Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Design of Detecting Contaminants in Façade Cleaning Applications

As the number of high-rise buildings is increasing, more methods of exterior-wall cleaning are being developed. There are a few models based on artificial intelligence that determine the type and level of contamination primarily by moving the cleaning area. In this study, we propose an system using YOLOv3 algorithm, color-detection, to install on façade cleaning robot and brightness-discrimination. There are three types of contaminant-detection parameters: size, color, and brightness, and these parameters are subjected to a robust optimization process to maintain a constant detection rate under different conditions. The three parameters are determined via Taguchi method with signal to noise ratio and noise factors. An environment for algorithm testing is established, and artificial contamination is implemented on the specimen. A field test with the detection algorithm shall be performed in the near future.
Medical Data Augmentation Using Generative Adversarial Networks : X-ray Image Generation for Transfer Learning of Hip Fracture Detection

In the medical domain, it is difficult to retrieve large datasets. Classic data augmentation methods such as flipping, rotating, and scaling, are helpful in classification tasks, but these methods usually are not good enough to increase diversities and variances in small datasets. In this study, we applied AC-GAN (Auxiliary Classifier GANs) for data augmentation of our Limb X-ray dataset. We pre-trained the model for hip fracture detection of our Pelvic X-ray dataset by utilizing transfer learning with the augmented Limb X-ray dataset, which contained both the original dataset and realistic synthetic images made by the AC-GAN. The final hip fracture classification results of the Pelvic X-ray dataset showed that our generative model not only succeeded in producing realistic Limb X-ray data but also helped improve the performance of the transfer learning model for hip fracture detection.
Hybrid Time-Frequency Domain Turbo Equalization for Single Carrier MIMO Underwater Acoustic Communication

Shallow seawater acoustic channel has serious multipath effect. We proposed hybrid time-frequency domain Turbo equalization (HTFDTE) and bidirectional time-frequency domain Turbo equalization (Bi-HTFDTE) for multiple-input multiple-output (MIMO) single carrier (SC) underwater acoustic system with PN code to improve the quality of underwater acoustic communication. At the receiving end, the channel is estimated using the known PN code. The frequency domain equalization with minimum mean square error (MMSE) criterion and the time domain Turbo iterative processing are combined together to further improve performance. To eliminate error propagation effect, the Bi-HTFDTE includes two parallel HTFDTE structures, which requires roughly twice as many computational operations per iteration as the HTFDTE but achieving a lower bit error rate (BER). From the simulation results, we can find that the BER performance of HTFDTE is better than frequency domain Turbo equalization (FDTE). At the order of 10−4, the HTFDTE with three iterations gains about 0.5dB and 1dB over the HTFDTE with first iteration and the FDTE with first iteration for QPSK modulation, respectively. The Bi-HTFDTE with three iterations gains about 0.15dB over the HTFDTE with three iterations.
Genetic Programming for Multiple Feature Construction in Skin Cancer Image Classification

Skin cancer is a common cancer worldwide, with melanoma being the most deadly form which is treatable when diagnosed at an early stage. This study develops a novel classification approach using multi-tree genetic programming (GP), which not only targets melanoma detection but is also capable of distinguishing between ten different classes of skin cancer effectively from lesion images. Selecting a suitable feature extraction method and the way different types of features are combined are important aspects to achieve performance gains. Existing approaches remain unable to effectively design a way to combine various features. Moreover, they have not used multi-channel multi-resolution spatial/frequency information for effective feature construction. In this work, wavelet-based texture features from multiple color channels are employed which preserve all the local, global, color and texture information concurrently. Local Binary Pattern, lesion color variation, and geometrical border shape features are also extracted from various color channels. The performance of the proposed method is evaluated using two skin image datasets and compared with an existing multi-tree GP method, ten single-tree GP methods, and six commonly used classification algorithms. The results reveal the goodness of the proposed method which significantly outperformed all these classification methods and demonstrate the potential to help dermatologist in making a diagnosis in real-time situations.
Intelligent IoT Sensing System Based on Compressive Sensing with Adaptively Learned Dictionary

