Miracle-dz/UniFAR-FTUs huggingface dataset:
Models and Features with Covariate Shift Adaptation for Suspicious Network Event Recognition

Detecting a small number of suspicious network events from large amount of network traffic data is a very challenging task. We extract time series features from the network log data and use models such as LightGBM and stacked CNN-LSTM deep neural networks to predict whether the investigated alerts are suspicious. We apply feature alignment, covariate shift adaptation to overcome the covariate shift between training data and test data. In the IEEE Big Data 2019 Cup: Suspicious Network Event Recognition Competition, our model scored the first place on the public board and the fourth place on the final board respectively.
Negative Capacitance Effect on MOS Structure: Influence of Electric Field Variation

We explicate the negative capacitance (NC) effect in MOS structures through TCAD simulation, and find the influence of electric field variation in ferroelectric layer. Furthermore, pre-researched time-dependent NC effect is explained in more detail. For this purpose, the electric field and potential difference in the ferroelectric layer are analyzed based on the measured data of metal-ferroelectric-metal (MFM) capacitors, and then the parameters for the occurrence of the NC effect in the NCFET were analyzed. This establishes the conditions necessary for the successful operation of the NCFET.
A Refined Analysis of Zcash Anonymity

With the continuous development and popularity of blockchain technology, anonymity of cryptocurrency has attracted wide attention. Zcash is an altcoin of Bitcoin aiming to protect blockchain anonymity. Its anonymity is highly guaranteed by zero-knowledge proofs. However, it is still practicable to decrease Zcash’s anonymity. In this paper, we provide a refined empirical analysis of Zcash anonymity. We improve current address clustering methods and increase the clustering rate by 9%. We also analyze the whole process of distributing mining reward and identify 87.5% addresses and 25.7% transactions. Besides, we simplify Zcash transaction network and then pick out nodes (edges) which play important roles in network connectivity. We show that these nodes are mostly mining pools. In particular, users participating in shieldedpool are mostly founders, miners and mining pools, although shieldedpool itself is designed for protecting anonymity of users with high privacy requirements. Our results, to an extent, are opposite to the original intention of Zcash.
Dual-Mode Operation of 3-Level 4-Leg AT-NPC Inverter for Microgrids

This study presents dual-mode operation and seamless transfer of 3-level 4-leg Advanced T-type Neutral Point Clamped (AT-NPC) inverter for low voltage microgrids. The proposed control technique is designed with Proportional Resonant (PR) based current controlled in grid-connected operation and with Proportional Multi Resonant (PMR) based voltage controlled in islanded operation. In order to ensure smooth transition between these operating modes, combined seamless transfer method is proposed. The proposed 3-phase 4-leg inverter system can supply both 1-phase and unbalanced 3-phase load groups in islanded mode. A laboratory prototype is installed for the experimental testing of the 3-phase 4-leg AT-NPC 3-level inverter system. The proposed system is simulated via PSIM software and the model-based embedded codes are generated with PSIM/SimCoder software. Real-time control algorithm of the inverter system is carried out with TMS320F28335 digital signal controller board. Finally, the operation performance of the inverter system is investigated both in the islanded/grid-connected mode and the seamless transfer process between these two operating modes.
Design of Programmable, High-Fidelity Haptic Paddle

In this study, we propose the design of a 1-DOF haptic interface for kinesthetic learning applications and educational platforms in some engineering and science courses. The proposed system is a directly-driven, high fidelity, easy-to-build, easy-to-program haptic device and yet still affordable for use in education. The major advantage of the system is that there is no transmission between the motor and human (i.e. direct-drive) motion which greatly reduces the mechanical complexity, improves robustness, reduces some calibration stages, and reduces some of the important costs. The main drawback associated with a direct-drive actuator interface is that effective motion measurement resolution is very low compared to other systems that utilize capstan drive like mechanisms. To overcome this problem, we used an affordable electronic gyroscope to measure angular velocities directly and integrate gyro and encoder measurements using a statistical fusion filter. The core component of the electronic hardware is a 13$ embedded microcontroller board which assured us of 1KHz hard-real-time measurements and programmable interfaces. The human sense of touch has dynamic characteristic whose bandwidth changes according to action and highest bandwidth with the highest stimulus intensity in an action like slippage has a bandwidth of 1KHz. Because of that, a tactile device being 1KHz hard-real-rime is important for many haptic applications. All these features can be used on various virtual tasks as a kinesthetic learning medium. Such a high fidelity and yet affordable haptic interface is suitable for in-class activities and improves the ability of perception of the students especially on physical systems.
Learning Natural Locomotion Behaviors for Humanoid Robots Using Human Bias

