Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy-enhanced Deep Packet Inspection at Outsourced Middlebox

Outsourcing middlebox services to public cloud has become a prevalent choice among enterprises to reduce the facility deployment and management costs. However, the packet payload and inspection rule are inevitably exposed to the cloud server when network traffic is redirected to the middlebox. Although HTTPS protocols are able to conceal the content of packet using end-to-end encryption, it becomes challenge to perform deep packet inspection (DPI) over the encrypted traffic. In this paper, we propose a privacy-enhanced DPI (PE-DPI) scheme that is compatible with current HTTPS protocols. PE-DPI leverages a homomorphic encryption system as the building block to achieve DPI functions including keyword matching and malware detection on two non-collusion cloud servers. PE-DPI allows all the connections to share the same encrypted inspection rule, which significantly reduces the connection initialization costs. PE-DPI is able to preserve the confidentiality of packet payload and inspection rule by processing DPI over ciphertext domain. Extensive experiments demonstrate that PE-DPI out-performs the existing schemes in inspection rule preparation, sender side encryption and receiver side verification overheads.
TorBot Stalker: Detecting Tor Botnets Through Intelligent Circuit Data Analysis

Botnets are collections of infected computers that are controlled centrally by a botmaster, often for sending spam or launching denial of service attacks. The task to take down these botnets is often a cat and mouse game with operators frequently changing domains for their control infrastructure. More recently, operators have moved to using Tor, a pseudo-anonymous network for hosting services whereby identification is difficult. Additionally, because connections to the Tor network are encrypted, we cannot use traditional methods like Domain Name System (DNS) and traffic signatures to detect infected hosts. In this paper, we introduce TorBot Stalker: the first mechanism for detecting, de-anonymizing, and destroying Tor botnets. We use machine learning to analyse and fingerprint the timings and frequency of Tor network circuit data when routing botnet traffic, and build a detection mechanism that is able to identify infected hosts at the Tor network border, in real-time, while preserving the privacy of legitimate users. TorBot Stalker can be implemented at any node in the Tor network and can differentiate between botnets and legitimate applications like Internet Relay Chat (IRC) coming from the same host. Experimental data demonstrates an accuracy of 99% with few false positives. We then apply the technique at the entry to the Tor network to measure the fraction of traffic which is for botnet. We observed that Torbot Stalker is able to de-anonymize real botnets in the Tor network and further identify infected hosts and control servers.
Self-Attention Based Network for Punctuation Restoration

Inserting proper punctuation into Automatic Speech Recognizer(ASR) transcription is a challenging and promising task in real-time Spoken Language Translation(SLT). Traditional methods built on the sequence labelling framework are weak in handling the joint punctuation. To tackle this problem, we propose a novel self-attention based network, which can solve the aforementioned problem very well. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Nets(RNN) and Convolutional Neural Nets(CNN). We conduct extensive experiments on complex punctuation tasks. The experimental results show that the proposed model achieves significant improvements on joint punctuation task while being superior to traditional methods on simple punctuation task as well.
Classifying the Ideational Impact of IS Review Articles: A Natural Language Processing Based Approach

By providing knowledge contributions and stimulating future research, review articles (RAs) play a vital role for cumulative knowledge development. Although many papers cite RAs, it is rarely transparent to which degree citation impact represents perfunctory citations as opposed to a deeper engagement with a RA’s knowledge contributions. This distinction between perfunctory and ideational impact has largely been neglected in the literature arguably because of the manual effort required for qualitative analysis. Against this background, our study aims at developing automated classifiers of ideational impact of IS RAs. We propose a machine learning model based on natural language processing to evaluate the feasibility of automated analyses. The evaluation results provide evidence for an effective and scalable classification approach that presents a reliable and reproducible solution to the ideational impact classification problem. We discuss implications for improving the capabilities of understanding how IS scholars build on their field’s body of knowledge.
Displaying the Events Flow of POS-Devices Network for Building an Effective Monitoring System

The article describes the main problems when monitoring the operation of POS networks. The urgency of the developing task a new method of POS-devices network cognitive mapping for effective monitoring of processes occurring in them is considered. The main requirements to this problem are formulated. Methods and algorithms that can eliminate actual problems when solving the task of monitoring a large number of POS-devices are proposed and described.
Segmenting Dynamic Network Data

Networks and graphs arise naturally in many complex systems, often exhibiting dynamic behavior that can be modeled using dynamic networks. Two major research problems in dynamic networks are (1) community detection, which aims to find specific sub-structures within the networks, and (2) change point detection, which tries to find the time points at which sub-structures change. This paper proposes a new methodology to solve both problems simultaneously, using a model selection framework in which the Minimum Description Length Principle (MDL) is utilized as minimizing objective criterion. The derived detection algorithm is compatible with many existing methods, and is supported by empirical results and data analysis.
A Novel Community Detection Method for Collaborative Networks

