Miracle-dz/UniFAR-FTUs huggingface dataset:
Device-to-Device Communication in 5G: Towards Efficient Scheduling

5G wireless networks are expected to carry large traffic volumes due to the growth of mobile devices and the increasing demand for high data rates from applications. Device to device communication is one of the suggested technologies to support this increasing load and enhance the capacity of networks. However, the implementation of D2D communication reveals many barriers that include communication scheduling, for which the architecture remains complex and obscure. In this paper, an overview of the available literature on the implementation of networks supporting D2D communication is presented, emphasizing the complexity of the offered solutions. This paper also offers a study of the impact of different device distribution models on the throughput of the devices. The paper introduces the challenges and makes the case for the need to find a more efficient D2D scheduler providing less complexity.
Development of an algorithm for forecasting and preventing emergency situations in industrial traffic control systems based on data analysis of multi-code labels

This article proposes a method for controlling the movement of industrial products based on data from multi-code labels using the algorithm for forecasting and preventing extraordinary situations. In the course of the work, possible extraordinary situations arising in the process of displacement were analyzed. Also on the basis of the developed algorithms, simulation modeling was carried out, according to the results of which it was shown the effective use of this method for controlling the movement of products in industrial enterprises.
Fluorescence in rectal cancer surgery

Fluorescence image-guided surgery (FIGS) is a relatively emerging optical surgical navigation modality. In the present overview, we will outline the potential role of FIGS applied to rectal oncological surgery. FIGS enables accurate real-time evaluation of bowel perfusion. It allows to visualize the draining lymphatics, providing intraoperative mapping and sentinel node identification to tailor the optimal oncological resection. Critical structures, including ureters, can be highlighted by means of fluorescence to prevent inadvertent injuries. Cancer-specific fluorescent molecules allow to precisely evaluate radical tumor removal and to estimate the surgical margins. Fluorescence-enhanced endoscopy might improve the diagnostic yield.
Evaluating and Developing Methods to Assess Business Process Suitability for Robotic Process Automation - A Design Research Approach

Robotic Process Automation (RPA) is an emerging approach to automation within or across information systems. While it has grown immensely during recent years, academic studies on the topic have been quite scarce. This study focuses on one aspect of this automation, namely how to select which processes to automate. The purpose of this study is to review existing methods for selecting processes to automate and if possible, suggest improvements to these. In addition, this study also aims to investigate if BPMN (Business Process Model and Notation) can be used to assist in the process selection. In order to fulfill this purpose, the study began by reviewing existing methods and gathered a total of 23 methods or recommendations for how to select processes. These methods were then applied at a partner company for four specific processes. The processes were selected during a set of interviews after which the processes were modelled using BPMN and assessed using the criteria from the existing methods. This revealed a number of issues with existing methods and the use of BPMN for this purpose. A new methodology was designed, the RPA Suitability Framework, which tried to address these issues, both with the existing selection methods as well as BPMN. The RPA Suitability Framework was then applied to five new processes from the partner company to see how it fared compared to previous methods. This evaluation was done by comparing the outcome of the methods, the content of the methods as well as during an evaluation workshop with the partner company. Many improvements could be seen, but further testing of the new framework would be needed before a general recommendation to use it could be given.
Supporting Faculty to Face Challenges in Design and Delivery of Quality Courses in Virtual Learning Environments

Existing guidelines, standards, and frameworks for addressing quality of online courses often highlight the importance of setting up effective systems to support faculty in overcoming the emerging challenges they face in design and delivery of these courses. However, not much information is available in literature on the means of tackling these difficulties, which are ever evolving in line with the dynamic nature of learning technologies and mode of teaching and learning in virtual environments. This qualitative study sought to identify these challenges and emerging solutions. Data was collected using interviews with academic administrators and faculty experienced in design and delivery of online courses and programs. As a result, several themes emerged, among them the need for an academic institution to dynamically adapt its mission and culture to the evolving nature of online teaching and learning. Other emergent challenges and suggested solutions are discussed.
Accurate, Energy-Efficient, Decentralized, Single-Hop, Asynchronous Time Synchronization Protocols for Wireless Sensor Networks

This paper concerns with the synchronization of infrastructure impoverished sensor networks under harsh conditions. It suggests three novel asynchronous, decentralized, energyefficient time synchronization protocols. The protocols require only single hop, sparse communication with unlabeled neighboring nodes of the network to determine accurately the time of the gateway node. The time of a node is considered as a dynamical variable of a discrete system whose evolution is asynchronously activated/inhibited by another dynamical switching system. The protocols are termed: Timed Sequential Asynchronous Update (TSAU), Unidirectional Asynchronous Flooding (UAF) and the Bidirectional Asynchronous Flooding (UAF). Along with intensive simulation, the protocols are implemented and tested on the MicaZ sensor node platform. A comprehensive evaluation of the energy consumption, memory requirements, convergence time, local and global synchronization errors of the proposed protocols are carried-out against Flooding Time Synchronization Protocol (FTSP) and Flooding Proportional Integral Time Synchronization Protocol (FloodPISync). All the analysis show that these protocols outperform the well known protocols. Also, being asynchronous, they are more realistic relative to the synchronous ones.
Successful multidisciplinary clinical approach and molecular characterization by whole transcriptome sequencing of a cardiac myxofibrosarcoma: A case report

