Miracle-dz/UniFAR-FTUs huggingface dataset:
Bot Believability Assessment: A Novel Protocol & Analysis of Judge Expertise

For video game designers, being able to provide both interesting and human-like opponents is a definite benefit to the game's entertainment value. The development of such believable virtual players also known as Non-Player Characters or bots remains a challenge which has kept the research community busy for many years. However, evaluation methods vary widely which can make systems difficult to compare. The BotPrize competition has provided some highly regarded assessment methods for comparing bots' believability in a first person shooter game. It involves humans judging virtual agents competing for the most believable bot title. In this paper, we describe a system allowing us to partly automate such a competition, a novel evaluation protocol based on an early version of the BotPrize, and an analysis of the data we collected regarding human judges during a national event. We observed that the best judges were those who play video games the most often, especially games involving combat, and are used to playing against virtual players, strangers and physically present players. This result is a starting point for the design of a new generic and rigorous protocol for the evaluation of bots' believability in first person shooter games.
Unobtrusive Continuous Monitoring of Fetal Cardiac Electrophysiology in the Home Setting

The current home fetal heart rate (fHR) measurement devices and clinical cardiotocography (CTG) use ultrasound Doppler detectors that are highly position and movement dependent making it difficult to capture the fHR, often causing unnecessary alarm and concerns. The U.S. Food and Drug Administration (FDA) has also been concerned about the unknown rate of repeat and prolonged use of ultrasound detectors. Fetal electrocardiogram (fECG) and HR monitoring devices based on bio-potential acquisition have been developed; however, they are bulky and intrusive, thus not widely accepted. Here, we present innovative non-contact electrode ECG sensors on a small unobtrusive patch which could be attached to the abdomen, or inside garment, incorporated with Bluetooth low energy (BLE) wireless communication, to transfer the acquired data to a smartphone. An Android app was developed to extract the mixed maternal/fetal ECG (f/mECG) signals. In this paper, we demonstrate a simple and robust scheme to provide accurate 24/7 monitoring of f/mHR, with the potential to expand to f/mECG monitoring in the home setting.
An Autonomous Mobile Robot Architecture for Outdoor Competitions

Outdoor autonomous navigation and obstacle avoidance are common challenges in robotic competitions, and solving them involves many different sets of skills. In this work, we present a modular architecture for controlling a robot in such tasks. It uses unscented Kalman filter and extended Kalman filter to fuse Global Positioning System, inertial measurement unit and odometry data; a model reference adaptive velocity controller, a proportional controller, and a deformable virtual zone algorithm for reactive obstacle avoidance. We also developed a computer vision target tracking algorithm. In the end, we show the importance of having quality sensors to work with.
A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network

Software Defined Network (SDN) is a revolutionary idea to realize software-driven network with the separation of control and data planes. In essence, SDN addresses the problems faced by the traditional network architecture; however, it may as well expose the network to new attacks. Among other attacks, distributed denial of service (DDoS) attacks are hard to contain in such software-based networks. Existing DDoS mitigation techniques either lack in performance or jeopardize the accuracy of the attack detection. To fill the voids, we propose in this paper a machine learning-based DDoS mitigation technique for SDN. First, we create a model for DDoS detection in SDN using NSL-KDD dataset and then after training the model on this dataset, we use real DDoS attacks to assess our proposed model. Obtained results show that the proposed technique equates favorably to the current techniques with increased performance and accuracy.
A Study of the Reading Behavior of University Students

Reading is part of our daily life. For university students, reading is extremely important for improving their professional knowledge and cultivating their personal hobbies. This work studied the relationship between the university students’ academic ranking and their reading behavior as well as reading quantities. By analyzing the data of library loan records and the examination performances of all the students who majored in software engineering in Chang’an University within their four-year-long undergraduate study, we set the reading quantities of professional books and other (non-professional) books as two essential factors, and established a model based on multiple linear regression machine learning algorithm, which successfully predicted the students’ academic ranking from their reading quantities. K-means was also used to perform simple clustering on different types of students.
Class Signature-Constrained Background- Suppressed Approach to Band Selection for Classification of Hyperspectral Images