This letter presents a new learning framework that leverages the knowledge from imitation learning, deep reinforcement learning, and control theories to achieve human-style locomotion that is natural, dynamic, and robust for humanoids. We proposed novel approaches to introduce human bias, i.e. motion capture data and a special Multi-Expert network structure. We used the Multi-Expert network structure to smoothly blend behavioral features, and used the augmented reward design for the task and imitation rewards. Our reward design is composable, tunable, and explainable by using fundamental concepts from conventional humanoid control. We rigorously validated and benchmarked the learning framework which consistently produced robust locomotion behaviors in various test scenarios. Further, we demonstrated the capability of learning robust and versatile policies in the presence of disturbances, such as terrain irregularities and external pushes.
A System for Analysis and Remediation of Attrition

With the increasing impetus on globalization, workplace modernization, and employee welfare, modern organizations are focusing more and more resources in developing their pool of human capital. Employee attrition poses a major challenge for organizations – be it in terms of operational cost or loss of talent. We present CLARA (CLustering for Analysis and Remedial of Attrition), a deployed end-to-end system applying descriptive, predictive, and prescriptive analytics, providing as output actionable remedial actions to be used by HR departments to improve employee retention. We propose a coupling between clustering and frequent pattern based scoring measure to identify candidate employees at a high risk of attrition, and subsequently recommend suggestive actions to improve their retention. Using publicly available IBM human resource (HR) dataset, we show that CLARA demonstrates comparable performance (compared to state-of-the-art techniques) in identifying such “high risk” employees. We further validate the quality of the recommendations provided by the framework in reducing the overall rate of human capital loss, and discuss the real-life implementation of the framework within our organization. CLARA attains up to 65% precision, on IBM HR dataset, in predicting employee attrition and, thanks to the remedial actions, up to 22.5% reduction in the predictive attrition score for the top-5 identified employees.
Automatic Superimposed Droop Frequency Control Scheme for DC Microgrids

This paper proposes a control strategy to improve the superimposed droop frequency control scheme in DC microgrids. Conventional droop-based schemes for DC microgrids control suffer from issues such as inaccurate power sharing among the sources, voltage control with insufficient quality, and adverse effect of the line resistance on the droop characteristic. To overcome these challenges, the superimposed droop frequency method has been introduced. However, the stable operation in terms of the load variations and improper voltage quality are still the challenges with this prior-art method due to the injected AC voltages. In this paper, a method is proposed to solve the stability problem and alleviate the overall voltage quality in the superimposed droop frequency scheme. The performance of the proposed method is verified by different simulation studies in MATLAB/SIMULINK environment.
Discovering Partial Periodic Spatial Patterns in Spatiotemporal Databases

Finding partial periodic patterns in very large databases is a challenging problem of great importance in many real-world applications. Most previous work focused on finding these patterns in temporal (or transactional) databases and did not recognize the spatial characteristics of items. In this paper, we propose a more flexible model of partial periodic spatial pattern that may be present in spatiotemporal database. Three constraints, maximum inter-arrival time(maxIAT), minimum period-support(minPS) and maximum distance(maxDist), have been employed to determine the interestingness of a pattern in a spatiotemporal database. The maxIAT controls the maximum duration in which a pattern must reappear to consider its occurrence as periodic within the data. The minPS controls the minimum number of periodic occurrences of a pattern within the data. The maxDist controls the maximum distance between the items in a pattern. All patterns satisfying these three constraints are returned. An efficient algorithm, called SpatioTemporal-Equivalence CLAss Transformation (ST-ECLAT), has also been described to discover all partial periodic spatial patterns in a spatiotemporal database. This algorithm employs a novel smart depth-first search technique to discover desired patterns effectively. Experimental results demonstrate that the proposed algorithm is efficient. We also present a case study in which we apply our model to find useful information in the air pollution database.
Banknotes Serial Number Coding Recognition

Banknotes serial number coding is the main basis of bank’s supervision, currency circulation and authenticity verification, the traditional manual identification method can hardly meet the current needs of currency circulation and security control. This paper solves the problem of banknotes serial number coding recognition based on deep learning algorithm. Our algorithm is composed of text localization and text recognition. The aim of the text localization phase is to get the region of the serial number coding. Based on YOLOv3 model, this paper replaces original convolution module with depthwise separable convolution. The idea of Inception is adopted in subsample stage. K-means algorithm is used to re-cluster width and height of serial number coding region to adjust anchor value, thus, improving the localization accuracy. The aim of the text recognition phase is to get the serial number coding of the banknotes, CNN+LSTM+CTC is taken as the model for text recognition. In this paper, the fourth edition of RMB data set is used for serial number coding recognition, 99.38% accuracy is obtained in experimental results.
An Informative Base of Positive and Negative Association Rules on Big Data

