Miracle-dz/UniFAR-FTUs huggingface dataset:
Increasing the Quality and Performance of N-Dimensional Point Anomaly Detection in Traffic Using PCA and DBSCAN

Detection of point anomalies is a very important issue in a large scale of fields from Astronomy and Biology to network intrusions. Clustering has been employed by many researchers to solve such problems and DBSCAN seems like the most efficient technique. Due to its high computational complexity, this work focused on decreasing it by decreasing the dimensionality of the data points. For this reason, Principal Components Analysis used and then DBSCAN applied on the new data sets provided by PCA. The quality of the experimental results was very promising proving that such an approach can be adapted. In addition, the performance of the combined PCA and DBSCAN was examined. Our analysis shows that a speedup of 25% was achieved while the quality was 80% reducing the dimensionality of data set to half.
Indexing Text Related to Software Vulnerabilities in Noisy Communities Through Topic Modelling

Despite efforts in the security community to quickly index and disseminate vulnerabilities as they are discovered and addressed, there are concerns about how to scale up the knowledge management of vulnerabilities given its dramatic growth rate. To address these concerns, recent research shifted towards more proactive approaches, in particular leveraging text mining methods to improve vulnerability identification and dissemination to security investigators. While providing a starting point for understanding vulnerability trends, recent methods are still reliant on curated identifiers, such as 'CVE-*', hence missing the majority of cybersecurity activity. We show that we can leverage overlapping textual themes in software vulnerabilities to identify related software vulnerability discussions without prior knowledge of identifiers. Our method obtained 86% accuracy in identifying related vulnerabilities with minimal pre-processing in a noisy community.
Hyperspectral Image Segmentation of Retinal Vasculature, Optic Disc and Macula

The most common approach for retinal imaging is the eye fundus photography which usually results in RGB images. Recent studies show that the additional spectral information provides useful features for automatic retinal image analysis. The current work extends recent research on the joint segmentation of retinal vasculature, optic disc and macula which often appears in different retinal image analysis tasks. Fully convolutional neural networks are utilized to solve the segmentation problem. It is shown that the network architectures can be effectively modified for the spectral data and the utilization of spectral information provides moderate improvements in retinal image segmentation.
Training a Neural Network on Analog TaOx ReRAM Devices Irradiated With Heavy Ions: Effects on Classification Accuracy Demonstrated With CrossSim

The image classification accuracy of a TaO<sub><italic>x</italic></sub> ReRAM-based neuromorphic computing accelerator is evaluated after intentionally inducing a displacement damage up to a fluence of 10<sup>14</sup> 2.5-MeV Si ions/cm<sup>2</sup> on the analog devices that are used to store weights. Results are consistent with a radiation-induced oxygen vacancy production mechanism. When the device is in the high-resistance state during heavy ion radiation, the device resistance, linearity, and accuracy after training are only affected by high fluence levels. The findings in this paper are in accordance with the results of previous studies on TaO<sub><italic>x</italic></sub>-based digital resistive random access memory. When the device is in the low-resistance state during irradiation, no resistance change was detected, but devices with a 4-<inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula> inline resistor did show a reduction in accuracy after training at 10<sup>14</sup> 2.5-MeV Si ions/cm<sup>2</sup>. This indicates that changes in resistance can only be somewhat correlated with changes to devices’ analog properties. This paper demonstrates that TaO<sub><italic>x</italic></sub> devices are radiation tolerant not only for high radiation environment digital memory applications but also when operated in an analog mode suitable for neuromorphic computation and training on new data sets.
Reverse Engineering IoT Devices: Effective Techniques and Methods

Recent Internet of Things (IoT) botnet attacks have called the attention to the fact that there are many vulnerable IoT devices connected to the Internet today. Some of these Web-connected devices lack even basic security practices such as strong password authentication. As a consequence, many IoT devices are already infected with malware and many more are vulnerable to exploitation. In this paper we analyze the security level of 16 popular IoT devices. We evaluate several low-cost black-box techniques for reverse engineering these devices, including software and fault injection-based techniques used to bypass password protection. We use these techniques to recover device firmware and passwords. We also discover several common design flaws which lead to previously unknown vulnerabilities. We demonstrate the effectiveness of our approach by modifying a laboratory version of the Mirai botnet to automatically add these devices to a botnet. We also discuss how to improve the security of IoT devices without significantly increasing their cost or affecting their usability.
Geometric Positioning Techniques Based on Tracking Algorithm for Indoor Dynamic Environments

This article presents location-estimation algorithm based on tracking algorithm for indoor dynamic environments. Wireless positioning techniques have gained more popularity in recent years. There are many approaches for indoor positioning systems, such as received signal strength (RSS), angle of arrival, time of arrival, and time difference of arrival. In terms of indoor positioning approaches by trilateration technique to determine the location of mobile devices, this article illustrates the concept of the measured range extract from time of arrival approaches. Then an easy geometric technique is applied. Furthermore, in order to enhance performance, Kalman filtering approach is used to track and correct the movement path of mobile device to effectively improve positioning accuracy.
CFC-ITS: Context-Aware Fog Computing for Intelligent Transportation Systems

