Reviewerly/exHarmony huggingface dataset:
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning

Deep reinforcement learning (RL) algorithms have recently achieved remarkable successes in various sequential decision making tasks, leveraging advances in methods for training large deep networks. However, these methods usually require large amounts of training data, which is often a big problem for real-world applications. One natural question to ask is whether learning good representations for states and using larger networks helps in learning better policies. In this paper, we try to study if increasing input dimensionality helps improve performance and sample efficiency of model-free deep RL algorithms. To do so, we propose an online feature extractor network (OFENet) that uses neural nets to produce good representations to be used as inputs to deep RL algorithms. Even though the high dimensionality of input is usually supposed to make learning of RL agents more difficult, we show that the RL agents in fact learn more efficiently with the high-dimensional representation than with the lower-dimensional state observations. We believe that stronger feature propagation together with larger networks (and thus larger search space) allows RL agents to learn more complex functions of states and thus improves the sample efficiency. Through numerical experiments, we show that the proposed method outperforms several other state-of-the-art algorithms in terms of both sample efficiency and performance. Codes for the proposed method are available at this http URL .
The Relationship between Job Characteristics and Transformational Leadership on Organization and Altruism Commitments and its Effects on Nurses’ Satisfaction and Performance in Private Hospitals of Samarinda City

The purpose of this study was to analyze and prove the relationship of Job Characteristics and Transformational Leadership on Organizational Commitment and Altruism and Its Effect on Nurses' Satisfaction and Performance in Private Hospitals in Samarinda City.The design of this study was explanatory research.The type of this research was observational study using a cross-sectional research design.The method used to collect the data in this study was survey method.The population of this study was nurses at Private Hospitals in Samarinda City.In this study, Slovin formula was used as the sampling method obtaining total of 225 respondents, while the data collection process used simple random sampling.This study used Structural Equation Modeling Partial Least Square (SEM-PLS) approach to analyze the data.The results of this study can be summarized as follows: Job characteristics provide a positive and significant effect on nurses' organizational commitment.Job characteristics provide a positive but not significant effect on nurses' satisfaction.Job characteristics have a positive but not significant effect on nurses' performance.Job characteristics have a positive and significant effect on nurses' altruism.Transformational leadership has a positive and significant effect on nurses' organizational commitment.Transformational leadership has a negative but not significant effect on nurses' satisfaction.Transformational leadership has a positive and significant effect on nurses' performance.Transformational leadership has a positive but not significant effect on nurses' altruism.Organizational commitment has a positive and significant effect on nurses' satisfaction.Organizational commitment has a positive and significant effect on nurses' performance.Altruism has a negative and insignificant effect on nurses' satisfaction.Altruism has a positive and significant effect on nurses' performance.Nurses' satisfaction provides a positive and significant effect on the performance of executive nurses at Private Hospitals in the City Samarinda.
How airplanes fly at power-off and full-power on rectilinear trajectories

utomatic trajectory planning is an important task that will have to be performed by truly autonomous vehicles. The main method proposed, for unmanned airplanes to do this, consists in concatenating elementary segments of trajectories such as rectilinear, circular and helical segments. It is argued here that because these cannot be expected to all be flyable at a same constant speed, it is necessary to consider segments on which the airplane accelerates or decelerates. In order to preserve the planning advantages that result from having the speed constant, it is proposed to do all speed changes at maximum deceleration or acceleration, so that they are as brief as possible. The constraints on the load factor, the lift and the power required for the motion are derived. The equation of motion for such accelerated motions is solved numerically. New results are obtained concerning the value of the angle and the speed for which the longest distance and the longest duration glides happen, and then for which the steepest, the fastest and the most fuel economical climbs happen. The values obtained differ from those found in most airplane dynamics textbooks. Example of tables are produced that show how general speed changes can be effected efficiently; showing the time required for the changes, the horizontal distance traveled and the amount of fuel required. The results obtained apply to all internal combustion engine-propeller driven airplanes.
Learning Likelihood Estimates for Open Set Domain Adaptation

