Miracle-dz/UniFAR-FTUs huggingface dataset:
CO-REGISTRATION OF SIMULTANEOUS HIGH-SPEED ULTRASOUND AND ELECTROMAGNETIC ARTICULOGRAPHY FOR SPEECH PRODUCTION RESEARCH

We outline a method for collecting simultaneous high-speed ultrasound and electromagnetic articulography (EMA) data using widely-available commercial hardware and software. Previous research demonstrates the utility of a simultaneous EMA-ultrasound set-up and reports the magnitude of probe rotation and displacement over an experimental session. We build upon this research by (1) combining systems with higher temporal and spatial accuracy than those previously used; (2) using a more principled method for temporal synchronisation; (3) reporting the effects of ultrasound on the accuracy of EMA tracking; (4) tracking probe movement using an alternative stabilisation method. Our system shows a high degree of temporal and spatial accuracy and can be easily implemented by other researchers.
Propose Marketing Strategies of Oasis Siliwangi Sport Hotel to Increase Brand Awareness and Purchase Intention

After the outbreak of the COVID-19 pandemic in 2020, there was a rise in tourist visitors from different parts of Indonesia to Bandung, resulting in a direct boost in the occupancy rate of star hotels in the city. Because of this growth, numerous hotels developed, with the majority being 3-star establishments. The Oasis Siliwangi Sport Hotel is a three-star establishment in Bandung, offering a range of facilities and an ideal location. Oasis Siliwangi Sport Hotel in Bandung is now struggling with a lack of brand awareness, resulting in potential customers being unaware of the hotel’s existence and room occupancy rates. Consequently, this has led to a decline in the intention to make reservations. The objective of this study is to determine the variables that influence the level of brand awareness for Oasis Siliwangi Sport Hotel and develop effective marketing strategies to enhance the likelihood of customers choosing to stay at Oasis Siliwangi Sport Hotel. Data collection comes from primary data and secondary data. The primary data collection strategy is quantitative, utilizing a questionnaire for consumer analysis and hypothesis testing. Secondary data will be acquired from national and international scientific journals, previous research, book literature, government reports, and other reliable source of internet sites. The survey was disseminated to 217 potential target market participants with prior experience lodging at the hotel. The gathered data is analyzed using SMART PLS. The research findings indicate that Advertising and Social Media Marketing positively influence Brand Awareness. Furthermore, brand awareness, price, and facilities influence customer’s purchase intention. Based on these findings, several recommendations and strategies are made. These include create exciting bundling package and loyalty program, optimize promotion activities, utilize advertising to reach consumers, and strengthen hotel facility.
UvA-DARE (Digital Academic Repository) Pilot-scale hybrid constructed wetlands for the treatment of cooling tower water prior to its desalination and reuse

Cooling towers are responsible for a large part of the industrial fresh water withdrawal, and the reuse of cooling tower water (CTW) effluents can strongly lower industrial fresh water footprints. CTW requires desalination prior to being reused, but various CTW components, such as total organic carbon (TOC), conditioning chemicals and total suspended solids (TSS) hamper physico-chemical desalination technologies and need to be removed from the CTW. A cost-efficient and robust pre-treatment is thus required, which can be provided by constructed wetlands (CWs). The present study is the first study that determined the CTW pre-treatment efficiency of hybrid-CWs and the impact of winter season and biocides in the CTW on the pre-treatment efficiency. The most efficient CW flow type and dominant removal mechanisms for CW components hampering physico-chemical desalination were determined. Subsurface flow CWs removed PO 43 (cid:0) , TSS and TOC as a result of adsorption and filtration. Vertical subsurface flow CWs (VSSF-CW) excelled in the removal of benzotriazole as a result of aerobic biodegradation. Horizontal subsurface flow CWs (HSSF-CW) allowed the denitrification of NO 3 (cid:0) due to their anaerobic conditions. Open water CWs (OW-CWs) did not contribute to the removal of components that hamper physico-chemical desalination technologies, but do provide water storage options and habitat. The biological removal processes in the different CW flow types were negatively impacted by the winter season, but were not impacted by concentrations of the biocides glutaraldehyde and DBNPA that are relevant in practice. For optimal pre-treatment, a hybrid-CW, consisting of an initial VSSF-CW followed by an OW-CW and HSSF-CW is recommended. Future research should focus on integrating the hybrid-CW with a desalination technology, e.g. reverse osmosis, electrodialysis or capacitive ionization, to produce water that meets the requirements for use as cooling water and allow the reuse of CTW in the cooling tower itself.
UvA-DARE (Digital Academic Repository) Learn global and optimize local A data-driven methodology for last-mile routing

