Miracle-dz/UniFAR-FTUs huggingface dataset:
Path-integral representation of diluted pedestrian dynamics

We frame the issue of pedestrian dynamics modeling in terms of path-integrals, a formalism originally introduced in quantum mechanics to account for the behavior of quantum particles, later extended to quantum field theories and to statistical physics. Path-integration enables a trajectory-centric representation of the pedestrian motion, directly providing the probability of observing a given trajectory. This appears as the most natural language to describe the statistical properties of pedestrian dynamics in generic settings. In a given venue, individual trajectories can belong to many possible usage patterns and, within each of them, they can display wide variability. We provide first a primer on path-integration, and we introduce and discuss the path-integral functional probability measure for pedestrian dynamics in the diluted limit. As an illustrative example, we connect the path-integral description to a Langevin model that we developed previously for a particular crowd flow condition (the flow in a narrow corridor). Building on our previous real-life measurements, we provide a quantitatively correct path-integral representation for this condition. Finally, we show how the path-integral formalism can be used to evaluate the probability of rare-events (in the case of the corridor, U-turns).
Reconstructing Out-of-Order Issue Queue

Out-of-order cores provide high performance at the cost of energy efficiency. Dynamic scheduling is one of the major contributors to this: generating highly optimized issue schedules considering both data dependences and underlying execution resources, but relying heavily on complex wakeup and select operations of an out-of-order issue queue (IQ). For decades, researchers have proposed several complexity-effective dynamic scheduling schemes by leveraging the energy efficiency of an in-order IQ. However, they are either costly or not capable of delivering sufficient performance to substitute for a conventional wide-issue out-of-order IQ. In this work, we revisit two previous designs: one classical dependence-based design and the other state-of-the-art readiness-based design. We observe that they are complementary to each other, and thus their synergistic integration has the potential to be a good alternative to an out-of-order IQ. We first combine these two designs, and further analyze the main architectural bottlenecks that incur the underutilization of aggregate issue capability, thereby limiting the exploitation of instruction-level and memory-level parallelisms: 1) memory dependences not exposed by the register-based dependence analysis and 2) wide and shallow nature of dynamic dependence chains due to the long-latency memory accesses. To this end, we propose Ballerino, a novel microarchitecture that performs balanced and cache-miss-tolerable dynamic scheduling via a complementary combination of cascaded and clustered in-order IQs. Ballerino is built upon three key functionalities: 1) speculatively filtering out ready-at-dispatch instructions, 2) eliminating wasteful wakeup operations via a simple steering technique leveraging the awareness of memory dependences, and 3) reacting to program phase changes by allowing different load-dependent chains to share a single IQ while guaranteeing their out-of-order issue. The net effect is minimal scheduling energy consumption per instruction while providing comparable scheduling performance to a fully out-of-order IQ. In our analysis, Ballerino achieves comparable performance to an 8-wide out-of-order core by using twelve in-order IQs, improving core-wide energy efficiency by 20%.
Indonesian Digital Bank Adoption Defence Determination Post-Privacy Data Leakage Issues

Banking service transformation from traditional services to bank digitalization has several advantages for both banks and customers. This study aims to examine several theories, including UTAUT, Meta UTAUT and TAM plus the development of variables from security and marketing theory in testing the behavior of digital bank customers in chronological perception by considering 19 hypotheses. The quantitative survey research was carried out by involving 206 respondents using the SEM PLS analysis technique. The results of hypothesis testing summarize the meaning of trust pre-use and post-satisfaction achieved by customers can encourage customer loyalty and intention to recommend digital banks to the public. The negative issue of a technology is a poison that can damage the sustainability of digital banks. So it is necessary to evaluate the improvement and development of digital banks both in terms of systems and services in order to continue to exist in the community.
Time-Varying Parameter Identification of a Tumor Growth Model Using Moving Horizon Estimation