We propose a condition monitoring IoT system using compressive sensing with a learned dictionary that is adaptively changed, at the edge terminal, to track the target state. The learned dictionary is meant to be fitted to the features of the training signals; thus, its reconstruction performance degrades when the measurements acquired include features that are not embedded in the dictionary. This characteristic is exploited in our system to directly detect a change of state, even in the case in which the new state is an unknown one. We apply this technique to condition monitoring of rotating machinery and we evaluate the dictionary reconstruction performance for different working conditions. Then, we apply the technique to the online detection of fault states. The evaluation reveals that this method can be effective to reduce the burden on the data transmitted to the cloud and it can overcome the problems faced for feature extraction when the information on the target is unknown.
ArticleDissolution and Diffusion based Reactions within YBa

This work presents a thorough identification and analysis of the dissolution and diffusionbased reaction processes that occur during the drawing of YBa2Cu3O7-x (YBCO) glass-clad fibers, using the molten-core approach, on a fiber draw tower in vacuum and in oxygen atmospheres. The results identify the dissolution of the fused silica cladding and the subsequent diffusion of silicon and oxygen into the molten YBCO core. This leads to a phase separation due to a miscibility gap which occurs in the YBCO–SiO2 system. Due to this phase separation, silica-rich precipitations form upon quenching. XRD analyses reveal that the core of the vacuum as-drawn YBCO fiber is amorphous. Heat-treatments of the vacuum as-drawn fibers in the 800–1200 °C range show that cuprite crystallizes out of the amorphous matrix by 800 °C, followed by cristobalite by 900 °C. Heattreatments at 1100 °C and 1200 °C lead to the formation of barium copper and yttrium barium silicates. These results provide a fundamental understanding of phase relations in the YBCO–SiO2 glass-clad system as well as indispensable insights covering general glass-clad fibers drawn using the molten-core approach.
Gas Discrimination Based on Single-Device Extraction of Transient Sensor Response by a MetalOxide Memristor Toward Olfactory Sensor Array

We propose single-device extraction of the transient response of gas sensors by metal-oxide memristors toward simple processing of transient response in an olfactory sensor array. Gas discrimination was carried out by storing the information of the transient sensor response as memristor resistance. Prior to the discrimination test, the analog reset behavior was confirmed to occur in the voltage range of 0.7 – 2 V, and the transient sensor responses were examined to be different between the test gases of acetone and ethanol. By applying a pulse train corresponding to the sensor outputs to the memristor, significantly different memristor resistances were obtained between the gases even under the condition that the discrimination was difficult by the steady-state outputs. Consequently, we successfully discriminated the gases based on the transient responses extracted by the memristors, demonstrating promising properties of our proposal for transient-based olfaction.
Power Quality Event Classification Using Transfer Learning on Images

Given the ever-increasing complexity of the electrical grid system, power quality events have been surging in frequency with each passing day. Due to their potential to cause massive losses for a wide variety of customers, it is crucial that such events are detected and classified immediately for appropriate response. In this paper, a novel approach has been developed wherein Transfer Learning techniques have been employed to classify power quality events using image classification. More specifically, the VGG16 model has been utilized to classify five distinct power quality issues by using scalograms as input images. 489 scalograms were generated via feature extraction using wavelet transforms. The VGG16 model has then been trained and tested using the same. Thereafter, the model performance has been evaluated, and the results have been discussed.
Taking over Cellular Energy-Metabolism for TBSV Replication: The High ATP Requirement of an RNA Virus within the Viral Replication Organelle

