Miracle-dz/UniFAR-FTUs huggingface dataset:
Wireless Ice Detection and Monitoring Using Flexible UHF RFID Tags

Owing to its low relative permittivity, very few microwave sensors have been developed for monitoring ice deposition. This paper presents the first use of UHF RFID tags for wireless RF ice sensing applications. Despite its low permittivity, the existence of ice as a superstrate on a planar ultra-thin dipole antenna can lower the resonance frequency of the antenna significantly. The RFID tags, having a measured unloaded range of 9.4 m, were evaluated for remotely detecting the formation of ice in various scenarios up to 10 m from the reader, as well as monitoring the ice thawing, based on the Relative Signal Strength (RSS) in a phase-free approach. In most RSS-based sensing approaches, the tag’s read-range is reduced as the RSS decreases in response to the stimulant. However, the proposed sensing mechanism does not reduce the tag’s range; the ice superstrate improves the impedance matching of the tags and increases their radar cross section (RCS), resulting in a 10 m loaded read-range with over 12 dB ice-sensitivity, in an echoic multi-path environment. The proposed sensing mechanism achieves an accuracy of 86% for tags positioned between 0.5 and 10.5 m with various obstructions. When subjected to line-of-sight metallic obstruction, the sensor still maintains 80% accuracy. The long range and high sensitivity of the proposed sensing mechanics show that UHF RFID is a promising method for detecting the formation of ice in future smart cities.
Residential floor plan recognition and reconstruction

Recognition and reconstruction of residential floor plan drawings are important and challenging in design, decoration, and architectural remodeling fields. An automatic framework is provided that accurately recognizes the structure, type, and size of the room, and outputs vectorized 3D reconstruction results. Deep segmentation and detection neural networks are utilized to extract room structural information. Key points detection network and cluster analysis are utilized to calculate scales of rooms. The vectorization of room information is processed through an iterative optimization-based method. The system significantly increases accuracy and generalization ability, compared with existing methods. It outperforms other systems in floor plan segmentation and vectorization process, especially inclined wall detection.
How Robots Should Approach People

To prepare for a future in which robots are more commonplace, it is important to know what robot behaviors people find socially normative. Previous work suggests that for robots to be accepted by people, the robot should adhere to the prevalent social norms, such as those related to approaching people. However, we do not expect that socially normative approach behaviors for robots can be translated on a one-on-one basis from people to robots, because currently robots have unique and different features to humans, including (but not limited to) wheels, sounds, and shapes. The two studies presented in this article go beyond the state-of-the-art and focus on socially normative approach behaviors for robots. In the first study, we compared people’s responses to violations of personal space done by robots compared to people. In the second study, we explored what features (sound, size, speed) of a robot approaching people have an effect on acceptance. Findings indicate that people are more lenient toward violations of a social norm by a robot as compared to a person. Also, we found that robots can use their unique features to mitigate the negative effects of norm violations by communicating intent.
Delegated Screening and Robustness [Click for Latest Version]

A policymaker relies on regulators or bureaucrats to screen agents on her behalf. How can she maintain some control over the design of the screening process? She solves a two-layer mechanism design problem: she restricts the set of allowable allocations, after which a screener picks a menu that maps an agent’s costly evidence to this restricted set. In general, the policymaker can set a floor in a way that dominates full delegation no matter how the screener’s objectives are misaligned. When this misalignment is only over the relative importance of reducing allocation errors or agent’s screening costs, the effectiveness of this restriction hinges sharply on the direction of the screener’s bias. If the screener is more concerned with reducing errors, setting this floor is in fact robustly optimal for the policymaker. But if the screener is more concerned with keeping costs down, not only does this particular floor have no effect: any restriction that strictly improves over full delegation is complex and sensitive to the details of the screener’s preferences. I consider the implications for regulatory
Horus: Interference-Aware and Prediction-Based Scheduling in Deep Learning Systems

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of-the-art resource managers are needed to increase GPU utilization and maximize throughput. While co-locating DL jobs on the same GPU has been shown to be effective, this can incur interference causing slowdown. In this article we propose Horus: an interference-aware and prediction-based resource manager for DL systems. Horus proactively predicts GPU utilization of heterogeneous DL jobs extrapolated from the DL model’s computation graph features, removing the need for online profiling and isolated reserved GPUs. Through micro-benchmarks and job co-location combinations across heterogeneous GPU hardware, we identify GPU utilization as a general proxy metric to determine good placement decisions, in contrast to current approaches which reserve isolated GPUs to perform online profiling and directly measure GPU utilization for each unique submitted job. Our approach promotes high resource utilization and makespan reduction; via real-world experimentation and large-scale trace driven simulation, we demonstrate that Horus outperforms other DL resource managers by up to 61.5 percent for GPU resource utilization, 23.7–30.7 percent for makespan reduction and 68.3 percent in job wait time reduction.
Appendix: Unsupervised 3D Keypoint Discovery for Shape Control

