Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloudification and Slicing in 5G Radio Access Network. (Cloudification et découpage des réseaux d'accès radio de cinquième génération)

The forthcoming fifth generation (5G) mobile networks is ambitious to evolve a broad variety of capabilities for the intended usage scenarios. To facilitate this vision, a paradigm shift is provided in 5G to establish a flexible and customizable communication system. Essentially, the 5G architecture shall be designed with a certain level control flexibility via incorporating the principles of softwarization and virtualization to turn a physical network into multiple customized end-to-end (E2E) logical network tailored to multiple service requests. Correspondingly, two key pillars are highlighted and investigated for the 5G network: slicing and cloudification. These two techniques are mostly challenging in the radio access network (RAN) domains due to its stringent requirements among real-timeliness, computing complexity and energy performance. To this end, this thesis is organized in tow parts to investigates these two techniques on the RAN domain. In the first part, we investigate the cloudification of the RAN, i.e., cloud RAN (C-RAN), in which the centralized RAN processing in a cloud can support efficient resource multiplexing and joint multi-cell processing. Despite its appealing, the C-RAN concept faces challenges in terms of the severe capacity and latency requirements of the fronthaul (FH) interface that connects distributed remote radio unit (RRU) toward the centralized baseband processing unit (BBU). We first investigate the impacts among several factors, such as functional split, packetization and packet scheduling, on the Ethernet-bsaed FH transportation to increase multiplexing gain. Further, two implemented functional splits over the OpenAirInterface (OAI) platform are presented and compared in terms of several KPIs to justify the C-RAN applicability. We also present a model and an analytical framework for the E2E RAN service delivery to maximize the network spectral efficiency by considering multiple design factors. In the second part, we focus on the RAN slicing to not only provide different levels of isolation and sharing to each slice but also enable the customization across control plane (CP), user plane (UP), and control logic (CL). Hence, we propose a flexible execution environment, denoted as the RAN runtime slicing system, in order to (a) virtualize slice service instances over the underlying RAN modules, and to (b) abstract radio resource to increase the multiplexing gain. Additionally, a number of new interfaces are identified for the communications between the RAN runtime and the orchestration system in support of slice-based multi-service chain creation and chain placement, with an auto-scaling mechanism to increase the performance. Finally, the runtime software development kit (SDK) is on top of the RAN runtime introduced to facilitate the development of control applications. These control applications can compose sophisticated and customized control logics that can be chained across different domains and planes.
OPTIMAL RESPONSE ARCHITECTURE FOR NETWORK OVERLAY PROTOCOL

Genetic networks are often designed to work with a single routing path, such as the shortest path, which is known as sub-optimal performance. On the other hand, the optimal performance policies suggested above (i.e. back pressure) require that each device in the network take dynamic routing decisions. In this paper, we examine the overlay structure of dynamic routing, so you only need a subset of devices (overlay nodes) to make dynamic routing decisions. We define the basic set of nodes that must form traffic to maximize network performance in many products. We apply the optimal node approach algorithm to many graphs and the results show that a small fraction of the overlapping nodes is sufficient for maximum performance. Finally, we propose a policy based on guiding thresholds and policy, which dynamically controls the traffic jams in overlapping nodes. The BP-T policy has been clarified to increase the performance of the situation when the underlying tracks do not overlap. In all simulated simulation scenarios, OBP not only achieves total productivity but also reduces delay compared to optimal performance guidance.
Factors Affecting Medicine-Induced Demand and Preventive Strategies: A Scoping Review