In last-mile routing, the task of finding a route is often framed as a Traveling Salesman Problem to minimize travel time and associated cost. However, solutions stemming from this approach do not match the realized paths as drivers deviate due to navigational considerations and preferences. To prescribe routes that incorporate this tacit knowledge, a data-driven model is proposed that aligns well with the hierarchical structure of delivery data wherein each stop belongs to a zone — a geographical area. First, on the global level
FastSNN: A CUDA-Based Programming Framework for Rapid Training SNNs

Spiking neural networks (SNNs) have attracted extensive attention due to their biological plausibility and low-power event-driven computation. However, since spiking neural networks have both temporal and spatial dimensions, more training time is required when training SNNs with a framework specially designed for artificial neural networks (ANNs), such as PyTorch and TensorFlow. In order to solve this problem, we built a programming framework FastSNN specially designed for SNN based on CUDA. The training time required for the network built using this framework can be significantly shortened. Furthermore, FastSNN focuses on allowing rookies to build SNN-based applications quickly. To this end, we have integrated all the components required to build SNNs in the framework. Specifically, it includes popular datasets (static/dynamic), encoding methods (frequency encoding/time encoding), commonly used spiking neurons, network modules, training methods, and visualization tools. Experimental results prove that the training time of the SNN built using our framework can be shortened by 2 to more than 10 times compared with the SNN built using PyTorch.
On Error Representativeness of Function Call Interfaces for C/C++ Program

Software fault injection is a fundamental technique for studying the dependability of software systems. The code mutation based defect injections encounter efficiency (i.e., dormant faults) and accuracy (when they are not done at the source code level) issues. Software error injections directly emulate the effects of software defects, which is more efficient and practical. But, whether the injected errors truly represent the impact of bugs in the real world is still an open problem. Several studies have investigated the representativeness problem of component interface (API) errors. However, the component API error injections are relatively coarse in granularity because they regard the component programs as black boxes. Thus, they cannot be used to analyze the internal error behaviors within the components. To overcome this issue, we propose a finite state machine for generating representative code mutations (as baseline) and a novel tracing approach for collecting function call interface data. That enables the analysis of the representativeness of program internal errors from the perspective of function call interface for C/C++ programs by fault-free and fault injection control experiments. The experimental results imply that the traditional error models cannot accurately emulate software defects at function call interfaces. We provide useful suggestions based on our findings for improving the representativeness of function call interface error injection.
A parallel tree node splitting criterion for fuzzy decision trees

Fuzzy decision trees are one of the most important extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Many fuzzy decision trees employ fuzzy information gain as a measure to construct the tree node splitting criteria. These criteria play a critical role in the construction of decision trees. However, many of the criteria can only work well on small‐scale or medium‐scale data sets, and cannot directly deal with large‐scale data sets on the account of some limiting factors such as memory capacity, execution time, and data complexity. Parallel computing is one way to overcome these problems; in particular, MapReduce is one mainstream solution of parallel computing. In this paper, we design a parallel tree node splitting criterion (MR‐NSC) based on fuzzy information gain via MapReduce, which is completed equivalent to the traditional unparallel splitting rule. The experimental studies verify the equivalency between the proposed MR‐NSC algorithm and the traditional unparallel way through 22 UCI benchmark data sets. Furthermore, the feasibility and parallelism are also studied on two large‐scale data sets.
Enhanced Non-preemptive Support of URLLC using Spread Spectrum Underlay Signalling

