Miracle-dz/UniFAR-FTUs huggingface dataset:
Research of Motif-Based Similarity for Link Prediction Problem

The link prediction problem in the network concerns to predict the existence of links between node pairs, which is a research hotspot in different scenarios with network applications. Methods of predicting links based on network topology and structures provide a number of measurements to reveal the underlying relationship between two nodes. In this paper, a motif-based similarity index for link prediction is proposed to calculate the similarity score of two nodes concerning their local environment, which takes advantage of existing similarity definitions and the motifs. This motif-based similarity can be generalized to more complicated cases by considering different motifs and keeps the same level of computational complexity with the existing indexes. Experimental results on 9 public benchmark datasets and 1 randomly generated dataset show the effectiveness of our proposed index, and accuracies on several datasets are significantly improved. The performance of motif-based similarity suggests that considering typical motifs on networks could improve the precisions of link prediction tasks, and exploring specific structure characteristics on networks will point out an important and effective direction for more research with network methods applied.
Toxic Speech Detection using Traditional Machine Learning Models and BERT and fastText Embedding with Deep Neural Networks

The introduction of social media brought about a revolution in the world of digitalization and communication. These platforms were initially developed with a purpose of connecting people across the global boundaries while allowing them to express their views and opinions and learn from others’ ideas. With the incoming of the pandemic, the usage of these sites has risen significantly be it by the businesses, educational institutions, students or general public. The increasing ubiquity of social media platforms like Twitter and Facebook has been an issue of major concern since a long time. Along with providing a way for enhanced communication, these platforms also allow internet users to voice their opinions which get circulated among the masses within seconds. Moreover, given the different backgrounds, believes, ethnicity and cultures that the users on these platforms come from, many of them tend to use mean, aggressive and hateful content during their discussions with people not hailing from a background similar to theirs. The amount of hate speech and offensive content has been increasing exponentially. Terms like "profane", "hate", and "offensive" are used interchangeably, and hence these have been classified under a broader category of "Toxic" content. A major part of our dataset focuses on conversations prevailing among the youth. After the preprocessing of this dataset using NLP and embeddings (Bert and fastText), a bunch of Machine Learning (LR, SVM, DT, RF, XGBoost) and Deep Learning algorithms (CNN, MLP, LSTM) have been performed, with CNN giving the best results.
Plant Pathology Disease Detection in Apple Leaves Using Deep Convolutional Neural Networks : Apple Leaves Disease Detection using EfficientNet and DenseNet

Over the years, many events of plant diseases have inflected suffering on untold millions of people worldwide by causing an estimated annual yield loss of 14% globally. Plant pathology is the science of plant diseases that attempts to improve the chances for survival of plants under unfavorable environmental conditions and parasitic microorganisms that cause disease. Temperature, pH, humidity and moisture are environmental factors contributing to development of plant diseases. Misdiagnosis can lead to misuse of chemicals causing economic loss, environmental imbalance and pollution and emergence of resistant pathogen strains. Current disease diagnosis is time consuming, expensive and based on human scouting. Automatic disease segmentation and diagnosis from plant leaf images can be reasonably useful than the existing one. Automatic plant disease detection involves image acquisition, pre-processing and segmentation, followed by augmentation, feature extraction and classification using models. This project uses Deep Convolutional Neural Networks models namely EfficientNet and DenseNet to detect Apple plant diseases from images of apple plant leaves and accurately classify them into 4 classes. The categories include "healthy", "scab", "rust and "multiple diseases". In this project, the apple leaf disease dataset is improved using data augmentation and image annotation techniques, namely Canny Edge Detection, Blurring and Flipping. Based on augmented dataset, models using EfficientNetB7 and DenseNet are proposed providing accuracy of 99.8% and 99.75% respectively and overcoming known shortcomings of convolutional neural networks.
Outage-Constrained Robust Multigroup Multicast Beamforming for Satellite-Based Internet of Things Coexisting With Terrestrial Networks

Satellite-based Internet of Things (IoT) is recognized as a cost-effective approach for global access. In this article, we aim at improving the spectrum efficiency of satellite systems to serve a huge number of IoT devices. To this end, we present a cognitive satellite-terrestrial framework, where a multibeam satellite system with full frequency reuse shares the spectrum with terrestrial networks based on the underlay paradigm. Considering DVB-S2X recommendations, geometric configurations, and channel characteristics, we investigate a robust multigroup multicast beamforming design for the satellite-based IoT coexisting with terrestrial networks in the presence of a phase error on channel state information, and characterize the achievable rate region under the outage probability constraint for the terminal and the power consumption constraint for the satellite. Based on the concept of rate profile, an associated optimization problem is formulated to design robust beamformers and determine the Pareto boundary of the region. To solve the intractable problem, we propose a two-level iterative algorithm on the basis of joint bisection search and penalty function enabled nonsmooth optimization. In particular, we develop a Bernstein-type inequality aided method and a large deviation inequality aided method to obtain a tractable and conservative approximation for the probabilistic constraint, respectively. Numerical results are provided to confirm the validity and superiority of our proposed scheme over the existing approaches and reveal the impact of key parameters on the achievable system performance.
ConNet: Deep Semi-Supervised Anomaly Detection Based on Sparse Positive Samples

