Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Complexity Hybrid Precoder Design for mmWave Multi-User MIMO Systems: A Non-Iterative Approach

Massive multiple-input multiple-output (MIMO) will be an important ingredient in millimeter-wave (mmWave) cellular communication systems. A natural application of massive MIMO is simultaneous transmission to multiple users. Unfortunately, the hardware constraints in massive MIMO make it difficult to apply the conventional fully digital beamforming techniques, especially in the mmWave bands. Thus hybrid beamforming (HB) in which the overall beamformer consists of a low-dimensional digital beamformer followed by an analog beamformer has been proposed for reducing the number of costly radio frequency (RF) chains in massive MIMO systems. This paper considers an HB design for multi-user massive MIMO systems. We design a sub-optimal analog precoder based on the downlink-uplink duality. To support the general situation where the number of RF chains is not necessarily equal to the number of users, we propose a greedy selection algorithm to allocate RF chains. Finally, we consider the hardware impairment in analog part and extend the proposed algorithm to support the use of finite resolution phase shifters. Simulation results demonstrate that the proposed HB method makes a better tradeoff between complexity and performance compared with existing methods.
A Blind Document Image Watermarking Approach Based on Discrete Wavelet Transform and QR Code Embedding

Recently there is a growing interest in digital watermarking; which is an effective solution for detecting copyright infringement and to avoid illegal use of digital products. In this paper, we introduce a robust digital watermarking method for every type of document image by embedding QR code information in a document image invisibly. The cover document image is decomposed into n levels of frequency channels by using Discrete Wavelet Transform (DWT) and then QR code is embedded in a sub-band of the final level of DWT. The experiment is carried out on heterogeneous contents document image dataset. The results show that our method can work well with various type of document images and the proposed approach is robust to JPEG compression, rotation, scaling, cropping and salt and pepper noise.
An Adaptive Clustering Scheme Based on Modified Density-Based Spatial Clustering of Applications with Noise Algorithm in Ultra-Dense Networks

As a key technology to increase the system capacity in 5th generation (5G) mobile communications systems, ultra-dense networks (UDN) is proposed by deploying high-density wireless access points in hot spots. To mitigate the serious inter-cell interference(ICI) arising in UDN, we propose an adaptive clustering scheme as the basis for coordinated multipoint transmission and reception (CoMP), which has been proven to effectively eliminate interference. Two machine learning algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Particle Swarm Optimization (PSO), are introduced into the design of clustering scheme. Simulation results have shown that the proposed scheme can achieve a higher system throughput compared with modified K-means scheme. Furthermore, to be consistent with the concept of green communication, geographically isolated points could be identified and processed to save communicate resources with the proposed scheme.
Moment representation in the lattice Boltzmann method on massively parallel hardware

The widely-used lattice Boltzmann method (LBM) for computational fluid dynamics is highly scalable, but also significantly memory bandwidth-bound on current architectures. This paper presents a new regularized LBM implementation that reduces the memory footprint by only storing macroscopic, moment-based data. We show that the amount of data that must be stored in memory during a simulation is reduced by up to 47%. We also present a technique for cache-aware data re-utilization and show that optimizing cache utilization to limit data motion results in a similar improvement in time to solution. These new algorithms are implemented in the hemodynamics solver HARVEY and demonstrated using both idealized and realistic biological geometries. We develop a performance model for the moment representation algorithm and evaluate the performance on Summit.
Scalable Mutation Testing Using Predictive Analysis of Deep Learning Model

Software testing plays a crucial role in ensuring the quality of software systems. Mutation testing is designed to measure the adequacy of test suites by detecting artificially induced software faults. Despite their potential, the expensive cost and the scalability of mutation testing with large programs is a big obstacle in its practical use. The selective mutation has been widely investigated and considered to be an effective approach to reduce the cost of mutation testing. In the case of large programs where source code has hundreds of classes and more than 10 KLOC lines of code, the selective mutation can still generate thousands of mutants. Executing each mutant against the test suite is cost-intensive in terms of robustness, resource usage, and computational cost. In this paper, we introduce a new approach to extract features from mutant programs based on mutant killing conditions, i.e. reachability, necessity and sufficiency along with mutant significance and test suite metrics to extract features from mutant programs. A deep learning Keras model is proposed to predict killed and alive mutants from each program. First, the features are extracted using the Eclipse JDT library and program dependency analysis. Second, preprocessing techniques such as Principal Component Analysis and Synthetic Minority Oversampling are used to reduce the high dimensionality of data and to overcome the imbalanced class problem respectively. Lastly, the deep learning model is optimized using fine-tune parameters such as dropout and dense layers, activation function, error and loss rate respectively. The proposed work is analyzed on five opensource programs from GitHub repository consisting of thousands of classes and LOC. The experimental results are appreciable in terms of effectiveness and scalable mutation testing with a slight loss of accuracy.
Welfare Technology, Healthcare, and Behaviour Modelling - An Analysis

