Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Identification of Leaf Stain Disease in Sugar Cane Plants Using Speeded-Up Method Robust Features

Cane (Saccharum officinarumL.) is a plant that is used as the main raw material in the sugar industry. One of the factors that can cause the reduction of sugar cane production and inhibit the development of the sugar industry is the late-diagnosed of leaf stain disease in sugarcane crops that cause crop failure. It is quite often the case because of the limited number of service facilities for communication and also the high cost to be incurred if the sugar cane entrepreneurs want to conduct the inspection process of the crop. Therefore, this study has a goal to identify leaf stain disease in sugarcane plants. This research used the image of sugar cane leaves that have stain spots as input for the image processing process using the Speeded-Up Robust Features (SURF) method. The identification process of leaf stains is divided into three types of leaf stain disease, namely ring stains, rust stains, and yellow stains. In this study, 60 images were used as preliminary data and 30 images for the testing process. Following the conducted test, it can be concluded that the early identification process of leaf stain disease in sugar cane has an accuracy rate of 90%.
Customer Segmentation based on RFM model and Clustering Techniques With K-Means Algorithm

Every day there is a transaction process performed by Customer. The process generates a lot of data where there are 82,648 transactions from the month of January-December 2017. This study aims to perform customer segmentation on Nine Reload Credit by utilizing data mining process based on RFM model and by using techniques Clustering. The algorithm used for cluster formation is K-Means algorithm. K-Means produces a visual cluster model with the Rapidminer 5.2 tools that represent the number of customers in each cluster by using RFM (Recency, Frequency, and Monetary) attributes. From 82,648 transactions that were then processed, based on RFM model it resulted in 102 Customers. Furthermore, we analyzed cluster by using K-Means algorithm with the result of 63 Customers in Cluster 1 and 39 Customers in Cluster 2. The result of this research can be used by company to know customer category, and then the company will know how to maintain the customer owned.
Evaluation of Gesture Description Language in the role of touchless interface for virtual reality environment

The main novelty presented in this paper is application and evaluation our Gesture Description Language (GDL) classifier in the role of touchless interface for virtual reality (VR) environment. In our VR system whole interaction is done by gestures and body movements analysis (so called natural user interface). For our needs we have adapted semi-realistic VR system (block engine). We have tested different aspects of proposed interface on a group of 26 persons with wide range of age (from 5 years to 40+) and both sexes. The results we obtained prove that GDL can be successfully applied in systems that require real time action recognition especially educational software games that aim at increasing the students’ motivation and engagement while they learn. Streszczenie. W tym artykule autorzy prezentują zastosowanie klasyfikatora o nazwie Język Opisu Gestów (GDL) w roli bezdotykowego interfejsu systemu wirtualnej rzeczywistości. Całość interakcji z zaprezentowanym w tej pracy systemem odbywa się poprzez rozpoznanie i analizę ruchu użytkownika (jest to tak zwany interfejs naturalny). Klasyfikator GDL został przetestowany w pseudo – realistycznym, wirtualnym środowisku, na grupie 26 osób obu płci w przedziale wiekowym od 5 do 40+ lat. Otrzymane rezultaty dowodzą, że zaproponowane podejście może być z powodzeniem użyte w aplikacjach wykorzystujących wirtualną rzeczywistość, w szczególności w grach edukacyjnych, których celem jest uatrakcyjnienie procesu zdobywania wiedzy. (Ocena działania Języka Opisu Gestów w roli bezdotykowego interfejsu w środowisku wirtualnej rzeczywistości).
Using LSTM for Detection of Wrist Related Disorders

We have build a device named RepaiR for strength measurements and isokinetic rehabilitation of the wrist joint. We have performed series of measurements on 25 healthy individuals and 10 patients with neuromuscular and traumatic impairments. Our initial goal was to verify that the measured data contain sufficient information to distinguish between healthy and not healthy subjects as a proof of concept. We have implemented Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) that processes the time structured measurements. LSTM effectively models the varying length of the input vector and the long term dependencies. We compare performances of our models on the data sets with varying minimal input vector lengths. We have proven that the measurements can be used to detect neuromuscular impairments and our best performing model worked with 77,6 % accuracy.
DPDA: A Moving Target Defense System Based on Detection and Prediction Mechanism

