Miracle-dz/UniFAR-FTUs huggingface dataset:
On the performance of hybrid beamforming for millimeter wave wireless networks

The phenomenal growth in the demand for mobile wireless data services is pushing the boundaries of modern communication networks. Developing new technologies that can provide unprecedented data rates to support the pervasive and exponentially increasing demand is therefore of prime importance in wireless communications. In existing communication systems, physical layer techniques are commonly used to improve capacity. Nevertheless, the limited available resources in the spectrum are unable to scale up, fundamentally restricting further capacity increase. Consequently, alternative approaches which exploit both unused and underutilised spectrum bands are highly attractive. This thesis investigates the use of the millimeter wave (mmWave) spectrum as it has the potential to provide unlimited bandwidth to wireless communication systems. As a first step toward realising mmWave wireless communications, a cloud radio access network using mmWave technology in the fronthaul and access links is proposed to establish a feasible architecture for deploying mmWave systems with hybrid beamforming. Within the context of a multi-user communication system, an analytical framework of the downlink transmission is presented, providing insights on how to navigate across the challenges associated with highfrequency transmissions. The performance of each user is measured by deriving outage probability, average latency and throughput in both noise-limited and interference-limited scenarios. Further analysis of the system is carried out for two possible user association configurations. By relying on large antenna array deployment in highly dense networks, this architecture is able to achieve reduced outages with very low latencies, making it ideal to support a growing number of users. The second part of this work describes a novel two-stage optimisation algorithm for obtaining hybrid precoders and combiners that maximise the energy efficiency (EE) of a general multi-user mmWave multiple-input, multiple-output (MIMO) interference channel network involving internet of things (IoT) devices. The hybrid transceiver design problem considers both perfect and imperfect channel state information (CSI). In the first stage, the original non-convex multivariate EE maximization problem is transformed into an equivalent univariate problem and the optimal single beamformers are then obtained by exploiting the correlation between parametric and fractional programming problems and the relationship between weighted sum rate (WSR) and weighted minimum mean squared error (WMMSE) problems. The second stage involves the use of an orthogonal matching pursuit (OMP)-based algorithm to obtain the energy-efficient hybrid
A Genetic algorithm for the optimal design of a multistage amplifier

The optimal sizing of analog circuits is one of the most complicated processes, because of the number of variables taken into, to the number of required objectives to be optimized and to the constraint functions restrictions. The aim is to automate this activity in order to accelerate the circuits design and sizing. In this paper, we deal with the optimization of the three stage bipolar transistor amplifier performances namely the voltage gain (AV), the input impedance (ZIN), the output impedance (ZOUT), the power consumption (P) and the low and the high cutoff frequency (FL,FH), through the Genetic Algorithm (GA). The presented optimization problem is of multi-dimensional parameters, and the trade-off of all parameters. In fact, the passive components (Resistors and Capacitors) are selected from manufactured constant values (E12, E24, E48, E96, E192) for the purpose of reduce the cost of design; also, the intrinsic parameters of transistors (hybrid parameters and the junction capacitances) are considered variables in order not to be limited in design. SPICE simulation is used to validate the obtained result/performances.
Wiretap Code Design by Neural Network Autoencoders

In industrial machine type communications, an increasing number of wireless devices communicate under reliability, latency, and confidentiality constraints, simultaneously. From information theory, it is known that wiretap codes can asymptotically achieve reliability (vanishing block error rate (BLER) at the legitimate receiver Bob) while also achieving secrecy (vanishing information leakage (IL) to an eavesdropper Eve). However, under finite block length, there exists a tradeoff between the BLER at Bob and the IL at Eve. In this work, we propose a flexible wiretap code design for degraded Gaussian wiretap channels under finite block length, which can change the operating point on the Pareto boundary of the tradeoff between BLER and IL given specific code parameters. To attain this goal, we formulate a multi-objective programming problem, which takes the BLER at Bob and the IL at Eve into account. During training, we approximate the BLER by the mean square error and the IL by schemes based on Jensen’s inequality and the Taylor expansion and then solve the optimization problem by neural network autoencoders. Simulation results show that the proposed scheme can find codes outperforming polar wiretap codes (PWC) with respect to both BLER and IL simultaneously. We show that the codes found by the autoencoders could be implemented with real modulation schemes with only small losses in performance.
Sparse Representation in 2-D Ultrasound Image of Heart

: To reduce energy minimization in 2D ultrasound images of cardiac motion, the input images are loaded with multiplicative Rayleigh noise . Spatial smoothness is done by sparse representation after the images are segmented into a number of pixel elements via patch. Dictionary learning is the next step to regularize the obtained solution. This proposed work provides accuracy and strain errors, then compare with state of the art algorithm. Furthermore, it is denoised by K-SVD to get an accurate clear image.
DataDeps.jl: Repeatable Data Setup for Reproducible Data Science

