Miracle-dz/UniFAR-FTUs huggingface dataset:
The Security–Reliability Tradeoff of Multiuser Scheduling-Aided Energy Harvesting Cognitive Radio Networks

We study the physical-layer security of a cognitive radio system in the face of multiple eavesdroppers (EDs), which is composed of a secondary base station (SBS), multiple secondary users (SUs) as well as a pair of primary transmitter (PT) and primary receiver (PR), where the SUs first harvest energy from their received radio frequency signals transmitted by the PT and then communicate with the SBS relying on opportunistic scheduling. We consider two specific user scheduling schemes, namely, the channel-aware user scheduling (CaUS) and the energy-aware user scheduling (EaUS). In the CaUS scheme, an SU having the best instantaneous SU-SBS link (spanning from SUs to SBS) will be activated to communicate with the SBS. By contrast, the EaUS scheme takes into account both the amount of energy harvested from the PT and the instantaneous quality of the SU-SBS link. We analyze the security–reliability tradeoff (SRT) of both the CaUS and EaUS schemes in terms of their intercept versus outage probability. We also provide the SRT analysis of traditional round-robin user scheduling (RrUS) used as a benchmarker of the CaUS and EaUS schemes. We demonstrate that the EaUS scheme achieves the best outage and secrecy performance in the high main-to-eavesdropper ratio (MER) region, but a worse secrecy performance than the CaUS method in the low-MER region. Moreover, from a security versus reliability perspective, the CaUS outperforms both the EaUS and the RrUS in the low-MER region. Surprisingly, this also implies that although the user scheduling criterion of EaUS exploits the knowledge of both the amount of harvested power and instantaneous channel state information (CSI), it exhibits a degraded physical-layer security in the low-MER region due to the fact that the increased harvested energy is beneficial not only for the legitimate SBS receiver but also for the EDs.
Reference Joint Trajectories Generation of CUHK-EXO Exoskeleton for System Balance in Walking Assistance

This paper introduces a wearable exoskeleton suit CUHK-EXO that can help paraplegic patients regain their mobility to stand up, sit down, and walk. An offline design and online modification (ODOM) algorithm is proposed for the exoskeleton to generate reference joint trajectories during walking assistance. First, reference trajectories of CUHK-EXO are designed offline based on motion capture data considering leg geometry constraints. Then the human–exoskeleton system (HES) including a pair of crutches is modeled as an eight-link system for analysis. Since the relative position of system center of pressure (COP) is an important factor to indicate system balance during walking, it is estimated in real-time and monitored for exoskeleton control. Based on the system COP position, this paper further proposes the reference trajectories online modification method for CUHK-EXO to counteract disturbances applied to the HES, and hence stabilize system balance in the walking assistance. Finally, walking tests are performed in both healthy subjects and a paraplegic patient to validate the effectiveness of the proposed ODOM algorithm. Testing results demonstrate that knowing the COP desired areas of the wearer, the exoskeleton CUHK-EXO can counteract perturbations and decrease the wearer’s efforts, so as to maintain system balance with the ODOM algorithm.
A Comparative Analysis of Android Malware

In this paper, we present a comparative analysis of benign and malicious Android applications, based on static features. In particular, we focus our attention on the permissions requested by an application. We consider both binary classification of malware versus benign, as well as the multiclass problem, where we classify malware samples into their respective families. Our experiments are based on substantial malware datasets and we employ a wide variety of machine learning techniques, including decision trees and random forests, support vector machines, logistic model trees, AdaBoost, and artificial neural networks. We find that permissions are a strong feature and that by careful feature engineering, we can significantly reduce the number of features needed for highly accurate detection and classification.
Transforming the Emotion in Speech using a Generative Adversarial Network

In recent years, natural and highly accurate outputs in domain transfer tasks have been achieved by deep learning techniques. Especially, the advent of Generative Adversarial Networks (GANs) has enabled the transfer of objects between unspecified domains. Voice conversion is a popular example of speech domain transfer, which can be paraphrased as domain transfer of speakers. However, most of the voice conversion studies have focused only on transforming the identities of speakers. Understanding other nuances in the voice is necessary for natural speech synthesis. To resolve this issue, we transform the emotions in speech by the most promising GAN model, CycleGAN. In particular, we investigate the usefulness of speech with low emotional intensity as training data. Such speeches are found to be useful when the training data contained
A CMOS-based Neuron Circuit for Spiking Neural Networks with Memristive Synapse