Existing semi-supervised anomaly detection methods usually use a large amount of labeled normal data for training, which have the problem of high labeling costs. Only a few semi-supervised methods utilize unlabeled data and a few labeled anomalies to train models. However, these kinds of methods usually encounter two problems: (i) since anomalies usually have different behavior patterns or the internal mechanisms that produce anomalies are complex and diverse, a few labeled anomalies cannot cover all anomaly types; and (ii) the amount of unlabeled data in the training set is substantially greater than the amount of labeled data, which leads to that unlabeled data with contamination often dominates the training process. To solve these two problems, we propose the semi-supervised anomaly detection method named ConNet and a new loss function named concentration loss. Specifically, ConNet consists of two stages. Firstly, we obtain the prior anomaly score of unlabeled data via prior estimation module and attach the prior anomaly score to unlabeled data as the training weight. Then, an anomaly scoring network is training to assign anomaly scores to data instances, which can ensure that the anomaly scores of anomalies significantly deviate from those of normal instances. We have conducted experiments on thirteen real-world data sets and tested the performance of our method in terms of detection accuracy, utilization efficiency of labeled data, and robustness to different contamination rates. The experimental results show that the performance of our method is significantly better than those of the state-of-the-art anomaly detection methods.
How Startup Stance IT Leadership: Case Study Business Incubator Program Universitas Indonesia

There is a phenomenon of successful and failed startup build by college students. While universities trying to support their innovation with business incubator program. The incubate program facilitates, accompany, and prepare their new business model to be able to compete in the market. With the demographic of students proposed as a tenant in the incubator program, the report found they are the generation that savvy with technology. Four quadrants used to understand the respective roles of IT Leadership which are support, factory, turnaround, and strategic. With that viewpoint, this initial research aimed to find out the IT leadership perception of the tenant. Survey conducted on the alumni of the incubator program. The open-ended question included in the questionnaire, profile of the tenant, news, and website added as our analytical material to get more insight. The results of this study indicate the tendency of startup business patterns based on questionnaire indicators, namely the turnaround quadrant with the total value of 2.286, Factory with value 1.714, strategic 1.429 and support 0.786. These results indicate that the role of IT in startup business incubators is used to search for new potential business models and innovation to develop new products or services. While most of the respondents have a perception of IT leadership for long term strategic vision.
Employee Turnover Prediction: The impact of employee event features on interpretable machine learning methods

The purpose of this study is to study the impact of employee event features on classifier performance to detect employee likelihood to turnover meanwhile maintaining the interpretability of classifiers for benefits of retention interventions development. In order to maintain feature importance four machine learning methods has been selected on this study, consist of (1) Logistic Regressions Method; 2) Random Forest Method; (3) Gradient Boosting Tree Method and (4) Extreme gradient boosting method. The result show that employee event features significantly improve classifiers performance. The extreme gradient boosting method has the best performance with 98.03% Accuracy, 90.79% F1 Score, 97.18% Precision and 85.19% recall. Top three feature importance to predict employee turnover are Merit increase compare with market, Ratio of annual leave hours on average - last month and Ratio of annual leave hours on average - last 2 months respectively.
The Analysis of Traffic Balancing for Data Centers Serving Requests of LEO Mobile Satellite Systems

One of the features of low earth orbit (LEO) mobile satellite systems is to deliver information to the data analytic centers. Many types of alarm information are collected by smart meters and video cameras that are placed where wired telecommunication is not possible due to the technical or economic meanings. It is supposed that requests for servicing are arriving to the data center following the Poisson model and each requiring exponentially distributed computing time. A data center consists of some number of computing units. Each unit processes the requests according to the Processor Sharing (PS) service discipline and cannot accept at the same time more than fixed number of requests. The mathematical model of load balancing between computing units is constructed. Two scenarios are considered: “static” when requests are routed to computing units according to the prescribed probabilities and “greedy” when requests are routed to the least loaded computing unit. Using the model, the main performance measures of interest are given with help of values of probabilities of model's stationary states. The recursive algorithm of performance measures estimation is suggested. The model and derived algorithms can be used for study the scenarios of traffic balancing at the data analytic centers.
FPGA Architecture To Enhance Hardware Acceleration for Machine Learning Applications