The advent of 5G NR (New Radio) technology has enabled a wide range of new use cases in commercial and tactical communications, particularly in the realm of Ultra-Reliable Low-Latency Communications (URLLC). In the conventional approach supported by the 3GPP standards, URLLC transmissions tend to preempt ongoing lower-priority transmissions (such as enhanced Mobile Broadband), degrading the Quality of Service (QoS) for the preempted transmissions. This paper proposes a novel frequency-domain spread spectrum underlay signal technique to address this challenge. The proposed approach enables the immediate transmission of URLLC traffic without preemption, increasing the network’s resiliency. This paper describes the design of the proposed underlay signaling for URLLC traffic, which is transmitted concurrently by the 5G base station (BS) with the non-URLLC traffic on the 5G PDSCH (Physical Downlink Shared Channel). The performance of the proposed underlay technique has been evaluated using comprehensive simulations. The simulation results demonstrate the ability of the proposed underlay signal to transport data with high reliability without disrupting concurrent 5G traffic.
PreFlush: Lightweight Hardware Prediction Mechanism for Cache Line Flush and Writeback

Non-Volatile Main Memory (NVMM) technologies make it possible for applications to permanently store data in memory. To do so, they need to make sure that updates to persistent data comply with the crash consistency model, which often involves explicitly flushing a dirty cache line after a store and then waiting for the flush operation to complete using a store fence. While cache line flush and write back instructions can complete in the background, fence instructions expose the latency of flushing to the critical path of the program's execution, incurring significant overheads. If flush operations are started earlier, the penalty of fences can be significantly reduced. We propose PreFlush, a lightweight and transparent hardware mechanism that predicts when a cache line flush or write back is needed and speculatively performs the operation early. Since we speculatively perform the flush, we add hardware to handle flush misspeculation to ensure correct execution of the code without the need for any complex recovery mechanisms. Our PreFlush design is transparent to the programmer (i.e. it requires no modification on existing NVMM-enabled code). Our results show that PreFlush can improve performance by up to 25% (15.7% average) for the WHISPER NVM benchmark suite and loop-based matrix microbenchmarks.
Efficiency Evaluation of Solar PV Array with Improved Performance

In photovoltaic (PV) systems, the irradiation power is the main mechanism for energy generation. The PV module's position, orientation, and tilt angle have a considerable impact on both its efficiency and durability. The roof-installed PV system thus met the domestic and industrial energy requirements. The outcome of this research demonstrates that for a small PV system, typical PV panels can provide up to 1KWh more power when stacked in a landscape orientation than when arranged in a portrait orientation. Connecting solar cells in photovoltaic (PV) modules is essential for involuntary constancy and resistive power consumption. While create a 3 × 3 mini module as of PERC solar with soldering pads and up to 20% increased efficiency for this research. During this module, we see a sizeable cell-to-module (CTM) power consumption of 8%. In microgrids (MGs), photovoltaic (PV) and energy generation is continuously rising. Battery energy storage systems (BESSs) reduce the fluctuations in PV outputs brought on by solar energy's sporadic availability. BESSs are helpful for MG operation stability, but they are still rather pricey. In order to guarantee security while taking into account the cost of the MG system, proper BESS size is necessary when operating within operational constraints during normal and emergency operations. In order to optimize the BESS size for MGs, the security-constrained optimal power flow (SCOPF) problem is solved, taking stochastic errors into account when predicting PV yields.
Boustrophedonic Frames: Quasi-Optimal L2 Caching for Textures in GPUs

Literature is plentiful in works exploiting cache locality for GPUs. A majority of them explore replacement or bypassing policies. In this paper, however, we surpass this exploration by fabricating a formal proof for a no-overhead quasi-optimal caching technique for caching textures in graphics workloads. Textures make up a significant part of main memory traffic in mobile GPUs, which contributes to the total GPU energy consumption. Since texture accesses use a shared L2 cache, improving the L2 texture caching efficiency would decrease main memory traffic, thus improving energy efficiency, which is crucial for mobile GPUs. Our proposal reaches quasi-optimality by exploiting the frame-to-frame reuse of textures in graphics. We do this by traversing frames in a boustrophedonic11Boustrophedon is a style of writing in which alternate lines of writing are reversed in order. This is in contrast to most modern languages, where the order of lines is the same, usually left-to-right. manner w.r. t. the frame-to-frame tile order. We first approximate the texture access trace to a circular trace and then forge a formal proof for our proposal being optimal for such traces. We also complement the proof with empirical data that demonstrates the quasi-optimality of our no-cost proposal.
Deep Learning Approach for Wind Energy Time-Series Forecasting with Environmental Factors