Recent discoveries on virus-driven hijacking and compartmentalization of the cellular glycolytic and fermentation pathways to support robust virus replication put the spotlight on the energy requirement of viral processes. The active recruitment of glycolytic enzymes in combination with fermentation enzymes by the viral replication proteins emphasizes the advantages of producing ATP locally within viral replication structures. This leads to a paradigm shift in our understanding of how viruses take over host metabolism to support the virus’s energy needs during the replication process. This review highlights our current understanding of how a small plant virus, Tomato bushy stunt virus, exploits a conserved energy-generating cellular pathway during viral replication. The emerging picture is that viruses not only rewire cellular metabolic pathways to obtain the necessary resources from the infected cells but the fast replicating viruses might have to actively hijack and compartmentalize the energy-producing enzymes to provide a readily available source of ATP for viral replication process.
Receiving Performance of Antenna-Coupled Electrode Electro-Optic Modulators for 5G Frequency Band

We have already proposed and developed an antenna-coupled-electrode electro-optic (EO) modulator for 28 GHz-band 5th generation mobile communication as a receiving antenna. The designed EO modulator was fabricated a metal ground plane backed linear dipole array using a 50-µm-thick LiNbO3 crystal and a 250-µm-thick SiO2 base. In this paper, we have shown antenna receiving patterns for 1-element dipole antenna. , 4-element dipole array antenna and 8-elemen dipole array antenna. Half power beam width for H-plane of our developed receiving antennas are 70, 25 and 11 degree for 1 element, 4-element and 8-element dipole array, respectively.
Design and Characterization of a Slot based Patch Antenna for Sub-6 GHz 5G Applications

This article presents the design, fabrication, and characterization of a dual-band microstrip patch antenna. The antenna is designed to operate at 3 GHz and 3.3 GHz, which are suitable bands for sub-6 GHz 5G applications. Rogers's 5880 substrate with a permittivity of 2.2 and a thickness of 1.57 millimeters (mm) is chosen for the antenna design and fabrication. The antenna model consists of a rectangular patch with three rectangular slots of different dimensions. For the feeding of the antenna, a 1 mm thick 50 Ω microstrip line is utilized. The overall size of the antenna is 50×30 mm 2. The simulation results show that the antenna exhibits a return loss of −20.6 dB at 3GHz, and −18.2 dB at 3.3 GHz, and a gain of 7.52 dBi at 3 GHz and 5dBi at 3.3 GHz. In addition, the radiation efficiency of the antenna is 97.6 % at 3 GHz and 88.5 %at 3.3 GHz. The antenna measurement results are in a reasonably good agreement with the simulation results which show that the antenna offers a gain of 4.7 dBi at 3 GHz and return loss of −22 dB at 3.05 GHz and −16 dB at 3.3 GHz. In order to show the effects of different parameters on the antenna performance, a parametric analysis is also performed which provides insightful guidelines about the antenna design and performance. Because of good results, the proposed antenna can act as a good candidate for sub-6 GHz 5G applications.
Design and Analysis of Single Beam Parabolic Reflector Antenna in LTCC for Millimeter Wave Automotive Radar

Abstract This paper designs and analyzes radar antenna for automobiles that can operate at 24 GHz and 77 GHz. This radar antenna is designed on LTCC substrate for highly integrated module. The LTCC substrate has a reflector at an arbitrary position to improve the antenna patterns. The pattern of the reflector antenna has dual beam characteristics that the central portion of the pattern is reduced. Therefore, this paper adds an air layer to the LTCC substrate to improve this disadvantage. As a result, the pattern has been improved to have a single beam characteristic. This paper designs and analyzes a single beam offset parabolic reflector(OPR) antenna for a highly integrated module that can operate in the millimeter wave. This paper also suggests the possibility of integration of monopole antennas for short and long distance radar systems.
Modulation of Mitochondrial Metabolic Reprogramming and Oxidative Stress to Overcome Chemoresistance in Cancer

