Miracle-dz/UniFAR-FTUs huggingface dataset:
Practically Feasible Recommender Systems for Cold Start Problems

Recommender systems are beneficial to both service providers and users, as they offer item recommendations to individual users based on their preferences. However, the recommender systems present cold start problems, which refer to the fact that it is difficult to recommend new items for newly added users. Recommender systems for existing users and items have been researched vigorously. On the other hand, cold start problems have received insufficient attention. In this paper, we propose methods for recommender filtering that can be applied to address cold start problems. Our models provide simple matrix approximation methods for analyzing large volumes of unlabeled data from potential items. We use matrix factorization techniques based on a pseudo-inverse matrix for rating approximation, which enables us to make cold start recommendations by using a user–characteristic matrix, item–feature matrix, and rating matrix. The proposed models can explicitly learn user demographics and item features to overcome the cold start problems. The results of a numerical experiment show that the proposed methods outperform several baselines when tested on the MovieLens 1M dataset.
Detection of Arabic Spam Tweets Using Word Embedding and Machine Learning

Twitter has become one of the most popular social networking platforms for sharing activities and opinions. In this study, we explore the idea of applying word embedding based features with machine-learning techniques to detect Arabic spam tweets. In addition, the effects of text domain of the collected corpus to learn word embedding is analyzed. This is evaluated using a publicly available dataset of 3503 tweets alongside with three popular classifiers for binary classification, namely: Naïve Bayes, Decision trees and SVM. The experimental results reveal that the proposed method outperforms the baseline approach in distinguishing between machine-generated tweets and human-generated tweets. An accuracy rate of 87.33% is achieved using skip-gram word2vec technique with SVM.
Chromatic Dispersion Estimation Based on CAZAC Sequence for Optical Fiber Communication Systems

We propose a chromatic dispersion (CD) estimation method based on constant amplitude zero-autocorrelation (CAZAC) sequence and fractional Fourier transform (FrFT) algorithm for optical fiber communication systems, which extends the application of CAZAC sequence. By applying FrFT on the CAZAC sequence signal, the optimal order is searched and employed to estimate the accumulated CD because that the CAZAC sequence shares similar properties with chirp signal. We demonstrate the feasibility of the proposed method by the simulation of a 28 GBaud polarization-division-multiplexed (PDM) quadrature phase-shift keying (QPSK) optical fiber communication system. The error of CD estimation is less than 70 ps/nm for the standard single mode fiber (SSMF) link of 2000 km with OSNR in the range of 10–20 dB and the launch power in the range of 0–4 dBm per channel in a five channel system. It shows that the method is robust to ASE noise and optical fiber nonlinearity. Furthermore, the proposed method has been experimentally verified with the error of CD estimation less than 50 ps/nm after 100–900 km fiber link transmission.
A Scalable Implementation for Real-Time Phased Antenna Array mmWave Testbeds

In this paper, a software-defined radio(SDR)-based phased antenna array mmWave testbed is proposed and implemented. We first provide the underlying system model pertaining to the mmWave testbed. Then, details of the mmWave phased antenna array and the transceiver baseband signal processing procedure are deliberately illustrated. Non-uniform linear beam coding is adopted in the beam codebook design and over-theair experiments are conducted to demonstrate the feasibility and validity. The results indicate that 6-bit phase shifter is sufficient for the 64QAM data transmission and phase noise resulted from the local oscillator can be neglected in our mmWave testbed. Besides, more attention should be paid to the phase quantization than the phase random error in the phased antenna array.
Secrecy Performance Analysis of Cognitive Radio Networks with Full-duplex Relaying