Generating power from wind has gained prominence as a viable renewable energy source due to its slew of benefits including cheap production and real estate friendliness. However, it suffers from volatility in energy production due to unpredictable wind speeds. As such, it is imperative to have a good estimation of wind energy forecasting such that the energy demands can be met. The introduction of deep neural networks such as Recurrent Neural Networks (RNN), Long Short Term memory (LSTM) Networks and Temporal Convolutional Networks (TCN) have improved wind energy forecasting in the long term. This paper explores the effect of covariates such as location information and hourly weather forecasts on the performance of wind energy forecasting. Results show TCN considerably and consistently outperforms other state-of-the-art models in terms of speed and forecasting quality, with obtained scores of sMAPE: 6.18, MSE: 21.9, RMSE: 4.69, MAE:3.01 and $R^{2}$: 0.997. This suggests the adaptation of such models for modelling complex renewable energy forecasting tasks jointly with contextual factors.
RSSI Fingerprinting Using Machine Learning for Position Estimation

In the Internet of Things era, sensor measurement results are beneficial if the accurate position of the sending node can be known. In a wireless network, the positioning of an object can be determined if the object's distance can be known to several reference nodes. RSSI, ToA, and TDoA methods can determine the distance between objects on a node. The RSSI method is simple but needs to be more accurate because the RSSI value fluctuates. This study proposes the RSSI Fingerprinting method to overcome the weaknesses in the conventional RSSI method. In RSSI Fingerprinting, the signal strength is measured from several transmitters at several locations first performed, which can represent the area as a whole. Furthermore, several existing methods in artificial intelligence, such as k-NN, Random Forest, SVM, Gaussian Process, and Deep Learning (FFNN, CNN, RNN, and LSTM), are used to model the RSSI Fingerprinting. All models obtained were measured for their performance to estimate the node position and evaluated by three parameters, Mean Square Error (MSE), Mean Absolute Error (MAE), and Training Time. In the simulation, it was found that the Random Forest method has advantages compared to other methods. Furthermore, this research also discusses how the model obtained can be applied to IoT devices using Edge Devices that support machine learning or processing in the cloud.
Winteraceae do Rio Grande do Sul, Brasil Winteraceae in the state of Rio Grande do Sul, Brazil

presents five genera, of which only Drimys occurring in the Neotropics. The taxonomy of the Brazilian species is still controversial, and depending on the author, one to three species is accepted. This paper presents the taxonomic study of Winteraceae in the state of Rio Grande do Sul, and aims to elucidate the Winteraceae species for the region. Revision of national and international herbaria, and collections in several places of the state were made in order to gather data for the taxonomic treatment and the morphometrics analysis (ANOVA). Based on the morphometric analyses, phenological and morphological data, two species are accepted for Rio Grande do Sul: Drimys angustifolia and D. brasiliensis. We provide an identification key, descriptions, commentaries, phenology and illustrations for both taxa.
GOOSEAttacker: Synthetic Attack Generation Tool for IEC61850

The digitization of critical infrastructures introduces both advantages and vulnerabilities, particularly within Digital Electric Substations (DES) operating under the IEC 61850 communications standard. Although there exist measures such as the cybersecurity IEC 62351 standard, significant security risks are still present, partially due to a lack of cybersecurity knowledge among operators. This work focuses on the Generic Object-Oriented Substation Event (GOOSE) protocol that provides a critical role in the delivery of event-related information within DES and its associated vulnerabilities. In this paper we introduce GOOSEAttacker, a tool that emulates potential attacks within a DES environment targeting the GOOSE protocols. The tool marks malicious traffic, stores packet captures and can be customized for specific attacks and scenarios. We assess GOOSEAttacker in a testbed built with real DES equipment, confirming that the generated attacks compromise the operation of the deployed infrastructure.
Abnormal Rhythm Detection from a Single-Lead ECG via a Recurrent Neural Network