As a new technology to change the rules of cyberspace games, moving target defense provides a new idea for reversing the asymmetry between the attacker and defender. This paper proposes a moving target defense system based on detection and prediction mechanism‐‐DPDA system (detection-prediction-defense-assessment system), which utilizes the centralized control and programming flexibility of the SDN network. DPDA system is modeled as a discrete event dynamic system (DEDS), and performs detection of existing attacks based on real-time events. On the other hand, the attack probability transformation is used to adjust the transformation period based on the log-normal distribution function for unknown attacks. Finally, the simulation test environment is built. The results show that DPDA can implement more efficient active defense than the system with fixed transformation period.
User Evaluation of Group-in-a-Box Variants

Group-in-a-box (GIB) is a graph-drawing method designed to facilitate the visualization of the group structure of a graph. GIB allows the user to simultaneously view group sizes and inter-and intra-group structures. Several GIB variants have been proposed in the literature; however, their advantages and disadvantages have not been studied from the perspective of human cognition. Therefore, herein, we used eye tracking analysis and user surveys to evaluate the user experience of four GIB variants: Squarified-Treemap GIB(ST-GIB), Croissant-and-Doughnut GIB (CD-GIB), Force-Directed GIB (FD-GIB), and Tree-Reordered GIB (TR-GIB). We found some trade-offs among the methods for each type of user task and that FD-GIB and TR-GIB are superior than the other variants. Although ST-GIB's results were good, links were difficult to read in this graph layout. Eye-tracking data was gathered to determine which elements in each visualization significantly affected user experience. The results of this study will promote the effective use of GIB to analyze networks such as social networks or web graphs.
A 280-325 GHz Frequency Multiplier Chain With 2.5 dBm Peak Output Power

This paper presents a single-chip 300 GHz amplifier multiplier chain in a 130 nm SiGe BiCMOS process. It consists of two balanced output doublers and an integrated high-gain high output power amplifier to drive the 300 GHz frequency doubler. The two stage LC balun based 150 GHz cascode topology power amplifier is measured to deliver 14 dBm saturated output power at 148 GHz. On-wafer measurement of the chip shows a competitive bandwidth over 280 to 325 GHz. With the sufficient drive power, a peak output power of 2.5 dBm is obtained at 284 GHz with a 0 dBm 71 GHz input signal and the output power is higher than 0 dBm from 282 to 314 GHz. To the authors’ best knowledge, this is the highest output power among any published silicon based frequency multiplier chains around 300 GHz.
Performance Analysis of Big Data Frameworks on Virtualized Clusters

Research on Big Data applications has become increasingly important for institutions and researchers worldwide. This trend is triggered by the increasingly use of systems and devices that leads to generate massive of electronic data each day. The implementation of conventional algorithms has been considered to be less efficient on managing and processing large datasets. In Big Data computation, Hadoop and Apache Spark are two open source frameworks that are commonly used and run on physical clusters. Since running these frameworks on a physical cluster costs more energy and rigid in management, in this research we evaluated their performance on virtualized clusters. Virtualization technology offers flexibility on managing cluster by sharing the resources to multiple instances. Our experiments show that in general Apache Spark is about 2–9 times better in execution time and throughput compared with Hadoop running on a virtualized environment.
Cross-domain Sentiment Classification Based on Transfer Learning and Adversarial Network

