Miracle-dz/UniFAR-FTUs huggingface dataset:
PENERAPAN ALGORITMA K-MEANS UNTUK CLUSTERING DATA ANGGARAN PENDAPATAN BELANJA DAERAH DI KABUPATEN XYZ

Penelitian ini bertujuan untuk mengelompokkan data APBD tahun 2006-2011 yang memiliki kemiripan karakteristik berdasarkan kedekatan jarak, menggunakan teknik clustering dengan algoritma K-Means. Pembentukan cluster diuji dengan 3 nilai centroid dan 2 nilai centroid, yang dilanjutkan dengan menghitung nilai SSE (Sum of Squared Error). Hasil cluster dengan nilai SSE terkecil dijadikan sebagai parameter untuk mengestimasi data anggaran belanja yang akan datang. Berdasarkan penelitian yang dilakukan dapat diketahui bahwa pembentukan cluster dengan 3 nilai centroid adalah cluster yang terbaik, karena memiliki nilai SSE terkecil yaitu 6.235. 940.663,88,. Untuk nilai pendapatan tahun 2012 sebesar Rp 394.083. 451.404,-, diperoleh hasil estimasi nilai belanja tidak langsung sebesar Rp 199.533. 334.595,76,- sampai Rp 203.564. 311.052,24. Serta estimasi nilai belanja langsung sebesar Rp 176.135. 846.021,76,- sampai Rp 180.166. 822.478,24,-
MGCN: Dynamic Spatio-Temporal Multi-Graph Convolutional Neural Network*

Traffic prediction plays an important role in urban planning and smart city construction. Reasonable forecasting of future traffic conditions can effectively avoid traffic congestion and allow planning time for people to travel. However, complex traffic networks and non-linear time dependence make traffic prediction very challenging, and existing methods often lack the ability to model the dynamic spatio-temporal correlation of traffic data, making forecasting results unsatisfactory. We therefore propose a dynamic spatio-temporal multi graph convolutional neural network (MGCN) based on graph convolution network and attention mechanisms to perform traffic prediction tasks. Specifically, we design a spatio-temporal graph convolution module to simultaneously capture traffic network structure information, dynamic neighbour node information and traffic variation information in the temporal dimension, and effectively fuse them to represent comprehensive and dynamic spatio-temporal correlation. Further, we transform the historical time series into a future time series representation and resolve the future traffic conditions along the temporal and spatial dimensions on two Decoders respectively, aggregating the multidimensional information. Adequate experiments were conducted on two real large scale datasets, and the experimental results demonstrate the effectiveness and superiority of our approach and achieve a better level of performance than other baseline methods.
Developing a Virtual Reality (VR) Lab for Improving the Safety of Pyrotechnicians

Research has shown that technology promotes learning environments that stimulate improved learning performance, learners satisfaction and completion rates. Virtual reality (VR) is a computer-generated environment with scenes and objects that look and feel real, making the user feel immersed in that world. This immersion provides learning and training opportunities in a variety of areas. This paper aims to investigate the perceived usefulness, ease of use and impression of the presented solution and whether it can improve the safety and effectiveness of field demining missions. A focus group study was conducted. The findings of the study are very positive, and show an affirmative attitude towards the use of the presented application for the purpose of educating and training new deminers.
Fairness-Aware Active Learning for Decoupled Model

Active learning (AL) aims to actively label training data for supervised learners to efficiently improve accuracy of the trained models. However, traditional AL techniques ignore model fairness, which is an important social norm in modern machine learning applications. In this paper, we propose the first fairness-aware active learning technique for decoupled model called D-FA2L. It labels data that receive disagreed predictions from the decoupled models, and we hypothesize it can simultaneously improve model fairness and model accuracy. We present theoretical justification of D-FA2L, proving it can reduce model unfairness under proper conditions. In experiments, we show D-FA2L can reduce model unfairness effectively, and the reduction is more efficient than both classic and state-of-the-art AL techniques on three data sets. In the meantime, D-FA2L is able to maintain similar improvement rate on model accuracy.
Vertical Split Learning - an exploration of predictive performance in medical and other use cases

In healthcare and other fields, data of an individual is often vertically partitioned across multiple organizations. Creating a centralized data store for AI algorithm development is cumbersome in such cases because of concerns like privacy and data ownership. Methods of distributed learning over vertically partitioned data could offer a solution here. While several studies have evaluated the feasibility, privacy and efficiency of such methods, an extensive evaluation of their impact on predictive performance compared to a centralized approach is missing. Vertical Split Learning (VSL) aims to provide vertical distributed learning through distributed neural network architectures. Our study adapts and applies VSL to 8 datasets, both in medicine and beyond, evaluating the impact of different network and (vertical) feature distributions on predictive performance. In most configurations VSL yields comparable predictive performance to its centralized counterparts. However, certain data and network distributions give an unexpected and severe loss of performance. Based on our findings we give some initial recommendations under which conditions VSL can be applied as a suitable alternative for data centralization.
Overview of the Application of Unmanned Aerial Vehicles in the Arctic Atmospheric Observation System

