Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaffolding Methodology Implementation for Training Program in an Engine Assembly Department of Indonesian Remanufacturing Company

Remanufacturing is a form of circular economy implementation that is crucial to drive the sustainability development goals. PT. XYZ, one of the most extensive remanufacturing companies in Indonesia aims to improve its competitiveness and sustainability by focusing on their human resources aspect, especially on the competencies of assembly mechanics. The challenge occurred when the learning development lead time is time consuming, thus found an urgent need for its optimization. In order to dealt with the challenge, this research is conducted by adopting the Scaffolding Methodology model of which consists of four stages, namely focused instruction, guided instruction, collaborative learning and independent learning. The training program emphasized not only on the enhancement of the curriculum and the practical concept, but also its training location and the adoption of E-Learning. Moreover, the use of scaffolding method as gradual release of responsibility framework has been deployed, as well as knowledge conversion in the form of assembly collaboration and re-layout of assembly lines. Finally, this study has succeeded in developing an integrated training program that able to generate a significant reduction in preparing mechanic's competencies.
Evaluation of Different Cutoff Frequencies of High-pass Filter for Online Spike Sorting

Despite the ever-increasing demand for online spike sorting to be applied in various closed-loop neuromodulation experiments and treatments, the performance and bandwidth are still constrained by the strict requirement for time complexity. Initiatives for improving online spike sorting performance mostly started with the implementation and designing of sorting algorithms, assuming standardized data preprocessing operations are applicable to all cases and separable for evaluating sorting performance. However, we postulated that the cutoff frequency of the high-pass filter could affect the sorting performance, given that spike waveforms are informative in a broad band and would be distorted if the frequency characteristics of the filter and noise do not match. Based on this rationale, we have evaluated how cutoff frequency affects the spike sorting performance on both the synthetic and real datasets. It was demonstrated that, the cutoff frequency can have a huge impact on the sorting performance. Further, this impact was noise-dependent. For neural signals with homogeneous noise, the cutoff frequency would lead to greater disparity when the signal-noise ratio decreased. While for signals with different types of noise, when the noise was subject to a “1/f” power spectrum, higher cutoff frequencies would render better performance. However, lower cutoff frequencies were advantageous when the noise deviated from the “1/f” noise. Therefore, according to the evaluation, when the cutoff frequency of the high-pass filter was adaptively switchable, the spike sorting performance would be enhanced while sidestepping the challenges in designing sorting algorithms.
Motor imagery EEG decoding based on multi-loss fusion FBCNet

Brain-computer interfaces (BCI) enable direct communication with external equipment, using neural activity as the control signal. Electroencephalogram (EEG) signals are usually selected as the control signal. For EEG signals obtained from a given experimental paradigm, a superior algorithm for feature extraction and classification is very significant. As one of the representative algorithms of deep learning, the convolutional neural network (CNN) has been widely used in the field of BCI. In this work, we introduce the filter-bank convolutional network (FBCNet) and propose an improved method. It mainly improves the network performance by modifying the loss function. The single loss function in the network is improved to the multi-loss fusion functions. Various loss functions are added to the network, and the characteristics of different loss functions are used to train the network to improve the network classification performance. This method of multi-loss fusion functions is validated on a dataset of 11 healthy subjects and compared with the other three benchmark algorithms. The result shows that the improved FBCNet produces a four-classes accuracy of 78.5%, which is superior to other algorithms.
Estimating counterfactual treatment outcomes over time in multi-vehicle simulation

Evaluation of intervention in a multi-agent system, e.g., when humans should intervene in autonomous driving systems, is challenging in various engineering and scientific fields. Estimating the individual treatment effect (ITE) using counterfactual long-term prediction is practical to evaluate such interventions. However, most of the conventional frameworks did not consider the time-varying complex structure of multi-agent relationships and covariate counterfactual prediction. Here we propose an interpretable, counterfactual recurrent network in multi-agent systems to estimate the effect of the intervention. Our model leverages graph variational recurrent neural networks and theory-based computation with domain knowledge for the ITE estimation framework based on long-term prediction of multi-agent covariates and outcomes, which can confirm the circumstances under which the intervention is effective. On simulated models of an automated vehicle with time-varying confounders, we show that our methods achieved lower estimation errors in counterfactual covariates.
Photovoltaic cells for energy harvesting and indoor positioning

