Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-supervised classification model based on Triple Generative Adversarial Networks for Weather Forecasting

The challenge of the wind power forecasting is inherent intermittent and stochastic nature of wind. The patterns of distributions have a significant impact on power systems forecasting. The conventional models are constructed based on statistical assumptions such as Gaussian distributed, Weibull and lognormal distributed. However, these inflexibility distribution models may not obtain the converged result since the real data may not fit the assumptions, especially for the extreme weather conditions. To alleviate this issue, Triple generative adversarial networks (Triple-GAN) based weather classification model is proposed to predict wind energy in a semi-supervised manner. The simulation results demonstrate that our proposed method can improve the diversity of the generated scenarios and capture the spatiotemporal correlation among multiple sites. Meanwhile, the generated scenarios can further improve the performance of classifier and it is robust for optimal operation of wind and storage systems.
A PWM-free DC-DC Boost Converter with 0.43 V Input for Extended Battery Use in IoT Applications

A new single stage dc-dc converter is presented in order to fully exploit the energy of a battery cell that aims to bridge the gap between low-voltage, low-current and high-voltage, high-current converters. A theoretical analysis is presented along with expressions that describe the operation and DC gain of the converter. Classic dc-dc converters include a digital block for pulse width modulation (PWM) that controls the output voltage as a function of the duty cycle. The proposed converter utilizes only a current mirror connected to a transformer in order to boost output voltage, thereby lowering the dissipated power by up to 28.86% as a PWM block is not required. The simplicity of the presented topology is traded off with a small decrease in peak power efficiency, which drops to about 80%. Nevertheless, measurements show that the proposed converter operates at input voltages as low as 0.43 V, appropriate for commercial battery cells, and provides tens of mA. In this way, the energy of a battery source is fully exploitable almost doubling the practical battery lifetime (1.83×) as shown through real-world scenarios and experimental results.
Optimal Link Scheduling for Low Latency Data Transfer over Small World WSNs

In recent years, small world characteristics (SWC) received huge attention due to their various advantages in the context of social, electrical, computer, and wireless networks. A wireless sensor network (WSN) exhibiting SWC is known as small world WSN (SW-WSN). Therefore, SW-WSN consists small average path length and large average clustering coefficient. Here, in this paper, a novel optimal link scheduling method is proposed to develop SW-WSN. The proposed method determines, optimal number of new links need to be created in the network. Additionally, the method also finds the optimal node-pairs towards creation of these links. The developed algorithm considers node betweenness centrality measure for the introduction of SWC. SW-WSN obtained using proposed method yields reduced time complexity towards its development. Moreover, it also results in optimal SWC when compared to other existing methods. A reduced data transmission delay is noted over SW-WSN developed using proposed method. Random, near-optimal, and sub-optimal methods of introducing SWC and their time complexities are also investigated and compared to the proposed method. The results are computed over simulated and real WSN testbed. Obtained results demonstrate the significance of proposed method and its utilization over large scale network applications.
A Low-Power Double-Tail fT-Doubler Comparator in 65-nm CMOS

This paper presents a novel low power double tail latched comparator for emerging high-speed internet of things (IoT) applications. The comparator is designed in a standard 65-nm complementary metal oxide semiconductor (CMOS) process and occupies 87. 7µm2. It employs fT-doubler topology which reduces the device capacitances, allowing faster switching and lower power. When connected to a 600-mV supply and clocked at 20 MHz, the post-layout simulations show that the proposed comparator achieves 11% smaller delay and 13% lower power than the conventional design. The proposed comparator demonstrates good performance with comparable power to the state-of-the-art designs when normalized to frequency.
A Joint Operation Method of Fuzzy Test and Dynamic Symbol Execution

Fuzzy testing and symbol execution are the main methods of vulnerability mining for binary programs. Among them, fuzzy testing is time-consuming due to randomness, while symbol execution is limited by path explosion due to traversal. At present, there is a certain research foundation about the mixed test of fuzzy test and symbol execution, but most of them focus on improving the code coverage and other indicators. There is no clear method for how to call each other in the hybrid test process of fuzzy test and symbol execution. In this paper, combined with the strong interpretability of decision tree algorithm, a hybrid test call strategy based on decision tree is proposed, which quantifies the factors that affect the efficiency of hybrid test, and clarifies the reasons for triggering call between fuzzy test and symbol execution, which is helpful to improve the efficiency of hybrid test.
Multi-Scale Context Aggregation for Strawberry Fruit Recognition and Disease Phenotyping