This paper proposes a novel context-aware fog computing framework for intelligent transportation systems (ITS) called CFC-ITS. This scheme consists of multiple intelligent tiers: Internet of Things tier, fog service tier, and global cloud service tier supporting edge analytics for ITS services in a connected car environment. Each tier on the fog senses different contexts like location, time, available resources, and estimated response time for efficiently processing tasks to provide delay-sensitive services while optimizing virtualized resources. A preliminary prototype and a testbed for this study were built to validate the robustness of the proposed approach.
OME: An Optimized Modeling Engine for Disk Failure Prediction in Heterogeneous Datacenter

Nowadays, there are lots of disks from various disk models in datacenter. It is a challenge to make failure prediction for all disk models with high precision and high coverage. One-for-one modeling, transfer learning modeling and one-for-all modeling are proposed to address the challenge. However, none of them works well for all disk models and the automation problem for method selection and parameter tuning still persists. In this paper, we propose OME, an optimized modeling engine for disk failure prediction in heterogeneous datacenter. It builds a basis predictive model with one-for-all modeling and searches for the optimized with one-for-one and transfer learning modeling for every disk model. To achieve automation, OME employs a simple but effective transfer learning method, does cross-validation for comparison, prunes the tuning space, and constructs a directed acyclic graph for parallelism. Evaluation on a dataset from a real-world datacenter shows that OME outperforms a one-for-all predictive model from previous work by 18.5% overall, and the improvement for 43.3% disk models reaches over 30%.
Pilot Pattern Optimization for Small Data Packet Transmission

We consider pilot pattern optimization for single user single-input-single-output orthogonal frequency division multiplexing transmission over doubly-selective channels, assuming least squares channel estimation with linear interpolation and extrapolation. Our focus is on small data packet transmission which is typical for machine-type communications. We compare diamond-shaped pilot patterns to rectangular-shaped pilot patterns. We optimize the density and spacing of pilot symbols in both pilot patterns with respect to the constrained capacity and demonstrate that the rectangular pilot pattern can outperform the diamond-shaped pattern in case of small data packet transmission.
Ground Surveillance Radar Targets Classification Using Wald Sequential Test

In this research the Wald sequential test is applied for ground surveillance radar real signal classification. Central Doppler frequency and spectrum width around it are used as features for classification. The classification of signal from vehicle apart from all other classes is performed. For estimation of the probability density function, the Parzen method is used. Conducted results show that the Wald sequential test could be applied to classification of radar signals whose duration is 0.5 seconds, with average number of observation for signals that origin from vehicle 1.61, and for signals that origin from other classes 1.22.
Assessment of Mobile Applications’ Credibility in Users’ Content-Searching Behavior based on PIT Theory

This work studies the assessment of mobile applications credibility in user’s content-searching behavior, using Fogg’s Prominence-Interpretation Theory (PIT). Though credibility is a subjective notion, it can be assessed in an objective manner when examined by both components of credibility assessment - prominence and interpretation. Two-phase of mix method sequential studies use to investigate the LINE Webtoon mobile app user. The quantitative phase was a quasi-experiment administered to 30 participants. Then, it was followed by the qualitative phase with in-depth interviews with five people from the experiment’s participants. The results show that the credibility of webtoon’s content was not related to the app’s credibility. Users might interpret the credibility of content without necessarily making reference to the app. This result is true for LINE Webtoon mobile app, but when it applied in different contexts, the results may differ.
Selective Compression Scheme for Read Performance Improvement on Flash Devices

The increasing density and capacity of NAND flash memory leads to degraded reliability. To address the reliability issue, low-density parity-check code (LDPC) has been deployed in NAND flash memories due to its strong error correction capability. The drawback of LDPC is that, to correct data with high raw bit error rate (RBER), read latency will be amplified. To improve read performance, this paper proposes to apply lossless compression to reduce RBER on data pages. However, compression and decompression incur time overheads. Compressing all the data pages for RBER reduction will degrade write performance. In addition, the variation of compression ratio leads to variation of RBER reduction, thus varied read latency reduction. In this work, a selective data compression scheme is proposed for read performance improvement. Both read frequency and compression ratio of data are taken into consideration. Data in a flash page with high read frequency and good compressibility are prioritized for compression. Experimental results show that the proposed scheme can improve read performance by 42% on average, without impacting write performance.
Low Area-Delay Complexity Digit-Level Parallel-In Serial-Out Multiplier Over GF(2m) Based on Overlap-Free Karatsuba Algorithm