Metabolic reprogramming, carried out by cancer cells to rapidly adapt to stress such as hypoxia and limited nutrient conditions, is an emerging concepts in tumor biology, and is now recognized as one of the hallmarks of cancer. In contrast with conventional views, based on the classical Warburg effect, these metabolic alterations require fully functional mitochondria and finely-tuned regulations of their activity. In turn, the reciprocal regulation of the metabolic adaptations of cancer cells and the microenvironment critically influence disease progression and response to therapy. This is also realized through the function of specific stress-adaptive proteins, which are able to relieve oxidative stress, inhibit apoptosis, and facilitate the switch between metabolic pathways. Among these, the molecular chaperone tumor necrosis factor receptor associated protein 1 (TRAP1), the most abundant heat shock protein 90 (HSP90) family member in mitochondria, is particularly relevant because of its role as an oncogene or a tumor suppressor, depending on the metabolic features of the specific tumor. This review highlights the interplay between metabolic reprogramming and cancer progression, and the role of mitochondrial activity and oxidative stress in this setting, examining the possibility of targeting pathways of energy metabolism as a therapeutic strategy to overcome drug resistance, with particular emphasis on natural compounds and inhibitors of mitochondrial HSP90s.
A Zero-Current-Switching Heater Based on Four-Resonant-State LC Converter for Low-Temperature Lithium-Ion Batteries of Electric Vehicles

A zero-current-switching (ZCS) heater based on four-resonant-state LC converter is introduced to warm low-temperature lithium-ion power batteries of electric vehicles (EVs). By using two opposite-state pulse-width modulation (PWM) signals, the proposed AC heating topology can produce a sinusoidal current with high frequency to internally warm cold batteries with zero switching loss. The proposed onboard AC heating topology has the features of easy control, compact size, low price, zero switching loss, and high security. Experimental results and analyses show that by motivating a sine-wave current among cells with the average amplitude of 4.2 C and the switching frequency of 17.9 kHz, the proposed ZCS AC heater can warm the cold batteries from ─20°C to 0 °C within 7.8 min, which consumes about 6% of battery energy. Therefore, the proposed heater is beneficial for realizing “all-climate” battery packs without the improvement of battery materials and structure.
Postoperative quality of life after laparoscopy‐assisted pylorus‐preserving gastrectomy compared with laparoscopy‐assisted distal gastrectomy for early gastric cancer

This study aimed to investigate the postoperative quality of life (QOL) between laparoscopy‐assisted pylorus‐preserving gastrectomy (LAPPG) and laparoscopy‐assisted distal gastrectomy with Billroth I anastomosis (LADGBI) in patients with middle‐third early gastric cancers (EGC).
Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness

Many proposed methods for explaining machine learning predictions are in fact challenging to understand for nontechnical consumers. This paper builds upon an alternative consumer-driven approach called TED that asks for explanations to be provided in training data, along with target labels. Using semi-synthetic data from credit approval and employee retention applications, experiments are conducted to investigate some practical considerations with TED, including its performance with different classification algorithms, varying numbers of explanations, and variability in explanations. A new algorithm is proposed to handle the case where some training examples do not have explanations. Our results show that TED is robust to increasing numbers of explanations, noisy explanations, and large fractions of missing explanations, thus making advances toward its practical deployment.
Delving Deeper into the Decoder for Video Captioning

Video captioning is an advanced multi-modal task which aims to describe a video clip using a natural language sentence. The encoder-decoder framework is the most popular paradigm for this task in recent years. However, there exist some problems in the decoder of a video captioning model. We make a thorough investigation into the decoder and adopt three techniques to improve the performance of the model. First of all, a combination of variational dropout and layer normalization is embedded into a recurrent unit to alleviate the problem of overfitting. Secondly, a new online method is proposed to evaluate the performance of a model on a validation set so as to select the best checkpoint for testing. Finally, a new training strategy called professional learning is proposed which uses the strengths of a captioning model and bypasses its weaknesses. It is demonstrated in the experiments on Microsoft Research Video Description Corpus (MSVD) and MSR-Video to Text (MSR-VTT) datasets that our model has achieved the best results evaluated by BLEU, CIDEr, METEOR and ROUGE-L metrics with significant gains of up to 18% on MSVD and 3.5% on MSR-VTT compared with the previous state-of-the-art models.
A Smartphone App Designed to Help Cancer Patients Stop Smoking: Results From a Pilot Randomized Trial on Feasibility, Acceptability, and Effectiveness