Community structure prevails in network graphs like social networks, web graphs and collaborative networks. Clique percolation is one popular method used for unfolding the community structure in networks. However, clique percolation method is inefficient as the computational time is high for merging the identified cliques. This paper proposes a novel technique for detecting overlapping community structure by addressing the problem of clique merging. We reduce the overall time for community detection by applying edge streaming technique. The proposed method is validated through experiments using real and synthetic data in comparison with conventional clique percolation algorithm. The performance parameters such as execution time and goodness of the cluster are used for comparison and the results are promising. This model is suitable for community detection in collaborative network.
A Delta-Structured Switched-Capacitor Equalizer for Series-Connected Battery Strings

The classical switched-capacitor (SC) equalizer is widely used in battery management systems because of the accurate balancing and ease of implementation. However, it only achieves the adjacent cell-to-cell equalization, and its balancing speed and efficiency become extremely low for multicell series-connected battery strings. Therefore, a delta-structured switched-capacitor equalizer is introduced to achieve a high-speed and high-efficiency any-cells-to-any-cells (AC2AC) equalization at arbitrary imbalanced status for any number of cells. The slow switching limit and fast switching limit impedances of the proposed equalizer are developed to provide a theoretical guidance for the optimized design of the SC equalizers. A prototype for four battery cells was built, and a comparison with existing equalizers was presented. Experimental and comparative results show that the proposed solution exhibits an automatic AC2AC balancing with high reliability and strong robustness. The measured balancing efficiency is up to 94.5%.
ECA Filter Effects on Ground Clutter Statistics in DVB- T Based Passive Radar

This paper tackles the analysis of the Extensive CAncellation (ECA) filter effects in the statistical characterization of ground radar clutter. Real radar databases acquired by a DVB- T based Passive Radar (PR) system were characterized considering the Cross-Ambiguity Function output as the observation space. Goodness-of-fit tests were applied, and skewness and kurtosis values were estimated. The Gaussian model was proved to be suitable for high Doppler shifts. However, Mixture of Gammas and Gaussians distributions were proposed to model the intensity and the in-phase and quadrature components, respectively, of the region centred in the zero Doppler line. These results prove that, although the ECA filter rejects the most part of the interference components, a non-homogeneous characterization was required for Doppler shifts close to zero, where ground clutter effects concentrate. The proposed theoretical distributions will be useful for the formulation of optimum detectors based on the Neyman-Pearson criterion, improving the detection performance of PR systems.
Incorporating high-level and low-level cues for pain intensity estimation

Pain is a transient physical reaction that exhibits on human faces. Automatic pain intensity estimation is of great importance in clinical and health-care applications. Pain expression is identified by a set of deformations of facial features. Hence, features are essential for pain estimation. In this paper, we propose a novel method that encodes low-level descriptors and powerful high-level deep features by a weighting process, to form an efficient representation of facial images. To obtain a powerful and compact low-level representation, we explore the way of using second-order pooling over the local descriptors. Instead of direct concatenation, we develop an efficient fusion approach that unites the low-level local descriptors and the high-level deep features. To the best of our knowledge, this is the first approach that incorporates the low-level local statistics together with the high-level deep features in pain intensity estimation. Experiments are evaluated on the benchmark databases of pain. The results demonstrate that the proposed low-to-high-level representation outperforms other methods and achieves promising results.
A Substrate Integrated Gap Waveguide Based Wideband 3-dB Coupler for 5G Applications

A wideband 3-dB coupler using substrate integrated gap waveguide (SIGW) is proposed based on the principle of the branch-line coupler structure. The SIGW coupler design is comprehensively studied. It is found that SIGW conducting ridge vias play a significant role and have a strong impact on the cavity resonance, passband, and impedance matching. A prototype of the proposed SIGW coupler is fabricated and tested, and the measured agrees well with the simulated. The coupler, better than the reported, has a passband of ~5 GHz from 23.59 to 29 GHz with the isolation above 20 dB, covering the 24.25–27.5-GHz and 27.5–28.5-GHz frequency bands of 5G.
Optimization of Laser-opening Pattern for the Efficiency Improvement of Industrial PERC Solar Cell