We present DataDeps.jl: a julia package for the reproducible handling of static datasets to enhance the repeatability of scripts used in the data and computational sciences. It is used to automate the data setup part of running software which accompanies a paper to replicate a result. This step is commonly done manually, which expends time and allows for confusion. This functionality is also useful for other packages which require data to function (e.g. a trained machine learning based model). DataDeps.jl simplifies extending research software by automatically managing the dependencies and makes it easier to run another author’s code, thus enhancing the reproducibility of data science research.
A Novel Local Motion Planning Framework for Autonomous Vehicles Based on Resistance Network and Model Predictive Control

This paper presents a novel local motion planning framework in a hierarchical manner for autonomous vehicles to follow a trajectory and agilely avoid obstacles. In the upper layer, a new path-planning method based on the resistance network is applied to plan behaviors (e.g. lane keeping or changing), where the human-like factors can be included to simulate different driver styles, such as the aggressive, moderate, and conservative. The planned results (i.e. the lane-change command and the local planned path) will guide the lower-layer planner to decide the local motion. In the lower layer, for the sake of simplicity and alleviation of the computational burden, two separate model predictive controllers (MPC) based on a point-mass kinematic model are utilized for both longitudinal and lateral motion planning. Finally, a super-twisting sliding mode controller (STSMC) based motion tracker is designed to show the feasibility of the proposed decoupled planning method and decide the desired control actions of autonomous vehicles. Several scenarios are defined to comprehensively test and demonstrate the effectiveness and the real-time applicability of the new motion-planning framework. The results show that the proposed method performs very well in the planning and tracking process and takes less than $\text{25 ms}$ for the whole planning process, which can be easily implemented in real-world applications.
‘Age Matters’: A panel study investigating the influence of communicative and passive smartphone use on well-being

ABSTRACT Communicative and passive online use have been shown to affect individuals’ well-being in opposite ways. While communicative use is often associated with beneficial outcomes, passive use may even harm individuals’ well-being. However, existing studies have paid insufficient attention to the influence of age on these associations. Additionally, little evidence is available regarding the impact of communicative and passive smartphone use on individuals’ well-being across the life-span. Drawing upon the theoretical approaches of Socioemotional Selectivity Theory, Social Convoy Model, and Social Compensation Hypothesis, we assumed that particularly communicative smartphone use may be beneficial for older adults’ well-being, helping them to maintain their personal relationships. Results of a two-wave panel survey (N T2 = 461) revealed a significant negative influence of passive smartphone use on well-being irrespective of age. In line with the Socioemotional Selectivity Theory and Social Compensation Hypothesis, communicative smartphone use, however, only had a positive influence on well-being for adults older than 63 years, but not for younger adults. Our findings contribute to a broader understanding of the consequences of different types of smartphone use on well-being across generations in the longitudinal context.
Enabling Data Trustworthiness and User Privacy in Mobile Crowdsensing

Ubiquitous mobile devices with rich sensors and advanced communication capabilities have given rise to mobile crowdsensing systems. The diverse reliabilities of mobile users and the openness of sensing paradigms raise concerns for data trustworthiness, user privacy, and incentive provision. Instead of considering these issues as isolated modules in most existing researches, we comprehensively capture both conflict and inner-relationship among them. In this paper, we propose a holistic solution for trustworthy and privacy-aware mobile crowdsensing with no need of a trusted third party. Specifically, leveraging cryptographic technologies, we devise a series of protocols to enable benign users to request tasks, contribute their data, and earn rewards anonymously without any data linkability. Meanwhile, an anonymous trust/reputation model is seamlessly integrated into our scheme, which acts as reference for our fair incentive design, and provides evidence to detect malicious users who degrade the data trustworthiness. Particularly, we first propose the idea of limiting the number of issued pseudonyms which serves to efficiently tackle the anonymity abuse issue. Security analysis demonstrates that our proposed scheme achieves stronger security with resilience against possible collusion attacks. Extensive simulations are presented which demonstrate the efficiency and practicality of our scheme.
A Method for Astral Microtubule Tracking in Fluorescence Images of Cells Doped with Taxol and Nocodazole