A nonlinear Moving Horizon Estimator (MHE) was developed which can estimate the time-varying parameters of a tumor growth model under chemotherapeutic treatment. We introduce a sequential estimation strategy using the Full Information Estimator (FIE) that is able to approximate an estimate to the average initial model parameters. The algorithm penalizes the estimation error and the deviation of parameters between each consecutive iteration of the FIE. We also describe the tuning process in detail, where we utilized a grid search process to find the best choice for the parameters of the MHE. The algorithm was tuned and validated using time-series data, originating from in vivo mice experiments.
Tissue plasminogen activator resistance is an early predictor of posttraumatic venous thromboembolism: A prospective study from the CLOTT research group

Early resistance to clot lysis with TPA after injury is an independent risk factor for VTE BACKGROUND Venous thromboembolism (VTE) remains a frequent postinjury complication with well established but nonmodifiable risk factors. We hypothesized that fibrinolysis shutdown (SD) as measured by thromboelastography (TEG) would be an independent risk factor for VTE in trauma patients. METHODS A subgroup of patients enrolled in the CLOTT-2 (Consortium of Leaders in the Study of Traumatic Thromboembolism 2), multicenter prospective cohort study had kaolin TEG and tissue plasminogen activator (tPA)–TEG data at 12 and 24 hours postadmission. Patients underwent a screening duplex venous ultrasound examination during the first week unless clot was already detected on computed tomography. Injury factors associated with early fibrinolysis SD (defined as kaolin TEG Ly30 ≤0.3%) and/or tPA resistance (tPA-R) (defined as kaolin TEG with tPA 75 ng Ly30 <2.1%) were investigated as was the association of the TEG measurements with the development of VTE. RESULTS A total of 141 patients had both TEG measurements at 24 hours, and 135 had both TEG measurements at 12 hours. Shutdown was evident at 12 hours in 71 of 135 (52.6%) patients and in 62 of 141 (44%) at 24 hours. Tissue plasminogen activator resistance was found in 61 of 135 (45.2%) at 12 hours and in 49 of 141 (34.3%) at 24 hours. Factors significantly associated with SD included receiving >4 U of FFP in the first 24 hours, the presence of a major brain injury or pelvic fracture, and the need for major surgery. In contrast, factors significantly associated with early tPA-R included >4 U of red blood cells transfused in the first 24 hours and the presence of a major chest injury or long bone fracture. Deep vein thrombosis was detected in 15 patients and pulmonary clots in 5 (overall VTE rate, 14.2%). Tissue plasminogen activator resistance at 12 hours was found to be an independent risk factor for VTE (hazard ratio, 5.57; 95% confidence interval, 1.39–22.39). CONCLUSION Early development of a hypercoagulable state as defined by tPA-R at 12 hours after admission represents a potentially modifiable risk factor for postinjury VTE. LEVEL OF EVIDENCE Therapeutic/Care Management; Level II.
A Comparative Study of ARIMA and LSTM in Forecasting Time Series Data

Forecasting time series data is an important subject in economics and business where the Autoregressive Integrated Moving Average (ARIMA) has been extensively used despite its weaknesses, from requiring a minimum number of data points to the assumed linearity of data. With recent advancement, the Long Short-Term Memory (LSTM) shows potential to address such weaknesses. This research is aimed to identify a more suitable model in handling irregular data. Performance metrics used are the model accuracy measured with RMSE and the model run-time performance measured with the Python Timeit library. This research concluded that LSTM is more accurate than ARIMA (RMSE of ARIMA 0.144887 to LSTM 0.051828) in a shorter dataset of 36 data points and this result is reverted in longer dataset of 228 data points (RMSE of ARIMA 0.006949 to LSTM 0.036025). In terms of run-time performance, ARIMA is significantly faster than LSTM while the LSTM modelling time is increasing proportionally to the number of the training data points.
Amphis: Managing Reconfigurable Processor Architectures With Generative Adversarial Learning