We propose SoLoc, a lightweight probabilistic fingerprinting-based technique for energy-free device-free indoor localization. The system harnesses photovoltaic currents harvested by the photovoltaic cells in smart environments for simultaneously powering digital devices and user positioning. The basic principle is that the location of the human interferes with the lighting received by the photovoltaic cells, thus producing a location fingerprint on the generated photocurrents. To ensure resilience to noisy measurements, SoLoc constructs probability distributions as a photovoltaic fingerprint at each location. Then, we employ a probabilistic graphical model for estimating the user location in the continuous space. Results show that SoLoc can localize the user at sub-meter accuracy in a real indoor environment.
Characterizing I/O in Machine Learning with MLPerf Storage

Data is the driving force behind machine learning (ML) algorithms. The way we ingest, store, and serve data can impact the performance of end-to-end training and inference significantly [11]. However, efficient storage and pre-processing of training data has received far less focus in ML compared to efforts in building specialized software frameworks and hardware accelerators. The amount of data that we produce is growing exponentially, making it expensive and difficult to keep entire training datasets in main memory. Increasingly, ML algorithms will need to access data from persistent storage in an efficient way.
Evaluating Critical Success Factors for Implementation of Internet of Things (IoT) Using DEMATEL: A Case of Indonesian Automotive Company

The implementation of technology is used to support the Making Indonesia 4.0 Program in various industrial sectors in Indonesia to achieve optimization of company performance. This is in line with the Indonesian Government Program through the Ministry of Industry, namely Making Indonesia 4.0. In the Making Indonesia 4.0 Program, there are 7 leading manufacturing sectors. The automotive sector is the object of this research because automotive sector is a leading player in the export of Internal Combustion Engines (ICE) and Electrified Vehicles (EVs). The technology discussed in this research is to support the Making Indonesia 4.0 Program, namely the application of the Internet of Things (IoT) to automotive companies. This research aims to evaluate the critical success factor (CSF) of IoT implementation in automotive companies in Indonesia and obtain a relationship from each dimension that becomes an evaluation of the critical success factor (CSF) of IoT implementation in automotive companies in Indonesia. In this research, the validation stage was carried out by 5 panels of experts in Indonesia who have experience and knowledge in the field of IoT application in automotive companies. The results of the research using the DEMATEL method obtained 4 critical success factors (CSF) for IoT implementation in automotive companies in Indonesia which are dispatchers who are the top priority because they have a greater influence. The four dimensions are the Marketing Dimension, Finance Regulation, and Resources. While the other 4 dimensions, namely the Dimensions of Innovation and Ideas and Resources, Operations, People, and Management, Technology became the receiver as the last priority because it received greater influence. The results of this research can be used as a reference for the automotive industry and various other industries in conducting evaluations related to the critical success factor (CSF) of IoT implementation in companies to improve company performance.
MicroInfer: an edge deep-learning inference framework for industry IoT

To address the difficult deployment, high memory overhead, and poor portability of edge deep learning frameworks in industry IoT, we propose MicroInfer which is an edge deep learning inference framework for Microprocessors. MicroInfer employs a combination of upper computer and lower computer architecture. The upper computer automatically implements model quantization, model precompilation, operator matching, and code generation, estimates in advance the memory overhead and inference acceleration scheme required on the lower computer chip, and automatically generates a customized AI plug-in. The lower computer utilize an improved memory management strategy to execute the algorithm with as little memory space as possible without affecting the inference speed. A custom XidianOS operating system for microprocessors is also designed for MicroInfer, and the AI framework is designed to support various AI inference frameworks. The proposed MicroInfer is compared with other inference frameworks, achieving significant improvements in memory usage and inference speed, accelerating the development of industry IoT.
Multi-view traffic sign localization with high absolute accuracy in real-time at the edge

In this paper, we present a system to localize traffic signs with high accuracy in real-time at the edge using 3D reconstruction of an environment. We use Structure-from-Motion (SfM) based 3D reconstruction with only a small number of tracked feature points in video frames. The 3D model obtained from SfM alone has an arbitrary scale and orientation, which is not suitable for localizing traffic signs absolutely. Therefore, we use the GNSS locations of the images to scale and orient the 3D model with the real world. Next, we compute the latitude-longitude location of a traffic sign using the mapping between the traffic sign bounding box and corresponding 3D points in the 3D model. We also present the design of other relevant system components such as vehicle location, timing, data synchronization, and sensor calibration, which are required to achieve high accuracy localization. We evaluated our system in city, suburb, and highway scenes on meticulously annotated ground truth datasets. According to evaluation, our system achieves the median localization accuracy of 2.76 meters with high localization success rate and runs on average 15× faster than a comparable baseline system.
Revisiting data lakes: the metadata lake