The brained-inspired spiking neural networks (SNNs), which can be applied on visual information processing and speech recognition, is attracting great attention especially when combined with emerging electronic synapse (i.e. memristor). The neuron, transmitting and receiving spiking signals, is a vital component to realize the biological rules of SNNs, such as leaky-integrate-and-fire (LIF), inhibitory period, winner takes all (WTA) and bidirectional transmission. In this work, we propose and implement a novel spiking neuron circuit based on complementarymetal-oxide-semiconductor (CMOS) technology. Experimental results show that the neuron circuit can generate spiking pulses, realize lateral inhibition and change the weight of synapse.
AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries

This paper examines the validity of forecasting economic variables by using machine learning. AI (artificial intelligence) has been improved and entering our society rapidly, and the economic forecast is no exception. In the real business world, AI has been used for economic forecasts, but not so many studies focus on machine learning. Machine learning is focused in this paper and a traditional statistical model, the autoregressive (AR) model is also used for comparison. A comparison of using an AR model and machine learning (LSTM) to forecast GDP and consumer price is conducted using recent cases from G7 countries. The empirical results show that the traditional forecasting AR model is a little more appropriate than the machine learning model, however, there is little difference to forecast consumer price between them.
An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option.
Energy Efficiency-Aware Joint Resource Allocation and Power Allocation in Multi-User Beamforming

As a conventional metric, spectrum efficiency has been investigated widely in communication systems due to the limited bandwidth resources and increasing subscriber density. Beamforming technology is used in TD-LTE-A and 5G downlink to improve the spectrum efficiency and system capacity, and a proper resource allocation algorithm can also be used to improve the spectrum efficiency on the premise of satisfying user equipment (UE) requirements. In recent years, energy efficiency has attracted more and more attention as the power consumption on the communication systems increases rapidly, which lays a heavy burden on the environment. In this paper, we propose a joint resource allocation and power allocation algorithm in multi-user beamforming mode, which aims at maximizing the energy efficiency, and also takes UE requirements and spectrum efficiency into consideration. The proposed algorithm first calculates the user priorities and gives a UE grouping method to allocate resource blocks (RBs) to different UEs under the assumption that the power is equally allocated among UEs on the same RB. After that, on each RB, it calculates the power allocation among different UEs to realize the improvement of energy efficiency. In addition, the satisfaction of UE requirements is added in the algorithm as a constraint. Simulation results demonstrate that the proposed algorithm shows an excellent performance on system capacity, i.e., spectrum efficiency, and also provides higher energy efficiency than existing algorithms. Moreover, the UE requirements can be better satisfied and system fairness can also be improved.
Emerging Trends in MIMO OFDM

Multiple-input multiple-output (MIMO) wireless technology together with orthogonal frequency-division multiplexing (MIMO-OFDM) is a horny air-interface solution for next-generation wireless native space networks (WLANs), wireless metropolitan space networks (WMANs), and fourth-generation mobile cellular wireless systems. this text investigates the impact of equal and unequal received powers on the performances of various MIMO-OFDM schemes for terrestrial digital TV. More exactly, we tend to specialize in 3 kinds of nonorthogonal themes: the BLAST scheme, the Linear Dispersion (LD) code and therefore the Golden code, and that we compare their performances to that of Alamouti theme. using 2 receiving antennas, we tend to show that for moderate attenuation on the second antenna and high spectral potency, Golden code outperforms alternative schemes. However, Alamouti theme presents the most effective performance for low spectral potency and equal received powers or when one antenna is dramatically broken. when 3 antennas area unit used, we tend to show that Golden code offers the best lustiness to power unbalance at the receiving aspect.
Physical Layer Security in Wireless Sensor Networks Using Distributed Co-Phasing