Most existing domain adaptation frameworks were based on the strict assumption that different domains share the same label space, which is too idealized for real-world applications. In this paper, we examine a realistic case of open set domain adaptation with partially shared classes in two domains and consider the negative transfer brought by the unknown class from the target domain. Therefore, how to distinguish the unknown class from the known ones plays a key role. To achieve this goal, we propose a new approach called Open Distribution Control Adaptation Network(OpenDCAN). Inspired by the idea of Out-of-Distribution Detection, our method tries to explicitly model the distribution of the known source samples to differentiate it from unknown class in probabilistic distribution, which is different from existing methods. We use OpenBP, an open set domain adaptation framework, to provide a rough decision boundary between known and unknown classes. Then we further boost the performance by modifying the recently proposed Adaptive Feature Norm algorithm for the open set setting. If the probabilistic distribution of source samples is modeled, a likelihood estimate can easily detect unknown class without requiring any unknown samples in the source domain to get the concept of “unknown”. Experiments on representative benchmark datasets demonstrate the effectiveness of our approach.
The Correlation between Students’ Metacognitive Awareness and Their Anxiety in Writing Performance

The aim of this study is to verify the relationship between students’ metacognitive awareness and their writing anxiety. The researchers used descriptive statistics and the Pearson’s correlation coefficient to find out the relation. Data were collected by distributing two questionnaires, Metacognitive Awareness Inventory (MAI) and Second Language Writing Anxiety Inventory (SLWAI) to 27 students who were at intermediate level and taking Academic Writing subject in English Department of State Islamic University of Raden Intan Lampung. The data were analyzed using SPSS. From the results , it revealed that there was a significant correlation between students’ metacognitive awareness and their writing anxiety. The finding proved that Metacognitive awareness played a necessary role in increasing students’ writing performance by reducing the anxious feeling in the writing activities
PackIt: A Virtual Environment for Geometric Planning

The ability to jointly understand the geometry of objects and plan actions for manipulating them is crucial for intelligent agents. We refer to this ability as geometric planning. Recently, many interactive environments have been proposed to evaluate intelligent agents on various skills, however, none of them cater to the needs of geometric planning. We present PackIt, a virtual environment to evaluate and potentially learn the ability to do geometric planning, where an agent needs to take a sequence of actions to pack a set of objects into a box with limited space. We also construct a set of challenging packing tasks using an evolutionary algorithm. Further, we study various baselines for the task that include model-free learning-based and heuristic-based methods, as well as search-based optimization methods that assume access to the model of the environment. Code and data are available at this https URL.
Exploration and Application of Teaching Mode of Innovative Informatization in Higher Vocational Colleges

With the development of science technology and network technology, the teaching mode of using information technology is gradually popularized in the major vocational colleges, but from the practical effect, there are more or less problems in the exploration and application of teaching mode in many vocational colleges. According to the current situation of information-based teaching mode in our country and abroad, combined with the common problems of information-based teaching in domestic higher vocational colleges, this paper studies and explores a "five steps" information-based innovative teaching mode, analyzes the design ideas of the innovative mode, and finally evaluates the teaching practice and effect of the innovative mode.
A Hybrid Swarm Particle Optimization Algorithm for Task Scheduling in Cloud Computing

Today, cloud computing experts seek internet-based service providing to share resources using service providing techniques. This environment provides users with an image of abundant resources. The present paper recommends a combination of particle swarm optimization algorithm and simulated annealing algorithm to obtain an improvement in the performance of task scheduling to resources considering the available bandwidth allocated to each virtual machine. The performance of the proposed algorithm is investigated by the use of the Cloudsim Simulator. Research results show that the proposed algorithm outperforms the Swarm Particle Optimization (SPO), bat, and raven roosting optimism algorithms in terms of task execution time, response time, and performance efficiency.
KAJIAN KARAKTER FIKSI ARTHUR PENDRAGON DALAM PENDEKATAN LEVELS OF IDENTITY DAN HUBUNGANNYA DENGAN TRANSMEDIA STORYTELLING