Background Persistent smoking after a cancer diagnosis predicts worse treatment outcomes and mortality, but access to effective smoking cessation interventions is limited. Smartphone apps can address this problem by providing a highly accessible, low-cost smoking cessation intervention designed for patients with a recent cancer diagnosis. Objective This study aimed to summarize our development process and report the trial design, feasibility, participant acceptability, preliminary effectiveness, and impact on processes of change (eg, cancer stigma) of the first-known smoking cessation smartphone app targeted for cancer patients. Methods We used an agile, user-centered design framework to develop a fully automated smartphone app called Quit2Heal that provided skills training and stories from cancer survivors focusing on coping with internalized shame, cancer stigma, depression, and anxiety as core triggers of smoking. Quit2Heal was compared with the National Cancer Institute’s QuitGuide, a widely used stop smoking app for the general population, in a pilot double-blinded randomized trial with a 2-month follow-up period. Participants were 59 adult smokers diagnosed with cancer within the past 12 months and recruited through 2 cancer center care networks and social media over a 12-month period. The most common types of cancer diagnosed were lung (21/59, 36%) and breast (10/59, 17%) cancers. The 2-month follow-up survey retention rate was 92% (54/59) and did not differ by study arm (P=.15). Results Compared with QuitGuide participants, Quit2Heal participants were more satisfied with their assigned app (90% [19/21] for Quit2Heal vs 65% [17/26] for QuitGuide; P=.047) and were more likely to report that the app assigned to them was made for someone like them (86% [18/21] for Quit2Heal vs 62% [16/26] for QuitGuide; P=.04). Quit2Heal participants opened their app a greater number of times during the 2-month trial period, although this difference was not statistically significant (mean 10.0, SD 14.40 for Quit2Heal vs mean 6.1, SD 5.3 for QuitGuide; P=.33). Self-reported 30-day point prevalence quit rates at the 2-month follow-up were 20% (5/25) for Quit2Heal versus 7% (2/29) for QuitGuide (odds ratio 5.16, 95% CI 0.71-37.29; P=.10). Quit2Heal participants also showed greater improvement in internalized shame, cancer stigma, depression, and anxiety, although these were not statistically significant (all P>.05). Conclusions In a pilot randomized trial with a high short-term retention rate, Quit2Heal showed promising acceptability and effectiveness for helping cancer patients stop smoking. Testing in a full-scale randomized controlled trial with a longer follow-up period and a larger sample size is required to test the effectiveness, mediators, and moderators of this promising digital cessation intervention. Trial Registration ClinicalTrials.gov NCT03600038; https://clinicaltrials.gov/ct2/show/NCT03600038
Learning Spatiotemporal Features via Video and Text Pair Discrimination

Current video representations heavily rely on learning from manually annotated video datasets which are time-consuming and expensive to acquire. We observe videos are naturally accompanied by abundant text information such as YouTube titles and Instagram captions. In this paper, we leverage this visual-textual connection to learn spatiotemporal features in an efficient weakly-supervised manner. We present a general cross-modal pair discrimination (CPD) framework to capture this correlation between a video and its associated text. Specifically, we adopt noise-contrastive estimation to tackle the computational issue imposed by the huge amount of pair instance classes and design a practical curriculum learning strategy. We train our CPD models on both standard video dataset (Kinetics-210k) and uncurated web video dataset (Instagram-300k) to demonstrate its effectiveness. Without further fine-tuning, the learnt models obtain competitive results for action classification on Kinetics under the linear classification protocol. Moreover, our visual model provides an effective initialization to fine-tune on downstream tasks, which yields a remarkable performance gain for action recognition on UCF101 and HMDB51, compared with the existing state-of-the-art self-supervised training methods. In addition, our CPD model yields a new state of the art for zero-shot action recognition on UCF101 by directly utilizing the learnt visual-textual embeddings. The code will be made available at this https URL.