In this paper, we describe an algorithm that performs automatic detection and tracking of astral microtubules in fluorescence confocal images. This sub-population of microtubules only exists during and immediately before mitosis and aids in the spindle orientation by connecting it to the cell cortex. Anomalies in their dynamic behaviour play a causal role in many diseases, such as development disorders and cancer. The main novelty of the proposed algorithm lies in the fact it provides a fully automated estimation of parameters related to microtubule dynamic instability (growth velocity, track length and track lifetime), and helps in understanding the effects of intermediate drug concentrations. Its performance has been objectively assessed using publicly available synthetic data and largely employed metrics. Moreover, we present experiments addressing cell cultures doped with different concentrations of taxol and nocodazole. Such drugs are known to suppress the microtubule dynamic instability, but their effects at intermediate concentrations are not completely assessed. The algorithm has been compared with other state-of-the-art approaches, tested on consistent real datasets. The results are encouraging in terms of performance, robustness and simplicity of use, and the algorithm is now routinely employed in our Department of Molecular Biotechnology.
EVALUASI PENERAPAN UNDIKNAS MOBILE: ANALISIS TECHNOLOGY ACCEPTANCE MODEL

Undiknas Mobile is an application to facilitate students in carrying out their education. Based on the Technology Acceptance Model (TAM) with the addition of output quality and social influences this study aims to analyze the application of Undiknas Mobile through user intentions to continue using this system until they graduate from Undiknas University. This study is conducted by carrying a survey of 263 students at Undiknas university and use statistic descriptive as a technique of data analysis. The result of this study is the students have an intention to continue using Undiknas Mobile until they complete their study since they perceived the use and the ease of use of Undiknas Mobile. The output quality that is produced by Undiknas Mobile also accurate. In terms of social influences, it can be seen that only injunctive norm that can influence the users’ intention to use Undiknas Mobile.
Speed Up Similarity Search of Time Series Under Dynamic Time Warping

Similarity search is a foundational task in time series data mining. Although there are many ways to measure the similarity of time series, a lot of evidence indicates that dynamic time warping (DTW) has the best robustness in many applications. Unfortunately, the expensive computational cost limits its application in large-scale databases. To speed up similarity search under DTW, we design a framework of two-stage similarity search for time series. In the first stage, we propose an improved lower bounding distance, which can be used to discard plenty of dissimilar series to get a set of candidate sequences. In the second stage, to efficiently get the retrieval result from the set of candidate sequences, we explore early abandoning strategy to avoid the full calculation of DTW. Extensive experiments are conducted on real-world data sets. The experimental results indicate that the proposed method can improve the retrieval efficiency of similarity search under DTW and guarantee no false dismissals.
Service user involvement in cancer professionals’ education: perspectives of service users

Abstracts Background Service user involvement has been demonstrated as an important aspect of cancer professionals’ education. There is some understanding of service users’ incentive to be involved, but little insight into what motivates them. This study explores this concept more fully. Purpose To explore the motivations and experience of service users’ involvement in radiotherapy and oncology education. Materials and methods One-to-one interviews were conducted with service users who have been involved in education at the university. Thematic analysis was conducted. Five participants were asked about their motivations and experience of taking part in a variety of educational activities. Results The experience of being involved gave the participants a sense of wellbeing and purpose. Three sub-themes were identified that related to service users’ motivation for being involved in educational activities with undergraduate students. These were to promote awareness by sharing their experiences of cancer; improve patient care through learning from negative experience; and personal reward skills for survivorship. Conclusions This study has highlighted the importance of hearing the voice of the service users; a two-way engagement for which there are benefits for both the students and service users. The involvement of service users in education becomes part of their personal journey.
ArguMessage: A system for automation of message generation using argumentation schemes

This paper describes a system that uses argumentation schemes and limited user input to automatically generate persuasive messages that encourage behaviour change. We have used this system in the domain of healthy eating, but are also exploring its use in other domains such as behaviour change for cyber-security. The argumentation schemes used have been selected and amended by mapping them to Cialdini’s principles [5].
A Stable Control Algorithm for Multi Robot Formation

This paper presents the developed trajectory tracking controller for a formation of nonholonomic robots, which combines features from the leader-follower and virtual-structure approaches. The implemented decentralized control strategy allows the robots to be relatively independent and to switch easily between the executed individual tasks and the collective tasks. Convergence is thoroughly analyzed and guarantied using the Lyapunov approach.
Proportional hazard model for cutting tool recovery in machining

The proportional hazard model is a statistical method capable of including information on environmental and operating conditions. In machining, in the reliability field of a cutting tool, the interest of using proportional hazard model is highlighted. On one hand, the environmental and operating conditions are described and taken into account as explanatory variables. Three covariates are considered, namely, the vibration signal, the hardness material, and the lubrication/cooling. On the other hand, a new baseline hazard function is designed according to phenomena of tiny tool breakage followed by a self-sharpening process. This latter phenomenon, which can be considered as a rare event, prompted us to study extreme value theory to propose an adequate baseline hazard function. The newly obtained baseline hazard function will be named generalized extreme value proportional hazard model. This function is obtained thanks to the Gumbel function and has the property to be non-monotonic, an increasing then decreasing function. An alternative option as a baseline hazard function, based on the flexible Weibull distributions, is also proposed. Results produced in this article show the impact of all these variables on the surface roughness of the machined parts. According to reliability studies, the premature replacement of the cutting tool implying financial losses can be delayed. This may be of particular significance and benefit, in terms of sustainable development, in the case of mass production, by limiting the frequency of tool replacement.
SKELETOME: An eResearch Platform for the Skeletal Dysplasia Domain

