Reviewerly/exHarmony huggingface dataset:
Adversarial Feature Desensitization

Neural networks are known to be vulnerable to adversarial attacks -- slight but carefully constructed perturbations of the inputs which can drastically impair the network's performance. Many defense methods have been proposed for improving robustness of deep networks by training them on adversarially perturbed inputs. However, these models often remain vulnerable to new types of attacks not seen during training, and even to slightly stronger versions of previously seen attacks. In this work, we propose a novel approach to adversarial robustness, which builds upon the insights from the domain adaptation field. Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs. This is achieved through a game where we learn features that are both predictive and robust (insensitive to adversarial attacks), i.e. cannot be used to discriminate between natural and adversarial data. Empirical results on several benchmarks demonstrate the effectiveness of the proposed approach against a wide range of attack types and attack strengths.
Development of car rental system based on geographic information system and decision support system with AHP (Analytical Heirarchy Process) and SAW (Simple Additive Weighting) method

Abstract The purpose of this study was to develop an application of a car rental system with recommendations using the AHP and SAW methods in a decision support system. The purpose of developing this system was to help the public in knowing the car rental system and carry out the car rental process complete with recommendations. The development of this system uses the ADDIE method which consists of five stages, namely Analysis, Design, Development, Implementation, and Evaluation. There are 3 tests carried out namely the black box test, white box, and user response test. This research succeeded in developing a car rental system after passing the black box test and white box test. User response test conducted on this car rental system is included in a very good range with the System Usability Scale (SUS) 93.13.
Stochastic Optimization with Non-stationary Noise

We investigate stochastic optimization problems under relaxed assumptions on the distribution of noise that are motivated by empirical observations in neural network training. Standard results on optimal convergence rates for stochastic optimization assume either there exists a uniform bound on the moments of the gradient noise, or that the noise decays as the algorithm progresses. These assumptions do not match the empirical behavior of optimization algorithms used in neural network training where the noise level in stochastic gradients could even increase with time. We address this behavior by studying convergence rates of stochastic gradient methods subject to changing second moment (or variance) of the stochastic oracle as the iterations progress. When the variation in the noise is known, we show that it is always beneficial to adapt the step-size and exploit the noise variability. When the noise statistics are unknown, we obtain similar improvements by developing an online estimator of the noise level, thereby recovering close variants of RMSProp. Consequently, our results reveal an important scenario where adaptive stepsize methods outperform SGD.
Extrinsic parameters calibration of multiple fisheye cameras in Manhattan worlds

With the advantage of having a large field of view, fisheye cameras are widely used in many applications. In order to generate a precise view, calibration of the fisheye cameras is very important. In this paper, we propose a method of extrinsic parameters calibration of multiple fisheye cameras working in man-made structures. A Manhattan Worlds space assumption is used, which describes man-made structures as sets of planes that are either orthogonal or parallel to each other. The orientation of the cameras is obtained by extracting vanishing points that denote orthogonal principal directions in different images captured by the cameras at the same time. With the proposed method, the calibration of extrinsic parameters is very convenient and the system can be recalibrated remotely.
Medical Concept Normalization in User Generated Texts by Learning Target Concept Embeddings.

Medical concept normalization helps in discovering standard concepts in free-form text i.e., maps health-related mentions to standard concepts in a vocabulary. It is much beyond simple string matching and requires a deep semantic understanding of concept mentions. Recent research approach concept normalization as either text classification or text matching. The main drawback in existing a) text classification approaches is ignoring valuable target concepts information in learning input concept mention representation b) text matching approach is the need to separately generate target concept embeddings which is time and resource consuming. Our proposed model overcomes these drawbacks by jointly learning the representations of input concept mention and target concepts. First, it learns the input concept mention representation using RoBERTa. Second, it finds cosine similarity between embeddings of input concept mention and all the target concepts. Here, embeddings of target concepts are randomly initialized and then updated during training. Finally, the target concept with maximum cosine similarity is assigned to the input concept mention. Our model surpasses all the existing methods across three standard datasets by improving accuracy up to 2.31%.
Avatar’s facial expression with “Manpu (Comic Symbols)” by using multiple biometric information

