Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensing the Noise: Uncovering Communities in Darknet Traffic

Darknets are ranges of IP addresses advertised without answering any traffic. Darknets help to uncover interesting network events, such as misconfigurations and network scans. Interpreting darknet traffic helps against cyber-attacks – e.g., malware often reaches darknets when scanning the Internet for vulnerable devices. The traffic reaching darknets is however voluminous and noisy, which calls for efficient ways to represent the data and highlight possibly important events. This paper evaluates a methodology to summarize packets reaching darknets. We represent the darknet activity as a graph, which captures remote hosts contacting the darknet nodes ports, as well as the frequency at which each port is reached. From these representations, we apply community detection algorithms in the search for patterns that could represent coordinated activity. By highlighting such activities we are able to group together, for example, groups of IP addresses that predominantly engage in contacting specific targets, or, vice versa, to identify targets which are frequently contacted together, for exploiting the vulnerabilities of a given service. The network analyst can recognize from the community detection results, for example, that a group of hosts has been infected by a botnet and it is currently scanning the network in search of vulnerable services (e.g., SSH and Telnet among the most commonly targeted). Such piece of information is impossible to obtain when analyzing the behavior of single sources, or packets one by one. All in all, our work is a first step towards a comprehensive aggregation methodology to automate the analysis of darknet traffic, a fundamental aspect for the recognition of coordinated and anomalous events.
Structural Equation Modeling for Acceptance of Cloud Computing

As the disruptive innovations are being penetrated to each level of society, the acceptance of modern technology is regulating the education activities. Managing the educational usage of computing through cloud applications is a matter of discussion nowadays. The aim of this study is to instigate the assessment of the adoption of cloud computing from end-users perspective. Technology acceptance model is applied to assess the behavioural intention towards the adoption of cloud computing. An online survey was conducted with close-ended questionnaire. Data was collected from 298 respondents. Structural equation modeling (SEM) was conducted to undertake the model fit and path analysis through AMOS software. The analysis demonstrates that perceived usefulness and personal innovativeness are most significant element towards behaviour shaping while the subjective norm is found insignificant towards cloud computing use. The study will help to focus on individual-level innovativeness in academia.
$\alpha$ -Stable Lévy State-space Models for Manoeuvring Object Tracking

In this paper we present multidimensional $\alpha$-stable state-space models for object tracking, expressed in continuous time as Lévy processes. In contrast with the conventional Gaussian models, these heavy-tailed $\alpha$-stable models are more likely to exhibit extreme noise values, thus showing the capability for modeling of erratic manoeuvring behaviour. Despite the potential benefits, such models are usually highly intractable for inference and therefore have not yet been widely adopted in the tracking field. Here the models are represented in a conditionally Gaussian series form, so that the marginal (Rao-Blackwellised) particle filter can be employed to perform tracking and smoothing very efficiently. As the result, the simulation tracks present some sharp manoeuvres, owing to the heavy-tailed property, and experiments demonstrate improved performance on an intent inference problem from automotive UI with highly perturbed pointing data.
Dynamic image for micro-expression recognition on region-based framework

Facial micro-expressions are involuntary facial expressions with low intensity and short duration natures in which hidden emotions can be revealed. Micro-expression analysis has been increasingly received tremendous attention and become advanced in the field of computer vision. However, it appears to be very challenging and requires resources to a greater extent to study micro-expressions. Most of the recent works have attempted to improve the spontaneous facial micro-expression recognition with sophisticated and hand-crafted feature extraction techniques. The use of deep neural networks has also been adopted to leverage this task. In this paper, we present a compact framework where a rank pooling concept called dynamic image is employed as a descriptor to extract informative features on certain regions of interests along with a convolutional neural network (CNN) deployed on elicited dynamic images to recognize micro-expressions therein. Particularly, facial motion magnification technique is applied on input sequences to enhance the magnitude of facial movements in the data. Subsequently, rank pooling is implemented to attain dynamic images. Only a fixed number of localized facial areas are extracted on the dynamic images based on observed dominant muscular changes. CNN models are fit to the final feature representation for emotion classification task. The framework is simple compared to that of other findings, yet the logic behind it justifies the effectiveness by the experimental results we achieved throughout the study. The experiment is evaluated on three state-of-the-art databases CASMEII, SMIC and SAMM.
2PSA: An Optimized and Flexible Power-Precision Scalable Adder