Timely harvesting and disease identification of strawberry fruits is a major concern for commercial level cultivators. Failing to harvest the grown strawberries can result in the fruit rotting which makes their damaged tissues more prone to grey mold pathogens. Immediate removal of the overgrown or diseased strawberries is inevitable to curb the mass spreading of the pathogen. In this paper, we propose a deep learning-based framework to identify three different strawberry fruit classes (unripe, partially ripe and ripe), as well as a class of overgrown or diseased strawberries. We equip the proposed convolutional encoder-decoder network with three different modules. One for adaptively controlling receptive filed size of the network to detect objects of multiple sizes. Second for controlling the flow of salient features (information) to the deeper layers of the network and the other for controlling the architecture’s computational complexity. These modules combined, outperform the previous state-of-the-art semantic segmentation networks on the task of strawberry fruit phenotyping. We also introduce a dataset collected from different farms to evaluate the performance of the network. Quantitative and qualitative results show that notwithstanding heterogeneity in the data and the effect of the real-field variations, our approach produced remarkable results with a 3% increase in mean intersection over union as compared to the other state-of-the-art networks and was able to recognize diseased fruits with a precision of 92.45%.
Efficient and Privacy-Preserving Multi-Party Skyline Queries Over Encrypted Data

One existing challenge associated with large scale skyline queries on cloud services, particularly when dealing with private information such as biomedical data, is supporting multi-party queries with curious-but-honest parties on encrypted data. In addition, existing solutions designed for performing secure skyline queries incur significant communication and computation costs due to ciphertext calculation. Thus, in this paper, we demonstrate the potential of supporting privacy-preserving multi-party skyline queries on encrypted data using additive homomorphic and proxy re-encryption cryptosystems. However, the secure computation based on these cryptosystems will further slow down query efficiency. To improve the efficiency of comparison on encrypted data, we redesign two lightweight secure comparison protocols. Meanwhile, we present an efficient method named “blind-reading” to securely obtain the skyline point. We also propose a novel method, Privacy Matrix, designed to reduce the scale of the dataset so that the computational cost is significantly decreased without privacy leakage. Then, we construct our secure skyline query protocol by integrating lightweight secure comparison protocols, “blind-reading” and Privacy Matrix techniques. Finally, we evaluate the security of our protocol, where we show it is secure without leaking information. The performance evaluation also shows that our proposed approach significantly improves the efficiency (at least $\times 4.5$ faster) compared to the-state-of-art and has the scalability of query processing under large datasets.
A Novel Deep Learning Model for Palmprint/Palmvein Recognition

In this article, a novel palmprint/palmvein recognition algorithm is proposed. The algorithm firstly extracts the palmprint/palmvein feature by the principal component analysis network (PCANet), and then classifies by collaborative representation classifier (CRC). The proposed method is validated on several palmprint and palmvein databases, and the experimental results show that the method is very effective. At the same time, this method has good robustness for small training set. In the datasets of blue illuminations of Hong Kong Polytechnic University (PolyU) multispectral palmprint database, the recognition rate can reach 100% with only one training sample of each class. PCANet can extract palmprint depth feature information without the intervention of prior knowledge, and the CRC classifier can achieve high recognition accuracy rate while having an extremely low computational complexity. This algorithm can be used to real-time application.
Auto-SCMA: Learning Codebook for Sparse Code Multiple Access using Machine Learning

Sparse Code Multiple Access (SCMA) is an effective non-orthogonal multiple access technique that facilitates communication among users with limited orthogonal resources. Currently, its performance is limited by the quality of the handcrafted codebook. We propose Auto-SCMA, a machine learning based approach that learns the codebook using gradient descent while using a Message Passing Algorithm decoder. It is the first machine learning based approach to generalize successfully on the Rayleigh fading channel. It is able to learn an effective codebook without involving any human effort in the process. Our experimental results show that Auto-SCMA outperforms previous methods including machine learning based methods.
A Real-Time Dynamic Object Segmentation Framework for SLAM System in Dynamic Scenes

To accurately detect dynamic objects in dynamic scenes (DSs), a detection framework equipped with visual-based measurement methods has been proposed in this article. First, to segment dynamic objects in real time, the real-time instance segmentation network, You Only Look At CoefficienTs (YOLACT), has been introduced. Second, the geometric constraints have been utilized to further filter the missing dynamic feature points outside the segmentation mask. The dense optical flow method with adaptive threshold has been introduced to detect the missing dynamic objects driven by humans. Third, a background inpainting strategy has been proposed to restore the features occluded by dynamic objects. In order to verify the effectiveness of the dynamic object detection, the proposed method has been embedded in the visual simultaneous localization and mapping (SLAM) system to improve its performance in dynamic environments. Experiments performed on the Technische Universität München (TUM) and KITTI datasets have proved that the proposed detection method has an excellent performance in DSs, which is of great significance to improve the robustness of the SLAM system.
Enhancements of Attention-Based Bidirectional LSTM for Hybrid Automatic Text Summarization