Figure 1: Farthest Point Keypoint regularizer ablation. We investigate the influence of the number J of sampled farthest points q used for the keypoint regularizer (Section 3.2 of the main paper) on the quality of discovered unsupervised keypoints. We show unsupervised 3D keypoints trained using two versions of regularization. First, we set the number of sampled farthest points to double of the number of keypoints (a, c). This is the setup that we use throughout the paper. Second, we set the number of sampled farthest points to the number of points in the point cloud representing the shape. This essentially results in a regularizer that is minimizing the Chamfer distance between the unsupervised keypoints and the object point cloud. Although the learned unsupervised keypoints have a good coverage (b, d) they are not as equally spaced and characteristic of the shape as (a, c).
Influence of Bacterial Strain Combination in Hybrid Fiber Reinforced Geopolymer Concrete subjected to Heavy and Very Heavy Traffic Condition

The effect of bacterial and fiber combination to enhance the properties of GGBS based geopolymer concrete to be used as paver block is investigated in this study. In this study, Bacterial combinations such as Bacillus Subtillis and Bacillus Sphaericus and high modulus glass fibers and low modulus polypropylene fibers were incorporated to produce hybrid fiber reinforced bacterial geopolymer concrete with increased energy absorption characteristics and better post cracking behavior under heavy loads. Combined and the discrete performance of bacteria and fiber over the mechanical properties of the geopolymer concrete were investigated. The influence of bacterial strain combination over self-healing of concrete is also studied by inducing artificial cracks of 1mm over concrete. The self-healed products were subjected to microstructural investigation such as SEM analysis and XRD analysis to understand the microstructure of precipitated products and the bioremedial action exhibited by the bacteria. Finally, the paver blocks were produced for the optimum specimens of geopolymer concrete along with fiber and bacteria and its performance over compressive, split tensile, flexural and water absorption characteristics were assessed to determine its adaptability to sustain loading under heavy and very heavy traffic conditions as per Indian Standard 15658 (2006). This research work lays a path for the sustainable development of production of eco-friendly self-healing high strength paver blocks.
Improved time domain synchronous averaging based on the moving interpolation and kurtosis criterion searching

The fault diagnosis of rotating machinery involves revealing the possible faults in advance to reduce dispensable breakdowns; the difficulty of this lies in identifying the periodic features of rotating machines contaminated by background noise. Time-domain synchronous averaging (TSA) has been studied to eliminate random noise in signals. However, TSA sometimes cannot extract useful signals more accurately because of the accumulative phase error caused by the discrete sampling process and the difficulties in obtaining accurate prior information. Hence, the moving interpolation and Kurtosis searching criterion are used for more accurate extraction of harmonics and transient impacts. Also, an improved compensation algorithm based on moving interpolation is proposed to overcome the amplitude attenuation caused by cumulative phase error for low signal to noise ratio (SNR) signal. To determine some parameters in the algorithm such as the number of periods and the time delay of windows which depend on a priori information relevant to the fault period, a searching method for the prior information in vibration signals including transient impacts and harmonics with Kurtosis and minimum mean square error criterion is proposed to optimize the algorithm in the process of feature extraction. Finally, the improved TSA (ITSA) is applied to extracting the fault features in a real factory, and the performance of fault feature extraction in low SNR signal conditions with the ITSA has been enhanced effectively.
One-Out-of-q OT Combiners

In 1-out-of- <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> Oblivious Transfer (OT) protocols, a sender Alice is able to send one of <inline-formula> <tex-math notation="LaTeX">$q\ge 2$ </tex-math></inline-formula> messages to a receiver Bob, all while being oblivious to which message was transferred. Moreover, the receiver learns only one of these messages. Oblivious Transfer combiners take <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> instances of OT protocols as input, and produce an OT protocol that is secure if sufficiently many of the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> original OT instances are secure. We present new 1-out-of-<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> OT combiners that are perfectly secure against active adversaries. Our combiners arise from secret sharing techniques. We show that given an <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula>-linear secret sharing scheme on a set of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> participants and adversary structure <inline-formula> <tex-math notation="LaTeX">$\mathcal {A}$ </tex-math></inline-formula>, we can construct an <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-server, 1-out-of-<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> OT combiner that is secure against an adversary corrupting either Alice and a set of servers in <inline-formula> <tex-math notation="LaTeX">$\mathcal {A}$ </tex-math></inline-formula>, or Bob and a set of servers <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula> with <inline-formula> <tex-math notation="LaTeX">$\bar {B}\notin \mathcal {A}$ </tex-math></inline-formula>. If the normalized total share size of the scheme is <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula>, then the resulting OT combiner requires <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> calls to OT protocols, and the total amount of bits exchanged during the protocol is <inline-formula> <tex-math notation="LaTeX">$(q^{2}+q+1)\ell \log q$ </tex-math></inline-formula>. We also present a construction based on 1-out-of-2 OT combiners that uses the protocol of Crépeau, Brassard and Robert (FOCS 1986). This construction provides smaller communication costs for certain adversary structures, such as threshold ones: For any prime power <inline-formula> <tex-math notation="LaTeX">$q\geq n$ </tex-math></inline-formula>, there are <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-server, 1-out-of-<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> OT combiners that are perfectly secure against active adversaries corrupting either Alice or Bob, and a minority of the OT candidates, exchanging <inline-formula> <tex-math notation="LaTeX">$O(qn\log q)$ </tex-math></inline-formula> bits in total.
Imprinted polymer and Cu2O-graphene oxide nanocomposite for the detection of disease biomarkers