Adders are the core of all arithmetic circuits and the proposition of efficient adders, in distinct perspectives, are a constant in the last decades, with a myriad of solutions focusing on a wide variety of applications. The emergence of approximate computing encouraged the development of a new generation of dedicated imprecise adders intending to reduce delay, area, power and/or energy, but none of the proposed solutions is able to support run time definition of distinct power-precision operation points. This article presents the Power-Precision Scalable Adder (2PSA) which is a dynamically configurable power-precision imprecise adder, where the number of power-precision operation points can be configured at design time and each supported power-precision operation point can be changed in run time. The obtained experimental results showed that 2PSA is a fully flexible and efficient imprecise adder, supporting a high variety of power-SNR pairs, as well as a wide range of applications. Considering 8-bit adders, the power-SNR pairs vary from 2%-55dB to 60%-13.75dB, where eight operation points are allowed. Considering 64-bit adders, the power-SNR pairs ranges from 2%-325dB to 60%-16.65dB and 64 operation points are allowed. 2PSA also reached expressive power (from 18% to 73%) and area (from 54% to 73%) savings when compared with non-optimized solutions supporting the same operation points.
Ultrafast Optical Switching and Power Limiting in Intersubband Polaritonic Metasurfaces

We experimentally demonstrate a nonlinear metasurface comprising plasmonic resonators coupled to multi-quantum well transition. Due to MQW saturation, the metasurface provides a giant χ<sup>(3)</sup> on the order of 3×10<sup>−13</sup> m<sup>2</sup>/V<sup>2</sup> with ∼2 ps response time.
Semantic Embeddings for Medical Providers and Fraud Detection

A medical provider’s specialty is a significant predictor for detecting fraudulent providers with machine learning algorithms. When the specialty variable is encoded using a one-hot representation, however, models are subjected to sparse and uninformative feature vectors. We explore three techniques for representing medical provider types with dense, semantic embeddings that capture specialty similarities. The first two methods (GloVe and Med-Word2Vec) use pre-trained word embeddings to convert provider specialty descriptions to short phrase embeddings. Next, we propose a method for constructing semantic provider type embeddings from the procedure-level activity within each specialty group. For each embedding technique, we use Principal Component Analysis to compare the performance of embedding sizes between 32-128. Each embedding technique is evaluated on a highly imbalanced Medicare fraud prediction task using Logistic Regression (LR), Random Forest (RF), Gradient Boosted Tree (GBT), and Multilayer Perceptron (MLP) learners. Experiments are repeated 30 times and confidence intervals show that all three semantic embeddings significantly outperform one-hot representations when using RF and GBT learners. Our contributions include a novel method for embedding medical specialties from procedure codes and a comparison of three semantic embedding techniques for Medicare fraud detection.
A multilevel mapping based pedestrian model for social robot navigation tasks in unknown human environments

Social robot navigation aims to generate human-friendly paths in human-robot interactive environments. This paper focuses on maintaining humans' physical safety and mental comfort during robot navigation in an unknown dynamic environment. To achieve this goal, we use a variant of a pedestrian model that is particularly developed for low or average density environments. Design decisions on the representation of the obstacle and pedestrian are important for smooth motion planning. Limiting the local obstacles as a region centered at the robot would be taken into consideration has weaknesses in terms of time complexity because a much detailed map has a great number of cells to be evaluated. The study contributes to the theoretical field with extensions such as the development of the obstacle representation model which aims to overcome the computational cost of the current solutions for smooth motion planning which can be a bottleneck for the entire system. The proposed method is tested on a physical mobile robot in hallway scenario both in real-world environment and simulation, and its success is experimentally shown.
Relational Facts Extraction with Splitting Mechanism

Relational fact extraction is aimed to extract triples from sentences. Recent years, Sequence-to-sequence learning has been utilized for this task because of its advantage of modeling three different entity overlapping types. In their model, they utilized the same RNN cell to decode entities and relation in a triplet. Actually, the information required to predict entities and relation are different. So we shouldn’t mix the process of extracting entities from the original sentence and predicting the relation between entities. Based on the above observation, we propose a novel mechanism to split the process of decoding entities and relation. We conduct extensive experiments on NYT and WebNLG datasets. The experimental results show that our Splitting-Mechainsm (SM) helps to promote performance.
A Hardware Design for 3D-HEVC Depth Intra Skip with Synthesized View Distortion Change