Dynamic resources management in reconfigurable processors often manifests as a hard online decision-making task, which should yield premier solutions that must meet Quality-of-Service (QoS) requirements while maximizing the system’s efficiency. Most prior works rely on a hard-to-train predictor to model the complicated relationships between processor configurations and performance. To decide the proper resource allocation, the predictor needs to tentatively evaluate a group of possible configurations, and then decide the best configuration for the workload. This tedious process has an expensive runtime overhead for resource configuration in processors. Besides, prior works focus on improving the prediction accuracy, however, higher performance prediction cannot guarantee a good system outcome. Inspired by recent advances in adversarial learning, we present a generative adversarial network (GAN)-based framework, Amphis, which can directly generate the on-demand processor configuration for any scheduled-in application. By evaluating Amphis on a reconfigurable processor with 18 different workloads, our results demonstrate that the GAN-based method provides tremendous overhead reduction (up to 90%) compared to the SOTA prediction-based method WNNM while providing higher resource utilization.
Wireless Real-Time Exercise System for Physical Telerehabilation

The COVID-19 pandemic has impacted every aspects of health delivery and encouraged researchers to replace in-person clinical visits with telecommunications. Such approach has great potential to support home-based exercises. A critical part of successful use of exercise equipment at patient homes is to account for potential patient’s needs, values and preferences for rehabilitation and exercise prescriptions. The goal if this pilot project is to conduct an initial usability assessment of a remotely controlled version of interactive bike (iBike) system which gives the clinical team the capability to monitor exercise progress in real time using simple graphical representation. The bike can be used for upper or lower limb rehabilitation. A customized tablet app was developed to provide user interface between the app and the bike sensors. The iBike system was tested using a quasi-experimental design based on pre-post single group comparison. Two separate sessions, with a gap of a week and no further training and use of iBikE system, were carried out to determine the usability of the iBikE system. The completion times of Task 1 and Task 2 changed from 8.6±4.7 seconds to 1.8±0.8 seconds and from 315.0±6.9 seconds to 303.4±1.1 seconds respectively. Between pre-post tests, the 3-item post-task survey scores increased and the usability scores increased from 92.0±8.6 to 97.0±3.3. From the question of “How likely are you to recommend this iBikE system to others? ” the scores increased from 9.4±0.9 to 9.8±0.5. We concluded that further development and evaluation of the iBike system in different patient subgroups is warranted.
Evidence of anomalous dispersion of the generalized sound velocity in glasses

The dynamic structure factor S(Q,ω), of vitreous silica, has been measured by inelastic x-ray scattering in the exchanged wave-vector (Q) region Q=4-16.5 nm - 1 and up to energies ∞ω= 115 meV in the Stokes side. The unprecedented statistical accuracy in such an extended energy range allows us to accurately determine the longitudinal current spectra and the energies of the vibrational excitations. The simultaneous observation of two excitations in the acoustic region and the persistence of propagating sound waves up to Q values comparable with the (pseudo-)Brillouin-zone edge allow us to observe a positive dispersion in the generalized sound velocity that, around Q5 nm - 1 , varies from 6500 to 9000 m/s; this phenomenon was never experimentally observed in a glass.
A Secure Lightweight Authentication Scheme in IoT Environment with Perfect Forward and Backward Secrecy

Emerging cutting-edge technology can change the daily life of human beings, for instance, smart transportation, intelligent healthcare systems, Smart cities, etc. Human can use their device to access other devices remotely to collect and utilize the information for their work. Internet of Things is the backbone of the autonomous environment aforesaid. However, IoT uses the public channel to transmit information, and it opens the opportunity for attackers to intercept, delete, and modify data. Therefore, security is an imperative aspect of IoT technology. One of the critical security features is perfect forward and backward secrecy. This article proposes new protocol authentication using biometrics and Physically Unclonable Function (PUF) with the primary security feature of achieving Perfect Forward and Backward Secrecy (PFBS). Our protocol also achieves other security features such as anonymity, unclonable device, and mutual authentication. In addition, we conduct informal and formal analyses to prove that the protocol achieves security features.
TinyML-based approach for Remaining Useful Life Prediction of Turbofan Engines