As a result of global warming, the climate in the Arctic region is undergoing dramatic changes. The Arctic atmospheric system is an important part of the Arctic climate, and it also plays a vital role in understanding the dramatic changes of the Arctic climate. Limited by many factors such as severe weather, observation location and safety of researchers, the accuracy of data obtained by the traditional platform for observing the Arctic atmospheric system is low. However, this kind of low-precision data cannot be used to establish an accurate Arctic climate model. As a new type of mobile observation platform, the unmanned aerial vehicles (UAVs) are used for in-situ observation. And they are widely used for scientific research in the Antarctic and Arctic regions because of their flexibility, low cost and applicability to high-risk areas. An overview of UAVs, which have been used in the Arctic atmospheric observation system over the past few decades, is provided in this paper to reflect the important role of UAVs in observing the Arctic atmosphere. Additionally, both the disadvantages and development trends of the above UAVs are also summarized in this paper.
Perception of physical and virtual agents: exploration of factors influencing the acceptance of intrusive domestic agents

Domestic robots and agents are widely sold to the grand public, leading us to ethical issues related to the data harvested by such machines. While users show a general acceptance of these robots, concerns remain when it comes to information security and privacy. Current research indicates that there’s a privacy-security trade-off for better use, but the anthropomorphic and social abilities of a robot are also known to modulate its acceptance and use. To explore and deepen what literature already brought on the subject we examined how users perceived their robot (Replika, Roomba©, Amazon Echo©, Google Home©, or Cozmo©/Vector©) through an online questionnaire exploring acceptance, perceived privacy and security, anthropomorphism, disclosure, perceived intimacy, and loneliness. The results supported the literature regarding the potential manipulative effects of robot’s anthropomorphism for acceptance but also information disclosure, perceived intimacy, security, and privacy.
SOH Degradation of Curved and Flat Li-ion Thin Flexible Batteries Subjected to Flex-to-Install Testing at Various Fold Diameters

The growing need for wearable devices, fitness accessories and biomedical equipment has led to the upsurge in research and development of thin flexible battery research and development. Wearable equipment and other asset monitoring applications require versatile installation of power sources on non-planar surfaces. For power sources in wearable electronics, perseverance towards repetitive mechanical stresses induced by human body motion is necessary along with the usual desirable characteristics such as high capacity, high C-rate capability and good life cycle stability. Prior studies that document the reliability of power sources subject to static and dynamic folding are scarce and at times fail to follow definitive test protocols which limit their application to real-life battery use scenarios. Particularly, the use of manual mechanical stressing of the power sources instead of a mechanical test setup is a key shortcoming in existing literature. Data is lacking on battery life cycling and in-situ mechanical stressing of the power sources including their impact of performance and reliability. In our previous studies, we have demonstrated test protocols for performing static and dynamic folding tests, which are repeatable, extensive and overcome the shortcomings of the prior studies in literature. However, with advancement in battery manufacturing technology, curved batteries with specific rest diameters have been developed to suit particular applications requiring power source attachment to conformal surfaces. Thus, this study will compare the performance of curved batteries in their rest (i.e., pre-folded) condition and at additional fold diameters for flex-to-install testing with the performance of flat thin flexible batteries which will be tested at the same conditions.
Closing the Dynamics Gap via Adversarial and Reinforcement Learning for High-Speed Racing

Autonomous racing has lately gained popularity because of its entertainment value and potential of advancing autonomous driving in high-speed situations. These high-speed racing efforts usually focus on a road domain with fixed dynamics. They cannot meet the challenge of policy adaptation between domains with large dynamics gaps. Meanwhile, existing policy adaptation methods either rely on experts to build new environments for policy training, or only handle a small dynamics gap for low-speed control tasks due to limited dynamics modeling and rigorous data collection assumptions. To overcome these drawbacks, we introduce DAARL, a novel policy adaptation algorithm that uses adversarial and reinforcement learning to bridge the large dynamics gap between different domains. It has two training stages. In the first training stage, a domain transfer function is learned by adversarial learning to better capture the dynamics gap. The single domain transfer function integrates with the source domain to implement the dynamics of different target domains virtually without the help of experts. We name these virtual domains the imaginary target domains. In the second training stage, the knowledge of the source-domain policy guides the reinforcement learning of a target-domain policy on an imaginary target domain. It improves the convergence of the target-domain policy. Five experiments have been conducted on a racing simulator with different road domains. All results show that DAARL outperforms baselines in terms of driving speed, stability, success rate, and domain scalability.
Correlation Encoder-Decoder Model for Text Generation