In this paper, we investigate the secrecy performance of cognitive radio networks (CRNs) with full-duplex (FD) relaying over Rayleigh channels, where the secondary transmitter (Alice) communicates with the secondary receiver (Bob) with the help of the FD relay in the presence of an eavesdropper (Eve). For secrecy performance enhancement, we propose two schemes at the FD relay, i.e., selection combining-zero forcing beamforming/maximal-ratio transmission (SC-ZFB/MRT) and SC-ZFB/ZFB. The closed-form and asymptotic expressions for the secrecy outage probability (SOP) and the secrecy throughput of the CRNs with SC-ZFB/MRT and SC-ZFB/ZFB schemes are derived, respectively. From the analysis, we demonstrate that: a) the proposed SC-ZFB/ZFB scheme can achieve better secrecy performance than that of the SC-ZFB/MRT scheme and b) when RS is fixed, the SC-ZFB/ZFB and SC-ZFB/MRT schemes always attain higher secrecy throughput than the previously proposed maximal-ratio combining (MRC)/MRT scheme.
A Flow Table with Two-Stage Timeout Mechanism for SDN Switches

In Software-Defined Network (SDN), OpenFlow implements fine-grained network policy by installing rules in the switch flow table. Limited by memory capacity, inactive flows have to be evicted from the flow table to make room for newer flows. Currently, OpenFlow uses the fixed timeout mechanism to manage the flow table. This may result in poor utilization of the flow table. In this paper, driven by an in-depth analysis on network traffic characteristics, we propose a novel flow table architecture with two-stage timeout mechanism to better identify and keep useful flow entries. First, each flow rule is installed into the primary table with a timeout calculated by the SDN controller. Each flow rule in the primary table is expected to be hit by a packet within its timeout, and then renew its timeout to receive more packets. Upon violation of this expectation, a timeout will expire, and the flow will be shelved onto the Inactive Flow Queue (IFQ). The IFQ provides a second chance for a flow to be promoted back to the primary table if a packet of this flow arrives before its eviction from the IFQ. With this design, this two-stage flow table keeps retiring the most useless flows. In particular, it enables shortlived flows to be evicted as soon as possible to avoid waste of flow table resources, and enables active flows to be better retained with the help of the IFQ. Experimental results using real network packet traces show 60% higher hit ratio than current OpenFlow with the same overall table size.
Lexical Entrainment in Interaction with Two Robots

When interacting with artificial agents such as a computer and robot, people tend to imitate the lexical choices of the agent. This phenomenon is called lexical entrainment. Most studies on lexical entrainment have focused on one-on-one conversation. However, in recent years, studies on human-agent interactions have suggested that a conversation with several agents had different effects on a person’s impression and behavior compared with a one-on-one conversation. Therefore, we conducted an experiment to compare the occurrences of lexical entrainment in a conversation with one robot and two robots. The results did not show explicit evidence that there was a difference in effects between lexical entrainment with one robot and two robots; however, through an additional analysis, we found that lexical entrainment might occur more frequently in a conversation with two robots compared to one robot. The findings indicate the potential of using several agents in a conversation to control human lexical choices effectively.
An Area-based Offloading Policy for Computing Offloading in MEC-assisted Wireless Mesh Network

Mobile edge computing (MEC) enables cloud computing capabilities at the edge of network with close proximity and low latency. Wireless mesh network (WMN) is a multi-hop communications network with features such as decentralization, scalability, and self-organization. The MEC-assisted WMN has very promising applications, which can be implemented in some special environments such as battlefield surveillance, disaster incidents, and emergency situations. In this paper, we use ns3 network simulator to build a mobile edge mesh computing (MEMC) system. We design an area-based offloading policy (ABOP) algorithm. Our numerical results demonstrate an improved performance, where mesh client can offload as many tasks as possible in different areas with low delay and high competition ratio.
Privacy-Preserving Authentication Protocol based on Hybrid Cryptography for VANETs