Welfare technology is a growing area of research due to the increase in the ageing population. In Nordic countries, public authorities are the primary healthcare providers. Therefore, there is significant of investment to help older people to live as long as they wish at their own home. At the University of SouthEastern Norway, a current project on welfare technology is being developed for this purpose, with a focus on human behaviour modelling. Through the research, gaps were found between the technology and the healthcare aspect of it. Consequently, difficulties for the consumer (the older people) arise. This article presents an analysis of connection and gaps between technology and the healthcare area of welfare technology for the ageing.
FailViz: A Tool for Visualizing Fault Injection Experiments in Distributed Systems

The analysis of fault injection experiments can be a cumbersome task. These experiments can generate large volumes of data (e.g., message traces), which a human analyst needs to inspect to understand the behavior of the system under failure. This paper introduces the FailViz tool for visualizing fault injection experiments, which points out relevant events for interpreting the failures. We also present a motivating example in the context of OpenStack, and point out future research directions.
Spectrum Sharing and Energy Cooperation in Wireless Powered Cognitive Radio Networks

In this work, we consider a cognitive radio system, where the primary user (PU) owns the spectrum but has scarce energy while the secondary users (SUs) have adequate energy but lack of spectrum. Thus, a spectrum sharing and energy cooperation scheme is proposed, where the SUs help transfer energy to the PU in the first phase, and in return, the PU allows the SUs to access the spectrum in the second phase. This is particularly beneficial when the PU is energy-limited wireless sensor node or internet of things and the transmitters of SUs are base stations or access points with sufficient energy supply. Without loss of generality, we aim to maximize the minimum data rate among all SUs by jointly optimizing the time- splitting factor between the two phases, the transmission power at the primary transmitter (PT) and the precoding vectors for secondary transmitters (STs) under the minimum data rate requirement of the PU, and the power constraint at each ST. We also guarantee the energy causality constraint at the PT, i.e., the total consumed energy should be no larger than the total available energy. To solve this non-convex problem, we propose an efficient iterative algorithm by applying the successive convex approximation (SCA) and further show that the proposed algorithm is guaranteed to converge. Simulation results are finally presented to show the effectiveness of our proposed scheme.
Crop Area Identification Based on Time Series EVI2 and Sparse Representation Approach: A Case Study in Shandong Province, China

The accurate and timely spatial distribution information of various crop types is vital for food security. In this study, the 2-band enhanced vegetation index (EVI2) data from Moderate Resolution Imaging Spectroradiometer (MODIS) were combined with sparse representation approach to identify the distribution of various crop types in Shandong Province, China. Three groups of input variables derived from EVI2 including annual time series EVI2 (TS), harmonic features (HF), and combined vector formed by harmonic features and texture features (HFT) were used. The online dictionary learning and orthogonal matching pursuit algorithms were applied to generate the dictionary and solve the sparse coefficients of the identified samples, respectively. Then, the label of the identified samples can be obtained according to the minimum residuals between the dictionary and the sparse coefficients of the identified samples. At the provincial level, the validation based on the statistical data showed that three groups of input variables presented lower than ±25% at relative errors, and input variables of HFT performed better than the other two. At the municipal level, the results achieved by using input variables of HFT also agreed well with the statistics with the coefficient of determination R2 > 0.85 for wheat and maize, as well as R2 > 0.71 for peanut and cotton during 2014-2016. These results demonstrate that the combination of the input variables of HFT derived from time series MODIS EVI2 data and sparse representation approach can be used for crop identification in the study area.
NOMA Concept for PC5-Based Cellular-V2X Mode 4 in Crash Warning System

This paper proposes a new concept Decentralized Broadcasting-NOMA (Non-Orthogonal Multiple Access) for PC5-based Cellular-Vehicle-to-Everything mode 4 (called mode 4) to accommodate more vehicles and pedestrians (called nodes) in Crash Warning System (CWS). The proposed concept is to apply NOMA to broadcast system with autonomous distributed random access. This paper develops two methods to enhance mode 4 in the proposed concept. The first method is that several nodes broadcast their signals in parallel with DB-NOMA. This method can boost the spectrum utilization efficiency. The second method is that relay nodes superpose their own transmission signal and other nodeâ€™s relay signal with DB-NOMA. This method can efficiently expand broadcast ranges without consuming the additional transmission slots. Simulations results revealed that the proposed method accommodated 119% more nodes compared with the original mode 4. Therefore, this paper concludes that the proposed concept is useful and the proposed methods based on the concept outperform the current mode 4.
Sparse Signal Reconstruction With Statistical Prior Information: A Data-Driven Method