The concept of informative base for association rules is the subject of many approaches. However, these approaches are based on positive rules but not on negative rules, and this with the less selective support-confidence pair. So that, these positive rules are not enough to cover all needs in context of Big Data, it also needs the negative association rules. In order to overcome these limitations, we propose a new approach for positive and negative association rules using the new selective pair, support -MGK. We also introduce NONREDRULES algorithm for mining all informative association rules. The experimental evaluation on the reference databases presents the extensive feasibility of our approach on the context of Big Data.
QoS Prediction Methods in IoT A Survey

Emergence of Internet of Things (IoT) concept is expected to deeply change everyday life through a vast number of services and applications. Heterogeneity of IoT networks and unique characteristics of individual services, causes requirement of different quality factors for services and applications. Research on Quality-of-Service (QoS) aims to satisfy quality requirements of different services on heterogeneous IoT networks. To ensure a certain level of QoS, QoS prediction methodology is used to enhance service selection for IoT users. In this survey, QoS prediction approaches are briefly explained. The advantages and disadvantages of the existing approaches are presented and response times of QoS prediction methods are compared using two statistical metrics, namely Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
Exploring Dietary Intake Data collected by FPQ using Unsupervised Learning

Populations in countries undergoing rapid transition are experiencing food- and nutrition-related problems. To acquire high-quality nutrition information, we need beside adequate data about food consumption, also efficient methods for the extraction of information from the collected data.Our aim was to develop a methodology for analyzing and reasoning about dietary intake data collected by a food propensity questionnaire (FPQ) and dependent 24-hour recalls (24HRs). We analysed a subset of data (about 197 participants) in the SI.Menu survey carried out in 2016/17 in Slovenia. The participants completed FPQs and 24HRs.We were able to identify four clusters. Two clusters represented participants with more healthy habits, e.g., low intake of animal fats, high breakfast frequency, and high intake of fruits and vegetables. The other two clusters represented participants with less healthy habits, e.g., high intake of animal fats, low breakfast frequency and increased BMI.The four clusters can be well separated by only four variables. This interesting discovery could lead to simplified FFQ questionnaires, which could significantly decrease the participants’ burden and could ensure participant compliance in similar studies. Having big national data set related to nutrition should ease the process of creating sustainable policies that will ultimately benefit agriculture, human health and the environment.
Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network

Intrusion detection system (IDS) plays an important role in network security by discovering and preventing malicious activities. Due to the complex and time-varying network environment, the network intrusion samples are submerged into a large number of normal samples, which leads to insufficient samples for model training and detection results with a high false detection rate. According to the problem of data imbalance, we propose a network intrusion detection algorithm combined hybrid sampling with deep hierarchical network. Firstly, we use the one-side selection (OSS) to reduce the noise samples in majority category, and then increase the minority samples by Synthetic Minority Over-sampling Technique (SMOTE). In this way, a balanced dataset can be established to make the model fully learn the features of minority samples and greatly reduce the model training time. Secondly, we use convolution neural network (CNN) to extract spatial features and Bi-directional long short-term memory (BiLSTM) to extract temporal features, which forms a deep hierarchical network model. The proposed network intrusion detection algorithm was verified by experiments on the NSL-KDD and UNSW-NB15 dataset, and the classification accuracy can achieve 83.58% and 77.16%, respectively.
Launching a User-Generated Content Campaign to Promote PUMA’s Social Contributions

This user-generated content (UGC) campaign is based on a capstone project whose purpose is to predict ideal locations for opening PUMA new stores using three years daily nationally online transactions data. This marketing promotion aims to increase PUMA’s public awareness and reinforce its level of corporate social responsibility in the new store locations. The campaign will take charitable activities into account and share them through stories on social media (Facebook and Instagram).
Characterization of Blood Flow Changes in Normal and Pathological Aortic Dilation from 4D Flow Magnetic Resonance Imaging

Aim: Maximal diameter (Dmax), which is commonly used to diagnose thoracic aortic aneurysm (TAA) was previously shown to be normal in 20-30% of patients who ultimately develop dissection. Besides, inner aortic flow is associated with its wall dynamics. Thus, our aim was to quantify aortic flow changes using 4D flow MRI in the setting of ascending aorta (AA) dilation.Methods: We studied 20 patients with TAA and tricuspid aortic valve (TAA) and 56 healthy controls (30 subjects, 36±9y ≤50 years named YC, 26 subjects, 65±9y >50 years named OC). All underwent 4D flow MRI. After aortic segmentation, regional volume of backward flow (VBF) was extracted in addition to in-cross-section velocity standard deviation (SD) as well as maximal velocity jet angle (Angle) and eccentricity (Ecc). Receiver operating characteristic (ROC) analysis was performed to assess ability of flow indices to characterize dilation.Results: While AA Dmax changed by 1.4 folds between TAA and OC, VBF changed by 6.5 folds, and Ecc, Angle and SD changed by 1.3 to 1.9 folds between the two groups. Moreover VBF varied consistently with age and was able to detect AA dilation with an accuracy of 0.98. Conclusion: 4D flow MRI indices of local aortic flow disorganization, specifically backward flow, were able to accurately characterize dilation.
A robust, efficient, and balanced parallel algorithm for finding connected components

