnected datasets. Although several data mining algorithms have been developed to extract knowledge from large databases, typically such algorithms restrict themselves to analyzing data in a single dataset, which imposes a limitation on the possibilities of exploration of the various connections between the datasets in the Web of data in search of new knowledge. To overcome this limitation, the present work proposes the MRAR+, a method for mining multirelation association rules in graphs that, unlike the methods of the state of the art, is able to identify new and useful knowledge involving resources of multiple datasets connected to the Web of data. However, the proposed method seeks to identify resources linked to other datasets that, when considered in the mining process, have the potential to increase the chances of obtaining new and useful knowledge in the analyzed dataset. MRAR+ was implemented based on the MRAR algorithm that extracts multirelation association rules in graphs. In addition, MRAR+ was applied in two case studies and has produced new and useful rules for the user, illustrating the feasibility of the proposed method for mining different datasets interconnected in the Web of Data.
A Model of Correction Mapping for Al-Quran Recitation Performance Evaluation Engine

The main objective of the research is to develop the correction-mapping model for Al-Quran recitation performance evaluation engine. Machine learning and Digital Signal Processing techniques are applied in representing and analyzing the recitation speech signal. Consequently, a form of recitation correction results is derived and formulated for the final performance evaluation. The proposed corrective mapping model demonstrated in this paper confronted, but not limited to, with the challenging issues of variability of speaker recitation, recitation representation, speaker adaptation, feature extraction, parameters estimation and threshold process classification. The experimental results concluded the Al-Quran automated correction system knowns as Intelligent Quran Recitation Assistant (nur-IQRA) will be able to fulfil the current and future trends of digital society.
Performance comparison of artificial intelligence techniques in short term current forecasting for photovoltaic system

&lt;span&gt;This paper presents artificial intelligence approach of artificial neural network (ANN) and random forest (RF) that used to perform short-term photovoltaic (PV) output current forecasting (STPCF) for the next 24-hours. The input data for ANN and RF is consists of multiple time lags of hourly solar irradiance, temperature, hour, power and current to determine the movement pattern of data that have been denoised by using wavelet decomposition. The Levenberg-Marquardt optimization technique is used as a back-propagation algorithm for ANN and the bagging based bootstrapping technique is used in the RF to improve the results of forecasting. The information of PV output current is obtained from Green Energy Research (GERC) University Technology Mara Shah Alam, Malaysia and is used as the case study in estimation of PV output current for the next 24-hours. The results have shown that both proposed techniques are able to perform forecasting of future hourly PV output current with less error.&lt;/span&gt;
TONE

Selecting the most appropriate candidate domain ontology is necessary to ensure that the ontology covers the domain of interest at a reasonable level of detail. Existing approaches have the following drawbacks:
A taxonomy of metrics for software fault prediction

In the field of Software Fault Prediction (SFP), researchers exploit software metrics to build predictive models using machine learning and/or statistical techniques. SFP has existed for several decades and the number of metrics used has increased dramatically. Thus, the need for a taxonomy of metrics for SFP arises firstly to standardize the lexicon used in this field so that the communication among researchers is simplified and then to organize and systematically classify the used metrics. In this doctoral symposium paper, I present my ongoing work which aims not only to build such a taxonomy as comprehensive as possible, but also to provide a global understanding of the metrics for SFP in terms of detailed information: acronym(s), extended name, univocal description, granularity of the fault prediction (e.g., method and class), category, and research papers in which they were used.
Optimal Active Target Localisation Strategy with Range-only Measurements

This paper investigates the problem of one-step ahead optimal active sensing strategy to minimise estimation errors with range-only measurements for non-manoeuvring target. The determinant of Fisher Information Matrix (FIM) is utilized as the objective function in the proposed optimisation problem since it quantifies the volume of uncertainty ellipsoid of any efficient estimator. In consideration of physical velocity and turning rate constraints, the optimal heading angle command that maximises the cost function is derived analytically. Simulations are conducted to validate the analytical findings.
Intuitive Operate the Robot with Unconscious Response in Behavioral Intention: Tsumori Control

In this work, we proposed the control method that enable intuitive operation, “tsumori control”. Tsumori control is implemented using human discrete intention of motion included in continuous and intuitive motion output. The experience operating a robot using tsumori control will be demonstrated for our control system.
Reducing the Large Class Code Smell by Applying Design Patterns