The Transmedia Storytelling strategy is one of many strategies that support the sustainability of an entertainment media, one of which is the game. Games are a medium of interactive narrative entertainment. The three elements that support the narrative are time, character, and world-building. Sustainability through the transmedia character approach can be done by conducting a qualitative study. The writer takes the character of Arthur Pendragon, who examined the level of his identity to explain this character’s visual identity in a different universe.
Neural-DINF: A Neural Network based Framework for Measuring Document Influence

Measuring the scholarly impact of a document without citations is an important and challenging problem. Existing approaches such as Document Influence Model (DIM) are based on dynamic topic models, which only consider the word frequency change. In this paper, we use both frequency changes and word semantic shifts to measure document influence by developing a neural network framework. Our model has three steps. Firstly, we train the word embeddings for different time periods. Subsequently, we propose an unsupervised method to align vectors for different time periods. Finally, we compute the influence value of documents. Our experimental results show that our model outperforms DIM.
Predicting Patient Length Of Stay at Time of Admission Using Machine Learning

This master thesis investigates the possibility of using machine learning methods to predict patient length of stay at the time of admission to a clinical ward from the emergency department. The ma ...
Identifikasi pola iris mata menggunakan metode support vector machine dengan ekstraksi ciri hue saturation value (hsv) histogram, gabor filter dan wavelet transform

Mengidentifikasi pola citra iris mata dengan metode support vector machine dengan ekstraksi ciri hsv histogram, gabor filter dan wavelet transform
SGX-IR: Secure Information Retrieval with Trusted Processors

To preserve the security and the privacy of the data need for cloud applications, encrypting the data before outsourcing has emerged as an important tool. Furthermore, to enable efficient processing over the encrypted data stored in the cloud, utilizing efficient searchable symmetric encryption (SSE) schemes became popular. Usually, SSE schemes require an encrypted index to be built for efficient query processing. If the data owner has limited power, building this encrypted index before data is outsourced to the cloud could become a computational bottleneck. At the same time, secure outsourcing of encrypted index building using techniques such as homomorphic encryption is too costly for large data. Instead, in this work, we use a trusted processor, e.g, Intel Software Guard eXtension (SGX), to build a secure information retrieval system that provides better security guarantee and performance improvements. Unlike other related works, we focus on securely building the encrypted index in the cloud computing environment using the SGX, and show that the encrypted index could be used for executing keyword queries over text documents and face recognition detection in image documents. Finally, we show the effectiveness of our system via extensive empirical evaluation.
Pengembangan Leaflet Berbasis Android sebagai Penunjang Bahan Ajar Pada Mata Pelajaran OTK Sarana dan Prasarana Kelas XI OTKP di SMK PGRI 2 Sidoarjo

The purpose of this study is to describe the development process, analyze the feasibility, and analyze student responses to android-based leaflets as supporting teaching materials in OTK Facilities and Infrastructure XI OTKP class subjects at SMK PGRI 2 Sidoarjo. This type of research is R &amp; D research, while the development model used is the 4D development model from Thiagarajan, namely: Define, Design, Develop and Disseminate. The subjects in this study were 20 students of class XI OTKP 1 at SMK PGRI 2 Sidoarjo. The research instruments used included study sheets and validation by material experts, linguists, graphic experts and student response questionnaires. The results showed that the results of the study and validation of android-based leaflets based on the aspect of material eligibility obtained a percentage of 93% with a very strong category, linguistic aspects obtained a percentage of 98% with a very strong category, the aspect of graphics obtained a percentage of 92% with a very strong category. Based on the results of a limited trial to 20 students of class XI OTKP 1 on android-based leaflets get a percentage of 99% with a very strong category. So it can be concluded that the Android-based leaflet as a support for teaching materials in OTK Facilities and Infrastructure class XI OTKP 1 which has been developed is suitable for the learning process.Keywords: android, leaflet, teaching materials.
Web Service Implementation in Logistics Company uses JSON Web Token and RC4 Cryptography Algorithm

