Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis of Twitter Data

Sentiment analysis of Twitter data became a research tread in the last decade. Among popular social network portals, Twitter had been the point to fascination to several researchers in important areas like prediction of democratic events, customer brands, movie box-office, stock market, reputation of personalities etc. The term sentiment refers to the feelings or opinion of person towards some particular domain. Analysis of sentiment (opinions) and its classification based on polarity is a challenging task. Other challenges are overwhelming amounts of information on one topic with all having different representation. Classification and clustering are two major methods applied to perform sentiment analysis of twitter data. We have used Possibilistic Fuzzy C-Means with SVM to improve accuracy on movie tweets and worked on upto 3-grams.
Information Structures and Uncertainty Measures in an Incomplete Probabilistic Set-Valued Information System

An information system is a database that represents relationships between objects and attributes. A set-valued information system is the generalized model of a single-valued information system. A set-value information system that contains probability distributions and missing values is called an incomplete probability set-value information system (IPSIS). Uncertainty measure is an effective tool for evaluation. This paper explores information structures and uncertainty measures in an IPSIS. According to the Bhattacharyya distance, the distance between two objects in a given subsystem of an IPSIS is first proposed. Then, the tolerance relation on an object set, induced by a probability set-valued information system by using this distance, is obtained. Next, the information structure of this subsystem is introduced by a set vector. Moreover, the dependence between two information structures is studied by using the inclusion degree. Finally, as an application for information structures, measures of uncertainty for an IPSIS are investigated, and to evaluate the performance of the proposed measures, effectiveness analysis is given from the angle of statistics. These results will be helpful for establishing a framework of granular computing and understanding the essence of uncertainty in an IPSIS.
Emotion Classification and Crowd Source Sensing; A Lexicon Based Approach

In today’s world, social media provides a valuable platform for conveying expressions, thoughts, point-of-views, and communication between people, from diverse walks of life. There are currently approximately 2.62 billion active users’ social networks, and this is expected to exceed 3 billion users by 2021. Social networks used to share ideas and information, allowing interaction across communities, organizations, and so forth. Recent studies have found that the typical individual uses these platforms between 2 and 3 h a day. This creates a vast and rich source of data that can play a critical role in decision-making for companies, political campaigns, and administrative management and welfare. Twitter is one of the important players in the social network arena. Every scale of companies, celebrities, different types of organizations, and leaders use Twitter as an instrument for communicating and engaging with their followers. In this paper, we build upon the idea that Twitter data can be analyzed for crowd source sensing and decision-making. In this paper, a new framework is presented that uses Twitter data and performs crowd source sensing. For the proposed framework, real-time data are obtained and then analyzed for emotion classification using a lexicon-based approach. Previous work has found that weather, understandably, has an impact on mood, and we consider these effects on crowd mood. For the experiments, weather data are collected through an application-programming-interface in R and the impact of weather on human sentiments is analyzed. Visualizations of the data are presented and their usefulness for policy/decision makers in different applications is discussed.
Digital Photography Using a High-Transmittance Electro-Optical Iris

In this paper, a tunable electro-optical iris with high transmittance, based on polymer dispersed liquid crystal (PDLC) material, and a relevant image enhancement method are combined for digital photography. Our device is controllable by adjusting the voltage and no mechanical and moving components are required so as to improve the stability. The experiments show that transmittance of light throughput is tunable from nearly 0% to 77% by gradually increasing driving voltage. The digital camera system using our PDLC iris can capture the high-contrast image in real-scene photography. To further improve the image quality, we develop an iterative image enhancement method that is able to improve the signal-to-noise ratio by ∼10 times and increase the image brightness simultaneously. The combination of the PDLC electro-optical device and a variant of the enhancement algorithm can also have the potential to be applied in optical imaging and sensing.
A Two-Tier System for On-Demand Streaming of 360 Degree Video Over Dynamic Networks

360° video on-demand streaming is a key component of the emerging virtual reality and augmented reality applications. In such applications, sending the entire 360° video demands extremely high network bandwidth that may not be affordable by today’s networks. On the other hand, sending only the predicted user’s field of view (FoV) is not viable as it is hard to achieve perfect FoV prediction in on-demand streaming, where it is better to prefetch the video multiple seconds ahead, to absorb the network bandwidth fluctuation. This paper proposes a two-tier solution, where the base tier delivers the entire 360° span at a lower quality with a long prefetching buffer, and the enhancement tier delivers the predicted FoV at a higher quality using a short buffer. The base tier provides robustness to both network bandwidth variations and FoV prediction errors. The enhancement tier improves the video quality if it is delivered in time and FoV prediction is accurate. We study the optimal rate allocation between the two tiers and buffer provisioning for the enhancement tier to achieve the optimal trade-off between video quality and streaming robustness. We also design periodic and adaptive optimization frameworks to adapt to the bandwidth variations and FoV prediction errors in realtime. Through simulations driven by real LTE and WiGig network bandwidth traces and user FoV traces, we demonstrate that the proposed two-tier systems can achieve a high-level of quality-of-experience in the face of network bandwidth and user FoV dynamics.
Energy Efficiency and Speed Optimization by Squad-Unit Genetic Algorithm for Bipedal Walking