– It is a challenging issue to generate avatar’s natural facial expressions from user’s facial images. One of the most difficult problems is to analyze user’s facial images, and estimate his emotions. We have already proposed a method that estimated user’s emotions combining both analysis on user’s facial images taken from a video camera and measuring heart rate information, pNN50. Here, pNN50 is a percent of difference between adjacent heartbeat intervals greater than 50 ms calculated from heart rate information. Each user's emotion was estimated as either of ‘Joy’, ‘Disgust’, ‘Sadness’, or ‘Anger’. Our previous method estimated user’s emotions and generated appropriately avatar’s face images. However, further improvement of measurement of each emotion is needed for the natural facial expressions of the avatars. In this paper, we propose a new method which measures the skin conductance level (SCL) by using a biometric sensor. We focus on “Russell's circumplex model of affect”, in which each emotion is described as a 2D vector of the ‘valence’ and ‘arousal’. Our method calculates the ‘valence’ by measuring pNN50, and the ‘arousal’ from the SCL. The four quadrants of “Russell's circumplex model of affect” correspond to ‘Joy’, ‘Disgust’, ‘Sadness’, and ‘Anger’, respectively. Based on results of multiple biological information, i.e., pNN50 and SCL, the proposed method estimates the accuracy of emotions and generates the avatar's natural facial expressions by adding comic symbols (called “Manpu”) successfully and appropriately. “Manpu” can emphasize emotions. By using “Manpu”, human emotions can be more easily communicated.
PENERAPAN IT GOVERNANCE BERBASIS COBIT 4.1 PADA LEMBAGA PENDIDIKAN

In line with the increasing use of information technology devices, organizationsneed to implement information technology strategies that are appropriate toorganizational goals. Likewise, the use of information technology for businessprocesses in the education industry needs to adopt information technologygovernance. This research aims to analyze the implementation of informationtechnology governance using the COBIT 4.1 framework. The subject of thisresearch is the Catholic University of Indonesia Atma Jaya Jakarta. Datacollection was carried out by survey method using a questionnaire aimed atinformation technology managers and several core users in several units.Descriptive analysis results found that the conditions of information technologygovernance and implementation at Atma Jaya Catholic University of Indonesiaare at level 3 using a scale of 0 to 5. This result means that the institution has aproper procedure and communicate the document. This research contributes toresearch related to the application of information technology governance,especially for educational institutions, as well as information technologymanagers as input in managing information technology resources in relatedinstitutions.information technology resources in related institutions.
A picking interface seamlessly connecting passive and active performance on guitar

We have designed an interface that allows a guitar player to manipulate another channel, such as the manipulation of visual effects, while playing the guitar. This interface connects passive and active performance seamlessly. Our goal is to create an interface that immediately reflects the performer's intentions without turning away from the guitar playing motion. We propose a method to distinguish 3 picking states (Chord stroke/Single note/Non-picking) by sensing pressure on the pick and the rotation angle of the player’s index finger. Our brief experiment showed that our method can correctly identify the picking state with high accuracy. We also introduce an application example which manipulates visual and sound effects through our interface.
Robotic path planning using evolutionary neural network

Path planning is a well-known problem in mobile robot. The robot needs to move from the starting point to the destination point and avoid obstacles. To create an agent that able to reach some destinations in the given environment, such agent need some abilities to processing a collection of information obtained by its sensors and roam freely in the environment. In this paper, we design mobile agents to solve local path planning problems in 3D environment by using Evolutionary Neural Network (ENN) algorithm. ENN combines Evolutionary Algorithm (EA) and Neural Network algorithm. We chose Genetic Algorithm (GA) for the EA part and designing a simple feed forward neural network for the neural networks part. We evaluate what kind of ENN configuration values that works best in a local path planning problem. Experiment results show that the lowest iterations rate is 1.8 with one hidden layer and 50 hidden nodes when the population size is 50.
Pre-training Polish Transformer-based Language Models at Scale

Transformer-based language models are now widely used in Natural Language Processing (NLP). This statement is especially true for English language, in which many pre-trained models utilizing transformer-based architecture have been published in recent years. This has driven forward the state of the art for a variety of standard NLP tasks such as classification, regression, and sequence labeling, as well as text-to-text tasks, such as machine translation, question answering, or summarization. The situation have been different for low-resource languages, such as Polish, however. Although some transformer-based language models for Polish are available, none of them have come close to the scale, in terms of corpus size and the number of parameters, of the largest English-language models. In this study, we present two language models for Polish based on the popular BERT architecture. The larger model was trained on a dataset consisting of over 1 billion polish sentences, or 135GB of raw text. We describe our methodology for collecting the data, preparing the corpus, and pre-training the model. We then evaluate our models on thirteen Polish linguistic tasks, and demonstrate improvements over previous approaches in eleven of them.
Zhu Chungeng. 2019. Chinese aspectual particle <i>le</i>: A comprehensive guide