Large Class code smell refers to the classes that tend to centralize the intelligence of the system. Large Class indicates weaknesses in design that can possibly slow down the development or increase the chance of failures in the future. In addition, it makes the system more difficult to understand, read and develop. The methodology presented is designed to address the Large Class issue by classifying the causes of the code smell and applying different design patterns to refactor the code to improve the quality of software. This paper analyzes the causes of the Large Class code smell and classifies them to corresponding types and proposes a design pattern to address each type to refactor the code.
"Something isn't secure, but I'm not sure how that translates into a problem": Promoting autonomy by designing for understanding in Signal

Security designs that presume enacting secure behaviors to be beneficial in all circumstances discount the impact of response cost on users’ lives and assume that all data is equally worth protecting. However, this has the effect of reducing user autonomy by diminishing the role personal values and priorities play in the decision-making process. In this study, we demonstrate an alternative approach that emphasizes users’ comprehension over compliance, with the goal of helping users to make more informed decisions regarding their own security. To this end, we conducted a three-phase redesign of the warning notifications surrounding the authentication ceremony in Signal. Our results show how improved comprehension can be achieved while still promoting favorable privacy outcomes among users. Our experience reaffirms existing arguments that users should be empowered to make personal trade-offs between perceived risk and response cost. We also find that system trust is a major factor in users’ interpretation of system determinations of risk, and that properly communicating risk requires an understanding of user perceptions of the larger security ecosystem in whole.
Modeling the optimal decision-making strategy of an organization using mathematical methods

Abstract The paper builds and studies a stochastic decision-making model under the influence of both negative and positive factors in a competitive environment. There is a finite set of company states, which is estimated by the value of the firm’s capitalization per unit of time. Depending on the decision taken, the company goes into one or another state. In each state, the set of valid strategies depends on this state. The purpose of the simulation is to find the optimal strategy that maximizes the expected capitalization of the company from the transition process to various states, having a finite number of stages.
IRTree Model: An Alternative Approach for Self-Reported Ordinal Data Analysis

이 연구는 심리적 구인 측정에 주로 사용되는 리커트 척도 문항을 통해 수집된 서열척도 자료 분석을 위한 대안적 방법으로 문항반응나무(item response tree: IRTree) 모형을 소개하고, 실제 서열척도 자료 분석을 통해 이 모형의 적용가능성을 탐색하는 것에 목적이 있다. 이를 위해 본 연구에서는 ‘한국아동·청소년패널조사’의 중학교 1학년 패널 3차년도(2012년 조사) 자료 가운데, 다양한 상관 수준을 보이는 6가지 심리적 구인(자아존중감, 자아정체감, 우울, 신체증상, 휴대전화 의존도, 다문화 수용도)에 대한 자기보고식 서열척도 자료를 활용하였다. 연구 결과에 따르면, 적합도 측면에서는 모든 구인에 있어 IRTree 모형이 기존에 일반적으로 자기보고식 서열척도 자료 분석에 사용되던 GRM보다 더 적합하며, 능력모수 측면에서는 IRTree 모형을 통해 추정된 각 구인에 대한 개인의 특성 정도가 GRM의 추정치와 비교하여 크게 다르지 않음을 보임으로써 IRTree 모형의 능력모수 추정치에 대한 신뢰성을 확인하였다. 반면, IRTree 모형을 통해 추정된 극단반응양식은 심리적 구인에 상관없이 모두 정적인 상관을 보임으로써, 반응양식은 도구가 측정하고자 하는 구인과 무관한 개인의 일관된 특성임이 확인되었다.In the present research, the item response tree model (IRTree model) was introduced and illustrated as an alternative approach for the analysis of ordinal data from Likert-type scales. This study used the ordinal data sets measuring six psychological constructs (i.e., self-esteem, self-identity, depression, physical symptom, mobile dependency, multicultural acceptability) from the 3rd year of middle school students cohort in ‘Korean Child and Youth Panel Survey.’ For six psychological constructs, the IRTree model fit the data better than the graded response model (GRM), which is one of the standard IRT models. Additionally, the individuals’ latent traits for each construct estimated by the IRTree model were almost identical to those by GRM. These results verified that the IRTree model is useful and reliable approach for the analysis of ordinal data.
Multitouch Keyboard Revisited

Moded interaction has been well studied and its insights applied to both physical and digital realms. However, it can be observed that many of today's digital applications are not exploiting the full capabilities of moded interaction. Addressing this gap as our research objective, we analyze and model existing multitouch keyboard typing experience from a mode perspective. Such a strategy helps to identify the complex mode hierarchy to be flattened and the missing mode transitions to be added. These changes were reflected in terms of interaction model and user interface design of the proposed system. This was quantitatively evaluated using the number of taps required to type common phrases and sentences. These findings will not only make typing experience more accessible for novices and more efficient for experts but also bring about a better understanding of the greater moded interaction landscape beyond typing.