The 3D-HEVC (Three-Dimensional High Efficiency Video Coding) is the state-of-the-art standard to compress three dimensional videos. One of the 3D-HEVC novel tools is the Depth Intra Skip (DIS) which is used to efficiently compress smooth and homogeneous areas of depth maps using four different prediction modes. The decision of which DIS mode will be used to predict the depth map region is done through a distortion metric and at this point the 3D-HEVC also brings a novelty: the use of Synthesized View Distortion Change (SVDC). The SVDC is much more complex than other metrics, however it brings an additional precision to decide the DIS mode, increasing the coding efficiency, and also the objective and subjective qualities of the encoded videos. This paper presents the first published hardware design for the 3D-HEVC DIS using the SVDC metric. The developed hardware was described in VHDL and synthesized for TSMC 40nm library. The design used 11.133k gates and it is able to process two views of 3D HD 1080p videos at 30 frames per second videos with a power dissipation of 4,174 mW.
Link Prediction Based on Information Preference Connection for Directed Network

Link prediction aims to predict undetected or unknown links through network information. Most prediction indicators depend on local topology but ignore the pivot role of nodes. Aiming at the problem of topological sparseness in some directed scale-free networks, a preference attachment method based on Shannon theory is proposed to measure the relevance of information from the perspective of nodes. This paper defined the nodes self-information while analyzing nodes interaction process and the conditional information while analyzing the conditional probability of effective interaction based on information theory, and use them designed a new predict indicator. Experiments on 9 scale-free networks show that the proposed method has a better improvement under the Precision metrics.
Is hybrid AI suited for hybrid threats? Insights from social media analysis

Social media create the opportunity for a truly connected world and change the way people communicate, exchange ideas and organize themselves into virtual communities. Both understanding online behavior and processing online content are of strategic importance for security applications. However, high volumes, noisy data and rapid changes of topics impose challenges that hinder the efficacy of classification models and the relevance of semantic models. This paper performs a comparative analysis on supervised, unsupervised and semantic-driven approaches used to analyze social data streams. The goal of the paper is to determine whether empirical findings support the enhancement of decision support and pattern recognition applications. The paper reports on research that has used various approaches to identify hidden patterns in social data collections where text is highly unstructured, comes with a mix of modalities and has potentially incorrect spatial-temporal stamps. The conclusion reports that the disconnected use of machine learning models and semantic-driven approaches in mining social media data has several weaknesses.
A Deep Neural Network-based Estimation of EMI Reduction by an Intermediate Coil in Automotive Wireless Power Transfer System

In this paper, we proposed a deep neural network (DNN)-based estimation of EMI reduction by an intermediate (Int) coil in automotive wireless power transfer (WPT) system. The Int coil can reduce electromagnetic interference (EMI) level in the WPT system with the proper resonant frequency of the Int coil. The previous study has explained the resonant frequency of the Int coil should be higher than the operating frequency. According to the resonant frequency of the Int coil, we can achieve the amount of EMI reduction. Therefore, the design of the Int coil is essential for optimization of the EMI reduction of the automotive WPT system. However, it is impossible to get the optimized results of EMI reduction by simulations. The proposed DNN-based estimation method can predict the amount of reduced EMI level in real cases consisted of ferrites and shielding structures.
Discovering Connected Objects in the Criminal Investigations

More and more things around us are becoming digital and connected to the Internet. This market ranges from smart devices, wellness and health control to smart cities. This development offers to malicious parties the possibility of carrying out attacks, directly impacting the consumers of these new services. Thus, the connected objects are actors or witnesses of events that have occurred. This opens up a challenge for security and forensic investigations on the Internet of Things (IoT). In this article, we present the problem of finding connected objects on an offence scene. In the absence of a technical solution, the investigators limit themselves to a manual search. Hidden objects are often neither detected nor found. Thus, we aim to give a clear and precise image of the current devices. We also want to determine their position.This work focuses on the study of the digital signature of the scene and the radio frequency characteristics of the objects. To understand the electromagnetic environment, we use a software defined radio (SDR) and we develop several tools: a sensor for a single protocol and a mesh network of sensors. The SDR returns the used frequencies. The single receiver offers a global mapping of the environment on a given protocol. The multi-sensor mesh network gives a precise and targeted vision of the infrastructure connected to several protocols and frequencies. We propose to assess the relevance of the measurement methods in relation to operational needs, on the basis of a use case and feedbacks.
Maximizing Throughput-per-Joule of a Hybrid Communication Infrastructure Through a Software-Hardware based DVFS Mechanism

