Miracle-dz/UniFAR-FTUs huggingface dataset:
Fluctuational-Electrodynamics Calculations of the van der Waals Potential in Nanoparticle Superlattices

Based on fluctuational electrodynamics and a Green’s tensor formalism, we present a theoretical method which provides the correct van der Waals potential of nanocolloidal assemblies. We calculate the total van der Waals potential in superlattices of metallic nanoparticles where we find, in particular, that the pairwise summation approximation fails notably even for dilute superlattices. Furthermore, by comparing our method with the traditional Hamaker theory for the van der Waals interactions in colloidal systems, we find that the retrieved corresponding Hamaker constant depends dramatically on the lattice constant of the superlattices of the same nanoparticle size, signifying the total breakdown of the Hamaker theory for such systems. Since the only input of the presented theory for the van der Waals potential is the size and the dielectric function of the nanoparticles, we show that we can predict the lattice constant of stable nanoparticle superlattices without the use of empirical parameters as in Ham.
Attacking Sequential Learning Models with Style Transfer Based Adversarial Examples

In the field of deep neural network security, it has been recently found that non-sequential networks are vulnerable to adversarial examples. There are however few studies to investigate the adversarial attack on sequential tasks. To this end, in this paper, we propose a novel method to generate adversarial examples for sequential tasks. Specifically, an image style transfer method is used to generate for a Scene Text Recognition (STR) network adversarial examples, which are only different from the original image on the style. While they will not interfere with the recognition of image information by human vision, the adversarial examples would significantly mislead the recognition results of sequential networks. Moreover, based on a black-box attack, both in digital and physical environments, we show that the proposed method can use cross text shape information and attack successfully the TPS-ResNet-BiLSTM-Attention (TRBA) and Convolutional Recurrent Neural Network (CRNN) models. Finally, we demonstrate further that physical adversarial examples can easily mislead commercial recognition algorithms, e.g. iFLYTEK and Youdao, suggesting that STR models are also highly vulnerable to attacks from adversarial examples.
The perception of pre-university students on STEM

STEM is an integrated teaching and learning approach in Science, Technology, Engineering and Mathematic. The decreasing enrolment of students in STEM-field is alarming and impacting the industry as well as the university students’ intake. This study is focusing on one hundred of pre-university (foundation) students by looking at their understanding on STEM education and the impacts of STEM module in nurturing their interest on STEM. The study applied naturalistic inquiry to assess their perceptions of STEM prior and subsequently after involve in a STEM module activities. The results showed that majority of students are female and from different background, they are interested in learning science shown by the increase of agreement to 38% and 40% after the activity with the decrease in the percentage of neutral and disagree level. The percentage of interest on STEM was decreased after the activity. However, the level of confident about their ability towards science was increased. The confident level lead the respondents to choose career related to science/STEM based on the increase level of agreement from 22% to 28% and from 24% to 32% for strongly agree and agree relatively. Hence, the outreach with attractive activities would potentially add value for students’ intrinsic motivation for science and science learning.
Novel Pd-BTP/SiO2 as an Effective Heterogeneous Catalyst for Heck Reactions

A highly efficient and reusable catalyst, palladium (II) complex supported on functionalized silica, was prepared by anchoring palladium (II) onto 2,6-bis(5,6-dimethyl-1,2,4-triazine-3-yl)pyridine [BTP]-modified mesoporous silica (labeled as Pd-BTP/SiO2). The catalyst was characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), and Brunauer Emmett Teller (BET) analysis. Its catalytic performance was tested for the Heck coupling reactions between aryl halides with acrylic acid or styrene, and good to excellent results (higher conversions 87–96% and yields 66–78%) were obtained. The catalyst system was stable under the reaction conditions and could be successfully reused more than five times without the loss of catalytic activity.
Performance Enhancement and Analysis of Shell and Tube Type Heat Exchanger

Nano-fluids are playing a very important role in thermal stream due to its physical and thermal properties. Now a day it was observed that there are lots of papers which have explained the deep study on nanofluids. There are so many areas in which nanofluids are used to increase the thermal efficiency and coefficient of heat transfer. In this review comprehension, many research works which are related to nanofluids used as a working fluid in shell and tube heat exchanger will be summarized in efficient way. And theoretical as well as experimental study is to be performed for shell and tube heat exchanger to determine what the effects on various parameters of graphene nanofluid, silver nanofluid, Al2O3 nanofluid and copper nanofluid. Graphite is the cheapest form of producing flanks of graphene and these samples of produced graphene were optimized with help of different type of methods. To increase the performance of heat transfer of shell & tube heat exchanger, the graphene nanofluid is used as a working fluid rather than any different type of ordinary fluid. The thermal efficiency and the coefficient of heat transfer depend upon the inlet temperature, flow rate, concentration of working fluid. The general outcomes show that by increasing in concentration of nanoparticle and flow rate of working fluids, there will be the increment in heat transfer rate, thermal conductivity and efficiency of heat exchanger.
Improving K-means clustering based on firefly algorithm