Many algorithms have been developed in the field of Machine learning and its sub-fields such as neural networks, Deep learning and so on, for applications such as pattern classification, image and video processing, statistical data mining and so forth. These algorithms perform such tasks with remarkable accuracy. However, when implemented in traditional processor core software, many of these algorithms get choked when the size of the network or computational demand of the algorithm scales up. FPGAs and ASICs offer higher computation capability, throughput and bandwidth. Features such as massive parallel processing, high performance and reliability, are strong arguments for FPGAs for Deep Neural Network applications. FPGAs offer reconfigurable, flexible architectures where even the most popular GPUs fall short. This paper proposes a robust, re-configurable architecture for deploying Machine learning algorithms and presents its advantages by implementing a Neural Network in an FPGA and comparing its results with an implementation in a Raspberry Pi.
Research on Flat Design Based on UI Design

This article mainly analyzes the popular trend of flat and simple design in UI design, and understands how flat design can contribute to creating a better user experience by analyzing the principles of flat design aesthetics. This article expounds the author’s understanding of the flat design style in terms of the connotation, advantage and disadvantages, design methods and the ultimate goal of UI design. Nowadays, almost all UI design works present a simple and flat design style. This frenzy has also gradually affected other design fields. This article will discuss the concepts, roots, design principles and techniques of flat design, and explore its future development trends.
Electrochemical Impedance Spectroscopy With Practical Rest-Times for Battery Management Applications

The State of Charge (SoC), State of Health (SoH), and State of available Power (SoaP) of Lithium-Ion Batteries (LiB) are critical quantities which cannot be measured but must be estimated by Battery Management Systems (BMS) instead. A technique known as Electrochemical Impedance Spectroscopy (EIS) provides a non-destructive way of measuring battery impedance, offering detailed insight into the battery state of operation. Several challenges must be solved in to utilize EIS as part of the BMS, including the defining of operating conditions at which to perform the sensitive EIS measurement. In laboratory conditions, several hours of rest are used to ensure a stable response of the battery, but such rest times are impractical for BMS applications. This paper proposes a methodology combining drift compensation and a Voigt-circuit used as a filter to obtain valid EIS data with short rest times under different operating conditions. Extensive tests were conducted on lithium nickel manganese cobalt (NMC) batteries to evaluate the methodology and show how the different operating conditions impact the rest time required for valid EIS measurement. The results show that rest times between 5 and 30 minutes can be used to obtain useful EIS data for a wide range of operating conditions.
Detection of Signals with a Random Moment of Occurrence Using the Cumulative Sum Algorithm

A method for detecting signals with a random moment of occurrence based on the algorithm of cumulative sums with a reflecting screen under the influence of non-Gaussian noise is presented. An example of detecting a signal exposed to non-Gaussian quasi-deterministic additive noise is used for obtaining the dependences of the probability of false detection on the value of the signal-to-noise ratio for different values of the detection threshold. An increase in the detection threshold value, as well as the value of the signal-to-noise ratio, leads to a decrease in the probability of false detection. It is shown that the case of coordinated noise has the highest probability of correct detection. The algorithm for detecting a signal against the background of pulse noise and fluctuating non-Gaussian noise is analyzed and curves for probability of correct detection are obtained. It is found that the mismatch of the cumulative sum algorithm and the real density of probability distribution of noise leads to a decrease in the probability of correct detection. It is shown that the cumulative sum algorithm allows not only to solve the problem of detecting a signal in real time, but is also simple enough to be used when solving some practical problems.
User-Pair Scheduling and Mode Selection in Asymmetric Full-Duplex Systems With Limited Feedback: Algorithm and Scaling Laws

Determining which users to simultaneously schedule on the uplink and the downlink in a full-duplex (FD) system is crucial to control the inter-user interference between them and achieve a high spectral efficiency. The user-pair scheduling algorithm and the efficacy of FD is, thus, closely linked to the availability of channel state information and the feedback scheme that conveys it to the base station (BS). We consider a reduced feedback scheme in which a user that is scheduled feeds back only a limited number of quantized inter-user interferences that are below a pre-specified threshold. For it, we propose a novel user-pair scheduling and mode selection algorithm (UPSMA). We analyze the uplink and downlink spectral efficiencies of UPSMA for two channel models that highlight the different influences of small-scale fading and large-scale shadowing. We derive insightful asymptotic scaling laws that quantify the dependence of the threshold and the uplink and downlink spectral efficiencies on the number of users. UPSMA with limited feedback achieves a higher sum spectral efficiency than a half-duplex system and conventional FD resource allocation algorithms. Its performance is close to the exhaustive search algorithm in which the BS knows all the inter-user interferences.
Voice Activity Detection in the Wild: A Data-Driven Approach Using Teacher-Student Training