Abstract This article reviews Chinese aspectual particle le: A comprehensive guide 978-988-8528-10-3
A study of AR advertising to estimate the volume of invisible contents in a packaged product

When a consumer purchases a product packaged in a box or a bag for the first time, it is difficult to estimate the volume of the invisible contents in the packaged product. The purpose of this study is to help the consumer estimate the volume of the contents in the packaged product. This study developed augmented reality (AR) advertising that presented a virtual model of the product to make the consumer perceive the sense of the product’s volume. In this paper, we compared the sense of the food product’s volume among with the product only, the AR advertising on a tablet terminal, the AR advertising on a head mounted display (HMD) and the point-of-purchase (POP) advertising. As a result, we found that presenting the virtual model as AR advertising was effective in estimating the actual volume of the product. In addition, our result suggested that it was easier to understand the sense of the product’s volume using the HMD than using the tablet terminal and that AR advertising using the virtual model was useful for increasing the purchase intention.
Translation, the process–product interface and cognition

This chapter addresses methodological considerations for interfacing process and product data. It explains how methods from different research branches in Translation Studies can be combined. The combination of corpus-based work (i.e. product-oriented research) with experimental studies (i.e. process-oriented research) enables not only the quantification and interpretation of translation properties and strategies but also their explanation concerning cognitive processes and resources. Recent advances in corpus architecture and corpus querying and increasing incorporation of methods from psycholinguistics and cognitive science into process-oriented research point to a desired combination of corpus studies with a more direct, experimental insight into processing efforts. The most common methods are introduced, their strengths and shortcomings are discussed, and examples are given.
Blocking gamelan instruments frequency in virtual reality

This study was an experiment that looked at the possibility of VR relationships with the frequency of gamelan in animated puppet shows that were considered based on the blocking instrument. On the other hand, gamelan instruments provide the clearest picture of the frequency spectrum that is affected by various types of treatment, regardless of the ability of the instrument itself to produce sound in many characteristics of each instrument. The key experiment in this study was to arrange the position of the instrument in a different semicircle with a conventional arrangement that is lined up. This experiment is believed by the author to bring its own impact in relation to VR. Gamelan are used Slendro and pelog to get two different data. It can be assumed that this blocking semicircular is able to produce a more focused frequency distribution for the proportion of the listening room and VR users. And in connection with the experiment on blocking semicircular it can be concluded that sound is more effective and easier to identify.
Innovations and Challenges in K–12 English Language Proficiency Assessment Tasks

This chapter discusses the innovative features that are embedded in large-scale standardized K–12 English language proficiency (ELP) assessments that are widely used in the United States. Innovative assessment features, such as technology-enhanced items, have been explored and implemented in computer-based ELP assessments to elicit student performances that can more meaningfully reflect their real language abilities compared to traditional test designs. This review focused on three major ELP assessments: (1) ACCESS for ELLs, (2) the English Language Proficiency Assessments for 21st Century (ELPA21), and (3) the English Language Proficiency Assessments for California (ELPAC). This chapter first presents an overview of these three large-scale, high-stakes ELP assessments. Then, three innovative features identified in the ELP assessment tasks—technology-enhanced items, scaffolding, and universal tools—are described along with sample items to exemplify these innovative features. Finally, the challenges associated with the development and implementation of innovative features, as well as future directions, are discussed.
“Hear That Lonesome [Police] Whistle Blow”

When introduced in the nineteenth century, railroads were one of humankind's most transformational technologies. Like similar transformations such as the printing press and the automobile, railroads did more than advance technology. They fundamentally altered the entire social and cultural fabric, including basic relationships between people and time, distance, geography, class, and gender. Railroads also introduced new types of crime and criminal organization to the nineteenth-century West, and indeed to the entire nation and globe. These criminal activities quickly spread across wide geographic expanses. Railroads and society at large responded by altering the measures of social control through new forms of law enforcement, only one of which was the creation of private railroad-company police forces. In our own era, similar revolutionary information and communications technologies erupted upon a cultural and institutional world woefully unprepared to deal with them. As was true for nineteenth-century railroading, the results today have been new, ever-evolving forms of crime that plague individuals, companies, institutions, and governments, while baffling ill-equipped law enforcement agencies. The internet spammers, hackers, phishers, stalkers, swindlers, pedophiles, money launderers, identity thieves, election fixers, and cyberterrorists of today share a lineage with the railroad pickpockets, arsonists, ticket forgers, train robbers and wreckers, and hatchet-wielding hoboes of yesterday. Examining the railroads' transformation of crime and violence can shed light on today's tribulations with information technology, as well as on possible ways to deal with them.
Augmenting automated kinship verification with targeted human input