In the recent years, artificial intelligence, machine learning and IoT technologies have enabled a great number of industrial applications with profitable results. Predicting the remaining useful life (RUL) of turbofan engines constitutes a successful example of industrial AI, and it has received thorough attention from the researchers worldwide, with numerous novel and effective methods being proposed in the literature. Meanwhile, TinyML is a recent trend that has emerged in the AI field and demonstrates, amongst others, promising potential to break through the existing barriers of trusting and deploying real-time critical industrial AI solutions. In this context, this paper aims to further contribute to the literature and demonstrate the realization of RUL predictions in the extreme edge via TinyML, using the popular C-MAPSS dataset from NASA Ames Research Center, X-CUBE-AI tool and STMF767ZI microcontroller for the deployment of ML models. We benchmark different ML algorithms, with a special focus on deep learning algorithms (LSTMs and CNNs). The results indicate that there is potential for deploying machine learning models for RUL prediction in resource-scarce IoT devices, with acceptable accuracy loss, while taking advantage of the benefits TinyML has to offer over cloud-based AI inference.
Factors Influencing Adoption of Mobile Payments during COVID-19 Pandemic

Since the lifestyle changes made during the COVID-19 period, the factors influencing the adoption of mobile payments are shown in this study during the COVID-19 Pandemic in Indonesia, a total of 408 respondents were collected and only 402 respondents are in accordance with the provisions of the research, data collection using google form and analyzed using SmartPLS, the questions were collected on google form and then disseminated using social media such as Line, WhatsApp, Discord, and Instagram starting on April 15th to May 23rd, 2022. The results showed that one hypothesis was rejected. Namely Performance Expectancy on Intention to use Mobile Payment. While the others are accepted for a positive impact, either directly or indirectly, on intentions to use mobile payments.
Automatic Domain-Specific SoC Design for Autonomous Unmanned Aerial Vehicles

Building domain-specific accelerators is becoming increasingly paramount to meet the high-performance requirements under stringent power and real-time constraints. However, emerging application domains like autonomous vehicles are complex systems with constraints extending beyond the computing stack. Manually selecting and navigating the design space to design custom and efficient domain-specific SoCs (DSSoC) is tedious and expensive. Hence, there is a need for automated DSSoC design methodologies. In this paper, we use agile and autonomous UAVs as a case study to understand how to automate domain-specific SoCs design for autonomous vehicles. Architecting a UAV DSSoC requires consideration of parameters such as sensor rate, compute throughput, and other physical characteristics (e.g., payload weight, thrust-to-weight ratio) that affect overall performance. Iterating over several component choices results in a combinatorial explosion of the number of possible combinations: from tens of thousands to billions, depending on implementation details. To navigate the DSSoC design space efficiently, we introduce AutoPilot, a systematic methodology for automatically designing DSSoC for autonomous UAVs. AutoPilot uses machine learning to navigate the large DSSoC design space and automatically select a combination of autonomy algorithm and hardware accelerator while considering the cross-product effect across different UAV components. AutoPilot consistently outperforms general-purpose hardware selections like Xavier NX and Jetson TX2, as well as dedicated hardware accelerators built for autonomous UAVs. DSSoC designs generated by AutoPilot increase the number of missions on average by up to 2.25×, 1.62×, and 1.43× for nano, micro, and mini-UAVs, respectively, over baselines. Further, we discuss the potential application of AutoPilot methodology to other related autonomous vehicles.
Supervisor design for an assembly line in the presence of faults

A supervisory control architecture is developed for the discrete event model of a manufacturing educational testbed of a car assembly line in the presence of faults. The task of the plant is the assembly of the chassis and the roof of a car. The backbone of the assembly line is a transporter that makes the link of all sections of the plant. The goal of the paper is the coordination of the line. To accomplish this goal, the model of the plant in the presence of faults is derived, appropriate regular languages describing the desired performance of the plant are developed and finally appropriate supervisors, satisfying the design requirements, are determined in modular form. To satisfy the class of design requirement considered here, general criteria for the physical realizability of a supervisor, with respect to any process, are established.
High-energy Gamma Rays from Ultra-high-energy Cosmic-Ray Protons in Gamma-Ray Bursts