In this paper, a method of motion planning and trajectory optimization is proposed to decrease energy consumption and improve speed performance for bipedal walking. For a long time, researches of bipedal walking have been concentrated on single supporting phase, in which dynamic model such as inverted pendulum model (IPM) is used. Originally, in this paper the trajectory of the center of mass (COM) is generated separately: trajectory of COM is first planned in double supporting phase (DSP), then the trajectory of COM in single supporting phase (SSP) is generated consequently based on inverted pendulum model (IPM). Trajectory of COM in DSP, derivating SSP trajectory is optimized to achieve minimum energy consumption and better speed performance for bipedal walking. The evaluation of energy consumption and speed is illustrated as total cost of transporting (TCOT). To optimize a three dimensional space trajectory in DSP, modified genetic algorithm (GA), squad-unit generic algorithm is conducted. In this method, the unit for evolution contains three members sharing the same fitness value and has the advantage over conventional that can only represent one-dimensional input variable. The effect of optimization of three dimensional trajectories is verified by simulation.
A Survey of Live Migration Techniques for SR-IOV Device Pass-Through

Single-Root I/O Virtualization (SR-IOV) is a modern specification proposed by PCI-SIG that allows a PCI Express (PCIe) device to be utilized as multiple virtual functions (VF). By assigning VFs to virtual machines (VM) as pass-through devices, the VMs can achieve superior I/O performance. However, SR-IOV device pass-through is not compatible with live migration, which is an indispensable infrastructure management feature in virtualization. Therefore, live migration solutions for SR-IOV device pass-through have been a hot topic in I/O virtualization research area. This paper studies the challenge of SR-IOV device pass-through and provides a comprehensive survey of corresponding live migration techniques. Our primary goal is to propose a research overview to demonstrate the state-of-the-art SR-IOV live migration issues.
Configurable CNN Accelerator Based on Tiling Dataflow

Convolutional neural networks are widely used in deep learning. Nowadays, many CNN accelerators have been designed because the specific accelerators have high energy efficiency. Especially, the Tiling dataflow accelerators have achieved a high performance. However, we find that the size of the hardware is larger and larger. And the utilization of the Tiling dataflow may be low with the number of the process elements (PEs)increasing. In order to achieve a high-utilization accelerator, we propose a Configurable CNN Accelerator based on the Tiling dataflow. As we all know, the convolution can be seen as a 6-layer unrolling loops. But the Tiling dataflow only exploit 2-layer unrolling loops. The high-performance accelerator needs develop more parallelism to repress the PEs idling. Through the configurable technology, the Configurable CNN Accelerator can exploit 4-layer unrolling loops. And the unrolling loop strategy is optional. On this foundation, we propose a partial configurable technology, which can not only improve the utilization when the number of the PEs is giant, but also reduce the hardware overhead as much as possible. At last, we apply the Configurable CNN Accelerator to test several mainstream CNNs. The accelerator can achieve 1.2-30x speedup compared with the 16*16 Tiling dataflow. And when the number of the PE exceeds 512, the utilization can be maintained at an average of 82%-90%.
Hyperspectral Image Classification Using Modified Convolutional Neural Network

Classification of hyperspectral images (HSI) signifies giving each and every pixel in the image a class on the basis of some information obtained. Convolutional neural networks (CNNs), in recent years, have been widely explored and applied for HSI classification as they have shown better performance than other existing techniques in terms of accuracy of classification. Most of the existing techniques such as traditional techniques using manually crafted features, feature reduction techniques like independent component analysis (ICA), decision trees (DT) and support vector machines (SVMs) consider only the spectral information for classification. They are unable to utilize the spatial information adequately which hinders classification performance. CNN has shown great promise as it is able to conceive the spatial information along with the spectral information thus enhancing classification accuracy. However, as HSI has very high dimensionality and insufficient samples for training, effectively and efficiently applying CNNs for classification of HSI remains a demanding task. Therefore, in this paper, we have proposed a modified CNN architecture to boost its discriminative capability for extraction of both spectral and spatial information from original HSI and using those information predict classification results using a softmax regression (SR) classifier. The proposed architecture is different from conventional CNN models as it makes use of 1×1 filters along with FxF filters during convolution operations in order to properly cope with the HSI information. A global average pooling layer (GAP) replaces the traditional fully connected (FC) layers in the proposed architecture which helps to alleviate the problem of overfitting to some extent. Comparing our work with considered techniques on two benchmark datasets, it is observed that our proposed approach gives better results in terms of overall accuracy (OA), average accuracy (AA) and kappa coefficient (K).
A Biologically Inspired Cable Climbing Robot: CCRobot - Design and Implementation