The automatic generation of a text summary is a task of generating a short summary for a relatively long text document by capturing its key information. In the past, supervised statistical machine learning was widely used for the Automatic Text Summarization (ATS) task, but due to its high dependence on the quality of text features, the generated summaries lack accuracy and coherence, while the computational power involved, and performance achieved, could not easily meet the current needs. This paper proposes four novel ATS models with a Sequence-to-Sequence (Seq2Seq) structure, utilizing an attention-based bidirectional Long Short-Term Memory (LSTM), with added enhancements for increasing the correlation between the generated text summary and the source text, and solving the problem of out-of-vocabulary (OOV) words, suppressing the repeated words, and preventing the spread of cumulative errors in generated text summaries. Experiments conducted on two public datasets confirmed that the proposed ATS models achieve indeed better performance than the baselines and some of the state-of-the-art models considered.
Adaptive Event-triggered Tracking Control for A Manipulator Based on Dynamic Neural Network

This paper is focused on the adaptive event-triggered tracking control of a manipulator subjecting to uncertain dynamics and unknown disturbance. To improve the accuracy of estimation and robustness, the adaptive control scheme based on disturbance observer is developed, in which the uncertain parameters are identified by an echo-state dynamic neural network. Moreover, the event-driven signal transportation mechanism is built in the channel of the controller to the actuator. Both stability and feasibility of the overall system under the proposed control scheme are verified based on Lyapunov theories. Finally, the simulation results demonstrate that our method could accomplish both motion tracking in a robust way, meanwhile could reduce communication.
The Effect of Plastic Bag Ban Policy Towards Waste Complaints in Jakarta Through JAKI and Qlue

Plastic bag ban policy has been implemented in Jakarta since July 1st, 2020. However, it is still unclear the impact of this policy on Jakarta to reduce waste. The public complaint can be used as a proxy to see how the public responds to the waste reports distributed in Jakarta. This research aims to look at the plastic bag ban policy's significance towards waste complaints through two applications that citizens used to report, JAKI and Qlue. In this study, non-parametric hypothesis testing was performed since the data are not normally distributed. The result shows that North Jakarta did not show any significant differences in the waste reports after the plastic bag ban policy. On the other hand, the rest of the districts show a considerable difference.
Standardization of the Safety Level of the Use of DMSO in Viability Assays in Bacterial Cells

The antibacterial potential of the most diverse medicinal plants has benefited humanity for centuries, and precisely because of this, the number of studies investigating the antimicrobial activity of essential oils and their components is increasing. However, the hydrophobic character of the essential oils has made the experiments difficult, requiring the use of organic solvents in the tests in order to avoid such complications. Among the most commonly used solvents are dimethylsulfoxide (DMSO / C2H6OS). To date, the literature has not yet determined a standardization of the usual concentration of DMSO suitable for bacterial experiments, so that its use does not check the efficacy of the tested phytoconstituent by interactions between the solvent and the exploited compound. In view of this reality, the present study intends to standardize the DMSO concentrations that do not interfere in the viability of the bacterial strains of Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 25923, Pseudomonas aeruginosa ATCC 27853, Proteus mirabilis ATCC 25922 and Enterococcus faecalis ATCC 29212. In order to reach this objective, the disc diffusion method in Muller Hinton Agar of a reagent impregnated on a filter paper disc was used, using different concentrations of DMSO to determine the minimum inhibitory concentration (MIC) of the five microorganisms listed. The experiment was carried out in triplicate in order to safeguard the effectiveness of the method tested. The incubation was done in an oven at 35o C, for a period of 24 hours. For the test method the conclusions of the tests performed were expressed by the arithmetic mean of the diameter of the inhibition halos formed around the disks. The test demonstrates that for the concentrations and microorganisms tested, DMSO provides safety in its use.
LoRa IoT-Based Architecture for Advanced Metering Infrastructure in Residential Smart Grid

Internet of Things (IoT) technologies are opening new opportunities and services for different smart grid applications such as advanced meter infrastructures (AMI), distributed energy resources, and electric vehicles. Among these applications, AMIs represent the first step toward future smart grid implementation by enabling the reading and recording of electric power consumption on request or a schedule. In this study, the main focus is designing an IoT-based architecture to support AMIs’ daily reporting and billing in a residential grid. First, the network modeling for AMI is discussed. Long range (LoRa) technology, as one of the promising candidates for long range low power wide area networks (LPWANs), has been studied and discussed. A comprehensive analysis of the LoRa-based architecture is given for a case study of an actual residential grid, Puerto Montt, Chile. The results are analyzed and discussed for packet delivery ratio, energy consumption, throughput, number of collisions, and frequency distribution.
High Efficiency & Low Area DC-DC Buck Converter with the Digital Feedback Loop for the Wireless Applications