Cross-domain sentiment classification have raised much attention in recent years. Due to the lack of large labeled corpus, it's very hard and usually fail to apply sentiment classification task in new domains even excellent deep learning models are used. To address the problem, we introduce a Shared Knowledge Learning and Transfer Model (SKLT) for the cross-domain sentiment classification task based on Transfer Learning and Adversarial Network. This SKLT model can extract the domain-independent shared knowledge through bi-GRU combined with adversarial network and redundant features penalty, and we transfer the knowledge extracted from multi source domains to the target domain with partial weight transfer. Experiments on multi domains of review dataset demonstrate that, the shared knowledge extracted from the SKLT model works well in the new domain task, and it can significantly outperform original methods with domain adaptation.
The Role of Working Memory Capacity in Graph Reading Performance

We process information in memory and different people have different memory capacity. It is therefore important to understand possible impact of memory capacity when it comes to graph comprehension. In an attempt towards this direction, we conducted a user study investigating the impact of working memory capacity on graph reading task performance. Forty-six university students participated in the study performing a graph reading task with one hundred graph drawings of different complexity levels. Their working memory capacity and task performance (accuracy and time) were measured and recorded. The results of regression analyses indicated that working memory capacity was a significant predictor of performance accuracy, but not for response time. In this paper, we present the details of the study and discuss our findings and limitations of the study. Possible future research directions are also suggested.
An Automatic Brain Tumor Image Segmentation Method Based on the U-net

Brain tumor images segmentation plays a crucial role in the auxiliary diagnosis of disease, treatment planning and surgical navigation. In order to accurately segment brain tumor images, this paper proposes an automatic brain tumor Magnetic Resonance Imaging (MRI) image segmentation algorithm based on the U-net model. Firstly, based on samples preprocessing, loss function and optimization algorithm, a U-net baseline model with optimal parameters structure which is suitable for segmentation task is constructed. Secondly, a feature recombination layer, i.e. a 1×1 convolutional layer, is added to the baseline model to linearly recombine the upper layer features, enrich extracted features, reduce network parameters, and improve the segmentation results. The algorithm is validated and evaluated on the Brain Tumor Segmentation Challenge (Brats) 2015 challenge training dataset. The experimental results show that the proposed algorithm has strong competitiveness compared with the existing brain tumor MRI image segmentation algorithm.
Research on Highlight Snippets Identification Technology Based on Sentiment Analysis of Bullet Curtain

At present, the method of extracting highlights snippets of video is restricted to image and audio analysis in sports videos. About this problem, this paper proposes a new method of using bullet curtain text as sentiment carrier for sentiment analysis, and then using its time coupling to identify the highlight snippets. Firstly, according to the particularity of bullet curtain text, a domain sentiment dictionary is constructed. Secondly, the paper proposes a new feature integrating time series of bullet curtain, and classifies bullet curtain text by support vector machine. Finally, bullet curtain with intense sentiment is superimposed on the video time scale, and the full video brilliance curve is obtained. The best threshold is selected to identify highlight snippets. Experimental results show that the proposed method has higher accuracy, precision and recall rate, and the validity of the proposed method is verified.
Real-Time Simulation of Brush Behaviors in Chinese Calligraphy with the Sense of Immersion

This paper presents a real-time system for simulating the brush behaviors in Chinese calligraphy. With regards to the geometric profile of the Chinese brush, the “skinned mesh” modeling method has been adopted and an algorithm for fast computing of mesh rotation parameters has been developed to realize the two deformation effects i.e. bending deformation and torsion deformation during the process of brush pen writing. An ink diffusion model based on particle system has been developed. It can control the diffusion effect of ink on the paper surface. Based on the above techniques, a brushwork simulation system featuring a head mount display and a force feedback device has been developed, achieving excellent real-time performance and immersion effect.
A Novel Solution to the Curse of Dimensionality in Using KNNs for Image Classification