α 1-Acid glycoprotein (α 1-AGp) is a critical plasma protein that acts as a biomarker for different diseases when produced in large amounts (>1.2 mg ml−1). Therefore, selective, label-free, and fast detection of α 1-AGp in human serum is essential. This article presents the development of selective coatings based on molecularly imprinted polymer (MIP) matrix with boronate-affinity and Cu2O-decorated reduced graphene oxide (Cu2O-rGO) nanomaterials. The MIP-Cu2O-rGO hybrid coatings are fabricated on quartz crystal microbalance (QCM) to develop biomimetic α 1-AGp sensors. The MIP:Cu2O-rGO ratio is optimized to enhance sensing properties. Thus-designed MIP-Cu2O-rGO/QCM sensor performs sensitive and specific detection of α 1-AGp in complex mixtures. The initial assessment of the MIP-Cu2O-rGO/QCM sensor reveals an eight-fold increase in the response toward α 1-AGp compared to non-imprinted polymer. The MIP-Cu2O-rGO/QCM sensor exhibits high sensitivity (16.28 Hz ng−1 ml−1) and a low limit of detection (0.25 ng ml−1). When compared with multiple biorelevant analytes such as bovine serum albumin, lysozyme, glucose, fructose, and uric acid, the sensor shows high selectivity due to suitably oriented imprints or interaction sites that are tailored for α 1-AGp recognition. Furthermore, the MIP-Cu2O-rGO/QCM sensor can effectively detect 150–200 ng ml−1 of α 1-AGp in spiked human serum samples with a recovery rate of ∼92%. The results achieved in this study are compared with the relevant literature. The MIP-Cu2O-rGO/QCM sensor can be suitably used for the label-free, precise detection of α 1-AGp in complex mixtures.
Simultaneous measurement of temperature and color spectrum of axisymmetric premixed flames using digital laser speckle photography and an image processing approach

The efficiency of different fuels in combustion processes has been an ongoing quest. Temperature measurements and color spectrum of premixed combustion flames are the accepted parameters to study. This work’s aim is to do so simultaneously, with a very simple setup and saving computational resources. This method does not ask to synchronize two sets of measurements, avoid the need for complicated setup alignments, and use a single digital camera. Instead, the temperature and color spectrum are measured simultaneously using digital laser speckle photography and an image processing approach. The recorded data for both measurements are acquired using only a color digital camera, where the red (r) color channel is used to record data for temperature measurements, and the blue (b) and green (g) color channels are utilized to record the flames’ color spectrum. To calculate the flames’ temperature field, the Abel inversion technique was carried out, while the image processing required crosstalk image correction, image segmentation, and image filtering. The crosstalk image correction is the main feature that establishes an unequivocal relationship between image color and color spectrum of flames. The technique was tested on flames produced with a liquefied petroleum gas fed torch using different equivalence ratios. The temperature values obtained with this method are in good agreement with those obtained with a thermocouple.
Near-infrared photodetection using random networks of single-walled carbon nanotubes with asymmetrical work-function electrodes

We investigate the use of random networks of single-walled carbon nanotubes for near-infrared photodetection. By increasing the number of nanotubes between asymmetrical work-function electrodes using dielectrophoretic assembly, the effect of Fermi-level pinning of nanotube-Schottky contacts was revealed in the linear current-voltage characteristic. The extracted device resistance showed an abrupt drop when the numerous intertube junctions formed densely packed networks in the electrode channel. Under the excitation of a near-infrared laser, we performed the photocurrent measurement at ambient temperature at different light powers. Our devices with densely packed nanotube networks showed enhanced photoconductive detection of responsivity, detectivity, and detection response. This is attributed to the increase in the photoabsorption area, the decrease of the channel resistance, and the formation of continuous conducting paths for high-efficient charge percolation. The photoconductive responsivity of up to 8.0 μA W−1 was found with a detectivity of about 4.9 × 105 cm Hz1/2 W−1, which is 4 orders of magnitude greater than that achieved in the channel with individual nanotubes deposited and comparable to that of suspended nanotube bolometers. The densely packed nanotube devices had a detection response of ∼ 4 ms under a finite bias that can be explained by the short-diffusion length of the photoexcited electrons and holes. However, the decrease in the photocurrent with time observed in our devices that exhibited photovoltaic characteristics indicates that electron-hole pair recombination in the nanotube networks occurs with differing characteristic time scales of the injected electrons and holes.
PREFACE