Cardiac arrhythmias affect millions of individuals worldwide and can lead to severe complications such as stroke or heart failure. They can be difficult to diagnose with ambulatory electrocardiogram monitors due to their transient nature. We propose a system for long-term ar-rhythmia monitoring that takes single-lead electrocardio-gram and tri-axis acceleration signals as inputs. It is composed of a beat detector to extract interbeat intervals and a classifier to detect arrhythmias. This system is evalu-ated on two datasets including 42 patients and achieves an accuracy of 0.988 for the abnormal class, 0.967 for the normal class, and 0.979 for the tachycardia class.
A 12-bit 625 MS/s Time-interleaved Pipelined ADC

This article presents a 12-bit 625 MS/s time-interleaved ADC analog-to-digital converter (ADC) that achieves an SFDR/SNDR of 64.47/59.08 dB near Nyquist rate. An input buffer with a replica capacitor is employed to effectively drive the sampling capacitor with high linearity. A bootstrapped sampling switch operating at 625 MS/s is adopted to eliminate timing mismatch and achieve high sampling linearity. Also, the pipelined ADC is utilized as the sub-ADC owing to its balance of sampling rate and revolution. The ADC is implemented in a 45 nm CMOS technology with 376 mW power consumption and a total layout area of 850μm $\times$ 590μm.
A 4th Order Chebychev Active-RC Low-Pass Filter With 5/10/20/30/40/50 MHz Tunable Bandwidth and Low Power

Based on 40nm CMOS process, a tunable fourth order Chebychev low pass filter design was proposed for analog baseband circuits. The filter consists of two cascaded Tow-Thomas biquads and a clock-controlled series peripheral interface. The Tow-Thomas biquads utilized feedforward-compensated operational amplifiers. Tunable Capacitors and resistors were introduced to adjust the bandwidth. Simulation results show that the filter achieves a bandwidth of 5/10/20/30/40/50MHz with in-band fluctuations no more than 0.4 dB and its input-referred third order intermodulation point is at 15.6 dBm. The power consumption of the whole circuit is less than 5 mW. The filter layout occupies 751$\times 551 \mu m ^{2}$.
Do Programs Dream of Electromagnetic Signals? Towards GAN-based Code-to-Signal Synthesis

The analysis of EM signals has become a popular approach as a means for achieving non-intrusive and external anomaly detection, particularly in the realm of embedded devices. Previous efforts have shown huge potential in terms of discriminative precision between those signals that correspond to normal versus anomalous operations. However, virtually all research in the area neglects the challenge of gathering high-quality signals for the purpose of fingerprinting all possible execution paths of a program. Today, such efforts are mainly human-driven and because there may be hundreds of alternative paths even in simple programs, EM-based anomaly detection approaches exhibit poor scalability. In this work, we introduce a set of methods for the generation of EM signals directly from code. To the best of our knowledge, this is the first effort that aims to address code-to-signal synthesis for purposes of side-channel analysis. The proposed framework is based on Generative Adversarial Networks (GAN) that receive the corresponding assembly (ASM) code of a program as input and in turn produce artificial signals which bear near-identical morphological characteristics as the corresponding real EM signals. Although the proposed method pertains across a wide range of applications we have evaluated our approach specifically for the task of training anomaly detection models. We experimentally showed that synthetic signals can train models that can then detect even stealthy code injection attacks i.e., very challenging minimal code alterations with great accuracy. More specifically, synthetically trained anomaly detection models achieve a near-perfect AUC score of 0.993 for the detection of four malicious instructions and 0.981 for the detection of two malicious instructions. Interestingly, applying rudimentary feature engineering i.e., considering the maximum amplitude of the signal per cycle as opposed to the entire raw signal leads to perfect detection accuracy.