Context: Increasing demand for medicines can influence the health system performance and impose a financial burden on individuals and society. Therefore, the current study aimed to investigate factors affecting the induced demand for medicines and provide strategies to address the issue. Evidence Acquisition: A scoping review was conducted using three databases including PubMed, Cochrane Library, and Embase from January 1990 until March 2017. By defining inclusion and exclusion criteria, 33 papers that considered the factors affecting the induced demand for medicines and the strategies to reduce the demand were included in the study. The papers were used to extract common themes. Results: The final factors were categorized into eight groups, including monitoring mechanisms, demand-side factors, supply-side factors, structural factors, legal-political factors, insurance coverage and subsidies, commercial interactions and advertisements, and educational factors. In addition, strategies to address them were extracted. Conclusions: Since in developing countries the most important factors are the lack of a comprehensive monitoring system and supply-side factors, it seems that monitoring mechanisms such as performance audit and guideline development can reduce the induced demand for medicines.
Total Oxidant and Antioxidant Status in Patients with Obstructive Sleep Apnea and the Effect of Continuous Positive Airway Pressure

Obstructive sleep apnea (OSA) is a common disorder of middle-aged adults, affecting 4% of men and 2% of women [1]. The incidence of cardiovascular mortalities and morbidities such as hypertension, ischemic heart disease, arrhythmias, and stroke is higher in patients with OSA than in the general population [2]. OSA is characterized by recurrent upper airway obstruction. Patients with OSA experience intermittent hypoxia, leading to hypoxemia, hypercapnia, and increased sympathetic activation. Several studies have reported increased oxidative stress in patients with OSA [3-5]. Changes in blood oxygen saturation levels during sleep in OSA may result in the oxidant-antioxidant imbalance in favor of higher oxidants, depending on recurrent hypoxic events [6]. Increased generation of reactive oxygen species in vivo can lead to the depletion of one or more antioxidants, and the loss can be measured as an index of oxidative stress [7]. However, there are Total Oxidant and Antioxidant Status in Patients with Obstructive Sleep Apnea and the Effect of Continuous Positive Airway Pressure
A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things

With the evolutionary development of latency sensitive applications, delay restriction is becoming an obstacle to run sophisticated applications on mobile devices. Partial computation offloading is promising to enable these applications to execute on mobile user equipments with low latency. However, most of the existing researches focus on either cloud computing or mobile edge computing (MEC) to offload tasks. In this paper, we comprehensively consider both of them and it is an early effort to study the cooperation of cloud computing and MEC in Internet of Things. We start from the single user computation offloading problem, where the MEC resources are not constrained. It can be solved by the branch and bound algorithm. Later on, the multiuser computation offloading problem is formulated as a mixed integer linear programming problem by considering resource competition among mobile users, which is NP-hard. Due to the computation complexity of the formulated problem, we design an iterative heuristic MEC resource allocation algorithm to make the offloading decision dynamically. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of execution latency and offloading efficiency.
Distributed Algorithm for Making Scale-Free Network by Preferential Rewiring Without Growth

The digital payment system that uses cryptocurrency, such as Bitcoin, is a distributed ledger working on a peer-to-peer network. We present a method to make a scale-free network for such applications. Using some biased physical quantities that are observable in sites, we can make the scale-free network through processes of cooperating distributed sites. Each node only proposes connecting to the more attractive node among randomly known nodes. The candidate node that is found by each node agrees to set two-way links if the requesting node is more attractive than the old node that is already connected. Once they establish the new bidirectional relationship, they, respectively, remove the outgoing link to the less attractive node. We analytically calculate the connectivity distribution and show that the scaling exponent is 2.5. By Monte Carlo simulations, we confirm that a power law distribution of the scaling exponent 2.5 describes the degree distribution of the topology.
Beyond the backchannel: tweeting patterns after two educational conferences

ABSTRACT Mathematics teachers are using Twitter as a backchannel at conferences. Understanding whether teachers are using Twitter differently at traditional conferences like the National Council of Teachers of Mathematics (NCTM) Annual as compared to new conference models like TwitterMathCamp (TMC) is important. Tweets surrounding the 2015 NCTM Annual and TMC2015 were collected and analyzed to determine the differences in tweeting behavior of the mathematics teachers. Our results showed teachers use Twitter in common ways, despite the attention placed on social media at new conferences.
PENERAPAN ALGORITMA BEST FIRST SEARCH (BFS) DALAM PENCARIAN LOKASI APOTEK K-24 BERBASIS ANDROID DI KOTA MAKASSAR