Text generation is crucial for many applications in natural language processing. With the prevalence of deep learning, the encoder-decoder architecture is dominantly adopted for this task. Accurately encoding the source information is of key importance to text generation, because the target text can be generated only when accurate and complete source information is captured by the encoder and fed into the decoder. However, most existing approaches fail to effectively encode and learn the entire source information, as some features are easy to be missed along with the encoding procedures of the encoder. Similar problems also confuse the implementation of the decoder. How to reduce the problem of information loss in the encoder-decoder model is critical for text generation. To address this issue, we propose a novel correlation encoder-decoder model, which optimizes both the encoder and the decoder to reduce the problem of information loss by enforcing them to minimize the differences between hierarchical layers by maximizing the mutual information. Experimental results on two benchmark datasets demonstrate that the proposed model substantially outperforms the existing state-of-the-art methods. Our source code is publicly available on GitHub1.
In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance

In power grids, short-term load forecasting (STLF) is crucial as it contributes to the optimization of their reliability, emissions, and costs, while it enables the participation of energy companies in the energy market. STLF is a challenging task, due to the complex demand of active and reactive power from multiple types of electrical loads and their dependence on numerous exogenous variables. Amongst them, special circumstances-such as the COVID-19 pandemic-can often be the reason behind distribution shifts of load series. This work conducts a comparative study of Deep Learning (DL) architectures-namely Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-with respect to forecasting accuracy and training sustainability, meanwhile examining their out-of-distribution generalization capabilities during the COVID-19 pandemic era. A Pattern Sequence Forecasting (PSF) model is used as baseline. The case study focuses on day-ahead forecasts for the Portuguese nationa115-minute resolution net load time series. The results can be leveraged by energy companies and network operators (i) to reinforce their forecasting toolkit with state-of-the-art DL models; (ii) to become aware of the serious consequences of crisis events on model performance; (iii) as a high-level model evaluation, deployment, and sustainability guide within a smart grid context.
RESOLVED MILLIMETER EMISSION FROM THE HD 15115 DEBRIS DISK

We have used the Submillimeter Array (SMA) to make 1.3 mm observations of the debris disk surrounding HD 15115, an F-type star with a putative membership in the β Pictoris moving group. This nearly edge-on debris disk shows an extreme asymmetry in optical scattered light, with an extent almost two times larger to the west of the star than to the east (originally dubbed the “Blue Needle”). The SMA observations reveal resolved emission that we model as a circumstellar belt of thermal dust emission. This belt extends to a radius of ∼110 AU, coincident with the break in the scattered light profile convincingly seen on the western side of the disk. This outer edge location is consistent with the presence of an underlying population of dust-producing planetesimals undergoing a collisional cascade, as hypothesized in “birth ring” theory. In addition, the millimeter emission shows a ∼ 3 &sgr; ? > feature aligned with the asymmetric western extension of the scattered light disk. If this millimeter extension is real, then mechanisms for asymmetry that affect only small grains, such as interactions with interstellar gas, are disfavored. This tentative feature might be explained by secular perturbations to grain orbits introduced by neutral gas drag, as previously invoked to explain asymmetric morphologies of other, similar debris disks.
Designing a 9.3μW Low-Power Time-to-Digital Converter (TDC) for a Time Assisted SAR ADC

The Time-to-Digital Converter (TDC) is an impor-tant circuit block for digitally quantifying the time displacement between digital events. Among several applications of the TDC, this work focuses on its application to low-power Successive-approximation Analog-to-Digital Converters (SAR ADC). The TDC can assist the SAR algorithm to improve the energy efficiency of capacitive DAC switching schemes, which constitute a significant portion of the SAR ADC power dissipation. This work presents the design of a coarse 8-bit deep TDC in a manufacturable 28 nm Bulk CMOS technology, which displays good coverage of the SAR ADC input after a calibration step using tunable delay cells that were optimized for 0.6 V supply. In our design approach, we optimized for energy the sizing of the both delay cells and the LV registers. The TDC had a simulated mean power dissipation of just $9.25 \mu W$ at this voltage, making it a good candidate for applications that are not very demanding in terms of precision.
Non-thermal processes in coronae and beyond

This contribution summarizes the splinter session “Non-thermal processes in coronae and beyond” held at the Cool Stars 17 workshop in Barcelona in 2012. It covers new developments in high energy non-thermal effects in the Earth's exosphere, solar and stellar flares, the diffuse emission in star forming regions and reviews the state and the challenges of the underlying atomic databases. (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Exploring First Impressions of the Perceived Social Intelligence and Construal Level of Robots that Disclose their Ability to Deceive

