Miracle-dz/UniFAR-FTUs huggingface dataset:
What Characterizes an Influencer in Software Ecosystems?

Software Development Tends to be unequal when analyzed from a contributor perspective. A critical factor is the rise of influencers who lead development and dictate how the project evolves. By studying influencers, we can understand the evolution of a software ecosystem (SECO) and predict the future of its platform. Taking GitHub projects as our object of study, a SECO refers to "groups of projects that are developed and coevolve in the same ecosystem. " For example, npm SECO is formed by GitHub projects that are related to its libraries and packages.
CI-DPF: A Cloud IoT based Framework for Diabetes Prediction

Smart healthcare technology is one of the highest explored areas which apply modern computing technologies and techniques in healthcare research. By making use of sensors in smart wearable devices, the patient-generated data can be sent to electronic devices or any health records. This enables doctors/caregivers to directly monitor the patient activity in real-time. Moreover, a high volume of medical information is continuously produced with every passing day. It is an intrinsic need to gather, store and learn from the medical data to predict health of such patients. An alarming increase in the number of diabetic patients has become an important area of concern for medical researchers. In this paper, a Cloud IoT based framework for diabetes prediction is proposed and presented. It incorporates sensors in smart wearable devices as set of connected IoT devices for continuous monitoring and collections of blood glucose data which is sent for storage in cloud environment where an ensemble model is used to predict diabetes in patients. Experiment on ten Ensemble models by pairing two out of five different machine learning methods is carried out. The ensemble model of Decision Tree and Neural Network achieved the highest accuracy of 94.5% when evaluated using “Pima Indians Diabetes” data set.
Enabling Efficient and High Quality Zooming for Online Video Streaming using Edge Computing

High quality zooming function for online video streaming from cloud servers remains a challenge due to the intertwined relationships among video chunk lengths, viewer’s fast changing Region of Interest (RoI), and network latency. It is possible to utilize tiled Video technique and store picture tiles in separate files with their unique URLs on the media server with smaller chunk sizes, however it introduces a significant burden on the network core due to increased total video length contributed by combined non-video bits from too many smaller chunks. To overcome this, in this paper we propose the use of edge computing to achieve high quality zooming function for video streaming. Our proposal includes the system architecture using Tiled-DASH (T-DASH) video encoding on edge servers, and a novel ROI prediction method combining online and offline models on the client side. Our evaluations show that a high level of ROI prediction accuracy is achieved by our approach, fulfilling a core condition for making the zooming function a reality.
A development of capacitive voltage sensor for non-intrusive energy meter

monitoring voltage and current waveform was costly and difficult to install in the past. However, nowadays technologies are moving towards to make easy ways to measure accurate power. The main aim of smart meter makes users aware of the energy consumption on daily basis and to promote energy saving. In this paper, we present a non-contact energy measurement system, which does not need any electrical contact with physical load or its conductor of the power cables, to obtain both voltage and current waveforms. We developed a contactless voltage measurement system based on parasitic capacitive coupling. The current waveform was acquired from a current transformer. The proposed system achieved accurate power measurement, in fact, the error is approximately 2.5% compared with standard wattmeter.
Weighted Multi-task Learning in Classification Domain for Improving Brain-Computer Interface

One of the major limitations of brain computer interface (BCI) is its long calibration time. Due to between sessions/subjects nonstationarity, typically a big amount of training data needs to be collected at the beginning of each session in order to tune the parameters of the system for the target user. In this paper, a number of novel weighted multi-task transfer learning algorithms are proposed in the classification domain to reduce the calibration time without sacrificing the classification accuracy of the BCI system. The proposed algorithms use data from other subjects and combine them to estimate the classifier parameters for the target subject. This combination is done based on how similar the data from each subject is to the few trials available from the target subject. The proposed algorithms are evaluated using dataset 2a from BCI competition IV. According to the results, the proposed algorithms lead to reduce the calibration time by 75% and enhance the average classification accuracy at the same time.
The Efficiency of Sign Language Recognition using 3D Convolutional Neural Networks

Sign language is a language that uses hand gesture to present a word within the hearing impairment communication. But, the important problem is sign language is not usual for ordinary people which is misunderstand meaningful. However, the advancement in computer vision and recognition process could be adapt in computer system to learn or recognize sign language. These are useful for the system to recognized and translated sign language which ordinary people understand. In this article presents a study in process and method which related with the recognition of sign language using deep learning. The algorithm is 3D-CNN used for recognized process and the images received from Kinect sensor. We found that the sign language recognition using 3D-CNN was effective, the highest accuracy of the recognition was 91.23%.
MUSE: A Portable Cost-efficient Lie Detector