The development of e-commerce in Indonesia in the last five years has significantly increased the growth for logistics service companies. The Indonesian Logistics and Forwarders Association (ALFI) has predicted the growth potential of the logistics business in Indonesia to reach more than 30% by 2020. One of the efforts of logistics business companies to improve services in the logistics services business competition is to implement web service technology on mobile platforms, to easy access to services for customers. This research aims to build a web service with a RESTful approach. The REST architecture has limitations in the form of no authentication mechanism, so users can access and modify data. To improve its services, JSON Web Token (JWT) technology is needed in the authentication process and security of access rights. In terms of data storage and transmission security, a cryptographic algorithm is also needed to encrypt and maintain confidentiality in the database. RC4 algorithm is a cryptographic algorithm that is famous for its speed in the encoding process. RC4 encryption results are processed with the Base64 Algorithm so that encrypted messages can be stored in a database. The combination of the RC4 method with the Base64 method has strengthened aspects of database security. This research resulted in a prototype application that was built with a combination of web service methods, JWT and cryptographic techniques. The test results show that the web service application at the logistics service company that was created can run well with relatively fast access time, which is an average of 176 ms. With this access time, the process of managing data and information becomes more efficient because before making this application the process of handling a transaction takes up to 20 minutes.
Nonnull interferometric testing of spherical gratings under Littrow conditions with opposite diffraction orders

Diffracted wavefront measurements are qualitative and comprehensive verifications for the spherical grating that was manufactured to specifications. Direct interferometric testing of the diffracted wavefront is convenient and implemented by tilting the spherical grating at a Littrow angle to obtain autoreflection and then results in a nonnull interferometric testing configuration. The diffracted wavefront of the spherical grating contains not only wavefront errors induced by the manufacturing imperfections but also inherent wavefront contributions from the autoreflection testing setup. The magnitudes of the latter are affected by both the spherical substrate and the groove pattern. Through the analysis of geometric aberrations of spherical gratings, the groove pattern contributions are demonstrated to be contrary for the opposite diffraction orders. A nonnull interferometric testing of spherical gratings is proposed without foreknowledge of the groove pattern, in which the wavefront errors contributed only by the manufacturing imperfections are derived from dual measurements under Littrow conditions with opposite diffraction orders. Simulations are implemented for varied line spacing (VLS) spherical gratings with an F-number slower than 1.5 and groove density varying from 150 to 300 lp/mm, and the residual error less than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>0.004</mml:mn> </mml:mrow> <mml:mi>λ<!-- λ --></mml:mi> </mml:math> RMS is obtained. The residual misalignment error after conventionally removing defocus and tilt is further analyzed and discussed. A VLS grating in which the NA is 0.13 and groove density is 200 lp/mm is chosen as an experimental sample, and the diffracted wavefront error with 0.018λ RMS is obtained.
A Study on the Exploration of the Core Capabilities of Design Future Talent against the Fourth Industrial Revolution

최근 교육환경은 4차 산업혁명 시대의 도래로 융·복합 전문 역량요인을 고려한 교육을 요구하고 있다. 본 연구에서는 이러한 필요성에 발맞춰 4차 산업혁명 시대가 요구하는 미래 디자이너의 구체적 전문 역량의 방향성을 제안하는데 그 목적이 있다. 대학생의 인식 조사를 중심으로 변화하는 4차 산업혁명 시대에 대응하는 디자인 중심 핵심역량 요인이 무엇인지 살펴보았다. 연구방법은 문헌연구를 통해 우선, 세계경제포럼(2016), 공병호(2008), 4차 산업혁명 핵심 역량 핵심역량, 미래핵심역량 요인, 융합디자인 교육 및 직무능력 요인으로 검증된 항목을 선정하였다. 자료수집은 서울지역에 위치한 전문대학 디자인 전공자 176명을 대상으로 온라인 조사를 통해 진행하였다. 분석결과 디자인의 고유 영역이라 여겨졌던 시각적 표현 능력 이외에 융복합 프로젝트 기획 역량과 이를 실현하는데 요구되는 디자인 핵심역량 요인을 확인하였다. 세부적인 핵심역량 순위는 유연성역량(Flexibility), 관리역량(Management Skills), 창의역량(Creativity), 문제예측역량(Problem Predicting), 문제해결역량(Problem Solving), 전문성역량(Expertise), 협동적 수행역량(Collaborative Skills), 정보판별력역량(Information Discernment), 지식활용역량(Knowledge Application), 표현(구현)역량(Expression and Embodiment), 시간관리역량(Time Management), 분석역량(Analysis), 융합역량(Interdisciplinary Convergence), 정보통신역량(ICT Competence), 자기주도적 인생설계역량(Selfdirected Career Planning), 자기주도적학습역량(Selfdirected Learning), 신기술로 제작하는 역량(Manufacturing with New Technology), 신기술활용능력역량(Using New Technology), 폭넓은 신기술 적용역량(Applying New Technology), 직업적응역량(Career Adaptation), 도덕심(Morality), 자기계발역량(Self- development), 진로개척역량(Career Development) 순으로 높게 나타났다. 본 연구를 바탕으로 4차 산업혁명 시대에 대응하기 위한 디자인 요구역량을 확인하였다. 관련 자료는 후속 연구에 활용할 뿐만 아니라 4차 산업혁명 융복합 교육에 활용하는데 기여할 것으로 기대한다.
The 2020 M 5.9 Falam Earthquake the Subduction-Induced Strike-Slip Earthquake, West Myanmar