Kinship verification is the problem whereby a third party determines whether two people are related. Despite previous research in Psychology and Machine Vision, the factors affecting a person's verification ability are poorly understood. Through an online crowdsourcing study, we investigate the impact of gender, race and medium type (image vs video) on kinship verification - taking into account the demographics of both raters and ratees. A total of 325 workers completed over 50,000 kinship verification tasks consisting of pairs of faces shown in images and videos from three widely used datasets. Our results identify an own-race bias and a higher verification accuracy for same-gender image pairs than opposite-gender image pairs. Our results demonstrate that humans can still outperform current state-of-the-art automated unsupervised approaches. Furthermore, we show that humans perform better when presented with videos instead of still images. Our findings contribute to the design of future human-in-the-loop kinship verification tasks, including time-critical use cases such as identifying missing persons.
Velocity Kinematics Analysis and Trajectory Planning of 5 DOF Robotic Arm

Trajectory planning is important in robots to achieve smooth path planning. This paper presents the velocity kinematics analysis and trajectory planning of a 5 DOF robotic arm. The Jacobian matrix is utilized to analyze the velocity kinematics and the third-order polynomial equation is used to determine the path of angle, velocity, and acceleration of the robotic arm. A 5 DOF robotic arm used with revolute joints and the motion of it is performed using the Arduino Mega2560 (microcontroller) which controlling on servo motors. The results of the velocity kinematics indicated the maximum linear velocity occurs with the z-direction and the cubic polynomial equation satisfied a smooth path for angle and velocity but a discontinuous path for acceleration
Integration of LAPAN’s remote sensing ground station control and monitoring system application

Abstract The National Institute of Aeronautics and Space (LAPAN) - Remote Sensing has developed different website-based application systems. The application system is used to control and monitor several series of ground station processes ranging from acquisition, recording, processing, storage, to cataloguing data and remote sensing satellite products. At present these applications are still working separately which are built from various platforms both operating systems, programming, databases, infrastructure and architecture. Therefore, an application system integration solution is needed in order to facilitate and speed up ground station operators to control and monitor the processes that take place and obtain useful and valid information for decision making. The design of this system integration application is done by preparing materials and defining layouts, coding Hypertext Pre-processor (PHP), testing designs, granting domains, and providing hosting. The User Interface (UI) uses a bootstrap framework to display responsive results for each screen size.
CrossMMLA in practice: Collecting, annotating and analyzing multimodal data across spaces

Learning is a complex processthat is associated with many aspects of interaction and cognition (e.g., hard mental operations, cognitive friction etc.) and that can take across diverse contexts (online, classrooms, labs, maker spaces, etc.). The complexity of this process and its environments means that it is likely that no single data modality can paint a complete picture of the learning experience, requiring multiple data streams from different sources and times to complement each other. The need to understand and improve learning that occurs in ever increasingly open, distributed, subject-specific and ubiquitous scenarios, require the development of multimodal and multisystem learning analytics. Following the tradition of CrossMMLA workshop series, the proposed workshop aims to serve as a place to learn about the latest advances in the design, implementation and adoption of systems that take into account the different modalities of human learning and the diverse settings in which it takes place. Apart from the necessary interchange of ideas, it is also the objective of this workshop to develop critical discussion, debate and co-development of ideas for advancing the state-of-the-art in CrossMMLA.
Face Recognition Using Improved Co-HOG Features

Face recognition is one of the most sought-after biometric technologies in the field of machine learning and computer vision in recent years. Histogram of oriented gradients (HOG) descriptor was originally developed for human detection and recently, it is also being applied to face recognition. However, when compared with other successful feature descriptors such as SIFT, LBP, Gabor, and so on, there is still considerable research space on the application of HOG features for face recognition. Co-HOG, a variant of HOG, uses a pair of gradient orientations as its basic building block, unlike HOG which uses a single gradient. Using the pair of orientations, the co-occurrence matrix is computed and histograms are calculated. However, in Co-HOG, gradient direction alone is considered and magnitude is ignored. It is believed that gradient magnitude also carries significant information about features. In this paper, we develop a face recognition system that utilizes Co-HOG features with embedded gradient magnitude information. The experimentation is done on ORL face dataset and it is observed that the proposed model is better than other existing methods with a maximum accuracy value of 97%.