The recent advanced in the low-cost, small-scale EEG scanners open the doors for many innovations in the domains of disease detection, social sciences, cyber security, and much more. In this paper, we investigate the possibility of using EEG signals as an indication of deception or lies. Further, we exploit one of the new EEG scanners, MUSE, to investigate its ability to provide what is needed to detect potential lies. Our experiments showed great success for the MUSE in providing what is needed for a moderate-quality lie detector, with very low-cost and complexity. Integrating machine-learning techniques into the presented results would provide a much more accurate detection opening the door for a vast number of potential applications.
Survey of Mobile Malware Analysis, Detection Techniques and Tool

The rapid increase in the use of smartphones, has contributed to the increase in mobile attackers. In most situations deceitful applications are infected with malicious contents to cause harm to both the hardware and the software. These malicious programs or malware are usually designed to disrupt or gather information from the device. By attempts to curtail these problems various techniques are proposed. This paper attempts to analyze the most popular and recent techniques and suggests which is better.
High Gain Omnidirectional Dipole Array Antenna with Slot Coupler

A high gain omnidirectional dipole array antenna with slot coupler is proposed in this paper. The omnidirectional antenna is consisting of two pairs of dipoles and a feed line with impedance matching terminal. Slot couplers on the ground are excited by feedline to generate differential signals to feed dipoles respectively. Meanwhile, one pair of dipoles is placed with a distance λg to the other pair to ensure currents on dipoles in-phase. Dipoles and feedline are printed on the top layer of the substrate and ground with slots is etched on the other side. An antenna working in WiMAX 2.4 GHz band is designed. At the center frequency of 2.4 GHz, the simulated maximum gain is 4.3 dBi with vertical polarization and the gain is maintained within 3.4±0.9 dBi.
Systematic Analysis of Direct Sparse Odometry

In the field of robotics and autonomous driving, the camera as a sensor gets more and more important, as the camera is cheap and robust against environmental influences. One challenging task is the localization of the robot on an unknown map. This leads to the so-called Simultaneous Localization and Mapping (SLAM) problem. For the Visual SLAM problem, a plethora of algorithms was proposed in the last years, but the algorithms were rarely evaluated regarding the robustness of the approaches. This contribution motivates the systematic analysis of Visual SLAMs in simulation by using heterogeneous environments in Blender. For this purpose, three different environments are used for evaluation ranging from very low detailed to high detailed worlds. In this contribution, the Direct Sparse Odometry (DSO) is evaluated as an exemplary Visual SLAM. It is shown that the DSO is very sensitive to rotations of the camera. In addition, it is presented that if the scene does not provide sufficient clues about the depth, an estimation of the trajectory is not possible. The results are complemented by real-world experiments.
Energy Efficient and Robust Beamforming for MISO Cognitive Small Cell Networks

This paper studies a cognitive small cell downlink network, where one cognitive base station (CBS) transmits information to a cognitive user and transfers energy to energy harvesting receivers (EHRs). The spectrum sensing interval, the spectrum sensing time, and the beamforming matrices of the CBS are jointly optimized to maximize the energy efficiency (EE) of the CBS and to minimize the energy cost of the CBS under both the bounded channel state information (CSI) model and the probabilistic CSI model. The interference constraints of the macrocell users, the secrecy rate constraint, the transmit power constraint of the CBS and the energy harvesting constraints of the EHRs are all considered in this paper. All the three formulated optimization problems are nonconvex, for which semidefinite relaxation, a 2-D line search method, S-Procedure, and Bernstein-type inequalities are exploited. This paper also derives the conditions under which the EE maximization problem and the energy cost of the CBS minimization problem using the bounded CSI model have rank-one solutions. Simulation results demonstrate that the proposed algorithms have significant gain on the CBS EE under the perfect CSI and also gain on the CBS energy cost under imperfect CSI, all compared to the benchmark scheme.
A Temperature-Aware Reliability Enhancement Strategy for 3-D Charge-Trap Flash Memory