An earthquake with magnitude 5.9 occurred in the east of Falam on 16th April, 2020 at 11:45:23 (UTC). The epicenter is situated at latitude 22.789°N, longitude 94.025°E, 38 km ESE of Falam, at the depth of 10 km. Focal mechanism solution for this event is normal faulting (USGS). The epicentral location is in the Kabaw Valley along which Kabaw fault runs through in N-S direction. The Kabaw fault is situated in forearc region at the eastern base of N-S trending Rakhine Western Ranges under which the India oceanic plate is obliquely subducting beneath the Burma continental plate. The 2020 M 5.9 Falam earthquake occurred along two closely linked tectonic settings: north-eastward oblique subduction zone and north-south trending Kabaw fault zone system in the forearc region.The Falam earthquake ruptured the Tripura segment, one of the segments of India subduction zone, located approximately between latitude 22°-24°N according to the geographical location. This event is a rare intraplate earthquake and a subduction-induced strike-slip earthquake that ever occurred for the recent time in Myanmar. The shock was felt by cities of Gangaw, Kalemyo, Kalewa, Mandalay, Kyauk-se, Monywa. This earthquake was preceded by a loud sound and shaking lasts 1 minute. A few aftershocks of magnitude &gt;3.5 followed the main shock in the vicinity of the epicenter. The vibration spread a wide area along Rakhine Yoma and Myanmar lowland area. The investigation of field survey from social media was found that the event reaches Modified Mercalli Intensity scale VIII based on people’s perception, indoor effects and damaged buildings. Damage is severe in some poorly built structures and upper parts of stupa and pagodas.
Combating False Negatives in Adversarial Imitation Learning (Student Abstract)

We define the False Negatives problem and show that it is a significant limitation in adversarial imitation learning. We propose a method that solves the problem by leveraging the nature of goal-conditioned tasks. The method, dubbed Fake Conditioning, is tested on instruction following tasks in BabyAI environments, where it improves sample efficiency over the baselines by at least an order of magnitude.
FACE RECOGNITION FROM A PARTIAL FACE VIEW BY PARTITIONING AND ROTATING FACIAL IMAGES

This paper presents a novel technique for Face Recognition from a Partial Face View (FRPV), which consists of three phases. The first phase uses an existing algorithm to detect faces in input images. The second phase includes splitting the input images undetected by the first phase into two, four, six, or eight parts. Then, every part is rotated by a new split and rotate face detection (SRFD) algorithm until it detects a face in one of these partial images. The third phase uses the Eigenfaces method with train and test databases to perform recognition. This phase compares the selected test image with images in the train database until it recognizes the person and updates the train database. The FRPV system was implemented using a head-pose image database where every person has multiple images with several poses having different Pitch and Yaw Angles ranging from â€“90Âo to +90Âo. The results showed that the FRPV system outperformed previous methods. Its accuracy rate was equal to 96% for faces that had different poses. In addition, the SRFD method achieved a detection success rate of 67%, which is better than other similar methods.
Threatening tendencies of organized cyber crime

The article deals with current trends in cyber crime, including its organized forms, and proposes to strengthen the counteraction to this phenomenon