In this paper, a novel biologically inspired design for a cable climbing robot called CCRobot is presented. It consists of two main components: the gripping module and the parallel manipulator. The weight of CCRobot is approximately equal to 15 kg, and its load capacity is at least 30 kg. Its maximum climbing speed is more than 3 m/min, which is a high value in the field of cable climbing robot. Moreover, CCRobot is capable of performing various actions such as moving along a cable trunk with a certain range of bending or with some obstacles. Numerous on-cable locomotion implementations of CCRobot show that the proposed design is effective.
End-to-end Control of Kart Agent with Deep Reinforcement Learning

Research on kart racing games provides an opportunity to develop real autonomous driving controllers using unsupervised learning methods and therefore worth studying. In this paper we bring forward a feasible end-to-end control technique using a novel DQN architecture. Recurrence is introduced to eliminate the effect caused by the partially observable environment. A weighting layer is created to strengthen the impact differences of the lengthy episode. A modified relay mechanism is applied to solve the problem of sparsity and speed up learning. The method is tested and verified under the Mario Kart64 environment. With the screen pixels being the only input during both training and testing period, a direct speed control signal being the output of the system, this method makes the agent perform out similar learning behaviours just like a human player. The resulting agent is able to recognize the environment features well and could act out advanced behaviours which are undiscovered in previous methods.
Performance Comparison of Classification Algorithms on Medical Datasets

This paper studies selected classification algorithms on medical datasets. The selected health datasets are Breast Cancer Data, Chronic Kidney Disease, Cryotherapy, Hepatitis, Immunotherapy, Indian Liver Patient Dataset (ILPD), Liver Disorders, and Liver disorders dataset. ILPD and Liver disorders, Pima diabetes, risk factors cervical cancer and Statlog (Heart) Data Set dataset are taken from the University of California at Irvine (UCI) repository. The classification algorithms considered in this study are Bagging, IBK, J48, JRip, Multilayer perceptron (MP) and Naive Bayes (NB) classifiers.
SVPWM Strategy Based on the Hysteresis Controller of Zero-Sequence Current for Three-Phase Open-End Winding PMSM

Owing to high driving power, good controllability, and strong fault-tolerant ability, a three-phase open-end winding permanent magnet synchronous motor driven by a dual inverter with common dc bus is considered as a competitive solution. However, a path for zero-sequence current is provided in this drive topology. Thus, the system loss would be increased, and the maximum output power is limited. In order to suppress the zero-sequence current, a novel space vector pulsewidth modulation (SVPWM) strategy based on the hysteresis controller of zero-sequence current is proposed in this paper. The voltage vectors, whose polarity of zero-sequence voltages (ZSVs) is opposite with the polarity of zero-sequence current, are selected as the basic voltage vectors of SVPWM for fundamental plane. Meanwhile, the linear modulation range is analyzed. According to the analysis results, the proposed strategy has the advantage of wide linear modulation range of ZSV under the condition of high fundamental modulation ratio. The experimental results show that the zero-sequence current is effectively suppressed by the proposed modulation strategy. Furthermore, the comparative evaluation of the proposed modulation strategy, the conventional SVPWM, the SVPWM with zero ZSVs, and the zero vector redistribution SVPWM with zero-sequence current proportional-resonant controller is conducted to demonstrate the superiority of the proposed strategy.
Philippine Indigenous Plant Seedlings Classification Using Deep Learning

Plant taxonomists have specialized knowledge on a specific plant species. The shortage of these experts and their unbalanced distribution throughout the globe remains to be a serious problem worldwide. Deep learning technology can be used to create tools that will help them speed up the process of plant classification. This study implements three deep learning models to classify images of Philippine indigenous plant seedlings into five species. AlexNet, GoogLeNet, and ResNet50 were fine-tuned for this purpose. In the three pre-trained models, the weight and bias learning rate factors of the fully connected layers were both increased to 20 to speed up learning. Augmentation techniques such as rotation, random flip, and random horizontally and vertically translations were performed to training images to avoid the effects of overfitting. The resulting deep learning models can be implemented to classify Philippine indigenous plant seedlings as they all achieved above 90% accuracy on the validation set, with ResNet50 exhibited the highest with 98.93%.
Research on Humor Recognition