Weighted $\ell _{1}$ minimization (WL1M) is a general and powerful framework for reconstructing sparse signals from underdetermined measurements. The performance improvement of WL1M owes to the incorporation of additional structural priors of signals by means of its weights. However, the selection of weights relies on hand-crafted designs in existing works, so that high-order structural priors of signals are hard to be captured. This paper proposes a data-driven method, namely RBM-WL1M, to alleviate this situation. In the RBM-WL1M, restricted Boltzmann machines (RBMs) are employed to learn the prior distribution of the signals from training data; furthermore, utilizing the RBM, high frequency support set and non-zero probabilities for each of the entries in signals can be estimated effectively, which are used to appropriately select the weights. In our experiments, the proposed framework demonstrates superior performance over several state-of-the-art CS methods on the Physikalisch-Technische Bundesanstalt(PTB) Diagnostic ECG Data set.
FIESTA: A Free Roaming Collaborative Immersive Analytics System

We present FIESTA, a prototype system for collaborative immersive analytics (CIA). In contrast to many existing CIA prototypes, FIESTA allows users to collaboratively work together wherever and however they wish---untethered from mandatory physical display devices. Users can freely move around in a shared room-sized environment, author and generate immersive data visualisations, position them in the space around them, and share and communicate their insights to one another. Certain visualisation tasks are also supported to facilitate this process, such as details on demand and brushing and linking.
Pilot-Assisted Sparse Channel Estimation Based on Mutual Incoherence Property

Properly designing scattered pilot pattern over orthogonal frequency division multiplexing (OFDM) subcarriers is important to improve the accuracy of the sparse channel estimation, while decreasing the number of the required pilots. In this paper, we propose a pilot-subcarrier allocation scheme that optimizes the pilot subcarrier patterns without any knowledge of channels, where channel estimation is done by interpolating the pilot- subcarriers scattered over frequency domain. The proposed pilot allocation scheme utilizes the mutual incoherence property (MIP) of the compressive sensing (CS) theory to formulate the pilot allocation problem into infinity norm problem. Then, MIP based weighted fast iterative shrinkage-thresholding algorithm (MIP-WFISTA) is proposed to solve the formulated problem. Simulation results validate that, compared with the conventional techniques, the proposed pilot design scheme achieves more accurate channel estimation and as a result better bit error rate (BER) performance while decreasing the number of the required pilots in frequency-selective fading environments.
Poster: Towards a Framework for Assessing Vulnerabilities of Brainwave Authentication Systems

In the quest to devise new alternatives to password-based authentication, behavioral biometrics have become more and more appealing due to the improved usability that comes with their unobtrusiveness. One such type of biometric are brainwaves, which can be nowadays easily measured and used to prove a person's identity. Given the potential for this technology to be adopted in the near future, it is paramount to analyze its security implications. Furthermore, recent advances in brain computer interfaces make feasible the usage of brainwaves to prove users' identity. This work presents a comprehensive framework for assessing the vulnerabilities of brainwave authentication systems, incorporating new attack vectors that target specific features of brain biometrics. Resting on this theoretical groundwork, we analyze the existing literature on attacks and countermeasures, identifying gaps and providing a foundation for future research. Furthermore, we evaluated a subset of attacks identified through the framework and report our preliminary results.
Parameter Estimation for Block Diagonalization Based Hybrid Beamforming in Massive MIMO Communications

Massive multiple-input multiple-output (MIMO) will be employed in the 5th generation mobile communications (5G). To implement the massive MIMO, hybrid beamforming (HB) is one of the most promising techniques, because HB is composed of the analog beamforming (AB) and the digital beamforming (DB) and thus can decrease the number of baseband and radio frequency (RF) circuits significantly. Furthermore, HB can reduce a much larger amount of cost and power consumption than the full DB (FDB) configuration, which requires one baseband and RF circuit for each antenna element. This paper proposes a scheme to estimate both phases for phase rotators in AB and a precoding matrix for DB so that HB can approximate the block diagonalization (BD), which can nullify interference from the undesired users and form efficient transmit beams. For time division duplex (TDD) multiuser MIMO communications, the proposed scheme estimates the FDB channel matrix in the uplink (UL) and the phase for the phase rotators of AB in the downlink (DL). After obtaining an equivalent channel matrix from both the estimated FDB channel matrix and the AB precoding matrix, the scheme estimates the DB precoding matrix in DL from the equivalent channel matrix so that HB can approximate BD in DL. Computer simulations demonstrate that HB employing the proposed scheme can achieve DL average bit error rate (BER) performances that are little inferior to those of FDB.
Knowledge Acquisition Framework from Unstructured Biomedical Knowledge Sources. (Environnement d'acquisition des connaissances à partir de sources de connaissances biomédicales non structurées)