Compared to the conventional planar flash memory, advanced 3-D flash memory adopts charge-trap technology that can significantly enhance cell density and storage capacity. Despite these advantages, 3-D charge-trap flash memory brings several new challenges. First, charge-trap flash is sensitive to temperature. Recent studies demonstrate that, the high temperature will incur both charge loss and retention degradation. This issue does not happen in 2-D flash memory which adopts floating gate technology. Second, current 3-D charge-trap flash integrates the extra large capacity physical block, and each block contains over 1024 physical pages. The large-capacity block infrastructure will cause extra garbage collection overhead, which makes the thermal issue more complicated. This paper presents TempCure, a temperature-aware reliability enhancement strategy for 3-D charge-trap flash memory. TempCure is a novel hardware and file system interface that can transparently allocate physical space based on the temperature status. TempCure adopts two reliability enhancement strategies, temperature mining and block allotment, to prevent the generation of hotspots and enhance the data integrity of 3-D flash memory. We conduct a set of experiments using standard benchmarks. Experimental results show that TempCure can effectively reduce the peak temperature and block erase counts with negligible timing overhead in comparison with representative schemes.
Artificial Intelligence to Enhance Learning Design in UOW Online, a Unified Approach to Fully Online Learning

The current article presents a framework for the design and support of UOW Online, an entirely new unified university strategy for fully online learning. To aid teachers in the learning design process, we aim to create more awareness for teachers by determining the underlying learning design of their subject. To ensure the approach can be scaled up to cater for potentially hundreds of subjects, the manual labeling serves as input for an Artificial Intelligence (AI) algorithm that will train a model to label intended learning activities automatically. In addition to student demographics and behavior, the learning design and subject content will be used to augment an AI model that predicts future student outcomes. Future work focuses on the collection of necessary learning activities and manual encoding of these learning activities.
TW-See: Human Activity Recognition Through the Wall With Commodity Wi-Fi Devices

Device-free passive human activity recognition plays an important role in many applications, such as smart homes, identification, health care, etc. However, existing human activity recognition systems either require a dedicated device or do not meet the scenarios of the signals through the wall. To address this challenge, we present TW-See, a device-free passive human activity recognition system with Wi-Fi signals, which does not require any dedicated device and meets the scenarios of the signals through the wall. TW-See mainly exploits two key techniques to recognize different human activities. First, we propose an opposite robust PCA (Or-PCA) approach to obtain the correlation between human activity and its resulting changes in channel state information values that can eliminate the influence of the background environment on correlation extraction. Second, we propose a normalized variance sliding windows algorithm to segment the human action time from the Or-PCA waveforms, which can distinguish the true or false of the start and end times of human actions. Furthermore, we implemented TW-See with commodity Wi-Fi devices and evaluated it in several different environments. Experimental results show that TW-See achieves an average accuracy of 94.46% when the signals pass through the concrete wall.
Controllability of Large-Scale Networks: An Output Controllability Approach

In this paper we consider the problem of controllability and energy consumption for large scale networks. Instead of controlling separately all the nodes of the network we control an output which is defined as some measurement (for instance the average) of the nodes which are not directly controlled. We thus exploit the concept of Output Controllability and the Output Controllability Gramian to analyze the properties of the system. In this context, we show that it is possible to obtain a reduced-order approximated model which makes the Gramian compution and control design much easier. Simulations show that the reduced model is consistent with the original one and, for low ratios of controlled nodes, more robust with respect to the original.
An Analysis of Depth Camera Devices for Engineering Applications

Depth sensors are able to calculate the distance of the points in the image from themselves. Depth cameras implement both RGB and depth sensors to return visual and spatial information in the output data. This paper describes how depth data from the depth sensor are obtained and the experimental setup for the measurements of indoor spaces. Also, an accuracy test of the sensor is performed. Finally, the results are discussed and some examples of applications are proposed.
A Novel Movement Intention Detection Method for Neurorehabilitation Brain-Computer Interface System

In brain-computer interface based on motor imagery for rehabilitation, false positive could be a major cause of undesired brain plasticity which ends up with the wrong reconstruction of damaged brain tracts. Moreover, the number of electroencephalogram (EEG) electrodes required would be the reason of practical difficulties to clinical use. To reduce the false positive and the number of electrodes required, we proposed a novel two-phase classifier based on detecting Mu band event-related desynchronization (ERD). Along with five channels to detect motor imagery, the algorithm only uses three channels to reject ERD-like noise or non-motor signals. The performance of the proposed algorithm was evaluated through two-day experiments with four healthy subjects. The total sensitivity was 60.83% and the total selectivity was 78.49%. Those experimental results show that the proposed method can reduce the rate of false positives with small number of EEG channels.
A Reconfigurable Integrated Control for Narrow Tilting Vehicles