Humor, as an important part of human communication, plays an important role in the field of Natural Language Processing. It is worth pondering how to establish an effective model of humor recognition. In this paper, we explore the differences between humorous examples and nonhumorous examples from three perspectives which include Emotion Dimensions, Conflict and Emotion Outburst. Based on these perspectives, we design a series of effective features which weaken the influence of domain difference and clustering on humor recognition. Experimental results show that our features are effective. And our method provides a new idea for future researches.
Performance of Nonbinary Cubic Codes

Cubic codes were proposed by Haah as candidates for self-error correction in three dimensions (3D). While these codes are not self-correcting, Bravyi and Haah showed that they are partially self-correcting. In this paper we are interested in generalizations of the cubic code to prime alphabet. Kim initiated the study of such codes over prime alphabet. Haah also proposed a framework based on modules that enables the study of cubic codes over higher alphabet. However, there are many open questions remaining, especially those pertaining to the performance of nonbinary cubic codes. Building on Bravyi and Haah’s decoder, we study the performance of nonbinary cubic codes over the quantum erasure and depolarizing channels. This is the first such study of nonbinary cubic codes.
Uncertain Prediction for Slope Displacement Time-Series Using Gaussian Process Machine Learning

The Gaussian process regression (GPR) model, which is a powerful machine learning tool for probabilistic prediction, is introduced into slope displacement prediction. Using this model, the displacements of the slope of the permanent ship lock of the Three Gorges Project, the Wolongsi slope, and the high slope of Longtan hydropower station were predicted. In addition, the predictive uncertainty index (PUI) for describing the uncertainty of the predicted results was proposed, and the corresponding classification of the PUI was established. The study results demonstrate that the GPR model can self-adaptively acquire model parameter values and has satisfactory adaptability for predicting nonlinear time series of slope displacement. The proposed PUI and its classification based on the GPR model enable quantitative uncertainty analysis and, in turn, reliability evaluation of the predicted results. The GPR model provides a new approach to displacement prediction and safety management in slope engineering.
D2D Communications Meet Mobile Edge Computing for Enhanced Computation Capacity in Cellular Networks

The future 5G wireless networks aim to support high-rate data communications and high-speed mobile computing. To achieve this goal, the mobile edge computing (MEC) and device-to-device (D2D) communications have been recently developed, both of which take advantage of the proximity for better performance. In this paper, we integrate the D2D communications with MEC to further improve the computation capacity of the cellular networks, where the task of each device can be offloaded to an edge node and a nearby D2D device. We aim to maximize the number of devices supported by the cellular networks with the constraints of both communication and computation resources. The optimization problem is formulated as a mixed integer non-linear problem, which is not easy to solve in general. To tackle it, we decouple it into two subproblems. The first one minimizes the required edge computation resource for a given D2D pair, while the second one maximizes the number of supported devices via optimal D2D pairing. We prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. Then, the optimal algorithm to the original problem is developed by solving two subproblems, and some insightful results, such as the optimal transmit power allocation and the task offloading strategy, are also highlighted. Our proposal is finally tested by extensive numerical simulation results, which demonstrate that combining D2D communications with MEC can significantly enhance the computation capacity of the system.
Moving Object Detection with an IP camera

Video analytics is fast growing and important area of research. In the earlier age, video surveillance device was used purely as security solutions which were only capable to capture and watch the incident. This kind of system depends on human beings to watch and review all the video, which is however not effective and practicable. To make the system more perspicacious, an incipient area is explored, which is denominated as video analytics (VA). There are many areas wherein VA finds application, categorically, has applications in a sensitive area like no man zone, military, conveyance, civil and secure. Many businesses can use this kind of video analytics system to detect any kind of motion during the time when business is being closed. Video analytics based different approaches for object detection and tracking are discussed in this paper. The system detects moving object with the help of image processing and MATLAB and notifies in terms of alarm or by sending a message to the concerned mobile phone.
Discrimination of Arrhythmia Using a Smartphone

The disturbed electrical activity of the heart which leads to Atrial Fibrillation (AF) poses a threat to the health and a lot of people in the country are suffering because of this. Its timely detection and treatment can increase the longevity of patients with AF. The proposed, Smartphone based inventive method of detection of AF utilizes Photoplythosmography (PPG) signal obtained from the fingertip using the camera in a smartphone. It differentiates Normal sinus rhythm (NSR) from AF, Premature Ventricular contraction (PVC) and Premature Atrial Contractions (PAC), with the combination of statistical methods like Root mean square of successive differences (RMSSD), in combination with Shannon Entropy (ShE) and Turning point ratio (TPR) method. The reformulated TPR is used along with pulse rise and fall times to increase the accuracy of detection. The acquired results show that this method can detect NSR with sensitivity of 0.9155, and gives overall accuracy of classification of 92%