Data clustering determines a group of patterns in a dataset which are homogeneous in nature. The objective is to develop an automatic algorithm which can accurately classify an unleveled dataset into groups. The K-means method is the most fundamental partitioned clustering concept. However, the performance of K-means method is fully depending on determining the number of clusters, K, and determining the optimal centroid for performing the clustering process. In this paper, an adaptive firefly optimization algorithm, which is a nature-inspired algorithm, is employed to improve the K-means clustering. The experimental results of clustering two real datasets show that the proposed method is able to effectively outperform other alternatives methods.
Validity of physics e-handouts based on the STEM approach to improve students’ knowledge competency

Physics e-handouts based on the STEM approach to improve students’ knowledge competence is able to facilitate students to increase knowledge optimally. Before using this e-handout, an e-handout feasibility test is needed. The purpose of validation is to determine the suitability of e-handout with physics learning material by using the Aiken’s V formula by 4 validators. Data collection by questionnaire method. Validation is assessed from 3 aspects, first the Material Substance Eligibility with a value of 0.80 is a valid category, secondly a visual display worthiness obtained of a value of 0.85 with a valid category. Third, the feasibility of the learning design obtained a value of 0.81 with a valid categorization. The overall conclusion is that the validity value of the physics e-handout based on the STEM approach is 0.82 with a Valid category. So that e-handouts are appropriate to be used in physics learning to improve the competency of high school students’ XI knowledge.
An evaluation of Lidar-based 2D SLAM techniques with an exploration mode

SLAM is a fundamental problem in robotic field and there have been many techniques on it. It is necessary to give an insight on weakness and strength of these techniques specific to the intended final application. This paper presents a study of three most common laser-based 2D SLAM techniques: Gmapping, KartoSLAM and Cartographer. Each technique was applied to construct maps combined with autonomous exploration. All the approaches have been evaluated and compared in terms of inaccuracy of constructed maps against the ground truth. In order to draw conclusions on the performance of the tested techniques, a metrics of average distance to the nearest neighbor (ADNN) was applied. Moreover, the computational load of each technique is examined.
The relationship between the corresponding author and its byline position: An investigation based on the academic big data

With the increase of international collaboration, author contribution is becoming a key issue in research evaluation. Different counting methods may result in different results. Given the importance of the byline position and the significant role of the corresponding author, this study investigates the relationship between corresponding author and its byline position. By analyzing over 11 million papers from Web of Science published from 2000 to 2008, we find that the relationship between the corresponding author and byline position varies with year, the number of authors, countries and research fields. Our findings can provide insights to scientific research evaluation.
Removal methods of EMG Artifacts from EEG Signals

Electromyography (EMG) is the superposition of motor unit action potential (MUAP) in many muscle fibers in time and space. In real measurement, EMG signals will contaminate Electromyography signals, therefore they bring great difficulties to the qualified analysis and interpretation of EEG signals, and it is a momentous step to remove EMG artifacts from EEG signals. In the recent years, new methods were developed for EEG artifacts removal such as Multivariate Empirical Mode Decomposition and Singular Spectrum Analysis. In particular, some researchers combined the two methods and used their respective advantages to remove artifacts more thoroughly without affecting the EEG signal, such as the combination of Independent Component Analysis and Wavelet Method. In this paper, new methods for muscular artifacts removal from EEG above are discussed. Moreover, traditional methods including signal transform, filtering methods and Blind source separation (BSS) are also reviewed.
Authentication based on electrocardiography signals and machine learning

Among the information security problems involved in Telemedicine Information Systems (TMIS), the authentication area of the various entities involved has been extensively discussed in recent years and shown a wide range of possibilities. The problems caused by the application of inadequate authentication processes may lead to the death of patients who depend on Mobile Healthcare (M-Health) services. User authentication can be based on several physiological traits (e.g., iris, retina, and fingerprint) for biometric recognition, including electrocardiography (ECG) signals. Some ECG patterns are relatively robust to daily changes associated with normal heart rate variability. In fact, the relative lengths of PQ, QR, and RS intervals, as well as Q, R, and S relative amplitudes constitute individual traits. A few studies have succeeded in using ECG signals as an accurate authentication input and offered some advantages in comparison to biometrics traditional approaches. ECG-based user authentication can be built on Machine Learning (ML) models, used for classification purposes and reductions in distortions caused by misinterpretation of ECG data. Among the ML models adopted for ECG signals classification, ensembles have shown a good research opportunity. Random Under-Sampling Boosting (RUSBoost), a boosting algorithm not yet explored (to the best of our knowledge) for such a problem, can achieve comparatively high performance after a supervised training stage, even from relatively few training examples. This manuscript reports on a comparison of RUSBoost with Nearest Neighbour Search (NNS) regarding the classification of ECG signals for biometric authentication applications. The two ML techniques were compared by a random subsampling technique that considers four analysis metrics, namely accuracy, precision, sensitivity, and F1-score. The experimental results showed the better performance of RUSBoost regarding accuracy (97.4%), sensitivity (96.1%) and F1-score (97.4%). On the other hand, NNS provided better precision (99.5%).
Object Detection using Deep Learning: A Review