Kebutuhan akan sarana penyedia obat perlu ditingkatkan seiring dengan banyaknya jenis penyakit yang muncul, baik yang disebabkan oleh virus, bakteri atau pun akibat pengaruh lingkungan dan alam seperti cuaca. Namun, salah satu kendala bagi masyarakat ketika adanya penyakit yang menyerang secara tiba-tiba di malam hari adalah sulitnya menemukan apotek yang buka 24 jam karena pada umunya apotek hanya beroperasi pada pukul 08.00-21.00 WITA. Oleh karena itu apotek K-24 memberikan solusi pelayanan obat 24 jam. Meskipun apotek ini sudah memiliki beberapa cabang di Kota Makassar, namun untuk menemukan lokasi keberadaannya masih sulit. Dengan menggunakan smartphone Android, pencarian lokasi dapat dilakukan dengan mudah. Dan dengan penerapan algoritma Best First Search (BFS) pada aplikasi pencarian, maka jarak tempuh yang terdekat menuju lokasi tersebut dapat diketahui. Algoritma BFS ini akan membandingkan jarak untuk setiap rute yang mungkin dapat ditempuh menuju lokasi pencarian dan akan menghasilkan jalur terpendek sehingga hal ini dapat lebih mengefisienkan waktu dan menghemat biaya perjalanan.
An Efficient Mobility Model for Improving Transmissions in Multi-UAVs Enabled WSNs

Multi-Unmanned Aerial Vehicle (UAV) enabled Wireless Sensor Networks (WSNs) provide a wide range of applications, covering civilian and military expeditions along with geographical navigation, control, and reconnaissance. The coordinated networks formed between the UAVs and the WSNs help in enhancing the issues related to quality as well as coverage. The overall coverage issues result in starvation as an effect of long waiting time for the nodes, while forwarding the traffic. The coverage problem can be resolved by an intelligent choice of UAV way-points. Therefore, a specialized UAV mobility model is required which takes into account the topological structure as well as the importance of strategic locations to fix UAV way-points and decide the data transmission paradigm. To resolve this problem, a novel mobility model is proposed, which takes into account the attraction factor for setting up the way-points for UAV movements. The model is capable of deciding between the locations which result in more coverage, increased throughput with lesser number of UAVs employed, as justified by the simulation results and comparative evaluations.
With a View to the Side: YouTube’s Sidebar and YouTuber’s Linguistic Style as Hints for Trust-Related Evaluations

ABSTRACT Based on a heuristic approach to information processing, thematic reference of YouTube’s sidebars and YouTuber’s linguistic style are viewed as cues that should impact viewers’ evaluation of videos. In this study, a 2 × 2 online experiment was conducted wherein these factors were varied systematically for a video about nutrition myths. 147 participants assessed the credibility of information, YouTuber’s trustworthiness, and the self-reported learning gain. Results showed that a sidebar referring to similar (vs. unrelated) videos increased participants’ perceived trustworthiness of a YouTuber who used a YouTube-typical (vs. formal) language. Moreover, participants judged the learning gain to be higher when YouTube’s sidebar referred to similar videos. However, thematic reference of sidebar and linguistic style did not impact participants’ credibility judgments. Since people seem to recognize YouTube’s sidebars when evaluating videos, YouTube’s recommendation criteria might not only mediate videos, but also influence people’s judgments of YouTubers and videos.
Deep learning for waveform estimation and imaging in passive radar