Finding connected components in an undirected graph has many practical applications. For example in a graph representing a social network, a connected component represents a group of related individuals with common interest. Also, finding connected components forms the basis for other clustering algorithms. In this paper, we will present a parallel algorithm which uses the well known sequential algorithm as the basis for finding connected components in an undirected graph. The algorithm can be adopted to run on a single computer with multiple cores or MapReduce. It is robust in the sense that it honors memory limits. This is important in today’s containerized environments. It balances the workload even in the presence of data skew. For the best known algorithm running in MapReduce, the number of iterations is the square of the logarithmic function of the number of vertices in the graph. For our algorithm, we will prove that the upper bound on the number of iterations is a logarithmic function of the maximum size of a connected component. In each iteration, the amount of data read from or written to a file system is bounded by four times the number of edges in the graph.
Heterotropia Diagnosis with Smartphone using Machine Learning and Computer Vision

In this paper, a deviation-based algorithm is proposed which exploits the power of TensorFlow, OpenCV, and machine learning to diagnose four forms of heterotropia - esotropia, exotropia, hypertropia and hypotropia. The mechanism developed for lateral, vertical, and angular deviation has been tested for K-Nearest-Neighbors (KNN), Support Vector Machine (SVM), and a trained neural network, based on 6 features. Finally, the accuracy, specificity, and sensitivity of the 2 MLC and convolutional neural network (CNN) was tested in a study of 337 users with and without heterotropia. Eyeris, an Android based application, has been developed as a proof of concept for this paper. The results yielded highest accuracy for SVM-based classification
An Access Control Mechanism for Pre-trained ML Models in Mobile Apps

Utilizing machine learning inference in mobile platforms have been made realistic since recent smartphones may have high-performance processors and hardware accelerators of signal and image processing. However, it could bring issues. One of the most significant issues is that any machine learning inferences can be unlimitedly applied to personal data which once the application got permitted to. Furthermore, it could be quite hard to understand the behavior of a ‘pre-trained’ machine learning model and the behavior of an installed application can be transparently modified afterword by replacing a used model. This research aims to implement access control against such functionalities of machine learning inference for mobile platforms. In this paper, we describe how to verify used pre-trained models and how to implement permission control at Android Neural Networks API for Android as a target platform. And we also show an evaluation result with a prototype implementation.
Investigate the Impact of Colour to Grayscale Conversion on Sound Recovery via Visual Microphone

Visual microphone is one of the remote sound acquisition methods utilizing the subtle vibrations caused by sound on an object to partially recover the sound that causes them via a video footage of the object. Although good quality sounds have been successfully recovered using the visual microphone technique, no study has considered the impact of the chosen colour-to-grayscale conversion methods on the sound recovery. Studies in computer vision areas such as image recognition found that the method used to convert colour to grayscale had significant influence on the performance and results. Thus, this work was performed to evaluate the impact of different colour to grayscale conversions on sound recovery using the visual microphone technique. An intelligibility metric was used to evaluate the sounds recovered using the different conversions. The results showed that the choice of colour to grayscale conversion did have an impact on sound recovery via the visual microphone technique. The conversion based on weighted averaged of pure colour channels (red, green and blue), e.g., Intensity and Luminance, and perceptually uniform grayscale representation used in International Commission on Illumination (CIE) 1976 L*a*b*, performed better when compared to other methods.
Isosbestic Point in Optical Mapping; Theoretical and Experimental Determination With Di-4-ANBDQPQ Transmembrane Voltage Sensitive Dye

Optical mapping methods utilize fluorescence dyes to measure dynamic response of cardiac tissue such as changes in transmembrane potential (Vm). For the commonly used Vm sensitive dyes, a dye absorption and emission spectra shift as Vm changes. Signals relevant to Vm are calculated as a relative fluorescence change with respect to the fluorescence baseline. The amplitude of the change depends on the long-pass (LP) filter cut-on wavelength, placed on the sensor side, and the excitation wavelength. An excitation wavelength near the absorption peak, termed the isosbestic point, results in minimal absorption coefficient change as absorption spectra shifts. Consequentially the fluorescence intensity virtually does not change, when fluorescence across the entire emission spectra is measured, irrelevant of Vm changes. In this study we experimentally determined the isosbestic point for a near infrared dye Di-4-ANBDQPQ. We then present a theoretical study examining the dye linear or non-linear response as the fractional fluorescence change of Vm change, due to emission spectra shift and amplitude change, over a range of excitation wavelengths and LP filters. Linear "optical" response is important to quantify certain aspects of cardiac dynamics such as the action potential (AP) shape and duration, especially when studying drug effects and dynamical substrates for arrhythmia development.