Object detection is one of the most critical and challenging tasks in computer vision. It is the process of finding objects belonging to some predefined categories and determining their location in an image or video. This paper reviews deep learning-based object detection models. The paper discusses some benchmark datasets. The performance evaluation of different detectors on different datasets based on mean Average Precision (mAP) is reviewed. Object detection is used in different fields in different forms. Applications of object detection like pedestrian detection, autonomous driving, face detection, etc., are presented. Finally, the future scope is discussed to work on new techniques for object detection.
Research on Pedestrian Multi Object Tracking Based on FairMot Transfer Learning

Object tracking is divided into two processes: multi object detection and multi object tracking. In this paper, the multi object detection is studied firstly, and the multi object detection algorithms based on prior box anchor and anchor-free are analyzed and compared. Then in the second process of multi object tracking, multi object tracking algorithm are researched: the multi object tracking algorithm based on prior box anchor, the multi object tracking algorithm based on anchor-free. Finally, Finally, an experiment is carried out to track the pedestrians on the road by using FairMot transfer learning, and the metrics of MOTA, IDF1, IDS, MT, ML and FPS are analyzed. Experiments show that under the method of using transfer learning, MOTA can be improved by 4.2 percentage points.
Sentiment analysis of GO-JEK services quality using Multi-Label Classification

One of the most popular online transportation providers in Indonesia is GO-JEK. At the first establishment, GO-JEK is only an online taxi motorbike service (in Bahasa Indonesia: ojek) that transforms from a conventional taxi motorbike. After several years GO-JEK began to develop more services, like GO-FOOD, GO-SEND, GO-CAR, GO-MART, GO-RIDE, GO-PAY, GO-TIX, GO-BOX, and GO-MED. As the GO-JEK services develop into more categories, it is more challenging to automatically analyze the sentiment polarity for each category of services category. An ordinary classification algorithm or single-label classification is concerned with learning from a set of examples associated with a single label classification. However, in this case, we want to classify GO-JEK services based on two-class targets, which are GO-JEK service categories and polarity sentiment classification. This research methodology contains Dataset Preparation, Feature Selection, Basic text mining process, train, and split dataset, and Classification. We implemented two classification methods: Multi-Label Classification and a simultaneous classification using the Random Forest Algorithm as a comparison. Based on this dataset, the most mentioned GO-JEK service is GO-FOOD followed by GO-SEND, GO-RIDE, GO-CAR, and GO-MART. Based on the service category, GO-FOOD gets the most positive reviews following by GO-RIDE and GOJEK 90. Some service categories like GO-SEND and GO-MART get more negative reviews than positive reviews. The accuracy of the Multi-label classification method raised 76%. Simultaneous classification accuracy using the Random Forest algorithm produce for service category classification yields 97% and only 78% for sentiment polarity classification. We can see that both algorithm, multi-label classification, and Random Forest algorithm, yields almost the same classification accuracy for polarity sentiment classification. We can conclude that the imperfect accuracy in polarity sentiment classification is related to the difficulty of identifying the most suitable polarity for each tweet. Many sarcasm tweets and slang words that cannot be identified.
A Fast Traffic Sign Detection Algorithm Based on Modified YOLOv3

Traffic sign detection is an important component of both automatic driving systems and driver assistance systems. However, it is still a big challenge to detect traffic signs quickly and accurately. In this paper, in order to solve this problem, a lightweight algorithm based on YOLOv3 is proposed, which can achieve real-time and accurate traffic sign detection. Firstly, a new lightweight backbone network is proposed by modifying the darknet53 network, which is the original backbone network of YOLOv3. Secondly, in order to further speed up the detection time, depthwise separable convolution is introduced into the proposed backbone network to further reduce the amount of parameters and computations of the model. Thirdly, dense connection is adopted to maintain the accuracy. Experiments on CCTSDB dataset illustrate that the algorithm has great real-time performance and accuracy, which is better than other state-of-the-art algorithms. The algorithm can detect a 416*416 image in only 3.4 milliseconds, which is 44.3% faster than YOLOv3. In addition, the mAP and F1 value are 97.2% and 91.6% respectively, which are better than YOLOv3.
Feature Point Matching Based on Local Relative Velocity Consensus