We argue that emerging federated data management architectures require a means of gathering, linking, curating and enriching metadata in a graph. We call the system that supports these tasks a metadata lake. We explain the underlying architectural principles that are required to achieve such a system and describe our current implementation. We show how our metadata lake is used to achieve certain advanced capabilities and report on its performance.
Study on fatty liver based on Pseudotime analysis

In recent decades, the unhealthy diet and sedentary lifestyles of people are taking their toll on growing cases of metabolic diseases worldwide, one of them being Nonalcoholic Fatty Liver Disease (NAFLD). This disease has become one of the most sophisticated medical and physiological puzzles because of its convoluted mechanisms of progression.Existing gene expression analysis methods like microarray or RNA-sequencing are unable to resolve the complex mechanisms of progression of non-alcoholic fatty liver disease (NAFLD) due to insufficient accuracy and lack of phenotypic data. Particularly, incomplete phenotypic data in public liver gene expression cohorts have cumbered many studies on the progression of NAFLD. To address this issue, the cutting-edge pseudotime analysis is adopted to estimate liver health status in human liver gene expression data. The identified DE genes separate the NAFLD patients and the healthy controls in hierarchical clustering, and their related biological pathways are highly relevant to liver signaling and injury, implying the close relationship between the DE gene expressions and NAFLD. What's more, the pseudotime analysis we conducted simulates the deterioration of NAFLD by using liver fat percent to represent NAFLD severity and aligning the candidate samples on the estimated trajectory according to their respective gene expression and covariates; we verified the pseudotime model using another microarray cohort.
Deception Detection Within and Across Domains: Identifying and Understanding the Performance Gap

NLP approaches to automatic deception detection have gained popularity over the past few years, especially with the proliferation of fake reviews and fake news online. However, most previous studies of deception detection have focused on single domains. We currently lack information about how these single-domain models of deception may or may not generalize to new domains. In this work, we conduct empirical studies of cross-domain deception detection in five domains to understand how current models perform when evaluated on new deception domains. Our experimental results reveal a large gap between within and across domain classification performance. Motivated by these findings, we propose methods to understand the differences in performances across domains. We formulate five distance metrics that quantify the distance between pairs of deception domains. We experimentally demonstrate that the distance between a pair of domains negatively correlates with the cross-domain accuracies of the domains. We thoroughly analyze the differences in the domains and the impact of fine-tuning BERT based models by visualization of the sentence embeddings. Finally, we utilize the distance metrics to recommend the optimal source domain for any given target domain. This work highlights the need to develop robust learning algorithms for cross-domain deception detection that generalize and adapt to new domains and contributes toward that goal.
Sentinel: a fast and memory-efficient serverless architecture for lightweight applications

Serverless computing is a new computing paradigm that enables application developers to focus on the core service logic of their applications. To protect the host kernel from attacks, serverless service providers need to ensure isolation between application sandboxes while keeping the startup latency and memory usage low. Existing architectures provide the warm start functionality to alleviate the startup latency. However, as warm starts are achieved at the cost of high memory usage, handling all requests as warm starts is virtually impossible. Therefore, mitigating the cold start overhead is key to improving startup latency. In the real world, serverless applications tend to be uncomplicated; they only issue simple system calls and do not modify the underlying filesystem. While existing architectures support full-fledged virtualization, serverless applications support many functionalities and data structures that are unnecessary for simple applications. This paper proposes Sentinel, a serverless architecture to target those simple applications. By stripping the unnecessary functionalities and providing the bare minimum OS virtualization needed to execute the targeted applications, Sentinel achieves drastic improvement compared to existing architectures; up to 10× shorter startup overhead, 8.13× shorter end-to-end execution latency, and 98% lower memory usage.
A mechanism for pockets of predictability in complex adaptive systems

We document a mechanism operating in complex adaptive systems leading to dynamical pockets of predictability (“prediction days”), in which agents collectively take predetermined courses of action, transiently decoupled from past history. We demonstrate and test it out of sample on synthetic minority and majority games as well as on real financial time series. The surprising large frequency of these prediction days implies a collective organization of agents and of their strategies which condense into transitional herding regimes.
Performance Evaluation of Systematic Option Trading Strategies Using Entry and Exit Points Predicted by Machine Learning