Overlap-free Karatsuba algorithm (OFKA) is one of the Karatsuba algorithms (KAs) which can be employed to reduce the computation complexity of fast/high-precision polynomial based multiplication (the space complexity of the product can be reduced from O(m^2) to O(m^log2^3)). Meanwhile, digit-level (DL) finite field multipliers over GF(2^m), generally can be categorized as DL parallel-in serial-out (DL-PISO) and DL serial-in parallel-out (DL-SIPO) styles, have gained substantial attentions in cryptographic related applications (such as elliptic curve cryptosystem (ECC)) recently due to their efficient performance in area-delay tradeoffs. In this paper, aim at deriving an efficient DL-PISO multiplier with sub-quadratic space complexity, we present a novel polynomial basis multiplication through a combination of three coherent interdependent efforts. First of all, a novel bivariate polynomial multiplication algorithm using two steps of reduction is presented. Then, a new DL-PISO polynomial basis multiplication algorithm using OFKA over GF(2^m) is introduced as well as its corresponding structure. Finally, the complexity and comparison are detailed given to confirm the efficiency of the proposed DL-PISO multiplier over the existing DL-PISO and DL-SIPO designs, i.e., the proposed one has lower area-delay product (ADP) when compared with the competing ones. The proposed DL-PISO multiplier is highly regular and hence can be applied in many resource-constrained environments.
Microstrip Aperture-Coupled Stacked Patch Antenna Array for Wide-Band Wide-Angle-Scan Applications

A low profile planar array is presented for the wide-band wide-angle-scan application. Here the aperture-coupled stacked patches are used as the array element, and a co-axial-to-stripline transition is constituted for the feeding structure, considering the integration with the active transmit/receive module. The performance of the array element under the periodic boundary condition is analyzed, and an $8^*4$ array demonstrator is built. From the simulated results, a desired wide-band wide-angle-scan characteristic is achieved, and a large phased array can be constituted from the modular tile based on the proposed array demonstrator.
Design of a Miniaturized UHF RFID Tag Antenna

This project researched and developed a miniaturized antenna by increasing the width of the folded arm and designing the bend structure of the arm. HFSS software is used for the main process of simulation design and optimization. The simulation and the experimental results showed that the input impedance is 18.3305 +j* 196. 1702Ω, which is nearly conjugate-matched up the SL3ICS1202 G2XL chip with impedance of 24-j* 195Ω at 915MHz. Its total size is 33mm ×67 mm ×3 mm, bandwidth is 66 MHz, gain is -8.7848dB and the efficiency is 0.17106. It meets the demands of miniaturization, low cost and the input impedance conjugate matching.
Multimodal Human Action Recognition Based on a Fusion of Dynamic Images Using CNN Descriptors

In this paper, we propose the use of dynamic-images-based approach for action recognition. Specifically, we exploit the multimodal information recorded by a Kinect sensor (RGB-D and skeleton joint data). We combine several ideas from rank pooling and skeleton optical spectra to generate dynamic images to summarize an action sequence into single flow images. We group our dynamic images into five groups: a dynamic color group (DC); a dynamic depth group (DD) and three dynamic skeleton groups (DXY, DYZ, DXZ). As action is composed of different postures along time, we generated N different dynamic images with the main postures for each dynamic group. Next, we applied a pre-trained flow-CNN to extract spatiotemporal features with a max-mean aggregation. The proposed method was evaluated on a public benchmark dataset, the UTD-MHAD, and achieved the state-of-the-art result.
Joint Design of Relay-User Selection in Direct-link Energy Harvesting Relay Networks

In this paper, we investigate the outage performance of the proposed relay-user selection (RUS) scheme for amplify-and-forward full-duplex multi-relay multi-user network, where the direct link between the source node and the user node is used to convey information. Meanwhile, each relay node is energy-constrained and the simultaneous wireless information and power transfer technology is employed to harvest energy. A power splitter is equipped in each relay node to split the received signal into energy harvesting part and signal processing part. The closed-form and a simple asymptotic expression for outage probability is provided. Moreover, the optimal power splitting factor of each relay node is calculated to minimize the outage probability of the system. Finally, the numerical simulations are provided to validate the analysis.
Hardware design of Euclidean Projection modules for ADMM LDPC decoding

The ADMM decoding is a novel LP-based algorithm that can improve the decoding performances of the conventional BP decoding algorithms for LDPC codes. However, it was shown that the Euclidean projection (EP) computation involved in the ADMM algorithm limits the throughput performances of the ADMM hardware decoders and drastically increases their silicon cost. Recently, different techniques have been proposed in the literature at the algorithmic level to improve the hardware characteristics of the EP module without penalizing the error correction performances of the ADMM decoder. In this article, we present the implementation results of optimized hardware design of these theoretical approaches. Then, an evaluation of their real interest for high throughput and low complexity ADMM LDPC decoder design is provided.
Puzzle Memory: Multifractional Partitioned Heterogeneous Memory Scheme