In this Paper, a high efficiency and low area dc-dc buck converter with the digital feedback loop is proposed for wireless device. The digital feedback loop is consisted of two-step digital pulse width modulation (DPWM) and low power self-tracking zero current detector (ST-ZCD). To implement a high-efficiency dc-dc converter, a hybrid DPWM core is proposed with high linearity and low power consumption. To reduce the output voltage ripple within 20mV, an adaptive window analog-to-digital converter is proposed. To minimize the reverse current, a dead time generator is implemented with the proposed ST-ZCD. The circuit is designed with a Samsung 28nm CMOS process that produces an output voltage of 1.8V using a standard supply voltage of 3.3V.
When Homecoming is not Coming: 2021 Homecoming Ban Sentiment Analysis on Twitter Data Using Support Vector Machine Algorithm

Homecoming, more traditionally known as Mudik, has become a trending topic on several social media platforms as soon as the 11-day homecoming ritual ban was announced on 7 April 2021. Opinions, varying from those in favor of and against the ban, start to rapidly appear. Twitter, a social media platform which is now considered to be an extension of oneself and often used to express ones’ opinion, has become flooded with comments on the homecoming ritual ban. The swarm of opinions in the form of tweets were then used as a dataset for sentiment analysis in order to understand how people perceive the ban. The algorithm used in this research is the classification algorithm using the Support Vector Machine method. The dataset was classified into three sentiments: positive, negative, and neutral. The use of the Support Vector Machine algorithm yielded a 62% accuracy with this dataset. The sentiment analysis showed that the keyword "mudik" had a neutral sentiment for the most part. Meanwhile, results of engagement analysis show that the largest forms of engagements were retweets and liking tweets that had a neutral sentiment. When the neutral sentiment was removed, we found that the largest sentiment on the homecoming ritual ban was negative. This is likely due to the release of an addendum to the Covid-19 Handling Task Force Circular Number 13 of 2021 on 22 April 2021 that imposes more restrictions on and extends the effective dates of the restrictions related to the homecoming ritual ban; exactly one day before the data scraping of 5000 datasets on tweets from 23 April 2021 was carried out. The researcher had already sampled the tweets with the most engagements (those with the most retweets and likes). It was found that some tweets had a negative sentiment, but the model classified it as having a neutral sentiment. This may be affected by inaccuracies of dataset training as some of the tweets were in Malay rather than Indonesian. A challenge that needs to be overcome is the limited number of datasets for NLP training or sentiment analysis for the Indonesian language in comparison to that of the English language. On the other hand, this has become an opportunity for the researcher to develop a more appropriate training model.
Impact of Code Deobfuscation and Feature Interaction in Android Malware Detection

With more than three million applications already in the Android marketplace, various malware detection systems based on machine learning have been proposed to prevent attacks from cybercriminals; most of these systems use static analyses to extract application features. However, many features generated by static analyses can be easily thwarted by obfuscation techniques. Therefore, several researchers have addressed this obfuscation problem with obfuscation-invariant features. However, to the best of our knowledge, no researcher has utilized deobfuscation techniques. To this end, we adopt a code deobfuscation technique with an Android malware detection system and investigate its effects. Experimental results indicate that code deobfuscation can successfully retrieve useful information concealed by obfuscation. Further, we propose interaction terms based on identified feature interactions. The proposed interaction terms aim to eliminate the interference caused by the size of the application and other features because many feature values are correlated to the size of the application. In addition, the experimental results indicate that these interaction terms have a high ranking in terms of feature importance values. Our proposed Android malware detection model achieves 99.55% accuracy and a 94.61% F1-score with the well-known Drebin dataset, which is better than the performance of previous works.
A Fast Wake-Up and High Accuracy Sensor Interface by Synchronous Sampling with Power-Efficient Switching Regulator

This paper proposes a high-accuracy readout system with a high-efficient DC-DC converter. Thanks to a switching DC-DC converter with high conversion efficiency, the power loss in a regulator can be reduced. In the proposed system, sampling is performed using a synchronization signal generated from the switching regulator to avoid deterioration of readout accuracy caused by the ripple noise of the switching converter. Since the proposed technique does not require a large capacitor for ripple reduction, the wake-up time can be reduced, which is suitable for IoT devices operating intermittently. A prototype system developed in this work has achieved a reduction in sensor resistance readout variation to 0.006% while maintaining a high-speed wake-up.