The k-Nearest Neighbors (KNN) is one of the simplest and widely used algorithms in Machine Learning applications such as Image Classification. Being based on the Euclidean distance the algorithm is quite simple and effective in most cases. However, it suffers from the problem of ""The Curse of Dimensionality"" as the Euclidean distance becomes meaningless when the dimension of data becomes significantly high. In this paper we present a novel solution to this problem by making use of the Convolutional Neural Network (CNN) which can extract the most important features automatically from the images. These features extracted by the CNN are of reduced dimensions and can effectively be used by the KNN to recognize the images. The results and comparisons show that the proposed method is also seen to reduce the time taken for testing while retaining high accuracy. The proposed technique achieved an accuracy of 96.92% on MNIST, 85.09% on Fashion MNIST and 95.17% on the A-Z Alphabets databases respectively.
REEFS: An SDN Hyper File System Based on Erasure Coding

This study proposes a hyper file system, named REEFS (Resilient Erasure-codE File System), which is based on erasure code (EC) and integrates the technologies of software defined networking (SDN) and the distributed file system (DFS). REEFS utilizes the EC to encode a data object or file into $\boldsymbol{n}$ blocks for storing in $\boldsymbol{n}$ storage nodes in the DFS. REEFS is fault-tolerant since any $\boldsymbol{k}$ out of the $\boldsymbol{n}$ blocks can recover the data object. An algorithm, called Residual Network Multiple Shortest Path (RNMSP), is developed for finding the shortest paths from the file source to $\boldsymbol{n}$ storage nodes to reduce the file data block transmission latency, while maintaining high data availability. The shortest paths are used by the SDN controller to manage SDN switches to forward block data. The RNMSP algorithm is compared with other related algorithms to show its superiority in terms of the average file transmission latency.
Multi-Feature Fusion Using Collaborative Residual for Hyperspectral Palmprint Recognition

Recently, hyperspectral imaging has attracted more and more considerable research attention because of its discriminative information. This paper presents a multiple feature fusion strategy for hyperspectral palmprint recognition, combining texture LBP feature, direction LDP feature and global deep convolutional feature (DCNN). Compared to single patterns, the proposed method is focused on the combined use to improve the distinguishability. The LBP, LDP and DCNN are applied to the hyperspectral palmprint images (of an individual) in order to extract its features forming three feature matrixes. Each matrix contains redundant information and long dimensions. Thus, the 2D-PCA is applied to reduce the dimension of every feature matrix and generates a uniform feature vector. At last, residuals of each patterns based on the collaborative representation between the test data and training data are fused. The collaborative residual of the three patterns is exploited on final recognition. The proposed methodology was experimented on a large hyperspectral palmprint dataset consisting of 53 spectral bands obtaining an EER of 0.11% and an accuracy of 99.76% at recognition.
Digitally-Intensive Fast Frequency Modulators for FMCW Radars in CMOS : (Invited Paper)

Digitally-intensive PLLs have already demonstrated their effectiveness as frequency synthesizers for cellular applications, being able to fulfill the specifications in terms of phase noise, spurs and power dissipation thanks to the adoption of embedded digital processing that efficiently reduces the analog impairments. In this paper we review how these techniques further enable other capabilities, namely both wideband and linear frequency modulation. The application considered as a case study is the fast chirp generation for frequency-modulated continuous wave radar at millimeter waves. We illustrate how the combination of the two-point modulation of a digital PLL and the background digital pre-distortion of the DCO enable both fast and linear modulation.
Long Time Sequential Task Learning From Unstructured Demonstrations