In biomedicine, the explosion of textual knowledge sources has introduced formidable challenges for knowledge-aware information systems. Traditional knowledge acquisition methods have been proved costly, resource intensive and time consuming. Automation of large scale knowledge acquisition systems requires narrowing down the semantic gap between biomedical texts and structured representations. In this context, this study proposes a knowledge acquisition framework from biomedical texts. This contributes towards reducing efforts, time and cost incurred to minimaize ontology acquisition bottlenecks. The proposed framework approximates, models, structures and ontologizes implicit knowledge buried in biomedical texts. In the framework, the semantic disambiguator approximates biomedical artefacts from biomedical texts. The conceptual disambiguator models and structures the biomedical knowledge abstracted from the domain texts. Ontologization presents an explicit interpretation of biomedical artefacts and conceptualizations. The components of the framework are instantiated with scientific and clinical text documents and produced about four million concepts and seven million associations. This set of artefacts is structured into the lower ontological knowledge structure where the upper ontology structure is reused from existing ones. The conceptual structure is represented with graph formalism. The formal interpretation is based on OWL DL language primitives and constructs, which generates a set of OWL DL axioms. The set of OWL DL axioms is referred as the OWL ontology (Ko). The extent of approximation and quality of structural design are evaluated using criteria-based methods. A set of metrics is used to measure each criterion and showed encouraging results. Correctness measurements for concept entity are 70% for accuracy, 82% for completeness, 68% for conciseness and 100% for consistency. Quality measurement showed complex ontology structure with metrics values of 986,448 for vocabulary size, 18.73 for connectivity density, 145,246 for tree impurity and 226, 698 for graph entropy. The ontology schema potential metrics values are also 0.80 for relationship richness, 3 for attribute richness and 13,253 for inheritance richness. Ontology clarity showed an average readability, which is 3 attributes on average. The proposed framework has limitations to address the acquisition of individuals and entity attributes, losing cardinality information in the acquisition of the ontological knowledge. These lead to limitations on the formal interpretation of biomedical semantics, which in turn lead to deploy only existential restriction based interpretations. Thus, a way forward has been recommended to enhance semantic disambiguation and ontologization of the proposed framework so that they enable to accommodate the acquisition of cardinality and attribute information.
Resource Aware Chaining and Adaptive Capacity Scaling for Service Function Chains in Distributed Cloud Network

With the development of network technology such as software-defined network (SDN) and network function virtualization (NFV), Internet service providers (ISPs) are increasingly placing the virtual network function(VNF) instances at the network edge to provide network service. However, there are some issues to be tackled in the distributed SDN/NFV enabled cloud. Firstly, VNF instances require to be chained in predefined order to provide network services. It is a challenge to optimally select and chain VNF instances from the multi-instances. Moreover, due to the capacity limitation of the distributed edge nodes. The capacity of the Virtual Machines (VMs) that host VNFs should be proactively adjusted to cope with traffic demands. Since most existing works ignore the vertical capacity scaling problem in routing commodities with Service Function Chain (SFC) requests. In this paper, a fine-grained scheduling scheme at VM-level is proposed. Firstly, we formulate the SFC chaining problem as an Integer Linear Programming (ILP) model aiming to embed SFC requests with minimum estimated latency cost. Furthermore, we formulate the adaptive VNF resource allocation (VNF-AR) problem as a convex optimization. The theoretical optimal capacity for each VM can be derived from the Karush-Kuhn Tucker (KKT) conditions. At last, a novel joint optimization approach of VNF chaining and adaptive scaling (VNF-CAS) is proposed to efficiently embed the SFC requests. Performance evaluation shows that VNF-CAS can achieve better performance in SFC requests acceptance rate, average effective throughput, average load utilization and VM load balancing when it is compared with other algorithms in existing works.
Cross-Vendor Knowledge Transfer for Managed Security Services with Triplet Network

Managed security services detect incidents, i.e., successful attacks such as malware infection, in real time from a large number of alerts based on vendors' and security operations center's (SOC's) detection rules. To immediately find incidents, professional analysts in a SOC prioritize alerts if their indicators, i.e., meta-information of detection rules in alerts, are highly correlated with incidents. Indicators are typically divided into two priority levels, i.e., primary and secondary. However, levels of new indicators are difficult to accurately determine with a conventional system. Such a system determines an indicator's level as primary if the conditional probability of incidents occurring given an observation of the indicator's alert is high. Therefore, we propose a system for accurately determining levels of new indicators by focusing on alerts not recognized as incidents. With this system, we analyze the correlation between indicators made by different vendors then transfer knowledge of incidents between different vendors with a triplet network. We evaluate the effectiveness of the proposed system using 4,919,791 alerts collected from a large-scale SOC for one month. Our system identified 24.3% more primary indicators undiscovered at the time of data collection than a system without correlation analysis at a 5% false positive rate.