In this paper, we proposed and demonstrated the application of a rear busbar-parallel line opening pattern (BLOP) to reduce the series resistance and improve the PERC type cell efficiency. The proposed cells with BLOP gave an average efficiency of 21.40%, series resistance of 0.631 $\omega $ -cm$^{\mathbf {2}}$ and fill factor of 80.1 % while the reference cells gave an average efficiency of 21.34%, series resistance of 0.646 $\Omega $ -cm$^{\mathbf {2}}$, and fill factor of 79.9 %. This BLOP method increases the cell efficiency and fill factor by providing a better path way for holes underneath the rear busbar to be collected at the rear contact. As the employment of BLOP does not affect the capacity of the mass production, it is a very practical and readily available way to improve the performance of PERC type cells in mass production.
Middleware IoT protocols performance evaluation for carrying out clustered data

Several IoT middleware protocols have been proposed for the wireless transfer of IoT data. The main representatives are the CoAP, the SOAP, the MQTT and the HTTP protocols. The main concerns on these technologies are the delay, packet loss, processing effort and power consumption that IoT devices demand, in order to successfully transfer wireless data to the cloud. In exchange for the reduction of the power consumption, many of these technologies try to lower the data throughput, minimize security, or even reduce the distance range of device coverage. In this paper authors compare the performance of IoT application protocols, based on M2M delay and the extra payload that they enforce to the transmitted data. Experimentation tests reveal which protocol is best suited for different network and application scenarios accordingly.
Modeling and Analysis of FeICIC in OFDMA HetNets with Limited Backhaul Capacity

Heterogeneous networks (HetNets) with dense deployment of small cells in co-channel manner are recognized as a key solution to boost network capacity. However, in addition to the severe inter-tier interference, the non-ideal backhaul is also a bottleneck in improving the performance of HetNets. This paper proposes an analytical model of further-enhanced intercell interference coordination (FeICIC) in orthogonal frequency division multiple access (OFDMA) HetNets with limited backhaul capacity, and further evaluates the performance of HetNets. Stochastic geometry is applied to derive the expression of rate coverage in two-tier HetNets, while modeling the BS interference in the context of OFDMA. The accuracy of analytical results is validated by Monte Carlo simulation. The superiority of FeICIC for rate coverage of HetNets is demonstrated, and the proper parameter settings are further investigated to provide design guidelines for future dense networks.
A new bag of visual words encoding method for human action recognition

Human action recognition in videos is one of the key problems in computer vision. Inspired by image classification models, techniques based on bags of visual words have become one of the most effective approaches to solve this problem. The most usual way to engage an interest point in a bag of words is by means of the closest word found in a previously trained codebook. However, the quality of representation decreases when interest points have different visual words at similar distances or when we map noisy interest points. The aim of this paper is to present a new encoding procedure to engage the interest points in a bag of visual words improving the quality of the representation. The encoding that we propose tries to map only the relevant interest points detected in the scene. We experimentally show that using the new encoding method we can significantly improve the classification ratio.
Event-Triggered Control for a Class of Cascade Systems

This paper addresses the event-triggered control of cascade connected systems. In particular, we present event- triggered mechanisms that guarantee the stabilization of cas- cade systems with partial state feedback without infinitely fast sampling. Our approach is based on growth conditions on the interconnection terms and does not follow the framework of input-to-state stability with respect to the subsystems and/or measurement errors.
A New Approach to Sample Entropy of Multi-channel Signals: Application to EEG Signals

In this paper, we propose a new algorithm to calculate sample entropy of multivariate data. Over the existing method, the one proposed here has the advantage of maintaining good results as the number of channels increases. The new and already-existing algorithms were applied on multivariate white Gaussian noise signals, pink noise signals, and mixtures of both. For high number of channels, the existing method failed to show that white noise is always the most irregular whereas the proposed method always had the entropy of white noise the highest. Application of both algorithms on MIX process signals also confirmed the ability of the proposed method to handle larger number of channels without risking erroneous results. We also applied the proposed algorithm on EEG data from epileptic patients before and after treatments. The results showed an increase in entropy values after treatment in the regions where the focus was localized. This goes in the same way as the medical point of view that indicated a better health state for these patients.
Eye Gaze as an Indicator for Stress Level Analysis in Students

Stress is the most common cause of ill health in our society. Intense psychological pressure can affect the health of a person. Higher levels of anxiety, depression, and stress-related problems are observed in students than they had in the past. Stress in studies is one of the reasons for committing suicides. Timely detection and proper counseling can help them to manage their stress. The intense use of the digital devices is the common causes of eye fatigue. It is a user friendly approach for analyzing the students' stress level using an Eye Tracker. Student's eye gaze parameters are recorded using an Eye Tracker. Various stimuli are used to analyze the increase in stress level with respect to the increase in cognitive load. At the end of experiment eye fatigue detection test is conducted for the detection of eye fatigue. Statistical analysis is performed on the eye measures and observed that the eye measures have strong correlation with the cognitive load.
A Noise-Robust Self-Adaptive Multitarget Speaker Detection System