Embedded systems have been demanding massive parallel computing encapsulated inside one single chip. As more cores are integrated into a single chip, higher bandwidth must be available, which conflicts with severe power constraints of embedded devices. Dynamic Voltage and Frequency Scaling techniques have been widely employed in homogeneous communication infrastructures to tune the available throughput of the fabric to best fit the application communication demands. However, homogeneous infrastructures have a restricted number of voltage and frequency pairs due to their uniform fabric shape, which limits the opportunities to maximize power savings. In this work, we propose a software-hardware based DVFS in a Hybrid Communication Infrastructure composed by Bus, Crossbar and NoC to maximize the Throughput-per-Joule. The proposed DVFS approach works at structure-level by adjusting voltage/frequency of each structure depending on the application demands. The proposed DVFS mechanism improves the Throughput-per-Joule (TpJ), on average, in 29% over the Hybrid Structure without DVFS. Moreover, it boosts TpJ gains over Network on Chip fabrics by 23% in scenarios where both fabrics offer the same throughput.
IoT-Malware Detection Based on Byte Sequences of Executable Files

Attacks towards the Internet of Things (IoT) devices are on the rise. To enable precaution and countermeasure against IoT malware, we present a cross-platform analysis of IoT malware programs based on static discriminating information extracted directly from ELF binaries. With experiments on a dataset composed of more than 222K samples cross 7 different CPU architectures, we demonstrate that efficient malware detection can be realized with near optimal accuracy.
Self-Interference Cancellation and Channel Estimation in Multicarrier-Division Duplex Systems With Hybrid Beamforming

Design of full-duplex (FD) wireless systems faces many challenges, including self-interference cancellation (SIC), capability to provide high capacity, high flexibility for operation, best usage of resources, etc. In this paper, we propose and investigate a multicarrier-division duplex (MDD) based hybrid beamforming system operated in FD mode, which is endowed with the advantages of both time-division duplex and frequency-division duplex. It also shares some merits of FD and allows to be free of self-interference (SI) in digital domain, but faces the same challenge of SI as the FD in analog domain. Hence in this paper, we first propose an adaptive beamforming assisted SI cancellation scheme with taking into account the practical requirement of analog-to-digital conversion (ADC). It can be shown that the proposed approach is capable of jointly coping with the desired signals’ transmission and SI suppression. Then, channel estimation (CEst) in MDD/MU-MIMO system is proposed by exploiting the reciprocity between the uplink and downlink subcarrier channels that is provided by MDD. Correspondingly, the orthogonality-achieving pilot symbols are designed, and the least-square (LS) CEst as well as linear minimum mean-square error (LMMSE) CEst are derived. Finally, the performance of MDD/MU-MIMO systems employing the proposed SIC method is investigated, with respect to the SI cancellation capability, sum-rate potential, CEst performance, and the effect of CEst on the achievable performance. Our studies show that MDD/MU-MIMO provides an effective option for design of future wireless transceivers.
Intuitive Planning, Generation, and Tracking of Trajectory for WMR With Mobile Computing Device and Embedded System

With the intention of delivering an intuitive and simple platform to plan, generate, and track trajectories with a WMR, this paper presents a technological integration of hardware and software. This technological integration consists of a mobile computing device with an app developed by authors for the trajectory planning and a differential drive WMR that possesses an embedded system, in which a trajectory generation algorithm and a trajectory tracking control algorithm –both proposed by authors – are programmed. The mobile computing device wirelessly drives the WMR through the embedded system. The app is developed on the basis of the sketch approach, since it allows drawing sketches of the trajectory intuitively on the mobile device. Also, the app geometrically scales the sketch coordinates to match them with the real WMR workspace. For the trajectory generation, these scaled coordinates are wirelessly sent to the embedded system, where the trajectory generation algorithm joins them by using Bézier polynomials and concatenation of straight-lines. Hence, the tracking task is carried out with the control algorithm, which is proposed departing from the dynamic model of the WMR and whose asymptotic stability proof is performed with the Lyapunov method. Experimental results verify that the technological integration successfully plans, generates, and tracks trajectories, even when the WMR is far from the initial coordinate of the desired trajectory. These results, corroborate the good performance and robustness of the proposed control algorithm. Thus, the introduced technological integration is intuitive and simple since users only have to draw a sketch on the mobile device to carry out the trajectory tracking task.
TeO2-Coated Si3N4 Waveguides with Engineered Dispersion and Enhanced Nonlinearity

We present designs of TeO<inf>2</inf>-coated Si<inf>3</inf>N<inf>4</inf> waveguides with engineered anomalous dispersion and calculated nonlinear parameter up to three times that of stoichiometric Si<inf>3</inf>N<inf>4</inf>. We observe four wave mixing in high-Q ring resonators on this platform.