If a robot tells you it can lie for your benefit, how would that change how you perceive it? This paper presents a mixed-methods empirical study that investigates how disclosure of deceptive or honest capabilities influences the perceived social intelligence and construal level of a robot. We first conduct a study with 198 Mechanical Turk participants, and then a replication of it with 15 undergraduate students in order to gain qualitative data. Our results show that how a robot introduces itself can have noticeable effects on how it is perceived–even from just one exposure. In particular, when revealing having ability to lie when it believes it is in the best interest of a human, people noticeably find the robot to be less trustworthy than a robot that conceals any honesty aspects or reveals total truthfulness. Moreover, robots that are forthcoming with their truthful abilities are seen in a lower construal than one that is transparent about its deceptive abilities. These results add much needed knowledge to the understudied area of robot deception and could inform designers and policy makers of future practices when considering deploying robots that deceive.
The Optical Gravitational Lensing Experiment. OGLE-III Long Term Monitoring of the Gravitational Lens QSO 2237+0305

We present results of the long term monitoring of the gravitational lens QSO 2237+0305 conducted during the OGLE survey. Light curves of all four components of the lens obtained during the second phase of the OGLE project (OGLE-II; 1997-2000) are supplemented with the data collected in the OGLE-III phase in the observing seasons 2001-2006. Calibration procedures to tie the new OGLE-III data with already calibrated OGLE-II light curves are described. The resulting homogeneous OGLE data set is the most extensive photometric coverage of the gravitational lens QSO 2237+0305, spanning now one decade - the seasons from 1997 to 2006 - and revealing unique microlensing activity of this spectacular object. All photometric data of the gravitational lens QSO 2237+0305 collected by OGLE are available to the astronomical community from the OGLE Internet archive.
Learning Grasping Strategies for a Soft Non-Anthropomorphic Hand from Human Demonstrations

Finding effective grasp strategies constitutes one of the main challenges in robotic manipulation, especially when dealing with soft, underactuated, and non-anthropomorphic hands. This work presents a Learning from Demonstration approach to extract grasp primitives using a novel reconfigurable soft hand, the Soft ScoopGripper (SSG). Starting from human demonstrations, we derived Gaussian models through which we were able to devise different grasping strategies, exploiting the SSG features. As the grasping strategies are tightly related to the characteristics of the object to be grasped, we tested two different ways of modeling objects in the training dataset and we comparatively evaluated the resulting primitives. Experimental grasping trials on unknown test objects confirmed the effectiveness of the learned primitives and showed how assuming different levels of knowledge about the object representation in the training phase influences the grasp success.
Timing-Specified Controllers with Feedback for Human-Robot Handovers

We develop and evaluate two human-robot handover controllers that allow end-users to specify timing parameters for the robot reach motion, and that provide feedback if the robot cannot satisfy those constraints. End-user tuning with feedback is a useful controller feature in settings where robots have to be re-programmed for varying task requirements but end-users do not have programming knowledge. The two controllers we propose are both receding-horizon controllers that differ in their objective function, and their user specified parameters, and subsequently their user-interface: One controller uses a minimum cumulative jerk (MCJ) objective function, and the other a minimum cumulative error (MCE) objective function. We implemented the controllers on a collaborative robot and conducted two controlled experiments to compare the user experience and performance of these controllers vis-à-vis a baseline proportional velocity (PV) controller. In each experiment, participants (n = 30) interactively tuned the controller parameters, and collaborated with a robot to perform a time-constrained repetitive task. We found that the timing controller with the MCE implementation can provide a better user experience, both while setting the parameters (p =0.011) and performing the handovers with the robot (p < 0.001), and fewer failures (p =0.016) compared to the PV controller, however the MCJ implementation did not provide better user experience compared to the PV controller. The MCJ controller also resulted in more failures than the PV controller. These results could inform the design of usable and effective end-user configurable controllers for human-robot interaction.
Loophole-Free Bell Test Based on Local Precertification of Photon's Presence

A loophole-free violation of Bell inequalities is of fundamental importance for demonstrating quantum nonlocality and long-distance device-independent secure communication. However, transmission losses represent a fundamental limitation for photonic loophole-free Bell tests. A local precertification of the presence of the photons immediately before the local measurements may solve this problem. We show that local precertification is feasible by integrating three current technologies: (i) enhanced single-photon down-conversion to locally create a flag photon, (ii) nanowire-based superconducting single-photon detectors for a fast flag detection, and (iii) superconducting transition-edge sensors to close the detection loophole. We carry out a precise space-time analysis of the proposed scheme, showing its viability and feasibility.