In hyperspectral image classification (HSIC), background (BKG) is generally excluded from consideration due to the fact that obtaining complete knowledge of BKG is nearly impossible in reality. Unfortunately, BKG has significant impact on classification and band selection (BS). This paper investigates both issues and presents a novel approach called class signature-constrained BKG suppression (CSCBS) approach to BS for HSIC, where class signatures can be obtained either by a priori or a posteriori knowledge or training samples, and BKG suppression can be accomplished by taking the inverse of the sample correlation matrix R. Its idea takes advantage of the concept of the linearly constrained minimum variance (LCMV) developed from adaptive beamforming by constraining class signatures of interest while minimizing the effect caused by the unknown BKG so as to enhance the classification performance. There are two immediate applications of CSCBS. One is its application to HSIC, in which it becomes a CSCBS classifier. The other is its use of the LCMV-suppressed BKG as a measure to derive the band prioritization (BP) criteria and BS. Experimental results demonstrate that generally CSCBS does not need the full-band set for HSIC since a partial band subset selected by CSCBS-BP/BS can actually improve the classification results using full-band information.
Cross-Layer Combining of Truncated ARQ with Adaptive Modulation and Coding in Full Duplex Systems

In this paper, we design a bidirectional communication system employing adaptive modulation and coding (AMC) at the physical layer combined with truncated ARQ at the data link layer. The AMC in this system is made more robust with the help of a maximum a posteriori (MAP) optimal estimation with pilot symbol assisted modulation (PSAM) channel prediction technique. This prediction technique takes into account the latency in receiving the channel state information (CSI). We then considered this system operating in in-band full-duplex (FD) configuration and compared it with its half-duplex (HD) counterpart. The performance of the two configurations was compared under various channel conditions in terms of average spectral efficiency (SE). Numerical results obtained show that, even with residual self-interference (SI) signal in the received signal, the system in FD configuration outperforms that in HD configuration. In fact, when the SI signal is assumed to be completely canceled and the channel prediction is imperfect, the FD average SE more than doubles the HD average SE at low average signal-to-noise ratio (SNR).
Short-Attention Mechanism for Generative Dialogue System

In recent years, generative dialogue has become the hottest topic in the field of Nature Language Process(NLP). Among the many suggested approaches, the Sequence-tosequence network framework, a variant of traditional Recurrent Neural Network(RNN), has attracted the attention of researchers because of its outstanding performance on many tasks. This model consists of an encoder which encoders the input sequence to a vector and a decoder that decodes the vector to the output sequence. Then attention was applied to the model, that is, the model assigns different weights to different parts to compute vector during decoding process. This end-to-end method enhances the ability to generate natural answers in the human-computer conversation process, while also increases its calculation costs. To solve the problem, we propose a novel short-attention mechanism, in which the original sequence is compressed to a shorter sequence before calculating weight and vector. We apply shortattention to dialogue systems tasks and the experimental results show that short-attention can shorten the computation time by about 20% compared to attention.
Performance Evaluation of Application Layer Protocols for the Internet-of-Things

Over the last years, the number of Internet of Things (IoT) applications has been increased. To provide a suitable machine-to-machine communication across the applications, specific application layer protocols are used. In contrast to traditional protocols like HTTP, IoT protocols are designed to transfer information through unreliable, low bandwidth networks. This paper compares the performance of the protocols Advanced Message Queuing Protocol (AMQP), Message Queue Telemetry Transport (MQTT) and Extensible Messaging and Presence Protocol (XMPP) in the context of a monitoring business application. For that purpose, a three-layered test-bed is designed with a variable latency and Packet-Loss-Rate (PLR). MQTT performs best regarding the characteristics bandwidth usage, reliability, latency and throughput. AMQP performs worse than MQTT comparing the bandwidth usage and throughput. In contrast, XMPP has the worst values in all categories.
Leveraging MAC Preambles for an Efficient Link Estimation

Estimating the quality of a link is a key primitive in WSNs, as upper layers use this piece of information in making performance-critical decisions. State-of-the-art estimators extract a single sample of the link state per packet. Thus, improving the accuracy through multiple samples requires multiple packets, resulting in a significant energy and traffic overhead and delay.To address this issue, this paper proposes Rep, a novel sampling scheme able to extract the link quality from the packet repetitions of low-power preamble sampling MACs. The experiments show that Rep reduces the energy and traffic used for link estimation by one order of magnitude, and increases the speed of the process by one order of magnitude, while maintaining state-of-the-art accuracy.
MCA-Based Clutter Reduction From Migrated GPR Data of Shallowly Buried Point Target