We consider a bistatic configuration with a stationary transmitter transmitting unknown waveforms of opportunity and a moving receiver, and present a Deep Learning (DL) framework for passive synthetic aperture radar (SAR) imaging. Existing passive radar methods require two or more antennas which are either spatially separated or colocated with sufficient directivity to estimate the underlying waveform prior to imaging. Our approach to passive radar only requires a single receiver, hence reduces cost and increases versatility. We approach DL from an optimization perspective and formulate image reconstruction as a machine learning task. By unfolding the iterations of a proximal gradient descent algorithm, we construct a deep recurrent neural network (RNN) that is parameterized by transmitted waveforms. We cascade the RNN structure with a decoder stage to form a recurrent-auto encoder architecture. We then utilize backpropagation to learn transmitted waveforms by training the network in an unsupervised manner using SAR measurements. The highly non-convex problem of backpropagation is guided to a feasible solution over the parameter space by initializing the network with the known components of the SAR forward model. Moreover, prior information regarding the waveform structure is incorporated during initialization and backpropagation. We demonstrate the effectiveness of the DL-based approach through extensive numerical simulations that show focused, high contrast imagery using a single receiver antenna at realistic SNR levels.
Fusobacterium nucleatum in the Colorectum and Its Association with Cancer Risk and Survival: A Systematic Review and Meta-analysis

Background: The gut microbiome, in particular Fusobacterium nucleatum, has been reported to play a role in colorectal cancer development and in patient prognosis. We aimed to perform a systematic review and meta-analysis of published studies to assess the prevalence of F. nucleatum in colorectal tumors and evaluate the association between F. nucleatum and colorectal cancer development and prognosis. Methods: MEDLINE, EMBASE, and Web of Science databases were systematically searched for studies published until January 2019. Random effects meta-analyses were used to assess the prevalence of F. nucleatum in patients with colorectal cancer or tissues relative to controls and survival in F. nucleatum–positive versus –negative patients. Results: Forty-five relevant articles were identified. Meta-analyses indicated higher odds of F. nucleatum being present in colorectal tissue samples from patients with colorectal cancer [n = 6 studies, pooled OR = 10.06; 95% confidence intervals (CI), 4.48–22.58] and individuals with colorectal polyps (n = 5 studies, pooled OR = 1.83; 95% CI, 1.07–3.16) compared with healthy controls. Similar results were apparent in fecal samples, and when comparing tumor with adjacent normal tissue. Meta-analyses indicated poorer survival in patients with colorectal cancer with high versus low F. nucleatum abundance (n = 5 studies, pooled HR = 1.87; 95% CI, 1.12–3.11). Conclusions: A consistent increase in the prevalence and/or abundance of F. nucleatum in colorectal cancer tissue and fecal samples compared with controls was apparent. High abundance of F. nucleatum in colorectal tumors was also associated with poorer overall survival. Impact: F. nucleatum could be useful as a diagnostic and prognostic marker for colorectal cancer or as a treatment target.
Physiotherapeutic treatment of a patient with diagnosis of ankle sprain

Title: Physiotherapeutic treatment of a patient with ankle sprain Author: Christoforos Koupparis Location of clinical work placement: C.L. P.A (Centrum léčby pohybového aparátu) Aim The aim is a further understanding of an anterior talofibular ligament sprain. Generally in my thesis you can see how the structure of an ankle joint is and the function of the same joint by a kinesiological and a biomechanical point of view. Furthermore it will be described in details a therapy plan about that diagnosis. Summary My bachelor thesis contains two main parts, the general part and the special part. In the general part I analyze the branches of anatomy, kinesiology and biomechanics for the ankle joint. In this part you can find also an overview of the ankle sprain and the physiotherapy examinations that need to be done in such situations. On the other hand, the special part is an extended report of my case study. There are included kinesiological examinations (initial-final) and the rehabilitation program that I followed day by day with my patient during my clinical work placement. At the end there are the final results and a conclusion Results The results of my case study were positive and this is deduced from the final outcome of my patient. At an early stage of the rehabilitation program, the patient had pain and swelling relief. By the end of the therapy sessions we managed to increase the ROM in the physiological levels, we resolved the joint play restrictions and the hypertonic muscles got relaxed. Strengthening of both lower limb muscles was a main goal that also achieved. Finally we fixed the bad stabilization of the ankle joint and the patient‟s posture became significantly balanced.
New Value Metrics using Unsupervised Machine Learning, Lexical Link Analysis and Game Theory for Discovering Innovation from Big Data and Crowd-sourcing.