This paper proposes a reconfigurable control strategy for vehicles with tilting capabilities including a new rollover control approach. It can be applied to the emerging narrow tilting cars for urban transportation as well as conventional vehicles with semiactive or active suspensions. This paper extends the integrated lateral stability controller by considering the roll degrees of freedom for motion control. An envelope approach based on rollover index is proposed for the high-level controller of the vehicle to manage the rollover stability and the control energy consumption. The desired stabilizing forces are then distributed to available actuators using a reconfigurable optimal control allocation. The reconfigurable approach provides the freedom to select different actuators for the system without redesigning the controller. The optimal control allocation also ensures the feasibility as well as system robustness. It is shown via the simulation in CarSim that, by adopting the proposed control approach, tilting control effort is reduced and the vehicle handling, as well as stability in both lateral and roll motions, can be furthered improved.
A CNN-RNN Neural Network Join Long Short-Term Memory For Crowd Counting and Density Estimation

Crowd counting and density estimation is a challenging task in the field of computer vision. Most of existing methods of this task are based on convolutional neural network (CNN), which have achieved good results in low-density scene. Usually, people who are far away from the camera appear to be denser and smaller, while those who are close to the camera are more sparse and larger, therefor, structure contains only CNN gives the poor performance in some high-density crowd scene because of the uneven distribution of the crowd through camera. To address this problem, this paper designs a CNN-RNN Crowd Counting Neural Network (CRCCNN), which introduces Long Short-Term Memory (LSTM) structure, we use CNN structure to extract the features of the whole image, and use the LSTM structure to extract the contextual information of crowd region. Since LSTM has a good memory of the input information of sequential samples, it can predict the crowd density very well even for the high density population. We perform our experiments on different datasets and compare with other existing methods, which achieve the outstanding results and demonstrate the effectiveness performance of CRCCNN.
Introduction to a New Array Processing Concept: Orientational Beamforming

The article explains the orientational beamforming concept from a perspective of spread-spectrum communication not in the frequency domain but in the spatial domain. In the orientational beamforming concept, radiated signal energy is spread over a certain part of or the entire angular range by a transmit array with randomly (practically pseudo-random) distributed antennas. At the receiving end, an identical pseudo-random array is employed so that the matching of the antenna distributions between the transmit and receive arrays spatially collects and recovers the signal of interest. An orientational alignment of identical arrays is exploited to perform the beamforming. We have observed that the newly discovered spatial domain can eliminate the necessity of knowing the signal direction to obtain beamforming. Consequently, the matched array processing system improves the interference suppression capability, especially when directional interfering signals exist. The concept is also applicable to the near-field scenario without being affected by spherical wavefront delays in forming an array beam. In this article, proposed orientational beamforming in combination with conventional directional beamforming is also discussed.
Design and Implementation of an Automatic Object Recognition System Using Deep Learning and an Array of One-Class SVMs

This work shows the design, development and evaluation of an automatic object recognition system, which implements a classification methodology that makes it possible to recognize objects from different contexts and environments. The evaluated categories are input as digital images by users through a mobile application. This system is also able to learn new categories and enhance its performance according with the user's feedback. The proposed architecture achieved an initial generalization error of 20% on the first 50 categories and we showed that this error decreased after a few days of interaction with users. Finally, the system learned three completely new categories with images collected through the mobile app.
Sequential Rank Aggregation Method

Rank aggregation is a fundamental technique of different application domains. In this paper, we propose a new rank aggregation method. This method models the rank aggregation problem as an assignment problem and solves it by integer programming, where the objective function is set to minimize the sum of the squared Euclidean Distance between each initial ranking and the aggregated ranking. To avoid the computational limitation in working with large datasets, a sequential aggregation approach has been adopted. This approach proceeds sequentially in several steps. In each step, only two rankings are aggregated. It thus reduces the computational limitation of the proposed method. An illustration of the proposed method using datasets of green car adoption in Taiwan is presented in this paper. The results show that the proposed method can solve the rank aggregation problem effectively and efficiently.
Using Data Mining and Machine Learning Approaches to Observe Technology-Enhanced Learning

Understanding technology-enhanced learning (TEL) in schools continues to be difficult. A key reason is the complexity of digital technology use in the classroom to support learning. One of the reasons for this is the difficulty observing classrooms for extended periods of time to capture learning and the relevance to teacher's practice. This paper demonstrates how new technologies, data mining and machine learning approaches can be employed to explore the natural TEL classroom over time and meaningfully visualize results for teachers. To do this, a low-disturbance classroom observation kit is used to collect data in a secondary Science classroom for two months. One learning topic is identified, multimodal data are analysed and presented to the teacher for reflection. Three audio patterns relating to classroom activities and two teacher behaviours are identified, which bear relation to pedagogy, digital technology use and teaching strategies. Implications future research are discussed.