Reducing clutter is an important process in terms of improving the detection of targets when using ground penetrating radar (GPR), especially in the case of overlapping target responses and clutter. We present an alternative scheme to remove clutter from monostatic GPR data by combining migration and morphology component analysis (MCA). A simple phase shift migration is first performed on received B-scan data. In the migrated B-scan, clutter, and useful target responses exhibit different morphologies. The clutter appears to be horizontal stripes and the hyperbolic reflection from the target is refocused into an enhanced point-like shape. Subsequently, an overcomplete dictionary is constructed to discriminatively and sparsely represent each morphology based on the simulated GPR data. In particular, the 2-D undecimated discrete wavelet transform is used for the target component, and the 2-D curvelet atoms are used for the clutter. Then, the MCA technique is exploited to sparsely separate the two different components, which leads to the clutter reduction. The sparse representation of 2-D finite-difference-time-domain-based synthetic GPR data illustrates the validity of the overcomplete dictionary. In this paper, the proposed clutter removal method is tested on a synthetic model of point-like targets and real data from buried landmines. The migration concentrates the refracted signals from landmines to their origins, and the shapes of the landmines can be clearly defined. The results of the clutter reduction suggest that the proposed method is more effective than the method implemented directly on the B-scan data and the traditional mean subtraction.
Employing Genetic Algorithm and Particle Filtering as an Alternative for Indoor Device Positioning

Radio signals may contribute to seamless interactions with physical objects providing means to guide users from their position to a particular object within a room or store for instance. To achieve such a goal, a mechanism is needed to allow users to identify and locate objects of interest. Trilateration, fingerprinting and particle filter are usually employed as mechanisms for position estimation in indoor environments. This paper explores the the use of Genetic Algorithms (GA) combined with Particle Filter (PF) mechanism as an alternative to estimate indoor object position. The proposed scheme, named EPF (Evolutionary Particle Filter) has been compared to particle filter and trilateration. Simulation results show that the proposed EPF improves positioning accuracy by 1.5 cm (10%) and 30 cm (300%) over particle filter and trilateration, respectively.
Evaluating Layer-Wise Relevance Propagation Explainability Maps for Artificial Neural Networks

Layer-wise relevance propagation (LRP) heatmaps aim to provide graphical explanation for decisions of a classifier. This could be of great benefit to scientists for trusting complex black-box models and getting insights from their data. The LRP heatmaps tested on benchmark datasets are reported to correlate significantly with interpretable image features. In this work, we investigate these claims and propose to refine them.
An Experimental Comparison of Text Classification Techniques

Text classification is the task of labeling text data from a predetermined set of thematic labels. It has become of increasing importance in recent years as we generate large volumes of data and require the ability to search through these vast datasets with flexible queries. However, manually labeling text data is an extremely tedious task that is prone to human error. Thus, text classification has become a key focus of machine learning research, with the goal of producing models that are more efficient and accurate than traditional methods. The objective of this work is to rigorously compare the performance of current text classification techniques, from standard SVM-based, statistical and multilayer perceptron (MLP) models to recently enhanced deep learning models such as convolutional neural networks and their fusion with graph theory. Extensive numerical experiments on three major text classification datasets (Rotten Tomatoes Sentence Polarity, 20 Newsgroups and Reuters Corpus Volume 1) revealed two results. First, graph convolutional neural networks perform with greater or similar test accuracy when compared to standard convolutional neural networks, SVM-based models and statistical baseline models. Second, and more surprisingly, simpler MLP models still outperform recent deep learning techniques despite having fewer parameters. This implies that either benchmark datasets like RCV1 containing more than 420,000 documents from 52 classes are not large enough or the representation of text data as tf-idf document vectors is not expressive enough.
Towards Large Scale Packet Capture and Network Flow Analysis on Hadoop

Network traffic continues to grow yearly at a compounded rate. However, network traffic is still being analyzed on vertically scaled machines that do not scale as well as distributed computing platforms. Hadoop's horizontally scalable ecosystem provides a better environment for processing these network captures stored in packet capture (PCAP) files. This paper proposes a framework called hcap for analyzing PCAPs on Hadoop inspired by the Rseaux IP Europens' (RIPE's) existing hadoop-pcap library but built completely from the ground up. The hcap framework improves several aspects of the hadoop-pcap library, namely protocol, error, and log handling. Results show that, while other methods still outperform hcap, it not only performs better than hadoop-pcap by 15% in scan queries and 18% in join queries, but it's more tolerant to broken PCAP entries which reduces preprocessing time and data loss, while also speeding up the conversion process used in other methods by 85%.
Enhanced Test for Reconfigurable Hardware Systems Based on Sequential Logic