The key concerns in VANET communication are the security and privacy of the vehicles involved, but at the same time an efficient way to provide non-repudiation in the ad-hoc network is an important requirement. Most schemes proposed are using public key infrastructure (PKI) or symmetric key encryption to achieve security in VANET; both individually lack in serving the required purpose of providing privacy preservation of the involved On-Board Units (OBUs) (while still being able to offer non-repudiation) and amount to very sizeable overheads in computation. This paper proposes a privacy-preserving authentication protocol that employs hybrid cryptography, using the best features of PKI and symmetric cryptography to form a protocol that is scalable, efficient and offers services of integrity, non-repudiation, conditional privacy, and unlinkability; while still keeping the computational overhead at a reasonable level. The performance and security analysis of this scheme is provided to support the propositions.
Research on Water Quality Prediction Based on SARIMA-LSTM: A Case Study of Beilun Estuary

As water environment is an important part of mangrove ecosystem, an efficient prediction of water quality is the foundation for judging the health of wetland ecosystem. And it also contributes a lot to the smooth development of environmental protection work. Based on the data of water quality and weather in Beilun Estuary, this paper chooses permanganate index and the content of ammonia nitrogen, which can reflect the water quality, as forecasting targets. We propose a multi-feature prediction method called SARIMA-LSTM on the basis of seasonal autoregressive integrated moving average model and long short-term memory. Through the combination of linear and non-linear model, this method can possess a better prediction effect considering the influence of weather on water quality. And the experimental results of four models show that this method has higher accuracy, stability and reliability.
Si3N4 Waveguide-Coupled Microdisk Resonators with a Quality Factor of 107

We design and fabricate dispersion-engineered ∼830nm-thick Si<sub>3</sub>N<sub>4</sub> waveguide-coupled microdisks. Our resonators show a loaded quality (Q)-factor of ∼1×10<sup>7</sup>. We observe optical parametric oscillations in a 345μm-radius microdisk with a threshold pump power of ∼50 mW in the waveguide.
Design and Optimization of Nonuniform Helical Antennas With Linearly Varying Geometrical Parameters

Nonuniform helical antennas have many degrees of freedom, which makes the search space for the optimal design very challenging. The objective of this paper is to systematically analyze nonuniform helical antennas with linearly varying geometrical parameters and to provide analytical equations that approximate the optimal design and the gain of the designed antennas. Using various optimization algorithms, we made a large database of the optimal nonuniform helical antennas with linearly varying geometrical parameters. Based on these results, we made analytical equations that approximate the optimal design and the gain of the designed antennas. These equations allow for a fast design procedure yielding all necessary parameters needed for the design and fabrication of nonuniform helical antennas that meet specified characteristics. Special attention is devoted to antenna losses. Antennas designed following the presented procedure achieve around 2.5 dB higher gain than uniform helical antennas of the same axial length, while maintaining the bandwidth and axial ratio. As a verification of the proposed design procedure, a helical antenna with the central operating frequency of 1 GHz was designed, simulated, fabricated, and measured. The comparison between measured and simulated results confirms the validity of the presented design procedure.
Wear-Leveling Aware Data Allocation for Chip Multiprocessors with Non-Volatile Memory

Non-Volatile Memory (NVM) has many advantages that provide new choice for embedded storage systems. However, the NVM has a limited number of writes and the overhead of write operation is greater than read operation. In order to overcome these drawbacks, it is necessary to reduce the read and write overhead of the NVM and make the NVM wear-leveling. In this paper, we use Iterational Optimal Data Allocation (IODA) algorithm to achieve the global minimum read and write overhead data allocation, and then, we propose Greedy and Global Balance Optimization Allocation (GBOA) algorithm to allocate NVM data into blocks to achieve NVM wear-leveling at the software-level. Experimental results show that the lifetime of NVM based on the Greedy algorithm is improved by 37.96% and the GBOA is improved by 49.52% compared to the Random algorithm on average, while we can obtain the global minimum read and write overhead.
The Impact of Cache Partitioning on Software-Based Packet Processing