Feature matching is still a crucial and challenging problem in image processing. In this paper, a novel feature matching algorithm based on local relative velocity consensus (LRVC) is proposed to find two accurate matched feature points. To remove the outliers accurately and robustly, the relative velocity between the putative matches and their corresponding neighbors are exploited. Based on the consensus of relative velocity and the consensus of neighborhood elements, the similarity of each putative matches is evaluated. With a two iteration outlier removal strategy, the feature points are matched accurately and robustly. In the experiment, VGG dataset are used to verify the accuracy and robustness of the proposed method LRVC. The matching results indicate that our method is superior to three other classic feature matching methods.
Diversity Regularized StarGAN for Multi-style Fonts Generation of Chinese Characters

The generation of stylish Chinese fonts plays a central role in many applications such as the design of art fonts and Chinese calligraphy generation. Most of existing methods focus on the generation of a single-style Chinese font, while few works focus on the multi-style font generation. In this paper, we exploit the star generative adversarial networks (StarGAN), a very popular generative adversarial networks (GAN) model recently developed in the literature, to realize the generation of multi-style Chinese fonts via a single model. Furthermore, in order to tackle the generation issue of Chinese characters having similar strokes for StarGAN, i.e., generating the same mode for these different but similar Chinese characters, we introduce a diversity regularizer such that the generator can generate high-quality characters with better diversity. A series of experiments are conducted on a handwritten Chinese character dataset called CASIA-HWDB1.1 and three standard printing font datasets to show the effectiveness of the proposed method. The experiment results show that the proposed method can effectively tackle the generation issue of Chinese characters having similar strokes in terms of the quality and diversity of generated results, via comparing to the baseline StarGAN, and is scalable to the multi-font generation via comparing to existing methods for the single-style font generation.
Viability of Lead-Free Perovskites with Mixed Chalcogen and Halogen Anions for Photovoltaic Applications

We assess the viability for photovoltaic applications of proposed Pb-free perovskites with mixed chalcogen and halogen anions, AB(Ch,X)3 (A = Cs or Ba; B = Sb or Bi; Ch = chalcogen; X = halogen), by examining critical issues such as the structural, electronic/optical properties, and stability through the combination of density-functional theory calculations and solid-state reactions. The calculations show that these quaternary Pb-free perovskites are thermodynamically unstable—they are prone to decompose into ternary and/or binary secondary phases or form phases with nonperovskite structures. Solid-state synthesis efforts confirm the theoretically predicted difficulty for preparing these compounds; all attempted reactions do not form the desired perovskite phases with mixed chalcogen and halogen anions under conditions examined. Instead, they form separate binary and ternary compounds. Despite earlier predictions of promising characteristics for these prospective perovskites for photovoltaics, our results.
Soil seed bank of an invasive species, Cecropia peltata L. in kapur limestone hills

Cecropia peltata is an invasive species which was initially introduced through Kebun Raya Bogor. Research on the presence of C. peltata and its effect on plant community are still limited and most were based on above ground vegetation observation. Information on seed storage in the soil is important for understanding the impact of plant invasion on an ecosystem. Aim of this tudy is to discover and analyze the impact of C. peltata in the soil seed bank of Kapur limestone hills. Seedling emergence and seed extraction was used for a method. The result showed 646 seedlings of C. peltata in the soil seed bank with seedling density approximately 23.07 ± 33.59 seedlings tray -1. The presence of large amounts of C. peltata in the soil seed bank has the potential to push the dominance of this species on the above vegetation which can subsequently cause loss of native species and habitat destruction.
Interface mechanical behavior of gold alloy wire bonding

The connection between the internal chip and external pins of the semiconductor package and the connection between the chip play an important role in establishing the electrical connection between the chip and the outside and ensuring the input/output between the chip and the outside. It is the key to the entire subsequent packaging process. Wire bonding has a dominant position in connection methods due to its simple process, low cost, and application of a variety of packaging forms. In high-reliability fields such as military and aerospace applications, bonding wires are usually used as chip interconnect materials. The bonding wires have good electrical conductivity, thermal conductivity, and oxidation resistance, but there are some reliability problems in bonding due to the production of Au-Al compounds. In this paper, Al element is used to modify the bonding alloy wire innovatively, the mechanical properties of Au-Al and Au-Pd system bonding points and the growth and evolution of IMC are studied, and the influence of Al and Pd elements on the reliability of bonding points is evaluated.