In option trading, there are various technical indicators that can be used to determine entry signals. There are also various setups for systematic option trading strategies. The objective of this research is to evaluate the performance of systematic option trading strategies using entry signals generated by machine learning with technical indicators as predictors. To achieve this objective, a two-phase study is conducted. During Phase 1, machine learning algorithm is utilized to generate entry signals based on support and resistance prediction. Based on these signals, various option trading setups are evaluated in the Phase 2 to find an optimal setup that yield the highest ROC. Neural network algorithm with 5 hidden layers and 100 neurons in each layer is found to be the best algorithm to predict support and resistance to be used as signals. The back-testing results show that option trading strategy that yield the highest ROC is debit spread strategy with delta 0.15 setup and DTE 30 setup, which results in 399% annual ROC. The benchmark test results show that the performance of option trading using machine learning entry signals prediction outperforms option trading with single technical indicator entry signals, and that option trading outperforms the regular stock trading.
Estimating photometric redshifts for X-ray sources in the X-ATLAS field using machine-learning techniques

The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the AHEAD project (grant agreement No. 654215). G.M. acknowledges financial support from the AHEAD project, which is funded by the European Union as Research and Innovation Action under Grant No: 654215. F.J.C. and A.C. R. acknowledge financial support through grant AYA2015-64346-C2-1-P (MINECO/FEDER). A.C. R. also acknowledges financial support by the European Space Agency (ESA) under the PRODEX program.
On the evolution of those nuclei of planetary nebulae that experiencea final helium shell flash.

We suggest that some of the central stars of planetary nebulae experience a final thermal pulse having achieved a white dwarf configuration and begun their descent along a cooling white dwarf sequence of nearly constant radius. A concrete theoretical calculation demonstrates that, during such a pulse, most of the hydrogen remaining in the star at pulse onset is incorporated into the helium-burning convective shell and completely burned, and that, following the pulse, the star swells briefly to red giant dimensions. The model then proceeds to burn helium on a long time scale, retracing in the H-R diagram approximately the same path that it followed while burning hydrogen during the initial excitation of the nebula, which has by now expanded considerably in extent. We identify as being in the postpulse, quiescent helium burning phase the central stars of the planetary nebulae d Abell 78, and the central stars of a group of related high-excitation objects. These nebulae all have the large radii often found in conjunction with central stars of low luminosity that are thought to be cooling along the white dwarf sequence; however, they have the high luminosities that are characteristic of much smaller nebulae whose nuclei are thought tomore » be proceeding for the first time through the planetary nucleus regime in the (log L, log T/sub e/)-plane. « less
Endoplasmic Reticulum Stress Response is Transmissible in vitro from Epithelial Cells to Fibroblasts via the Mediation of Unfolded Protein Response

The unfolded protein response (UPR) can restore cellular homeostasis and induce apoptosis during endoplasmic reticulum (ER) stress. Its activation has been observed during inflammation and fibrogenesis, which undermine tissue architecture and organ function. Nonetheless, the escalation from cell-intrinsic UPR to tissue-wide damage remains unclear. This study investigated whether UPR-induced signals in epithelial cells are transmissible to fibroblasts and if they could alter fibroblast gene expression towards fibrotic phenotype. An engineered epithelial cell line HEK DAX in which UPR pathways can be activated is co-cultured with fibroblasts to mimic the tissue environment. Fibroblast gene expression was analyzed using fluorescent stress bioassay, Western blot, and immunocytochemistry. Changes in the expression of ATF6- and XBP1s-induced genes and altered fibroblast endosomal and secretory compartments were observed. Increased fibroblast proliferation was measured. These findings suggested that this epithelial UPR-initiated stress communication has direct influences on fibroblast, providing new insights into the pathophysiological roles of the UPR in fibrogenesis.
64. Huzenbacher See

During the last Ice age, the northern Black Forest was covered only by local glaciers, which resulted in many cirques, the deepest still containing lakes (Lang 2005). One of them, Huzenbacher See (8° 20′ 58′′ E, 48° 34′ 33′′ N, 747 m above sea level [a.s.l.]), is surrounded by ridges with elevations up to 940 m a.s. l. The lake covers an area of 2.5 ha, has a maximum depth of 7.5 m and is surrounded by a fringe of oligotrophic mires and by coniferous forest dominated by spruce. The closest larger areas with open vegetation are the Murg valley, about 4 km to the east, and the Acher valley more than 10 km to the west, where the Black Forest becomes lower and eventually bounds the Upper Rhine Rift. In modern times, as most lakes of the Black Forest, Huzenbacher See was dammed to rise the water table and to have water for flushing timber down to the Murg valley. With the rising water table parts of the surrounding peat were elevated and floated, resulting in a concentric ring of quaking bog surrounding the lake’s centre and separating it from the littoral water.