As current main memory technology scaling is coming close to an end due to its physical limitations, many emerging memory technologies are coming to the market to fill the scaling gap. Future memory systems will require a heterogeneous memory architecture where one technology acts as a low latency memory whereas the other acts as a high capacity memory. This will allow the future main memory system to continue to scale in terms of capacity, yet have similar or slightly better latency than today's DRAM technology. Prior work on data management in heterogeneous memory has optimized one or a maximum of two components in the computing stack. However, different components are good at different tasks in data management, so in the era of heterogeneous memory, it is inevitable that cooperative multi-component data management will be adopted in future systems. We propose a heterogeneous memory layout where two memories are laid out asymmetrically. The operating system is aware of this layout and places pages with different locality characteristics in different regions of memory. Finally, a custom hardware performs the data remapping to optimize the data placement at finer granularity than what is visible to the operating system. In the end, we show that our multi-component cooperative data management scheme can improve the overall system performance by up to 40%.
Event-based Near Optimal Sampling and Tracking Control of Nonlinear Systems

This paper presents a near optimal event-based tracking control scheme for nonlinear continuous time systems. In order to simultaneously design the event-based sampling intervals and the control policy, the problem of designing the event-triggering mechanism and the feedback controller is posed as a min-max optimization problem. Using the resultant saddle point solution, the feedback control policy and the threshold for the event-based sampling condition is designed. The proposed control scheme is realized by approximating the solution to the associated Hamilton-Jacobi-Issac (HJI) equation using event-based neural networks (NN). The NN weights are updated using an impulsive update scheme. Extension of Lyapunov stability analysis for the impulsive hybrid dynamical system is utilized to prove the local ultimate boundedness of the tracking and NN weight estimation errors. Furthermore, Zeno free behavior of the event-triggering mechanism is guaranteed along with the numerical simulation to corroborate the analytical design.
Percolative Learning: Time-Series Prediction from Future Tendencies

Multimodal learning has received considerable attention in recent years. Most prevailing approaches use multiple networks to learn a shared representation across modalities. However, these methods are limited to the laboratory scale because of unavailability of multimodal data. Percolative learning is a straightforward framework for solving this problem; we train our model using data from multimodalities. The model performs well during the testing phase, although only data from a single modality is provided. In this paper, we extend this framework to the problem of time-series prediction, specifically with focus on applications on maritime indices. Then, we attempt to exploit the potential of percolative learning. We also attempt to employ a genetic algorithm to find suitable architectures, which is one of the most important ingredients of percolative learning. The experimental results demonstrate the potential of our method and its superiority to other baselines.
Active Learning to Develop Key Research Skills in Master's Level Computer Science Coursework

This paper provides insights from a preliminary study on how embedding critical academic literacy skills using active learning strategies in a small Master's level computer science course might impact student performance, skills development, and engagement. One goal of higher education is to develop students' critical thinking and research skills. Prior work suggests these skills are best taught explicitly, when they are embedded in the curriculum, using active learning strategies, and using recent high-quality research as a basis for inquiry. Using multiple methods, this paper examines how teaching critical academic literacy skills through active learning strategies impacts student critical thinking ability and engagement. Results suggest substantial student engagement and improvements in critical literacy skills.
DQ-Voltage Droop Control and Robust Secondary Restoration With Eligibility to Operate During Communication Failure in Autonomous Microgrid

The combination of droop control and communication-based secondary control is eligible to maintain the steady voltage and frequency at the point of common coupling (PCC) together with the property of power sharing between the distributed generators. However, the performance of this combination is threatened by the slow dynamic and communication failure. In this context, an improved droop control is proposed to address the issue of slow dynamic and enhance the power-sharing accuracy in the autonomous microgrid. The droop coefficients are determined through a comprehensive analysis to assess the eigenvalues and plot the loci of the dominant poles. Robust secondary control is subsequently proposed to restore the voltage and frequency to their nominal values with a novel feature that ensures the performance continuity during the communication failure. The key idea of the proposed secondary control relies on utilizing the predictive values of the PCC voltage in the dq-rotating frame using two dynamic lookup tables during communication loss. The proposed control is evidenced by the simulation results in the MATLAB environment.
Community Detection System in Online Social Network

Due to the important number of online social network (OSN) users, the amount of data made available via this OSN is exponentially increasing. Such data is an opportunity to analysts and decision makers to discover new insights that allows better decisions. To analyse such network, one of the most important tasks consists in discovering community within such networks based on the users interests and activities. In this work, we present a design and an implementation of a big data solution that allows effectively to detect communities within the Twitter Social network. Our solution allows a multi-criteria search over the Twitter social graph to get more accurate results.