Traditionally, network functions were often implemented on expensive special networking hardware (e.g. middle-boxes). The architectural concept of network functions virtualization (NFV) enables the implementation of network functions (e.g. routing, firewall) as virtual network functions (VNF) in software to execute them on off-the-shelf x86 hardware. To leverage the capabilities of multi-core processors, the VNFs must be distributed on several cores. However, when many VNFs are executed on different CPU cores, the VNFs compete for the last level cache (LLC) which is a shared resource between all CPU cores and might become a performance bottleneck. With the help of cache partitioning, it is possible to divide the LLC into dedicated blocks for each VNF. This paper investigates if a performance gain can be achieved by cache partitioning. With the help of real testbed measurements, a Linux router as well as a DPDK-based router is examined. Our results show that cache partitioning can be beneficial if the cache partition size is sufficiently large.
Performance Analysis and Beamforming Design for Secure Visible Light Communications

This paper analyzes the secrecy rate and designs the secure beamforming for an indoor visible light communication (VLC) network consisting of one transmitter, one legitimate receiver, one eavesdropper, and several friendly jammers. In the VLC network, both the main and wiretapping channels have a uniformly distributed signal input and corrupted with additive white Gaussian noise. The imperfect channel state information (CSI) is considered by using the ellipsoidal approximation method. Under the amplitude constraints of the data and the interfering signal, the secrecy rate for the VLC network is analyzed. A beamforming optimization problem is proposed to maximize the secrecy rate with the given constraints. It is shown that the optimization problem is non-convex. In this paper, it is transformed into a solvable quasi-convex linear search problem. Numerical results demonstrate the performance gain of the optimal beamformer compared to the zero-forcing scheme. Moreover, the secrecy performance under different CSI uncertainties is also provided.
Dynamic MRI Reconstruction Exploiting Partial Separability and t-SVD

In this paper, we proposed a new method to reconstruct dynamic magnetic imaging (dMRI) data from highly undersampled k-t space measurements. First, we use the partial separability (PS) model to capture the spatiotemporal correlations of dMRI data. Then, we introduce a new tensor decomposition method named as tensor singular value decomposition (t-SVD) to the reconstruction problem. PS and low tensor multi-rank constrains are jointly enforced to reconstruct dynamic MRI data. We develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the proposed optimization problem. The experimental results demonstrate the superior performance of the proposed method.
QOBST: An Adaptable Matching Model for Content-Based Pub/Sub Systems

The matching efficiency of content-based publish and subscribe model(pub/sub model) can be more optimized since users on Internet have different needs for data and popular data is always changing. In this paper, a brand new matching model - Quasi-Optimal Binary Search Tree (QOBST) which is adaptive was proposed. Based on the idea of optimal binary search tree and avoiding the complex construction process of optimal binary search tree, QOBST is designed as an approximate optimal solution with time complexity of O(log2N). The subscription division of QOBST can completely avoid the secondary matching. Its index structure is changed with the changes of subscription and inputting data, making it easier to match the data with high frequency. Experimental results show that QOBST can be applied to content-based pub/sub models, especially distributed content-based pub/sub models.
Empirical Analysis of Shallow and Deep Architecture Classifiers on Emotion Recognition from Speech

Emotion detection or recognition from speech is currently a very crucial area of research with a plethora of applications in day to day life. Human communication depends heavily on mood, emotions and feelings. Availability of advanced signal processing techniques and artificial intelligence techniques like machine learning architecture (shallow classifiers) and neural network architecture (deep classifiers) have made this domain a booming area of research with increased efficiency and accuracy. This paper aims to empirically analyze various statistical machine learning algorithms like Naive Bayes, Support Vector Machine, Random Forest and deep learning algorithms like Convolutional Neural Network, Long Short Term Memory over emodb dataset which is publicly available for emotion classification into angry, sad, happy, neutral, other classes. A comparison of shallow classifiers on the basis of accuracy will help future researchers in providing hindsight into the field of emotion detection. Same goes for the comparison between the deep learning techniques.
Enhanced Sensing-Based Resource Scheduling Algorithm for 5G V2V Communications