This paper revolves around a test of a reconfigurable hardware system based on sequential logic (abbreviated, RHSS). The latter is a sequential hardware device that allows to change the hardware resources at run-time in order to modify the system functions and therefore to dynamically adapt the system to its environment. In order to reduce time and effort for finding test patterns and analyzing a circuit for its faults, we endeavor to show the way inter-circuits fault collapsing for RHS based on combinational logic remains valid and can be extended to RHSS. The application of the inter-circuits technique reduces the number of faults dealt with by eliminating redundant faults existing between circuits composing the RHSS. For a scalable RHSS, it is often impractical to reach an overall fault coverage. We propose two test techniques that target a minimal necessary set of faults while ensuring an acceptable fault coverage that answers the manufacturer requirements. For so doing, we promote a guiding method for rating potential faults in terms of their occurrence and severity where a fault is weighted according to how serious its consequence and how frequent it can occur. The proposed approach is implemented in a new version of the original TnTest tool.
Weighted Res-UNet for High-Quality Retina Vessel Segmentation

Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. Recently, deep learning based retinal vessel segmentation methods have reached the state-of-the-art performance. Due to the extreme variations in the morphology of the vessels against the noisy background, these methods still have issues of dealing with small thin vessels, low discriminative ability at the optic disk area, etc. In this paper, we proposed a U-Net-like model with the weighted attention mechanism and the skip connection scheme for addressing these issues. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed methods.
Optimization Design of High-Power High-Frequency Transformer Based on Multi-Objective Genetic Algorithm

With the increase of the transformer capacity and converter switching frequency, the transformer efficiency, stray parameters, insulation and heat dissipation should be considered when designing a transformer. Based on the transformer losses and stray parameters calculations, this paper focuses on the transformer optimization considering the transformer losses, insulation and leakage under constrained conditions using NSGA-II algorithm. A 220V/3.52kV, 3.52kW/20kHz transformer prototype was built and the experimental results verified the correctness of the calculation and the validity of the proposed optimization method.
A Large-Scale Analysis of Facebook’s User-Base and User Engagement Growth

Understanding the evolution of the user base as well as the user engagement of online services is critical not only for the service operators but also for customers, investors, and users. While we can find research works addressing this issue in online services, such as Twitter, MySpace, or Google+, such detailed analysis is missing for Facebook, which is currently the largest online social network. This paper presents the first detailed study on the demographic and geographic composition and evolution of the user base and user engagement in Facebook over a period of three years. To this end, we have implemented a measurement methodology that leverages the marketing API of Facebook to retrieve actual information about the number of total users and the number of daily active users across 230 countries and age groups ranging between 13 and 65+. The conducted analysis reveals that Facebook is still growing and geographically expanding. Moreover, the growth pattern is heterogeneous across age groups, genders, and geographical regions. In particular, from a demography perspective, Facebook shows the lowest growth pattern among adolescents. Gender-based analysis showed that growth among men is still higher than the growth in women. Our geographical analysis reveals that while Facebook growth is slower in western countries, it has the fastest growth in the developing countries mainly located in Africa and Central Asia; analyzing the penetration of these countries also shows that these countries are at earlier stages of Facebook penetration. Leveraging external socioeconomic datasets, we also showed that this heterogeneous growth can be characterized by indicators, such as availability and access to Internet, Facebook popularity, and factors related with population growth and gender inequality.
Forecasting of the Urban Area State Using Convolutional Neural Networks

Active development of modern cities requires not only efficient monitoring systems but furthermore forecasting systems that can predict future state of the urban area with high accuracy. In this work we present a method for urban area prediction based on geospatial activity of users in social network. One of the most popular social networks, Instagram, was taken as a source for spatial data and two large cities with different peculiarities of online activity-New York City, USA, and Saint Petersburg, Russia - were taken as target cities. We propose three different deep learning architectures that are able to solve a target problem and show that convolutional neural network based on three-dimensional convolution layers provides the best results with accuracy of 99%.
Teachers' Practices in Technology Integrated Classrooms in the Indian Context

This study attempted to study the use of technological resources in the Indian context by studying the use of Interactive Whiteboard by teachers in three schools situated in three different cities in India. Primary focus was laid on understanding teachers' instructional practices using the Interactive Whiteboard. Teachers' beliefs and practices were examined in order to understand the factors affecting their practices with respect to their use of technology. The data was collected through classroom observation, teacher interviews and a beliefs and practices questionnaire. The findings indicate that teachers' technology-assisted teaching was very minimal and was limited to playing video modules and completing activities that followed the video. Moreover, their classroom practices were not in agreement with their beliefs of technological integration. External factors such as school policies and lack of time have been found contributing to the inconsistencies in their beliefs and practices.