Voice activity detection is an essential pre-processing component for speech-related tasks such as automatic speech recognition (ASR). Traditional supervised VAD systems obtain frame-level labels from an ASR pipeline by using, e.g., a Hidden Markov model. These ASR models are commonly trained on clean and fully transcribed data, limiting VAD systems to be trained on clean or synthetically noised datasets. Therefore, a major challenge for supervised VAD systems is their generalization towards noisy, real-world data. This work proposes a data-driven teacher-student approach for VAD, which utilizes vast and unconstrained audio data for training. Unlike previous approaches, only weak labels during teacher training are required, enabling the utilization of any real-world, potentially noisy dataset. Our approach firstly trains a teacher model on a source dataset (Audioset) using clip-level supervision. After training, the teacher provides frame-level guidance to a student model on an unlabeled, target dataset. A multitude of student models trained on mid- to large-sized datasets are investigated (Audioset, Voxceleb, NIST SRE). Our approach is then respectively evaluated on clean, artificially noised, and real-world data. We observe significant performance gains in artificially noised and real-world scenarios. Lastly, we compare our approach against other unsupervised and supervised VAD methods, demonstrating our method's superiority.
Distributed Tracking Stopping Time Using Average Consensus

We study the problem of tracking stopping time in a distributed circumstance. More specially, a Gaussian random walk with positive drift is observed simultaneously by multiple detectors in a noisy fashion. The goal is to estimate the first-passage time when the random walk reaches a predefined level. After characterizing the optimal estimator with mean absolute p-moment, a distributed tracking stopping time estimation algorithm is proposed, where each detector only exchanges observation with their neighbors based on the average consensus scheme. Then, we provide theoretical performance bound and testify the asymptotic optimality of proposed distributed stopping time estimator. Finally, performance analysis is depicted by the numerical simulation.
Dynamic Multicast Routing Management for Robust Wide Area Power Grid Communication

High-volume synchrophasor data generated by the wide area measurement system (WAMS) present new challenges for the design of scalable and robust communication architectures suitable for the large-scale deployment in power grid monitoring and control applications. Features of information and communication technology (ICT) can be harnessed to devise a more flexible infrastructure than the conventional unicast-based hierarchical architecture. Multicast has been suggested as a potential solution for scalability issues in the current WAMS architecture. This article proposes a decentralized dynamic multicast routing management algorithm that manages network traffic efficiently, improves scalability, and makes the system resilient to component failures. The algorithm models the creation of alternate links as a stochastic process using the random-graph theory and manages connections between end hosts at the session layer. This protocol can be implemented using existing ICT protocols at a minimal cost. Numerical simulations and study of frequency control in an IEEE 39-bus system testbed show that the proposed algorithm improves the topological efficiency and resilience of the network.
Multi-Energy Flow Calculation Model and Solution Method for Regional Integrated Energy System

The regional integrated energy system, as an important physical carrier of the energy Internet, means great significance for improving regional comprehensive energy efficiency, ensuring energy safety, and achieving substantial consumption of renewable energy. This paper proposes an expanded load flow calculation method for a multi-energy flow supply network containing electricity, gas and heat. First, the steady-state mathematical model is established for power, heat, and natural gas networks. Then, based on the general energy bus model, mathematical expression of the energy coupling link is established. On this basis, the Newton-Raphson method is taken for decoupling calculation of the multi-energy load flow model. Finally, effectiveness of the method proposed herein is verified by practical examples.
Light-Controlled Gap-Type TFT Used for Large-Area Under-Screen Fingerprint Sensor

In the fierce market competition of different biometric methods in smartphones, where consumers are pursuing a high screen ratio, optical fingerprint recognition under OLED screen is a well-established approach. However, using thin-film transistor (TFT) technology to make large-area optical sensors generally has the issue of low sensitivity, which will make the signal difficult to be read out. In this paper, we propose to use gap-type TFT with high photosensitivity current and process compatible with display panels as the sensing device to build the sensing array. The pixel circuit can be simplified to structure containing only of two thin-film transistors (TFTs). Using this gap-type TFT sensing array under OLED panel for fingerprint recognition is proven to be effective by the clear images captured with the readout system.