Vehicle-to-vehicle $(V2V)$ communication is regarded as a promising technology in the intelligent transportation system to improve road safety and traffic efficiency. The most widely used resource scheduling algorithm inV2V communications is sensing-based semi-persistent scheduling (SPS) algorithm. However, traditional sensing-based SPS algorithm is challenged by lack of resource preemption and reselection mechanisms to satisfy the diverse performance requirements of the fifth generation (5G)V2V services. In this paper, an enhanced sensing-based resource scheduling algorithm is proposed for $5G V2V$ communications. Specifically, in order to guarantee the stringent performance requirements of the V2V services with higher priorities, a priority-based resource preemption mechanism is given. Then, a resource sensing and one-shot reselection mechanism is proposed to avoid resource collision of diverse services. The system level simulation results suggest that the proposed algorithm outperforms traditional sensing-based SPS algorithm, and the maximum gain of average packet reception ratio can achieve 5% in highway case.
Branch Use in Practice: A Large-Scale Empirical Study of 2,923 Projects on GitHub

Branching is often used to help developers work in parallel during distributed software development. Previous studies have examined branch usage in practice. However, most studies perform branch analysis on industrial projects or only a small number of open source software (OSS) systems. There are no broad examinations of how branches are used across OSS communities. Due to the rapidly increasing popularity of collaboration in OSS projects, it is important to gain insights into the practice of branch usage in these communities. In this paper, we performed an empirical study on branch usage for 2,923 projects developed on GitHub. Our work mainly studies the way developers use branches and the effects of branching on the overall productivity of these projects. Our results show that: 1) Most projects use a few branches (<5) during development; 2) Large scale projects tend to use more branches than small scale projects. 3) Branches are mainly used to implement new features, conduct version iteration, and fix bugs. 4) Almost all master branches have been requested by contributors to merge their contributions; 5) There always exists a branch playing a more important role in merging contributions than other branches; 6) Almost all commits of more than 75% branches are included in the master branches; 7) The number of branches used in a project has a positive effect on a project's productivity but the effect size is small, and there is no statistically significantly difference between personal projects and organizational projects.
Achievable Rate optimization for WET Enabled mmWave D2D Communication Systems

This paper focuses on the achievable rate optimization of relay-assisted millimeter wave (mmWave) Device-to-Device (D2D) communication systems. In the system, full-duplex relays with multiple antennas are deployed to improve the performance of data transmission between D2D users. In addition, the source node can wirelessly harvest the energy from relays for D2D data transmission through wireless energy transfer (WET). With respect to the proposed system, the closed-form expressions of both the achievable rate and harvested energy are derived. Based on those expressions, the impact of pilot sequences on WET and information transmission are analyzed. A novel scheme is proposed to determine the optimal pilot sequence that can maximize the system achievable rate. Simulation results confirm the accuracy and optimality of the proposed scheme.
Let's Meet: A Smartphone Co-Navigation System Based on Relative Direction and Proximity Change for Indoor Environments

Indoor navigation systems are important to many location-based services. Most existing systems require pre-trained databases, extra beacons and/or detailed indoor layout, which are rather expensive and cumbersome. In this paper, we propose a co-navigation system called Let's Meet to help two users to meet up with each other without incurring such burdensome requirements. In particular, the proposed system provides real-time movement instructions for two users based on the estimated users' relative direction and proximity change. Two modes are defined in the Let's Meet for computing relative direction and proximity change during the co-navigation process. In the far mode, the satellites data are exploited to provide the users with the relative direction and proximity change estimation; In the near mode, a decision factor that quantifies the change of access points and their signals to estimate the proximity change. In addition, trajectories of both users are presented in real-time during the navigation process to provide intuitive instructions for users. We have implemented the system on the Android platform and tested its effectiveness in practical indoor environments.