In this paper, we consider physical layer security in wireless sensor networks (WSNs) using distributed co-phasing (DCP)-based transmissions. For this protocol, we first analyze the achievable ergodic secrecy rate of a single stream DCP system in the presence of one or more eavesdroppers. We show that the coherent combining gain offered by DCP leads to the signal-to-interference-plus-noise-ratio (SINR) over the main channel increasing as the square of the number of SNs <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> and that over the eavesdropper channel increasing linearly with <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>. This results in a strictly positive ergodic secrecy rate that increases as <inline-formula> <tex-math notation="LaTeX">$\log {N}$ </tex-math></inline-formula>. We then analyze the performance of multi-stream DCP and show that using <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> data streams in DCP leads to a <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula>-fold increase in the achievable secrecy rate at high SNRs. We also discuss an alternative power allocation scheme for multi-stream DCP, such as distributed maximal ratio transmission with a per-user power constraint and show that this improves the achievable secrecy rates as compared to standard multi-stream DCP. Finally, we analyze the role of artificial noise in improving the achievable secrecy rates. We validate the accuracy of these derived results and illustrate the efficacy of DCP in ensuring secure data fusion in WSNs using Monte Carlo simulations.
Click Prediction for Image Re-Ranking using Query-Specific Semantic Signature

Image re-ranking is a successful method to get better results of text-based image search. User click data has been used in image re-ranking, clicks have been exposed to more exactly express the relevance of retrieved images to search query. A significant problem for click-based method is the need of click data, since only a short number of web images have been clickedon by users. To solve this problem with predicting image clicks, we propose a query specific method for image click prediction, and apply the obtained click information to the image reranking. A query keywords given, from a pool of images is first retrieved based on textual information. The user have to pick a query picture from the pool, the remaining images are re-ranked based on their optical similarity with the query picture. A most important challenge is that the similarities of visual features do not well associate with images semantic meanings which interpret users search purpose. The proposed method is based on query-specific which is significantly to increase both the accurateness and effectiveness of image re-ranking. Keyword— Image search, image re-ranking, click.
Handover Skipping for LiFi

This paper studies handover skipping, which enables handovers between two non-adjacent access points (APs), in light fidelity (LiFi) networks. LiFi is an emerging wireless communication technology, which operates in a way similar to wireless fidelity (WiFi) but uses light waves as a medium. Compared with WiFi, LiFi has a relatively shorter range with a single AP. This could possibly cause more frequent handovers, and thus, handover skipping techniques are required. Conventional handover skipping methods rely on information about the user’s trajectory, which is not ready to use at the AP. In this paper, a novel handover skipping scheme based on the reference signal received power (RSRP) is proposed. The new approach combines the value of RSRP and its rate of change to determine the handover target. Since RSRP is already used in the current handover schemes, the proposed method does not require additional feedback. The results show that compared with the standard handover scheme and the conventional handover skipping method, the proposed method can reduce handover rate by up to 29% and 17% and improve throughput by up to 66% and 26%, respectively.
A Data Visualization Approach for Intersection Analysis using AIS Data

Automatic Identification System data has been used in several studies with different directions like traffic forecasting, pollution control or anomalous behavior detection in vessels trajectories. Considering this last subject, the intersection between vessels is often related with abnormal behaviors, but this topic has not been exploited yet. In this paper an approach to assist the domain experts in the task of analyzing these intersections is introduced, based on data processing and visualization. The work was experimented with a proprietary dataset that covers the Portuguese maritime zone, containing an average of 6460 intersections by day. The results show that several intersections were only noticeable with the visualization strategies here proposed.
Scrutinizing News Media Cooperation in Facebook and Twitter