This is the 1st International Conference on Small Islands Community (ICSIC) organized by Faculty of Fisheries and Marine Science, Pattimura University, Maluku, Indonesia. As an annual scientific activity of Faculty of Fisheries and Marine Science, Pattimura University, this conference aims to facilitate researchers to publish and share their current research results. As we may be aware, the World Health Organization officially declared the novel coronavirus COVID-19 a pandemic. This global health crisis was a unique challenge that has impacted all countries in the world. In Indonesia, at that time, COVID-19 was widespread with continuing transmission across the country. The Indonesian government has instituted policies including mandatory compliance of travel restriction and social distancing. These restrictions were applied in each province, city, and/or regency. Considering this situation, ICSIC 2020 has been held virtually on December 12th, 2020. The uncertainty of the end of the pandemic is the main reason for the holding of this annual scientific conference and not being postponed. The theme of this conference was Momentum of Transformation of Small Islands Community in New Normal Era. and consisted of plenary and parallel sessions. In the plenary sessions, the Keynote Speaker, Dr. Lucky Adrianto from IPB University started the session and followed by six invited speakers Dr. Sophie Webber from The University of Sydney, Australia; Prof Dr. Sota Yamamoto, and Dr. Da-Jeng Song from Kagoshima University, Japan; Prof Dr. N Prachanat from Buriram Rajabath University, Thailand; Prof Dr. H Soselisa from Pattimura University, Indonesia; and Aleixo Leonito Amaral, MSc from National University of Timor Lorosa’e. The Keynote Speaker was given 30 minutes to deliver the current condition of Indonesia Small Islands Community related to the New Normal Era, and all the Invited Speaker were given 20 minutes each to talk about their current research and experience of the readiness of small islands community in their own country in welcoming new normal era. We accepted thirty-two abstracts from researchers who presented their most recent research in the Conference, and twenty-eight articles can be found in this proceeding. The parallel sessions were divided into four classes, and each class consisted of eight presenters. Presenters were given 10 minutes each to speak, and after the fourth person in each parallel session a 20-minute Q&A session was available. Zoom Meeting was used as a technology to deliver this Conference. We understand that the success of the conference depends ultimately on many people who had worked together in managing both the technical program and supporting social arrangements. Therefore, we would like to thank all committee members who had worked extremely hard for the details of important aspects of the conference programs. Thank you for your participation in this conference, we do hope to see you again in the next Conference. List of Committee, International Editorial Board, Local Editor, Organizing Committee are available in this pdf.
Appearance-based Gaze Estimation using Attention and Difference Mechanism

Appearance-based gaze estimation problem received wide attention over the past few years. Even though model-based approaches existed earlier, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. In this paper, we proposed two novel techniques to improve gaze estimation accuracy. Our first approach, I2D-Net uses a difference layer to eliminate any common features from left and right eyes of a participant that are not pertinent to gaze estimation task. Our second approach, AGE-Net adapted the idea of attention-mechanism and assigns weights to the features extracted from eye images. I2D-Net performed on par with the existing state-of-the-art approaches while AGE-Net reported state-of-the-art accuracy of 4.09◦ and 7.44◦ error on MPI-IGaze and RT-Gene datasets respectively. We performed ablation studies to understand the effectiveness of the proposed approaches followed by analysis of gaze error distribution with respect to various factors of MPIIGaze dataset.
VOJO RADOIČIĆ’S ARTWORK AS A STIMULUS FOR EARLY AND PRESCHOOL-AGED CHILDREN’S ARTISTIC CREATIVITY – CASE STUDY

Art appreciation is an interactive experience, that leads to the development of artistic sensibility, but also encourages creative thinking. Therefore, it is important to expose children to a variety of artistic stimuli that also include artworks. In this paper, posibilities are being researched to use reproductions of Vojo Radoičić’s artworks to encourage art expression in preschool children, as well as the impact of this kind of incentive on the development of creativity and motivation in the tested group of children. Vojo Radoiĉić is a famous artist from Rijeka and an unavoidable figure in the Croatian art scene of the second half of the 20th century. His art expression is characterised as poetic, playful and is well known for its lively coloring, resulting in his art pieces to often be compared to spontaneous children art expression. The research results showed that the selected artworks as a stimulus for children’s artistic expression had a positive impact on the development of art creativity and motivation in children who participated in the research. These positive qualitative changes can help preschool teachers in planning future art activities in kindergartens.