Learning from demonstration (LfD), which provides a natural way to transfer skills to robots, has been extensively researched for decades, and an army of methods and applications have been developed and investigated for learning an individual or low-level task. Nevertheless, learning long time sequential tasks is still very difficult as it involves task segmentation and sub-task clustering under an extremely large demonstration variance. Besides, the representation problem should be considered when doing segmentation. This paper presents a new unified framework to solve the problems of segmentation, clustering, and representation in a sequential task. The segmentation algorithm segments unstructured demonstrations into movement primitives (MPs). Then, the MPs are automatically clustered and labeled so that they can be reused in other tasks. Finally, the representation model is leveraged to encode and generalize the learned MPs in new contexts. To achieve the first goal, a change-point detection algorithm based on Bayesian inference is leveraged. It can segment unstructured demonstrations online with minimum prior knowledge requirements. By following the Gaussian distributed assumption in the segmentation model, MPs are encoded by Gaussians or Gaussian mixture models. Thus, the clustering of MPs is formulated as a clustering over cluster (CoC) problem. The Kullback–Leibler divergence is used to measure similarities between MPs, through which the MPs with smaller distance are clustered into the same group. To replay and generalize the task in novel contexts, we use task-parameterized regression models such as the Gaussian mixture regression. We implemented our framework on a sequential open-and-place task. The experiments demonstrate that the segmentation accuracy of our framework can reach 94.3% and the recognition accuracy can reach 97.1%. Comparisons with the state-of-the-art algorithm also indicate that our framework is superior or comparable to their results.
Comparison of Color Constancy Approaches on Images with Unbalanced Color Distribution

Color images often suffer from degradations due to the acquisition and condition of light. Some of the problems are under-exposure and color cast which can be categorized as unbalanced color distribution. Two approaches of color constancy used to remove the unwanted color cast of test images; they are Gray-world assumption and Histogram matching. This paper discusses the difference between the two approaches and compares the result images. Both approaches can successfully remove color casts of the images with different characteristic of the result image.
Segmentation of Overlapping Areas on Pap Smear Images with Color Features Using K-Means and Otsu Methods

Separation of overlapping areas on overlapping Pap smear image is still a tough thing to do. In addition to the process of identification of overlapping cells which is still difficult to do, sometimes there are components in the cells that complicate the process such as the presence of inflammatory cells that resemble the nucleus though the process of overlapping cell identification on Pap smear image is needed in early detection of Pap smear test to recognize a cell that is healthy or classified as cervical cancer cells. This study aims to segment the overlapping areas of Pap smear image by utilizing the color features of the cell. Stages of identification and separation of the area use K-means method combined with Otsu method. The results obtained in some cells of overlapping areas can be perfectly separated while in some other cells the results have not been encouraging and still require further research, especially to overcome the existence of inflammatory cells that complicate the separation process.
How to Implement NDN MANET over ndnSIM Simulator

In this paper, we discuss about how to implement a mobile ad hoc network (MANET) over the named data networking (NDN) architecture. NDN is focused as a network architecture for the future Internet, but its implementation is studied from the view of wired networks with point-to-point links. Although many NDN based MANET schemes are proposed, how to implement it is not still clear. This paper shows the implementation details of NDN MANET over ndnSIM simulator and the performance evaluation results.
Adaptive Composite Phases Modulation/Demodulation Based on Transform Domain Communication System

Transform domain communication system (TDCS) is a cognitive anti-interference system that can dynamically generate interference-orthogonal waveform. However, the low spectrum efficiency of the cyclic code shift keying limits its potential practical applications. Besides, TDCS only provides adaptive waveform rather than adaptive modulation. In this paper, an adaptive composite phases modulation/demodulation method is proposed to solve the low spectrum efficiency problem. Compared with the existing methods, it can achieve a considerable high spectrum efficiency and adaptive transmitting rate without signaling overhead.
A 0.84pJ/cycle Wheatstone Bridge Based CMOS RC Oscillator with Reconfigurable Frequencies

A low-power CMOS RC relaxation oscillator supporting reconfigurable frequencies is presented for timekeeping in low-power Internet of Things (IoT) devices. The oscillator employs a Wheatstone Bridge (WB) scheme to relax the constraint between reference current and reference resistance existing in prior arts. In addition, adjusting the resistor ratios in WB supports reconfigurable frequencies and allows the use of smaller resistors for lower frequencies, saving chip area. Measurements of the prototype in 180nm CMOS show an average temperature coefficient down to 9.6ppm/°C at nominal 67kHz across -20°C to 100°C, with an energy efficiency of 0.84pJ/cycle and an active area of 0.145mm2. Oscillations at 17kHz and 154kHz are also demonstrated by tuning the resistor ratio of WB.