We demonstrated a machine learning and artificial intelligence method, i.e., lexical link analysis (LLA) to discover innovative ideas from big data. LLA is an unsupervised machine learning paradigm that does not require manually labeled training data. New value metrics are defined based on LLA and game theory. In this paper, we show the value metrics generated from LLA in a use case of an internet game and crowd-sourcing. We show the results from LLA are validated and correlated with the ground truth. The LLA value metrics can be used to select high-value information for a wide range of applications.
Large Scale Image Retrieval based on User Generated Content

Online photo sharing systems provide a valuable source of user generated content (UGC). Most Web image retrieval systems use textual annotations to rank the results, although these annotations do not only illustrate the visual content of an image, but also describe subjective, spatial, temporal, and social dimensions, complicating the task of keyword based search. The research in this thesis is focused on how to improve the retrieval of images in large scale context , i.e. the Web, using information provided by users combined with visual content from images. Di erent forms of UGC are explored, such as textual annotations, visual annotations, and click-throughdata, as well as di erent techniques to combine these data to improve the retrieval of images using visual information. In conclusion, the research conducted in this thesis focuses on the importance to include visual information into various steps of the retrieval of media content. Using visual information, in combination with various forms of UGC, can signi cantly improve the retrieval performance and alter the user experience when searching for multimedia content on the Web.
Probabilistic, Variable and Interaction-aware Situation Recognition

Future advanced driver assistance systems (ADAS) as well as autonomous driving functions will extend their applicability to more complex highway scenarios and inner-city traffic. For these systems it is a prerequisite to know how an encountered traffic scene is most likely going to evolve. Situation recognition aims to predict the high level behavior patterns traffic participants pursue. Thus, it provides valuable information that helps to predict the next few seconds of a traffic scene. The extension of ADAS and autonomous driving functions to more complex scenarios poses a problem to state-of-the-art situation recognition systems due to the variability of the encountered scene layouts, the presence of multiple interacting traffic participants and the concomitant large number of possible situation classes. This thesis proposes and discusses approaches that tackle these challenges. A novel discriminative maneuver estimation framework provides the possibility to assess traffic scenes with varying layout. It is based on reusable, partial classifiers that are combined online using a technique called pairwise probability coupling. The real-world evaluations indicate that the assembled probabilistic maneuver estimation is able to provide superior classification results. A novel interaction-aware situation recognition framework constructs a probabilistic situation assessment over multiple traffic participants without relying on independence assumptions. It allows to assess each traffic participant individually by using maneuver estimation systems that determine complete conditional distributions. A real-world evaluation outlines its applicability and shows its benefits. The challenges associated with the increasing number of possible situation classes are addressed in two ways. Both frameworks allow to reuse classifiers in different contexts. This reduces the number of models required to cope with a large variety of traffic scenes. Moreover, a situation hypotheses selection scheme provides an efficient way for reducing the number of situation hypotheses. This lowers the computational demands and eases the load on subsequent systems.
Optimization of Counterwind Disturbance Compensation Control Algorithm for UAV Flight

The mechanical parameters of UAV in the headwind flight are easily disturbed by nonlinear variation of the wind force, so the optimal control design is needed. A counterwind disturbance compensation control algorithm for UAV flight based on static anti-saturation yaw suppression is proposed. The dynamic parameter model and control constraint parameter model of UAV flight is constructed under the action of headwind disturbance to realize the optimal configuration of UAV flight dynamics in uncertain environment. The vector velocity and radial acceleration of UAV flight are optimized and matched in the vortex center polar coordinate system. The static anti-saturation yaw suppression method is used to compensate the disturbance and correct the yaw in the headwind flight environment of UAV. Under the optimal control law, the UAV carrier is guided to fly headwind along the predetermined flight trajectory, and the stability control of UAV flight is realized. The simulation results show that the proposed algorithm has good output stability and accurate tracking ability, reduces the flight control error and improves the control quality.