We describe a multitarget speaker detection system that provides a robust way to classify the utterance of a speaker in noisy environments. The multitarget detection problem is known to be much more difficult to tackle than single target speaker verification tasks, especially when the target set is large and the data is corrupted by noise. In this work we aim to improve the performance of our multitarget speaker detection system in real-world settings, where complicated background noise and unpredictable speaker behavior are present. We make three major improvements that contribute to our goal. First, we discover an effective noise-filtering method using GMM-based voice activity detector followed by unsupervised bottom-up clustering. Second, we incorporate a Highway-LSTM network to estimate posterior distributions of senones, replacing the traditional GMM-UBM with senone posteriors. Finally, we apply a self-adaptive approach on the classifier back-end so that our PLDA parameters and S-normalization subsets can be updated online.
A MatlablSimulink Based Five Level Cascaded Multilevel Inverter for PV Systems

This paper presents an efficient five level H-bridge CMLI (Cascaded Multilevel Inverter) topology for PV systems. At first maximum power from solar PV (photo voltaic) array is extracted by providing controlled duty cycle to DC-DC boost converter. Control of Duty cycle is done using P&O (Perturb and Observe) type MPPT (Maximum Power Point Tracking) algorithm. After that control switching pulses are applied to five-level CMLI for conversion of DC voltage into AC. Complete details of switching sequence of cascaded multilevel inverter and working parameters for PV array is presented in this paper. Simulation results of voltage and current waveform of PV array, stabilized output of MPPT and of CMLI is also shown.
Sequential Fish Catch Forecasting Using Bayesian State Space Models

A new state space model suitable for fixed shore net fishing is proposed and successfully applied to daily fish catch forecasting. Accurate prediction of daily fish catches makes it possible to support fishery workers with decision-making for efficient operations. For that purpose, the predictive model should be intuitive to the fishery workers and provide an estimate with a confidence. In the present paper, a fish catch forecasting method is developed using a state space model that emulates the process of fixed shore net fishing. In this method, the parameter estimation and prediction are sequentially performed using the Hamiltonian Monte Carlo method. The experimental comparisons using actual fish catch data and public meteorological information demonstrated that the proposed forecasting system yielded significant reductions in predictive errors over the systems based on decision-trees and legacy state-space models.
Flexible and Discriminative Non-linear Embedding with Feature Selection for Image Classification

In the past years, various graph-based data embedding algorithms were proposed and used in machine learning and pattern recognition fields. This paper introduces a graph-based non-linear embedding learning algorithm for image classification and recognition. The proposed embedding method can be used for supervised and semi-supervised learning settings. The proposed criterion allows the simultaneous estimation of a linear and a non-linear embedding. It integrates manifold smoothness, Sparse Regression and Margin Discriminant Embedding. The deployed sparse regression implicitly performs feature selection on the original features of the data matrix and of the linear transform. The proposed method is applied to four image datasets: 8 Sports Event Categories dataset, Scene 15 dataset, ORL Face dataset and COIL-20 Object dataset. The experiments demonstrate the effectiveness of the proposed embedding method.
Load-Dependent Handover Margin for Throughput Enhancement and Load Balancing in HetNets

Inbound handover (HO) or hand-in is done when the user equipment (UE) performs HO from a macrocell (MC) to a small cell (SC), while the outbound HO or handout is done when a UE hands over from SC to MC. On the other hand, the inter-SC HO is done when a UE performs HO between two SCs. The outbound HO is not as complex as the other two types, because the UE has only one HO target base station, i.e., the MC. Therefore, in this paper, we only consider the inbound and inter-SC HO types. The user may associate with a small cell for a very short time of stay (ToS), smaller than a short time of stay threshold, and this may cause frequently unnecessary HOs and result in service interruption causing degradation in the quality of service (QoS). In this paper, we propose a novel HO method for the purpose of load balancing and throughput improvement in heterogeneous networks (HetNets). The influence of interference from both MC and SC base stations is taken into account so as to offloaded the user from the congested cell and forced it to HO to the SC that gives a good data rate by choosing the best SC, which has the highest signal to interference plus noise ratio (SINR), from a reduced neighbor cell list (NCL). The NCL is optimized utilizing the SINR threshold and ToS. The proposed method utilizes a modified A3 HO initiation event taking into account the cell load and the interference. Results show that the proposed method can perform HO while maintaining the throughput to a good level. In addition, the proposed method has significantly reduced the inter-SC HOs and inbound HO and radio link failures compared to the existing methods. Under different network conditions, load factors, and call arrival rates, results show that the proposed method can give significantly better performance, thereby producing higher throughput for the user and the network.