In recent times, news medias avail oneself of online social media platforms for news promotion, sharing and commentary to a large extent mainly in Twitter, Facebook, and Reddit. Therefore, in the literature, researchers have been used machine learning and text mining techniques to attain useful insights from the news media data in social media in-order to understand the factors for gaining large audience attention. Different to the previous studies, analyses of the news media in this work are based on a set of new features; content features such as the originality of a news item, context features such as time and circadian patterns of a news media, and reader reactions. Our dataset includes 238K tweets and 128K Facebook posts of 48 most popular news medias shared during May-June 2017. In this study we explored; news producers, news consumers, inter news production patterns, inter news dissemination behaviors, sharing similar news items within Twitter and Facebook (cross-posts), and news readers reactions on news items. In addition, we investigated the best time period to receive highest readers’ attention towards their news items as this information is useful for other news medias to understand the best time duration to publish news items. Finally, we proposed a predictive model to increase news media popularity among readers and the results manifested that, a news media should disperse its own content and need to publish at first before other news media publish the same content in social media in-order to be popular and attract the attention from readers.
User Behavior Prediction Using Enhanced Pattern Tree Data Structure and Web Usage Mining

As we all know that, in today’s era World Wide Web places a measurable role by providing lots of information to the user which will be more useful. Thus, to know the process of discovering and analyzing usage patterns of user, the paper aims to predict the behavior of the user from the web logs. The weblogs is termed as web log mining or web usage mining, every click made by the user will be automatically entered into the weblogs of the corresponding web server. The proposed data structure, pattern tree can be used efficiently to store the usage patterns and their frequency of all the users using the path sharing. The Enhanced Pattern Tree (EPT) maintains the relationship between different patterns and the corresponding users as rules. Search for the specific pattern would yield the corresponding user and vice-versa in minimum no. of searches as rule sharing is used by the pattern tree data structure. This research work aims to develop a framework for analyzing user behaviour through user patterns obtained from the web server logs and supports the use of association rules for representing the relationship between user and patterns.
A Petri Net-based Method to Verify the Security of the SDN

Due to the widespread research on Software Defined Networks (SDNs), the network security has been duly noted. But most of those efforts ponder over SDN security from the protocol perspective. To the best of our knowledge, none of the attempts has paid attention to the security analysis and modeling of state and communication in the SDN. Therefore, this paper provides a different approach to security analysis. Our objective is to analyze the security via the transformation state of communication model. Additionally, we present the security analysis method based on token to explore the potential threatens. Finally, we analyze the SDN via the combination of the number of token and time series based on Petri Net, and give the results. Our results are very bright in using such models to analyze such security objectives.
Malayalam Word Sense Disambiguation using Maximum Entropy Model

Word Sense Disambiguation (WSD) is one of the pressing problems in Natural Language Processing (NLP), which determines the right sense of an ambiguous word in the specific context. WSD is considered as a harder problem as it depends on a set of classes, which vary depending on the context. There have been a number of researches performed in WSD in many languages, from dictionary-based methods to supervised learning methods and unsupervised learning approaches. In this paper, an attempt is made for the disambiguation of Malayalam words, which has a rich set of ambiguous words having different meanings. A semi supervised machine learning techniques combined with statistical approach mainly Maximum Entropy is experimented for Malayalam, which shows promising result for a set of corpus trained.
Human Intention Understanding From Multiple Demonstrations and Behavior Generalization in Dynamic Movement Primitives Framework

Human’s interference in the process of skill learning can improve the performance of the robot greatly. However, learning from demonstration to generate a new action with human behavioral characteristics in the varying situation is challenging. Generally, dynamic movement primitives (DMPs) method can generalize the trajectory imitating the demonstration, but cannot integrate the feature of multiple trajectories of different targets. In this paper, the proposed method contains two aspects of learning and generating. The statistical method Gaussian mixture model and Gaussian mixture regression (GMM-GMR) is used to extract the common characteristic and eliminate the uncertainty of the multiple demonstrations. To exert the ability of DMPs to generate a human-like motion to a new goal, and we model the shape parameter with locally weighted regression (LWR) method. To enhance the ability of DMPs in multiple trajectories learning, we propose the multivariate Gaussian process regression (MV-GPR) method to construct the regression model of shape parameters to reflect the human intentions, with respect to the target position. To verify the feasibility of the proposed method, we design a peg-in-hole experiment with proving generalization and obstacle avoidance performance. The results have shown that the strategy integrated the generalization of DMPs and feature regeneration ability of MV-GPR method, and the generated valid trajectory could achieve the peg-in-hole task with 6-DOF whole-arm avoidance.