It has recently been proposed that ultra-high-energy (≳1019 eV) cosmic rays (UHECRs) are accelerated by the blast waves associated with GRBs. We calculate the observed synchrotron spectrum from protons and energetic leptons formed in the cascades initiated by photopion production, taking into account γγ attenuation at the source. Normalizing to the emission characteristics of GRB 970508, we predict ~10 MeV-100 GeV fluxes at a level that may have been observed with EGRET from bright GRBs and could be detected with the proposed GLAST experiment or with ground-based air Čerenkov telescopes having thresholds ≲ several hundred GeV. The temporal decay of the UHECR-induced high-energy γ-ray afterglows is significantly slower than that of the lower energy burst and associated synchrotron self-Compton (SSC) radiation, which provides a direct way to test the hadronic origin of a high-energy GRB afterglow. Besides testing the UHECR origin hypothesis, the short-wavelength emission and afterglows can be used to probe the level of the diffuse intergalactic infrared radiation field or constrain redshifts of GRB sources.
Evaluating the Effectiveness of Using the 4-radar System for the Contactless Fall Detection

The problem of developing an effective system for the elderly fall detection, as of today, still remains one of the most relevant problem of the geriatrics. Bioradiolocation-based methods have already shown their efficiency in the contactless fall recognition tasks, however, since the aforementioned methods are still on the early stage of development, there is a wide range of areas to improve. In particular, in the present paper we consider using a more complex 4-bioradar system in combination with wavelet transform and machine learning methods. To form a data set two series of experiments with a different radar configurations and different movement types have been conducted. Then, for the classification task, signal scalograms have been fed into a pre-trained convolutional neural network. The proposed system have shown a noticeable improvement is the fall recognition performance (accuracy and Cohen’s kappa of 96% and 92% respectively) in comparison with previously used 2-bioradar system (accuracy and Cohen’s kappa of 90% and 80% respectively)
Abrupt Seasonal Change of Large-Scale Convective Activity over the Western Pacific in the Northern Summer

Seasonal variations of large-scale convective activity and wind over the western Pacific are examined using Geostationary Meteorological Satellite infrared equivalent blackbody temperature (T BB ) and European Center for Medium range Weather Forecast (ECMWF) global analyses over a 10-year period from 1980 to 1989. In particular, this study describes an abrupt northward shift of large-scale convective activity over the western Pacific around 20°N, 150°E in late July. The enhanced convective activity is coincident with strong cyclonic circulation there which induces westerlies to the south of the cyclone and easterlies to the north of it. It is emphasized that this strong cyclonic circulation appears suddenly over the subtropical western Pacific region. Monsoon westerlies to the west of 110°E are not similary accelerated at the same time, indicating that this abrupt change is independent of the Asian monsoon system. To the north, an anticyclonic circulation is generated, which corresponds to the withdrawal of the Baiu season over Japan. Furthermore, this abrupt northward shift of large-scale convective activity is shown to be associated with tropical cyclone activity. In the mid latitudes, geopotential height pattern between pre- and post-northward shifts of the large-scale convective activity in late July exhibit equivalent barotropic vertical structure, suggesting the Rossbywave propagation emanating northeastward from the enhanced convective region around 20°N, 140°E (western Pacific) to as far north as 60°N, 180° (Bering Sea). Another feature is that the seasonal increase of sea surface temperature (SST) over the key area (20°N, 150°E) precedes abrupt convective enhancement by about 20 days, exceeding 29°C in early July. It is inferred that the northeastward extension of the warm SST tongue is intimately associated with the enhanced convection in late July. This result suggests that SST warming is not a sufficient condition but certainly one important ingredient for the abrupt northward shift of convections.