Skeletal dysplasias are a heterogeneous group of genetic disorders affecting human skeletal development. Currently, there are over 440 recognized types, categorized into 40 groups. Patients with skeletal dysplasias have complex medical issues, such as short stature, degenerative joint disease, scoliosis or neurological complications. Since most skeletal dysplasias are very rare (<1:10,000 births), data on clinical presentation, natural history and best management is sparse. The lack of data makes existing patient cases a precious resource for biomedical research because they enable scientists to study, among other things, the effects of single genes on human bone and cartilage development and function. The resulting insights may lead to a better understanding of the pathogenesis of more common connective tissue disorders, such as arthritis or osteoporosis.
Demonstration of GenoMetric Query Language

In the last ten years, genomic computing has made gigantic steps due to Next Generation Sequencing (NGS), a high-throughput, massively parallel technology; the cost of producing a complete human sequence dropped to 1000 US$ in 2015 and is expected to drop below 100 US$ by 2020. Several new methods have recently become available for extracting heterogeneous datasets from the genome, revealing data signals such as variations from a reference sequence, levels of expression of coding regions, or protein binding enrichments ('peaks') with their statistical or geometric properties. Huge collections of such datasets are made available by large international consortia. In this new context, we developed GenoMetric Query Language (GMQL), a new data extraction and integration language. GMQL supports queries over thousands of heterogeneous datasets; as such, it is key to genomic data analysis. GMQL queries are executed on the cloud, after being translated and optimized; our best deployment uses Spark over Hadoop. Datasets are described by the Genomic Data Model (GDM), which provides interoperability between many data formats; GDM combines abstractions for genomic region data with the associated experimental, biological and clinical metadata. GMQL is targeted to the bio-informatics community for facilitating data exploration and for integrating data extraction and data analysis; this demonstration highlights its usability and expressive power. We show GMQL at work from a Web-based user interface and from a language embedding (Python).
Property A and best L1-Approximation

An important property within the linear theory of best L1-approximation, the so-called Property A, is discussed. Indeed, this property characterizes all finite-dimensional L1(μ)unicity subspaces of continuous functions with respect to measures μ of a big class. Moreover, some extensions are considered: Property Ak, characterizing those subspaces where the sets of best L1(μ)-approximations have at most dimension k, and Property Aper characterizing L1(μ)-unicity subspaces of continuous periodic functions.
Phase I Trial

Los objetivos del estudio del estudio Fase 1 en el desarrollo de drogas anticancer son: determinar la dosis mas alta tolerada, programas de administracion, patrones de toxicidad, propiedades farmacoquineticas, y si fuera posible, potencial terapeutico. Su diseno depende grandemente de datos obtenidos en el desarrollo preclinico. Muchos protocolos requieren una dosis inicial a nivel de 1/10 de la dosis letal en ratones (en mg/m2), escalando por etapas, siguiendo un esquema tipo Fibonacci, y administrando la droga una vez o diariamente por cinco dias cada tres o cuatro semanas. Los estudios Fase 1 se limitan a pacientes con funcion hepatica y renal relativametne buena y con neoplasias clinicamente estables que han sido confirmadas histologicamente y son resistentes a teapia convencional. Los hallazgos correlacionados dosis/programa con niveles plasmaticos/toxicidad son incorporados en el diseno de estudios terapeuticos subsiguientes
Effect of a preventive intervention programme on the prevalence of anterior knee pain in volleyball players

Abstract Chronic knee disorders, such as patellofemoral pain syndrome and patellar tendinosis, are common injuries in volleyball players. Using a randomized clinical trial, the aim of the present study was to determine the effect of a 4-month in-season intervention programme on the prevalence of anterior knee pain in volleyball players. No significant differences were observed between the intervention and control group for the prevalence of anterior knee pain after the intervention programme (odds ratio =1.58 [95% confidence interval: 0.60–4.20]). Also, no significant differences were observed for individuals with previously sustained anterior knee pain (OR =0.81 [95% CI: 0.33–2.20]). It was revealed that the risk for recurrent anterior knee pain is significantly higher than the risk for newly sustained anterior knee pain (OR =5.79 [95% CI: 1.59–21.00]). In conclusion, we were unable to confirm any preventive effect of the intervention programme on the prevalence of anterior knee pain. However, a previous study using the same intervention and measuring the effect on the intrinsic risk factors of anterior knee pain showed a positive effect.