BACKGROUND Cardiac tumors are rare and complex entities. Surgery represents the cornerstone of therapy, while the role of adjuvant treatment remains unclear and, in case of relapse or metastatic disease, the prognosis is very poor. Lack of prospective, randomized clinical trials hinders the generation of high level evidence for the optimal diagnostic workup and multimodal treatment of cardiac sarcomas. Herein, we describe the multidisciplinary clinical management and molecular characterization of a rare case of cardiac myxofibrosarcoma in an elderly woman. CASE SUMMARY A 73-year-old woman presented signs and symptoms of acute left-sided heart failure. Imaging examination revealed a large, left atrial mass. With suspicion of a myxoma, she underwent surgery, and symptoms were promptly relieved. Histology showed a cardiac myxofibrosarcoma, a rare histotype of cardiac sarcoma. Eight months later, disease unfortunately relapsed, and after a multidisciplinary discussion, a chemotherapy with doxorubicin and then gemcitabine was started, achieving partial radiologic and complete metabolic response, which was maintained up to 2 years and is still present. This report is focused on the entire clinical path of our patient from diagnosis to follow-up, through surgery and strategies adopted at relapse. Moreover, due to their rarity, very little is known about the molecular landscape of myxofibrosarcomas. Thus, we also performed and described preliminary genome analysis of the tumor tissue to get further insight on mechanisms involved in tumor growth, and to possibly unveil new clinically actionable targets. CONCLUSION We report a case of cardiac myxofibrosarcoma that achieved a very good prognosis due to an integrated surgical, cardiac and oncologic treatment strategy.
A Physics-Specific Change Point Detection Method Using Torque Signals in Pipe Tightening Processes

Change point detection in torque signals has widely been adopted for quality inspection during pipe tightening processes. Previous studies on the change point detection in this process generally focus on directly detecting the change points throughout torques without considering the underlying mechanism that generates various quasi-periodic nonlinear profiles, thereby introducing a series of false change points and increasing the risk of releasing defective pipes. To overcome this problem, we propose a novel change-point detection approach by fully considering the profile generating mechanism, and introduce a similarity-weighted matrix with an integration of dynamic time warping and tightening physics. Thus, the probability of false detection of change points is reduced. A weighted regression model is developed to determine the authentic change points by introducing the tightening process constraints. The performance of the proposed approach is demonstrated by both numerical and real case studies, and results show that the proposed method achieves a more effective detection power than the other existing methods in the pipe tightening processes. Note to Practitioners—This paper was motivated by the real industrial needs of change point detection in torque signals during pipe tightening processes. Identifying the change points that substantially captures the tightening process conditions, and thus assuring the quality of pipe connections, is of great practical interest. A key challenge to this problem is that multiple uncertainties exist in the pipe tightening process, thereby leading to various nonlinear profiles with different lengths in torque signal. This fact introduces a number of fake change points and consequently mixes with the real change points regarding the process conditions. To address this issue, we propose to generate a “physical basis function” to adaptively assign the weights to those nonlinear profiles that may produce the fake points, by using dynamic time warping. To better use this method for change point detection, two factors should be noted: 1) the generated basis function should fully comply with the process physics and 2) this method can be widely used in torque signals with different lengths and multiple nonlinear profiles.
Device-to-Device Communication Facilitating Full-Duplex Cooperative Relaying Using Non-Orthogonal Multiple Access

This letter presents a device-to-device (D2D) enabling cellular full-duplex (FD) cooperative protocol using non-orthogonal multiple access (NOMA), where an FD relay assists in relaying NOMA far user's signal and transmits a D2D receiver's signal simultaneously. The ergodic capacity, outage probability, and diversity order of the proposed protocol are theoretically investigated under the realistic assumption of imperfect self and known interference cancellation. The Outcome of the investigation demonstrates the performance gain of the suggested protocol over conventional FD cooperative NOMA system.
Computational analysis of world music corpora

The comparison of world music cultures has been considered in musicological research since the end of the 19th century. Traditional methods from the field of comparative musicology typically involve the process of manual music annotation. While this provides expert knowledge, the manual input is timeconsuming and limits the potential for large-scale research. This thesis considers computational methods for the analysis and comparison of world music cultures. In particular, Music Information Retrieval (MIR) tools are developed for processing sound recordings, and data mining methods are considered to study similarity relationships in world music corpora. MIR tools have been widely used for the study of (mainly) Western music. The first part of this thesis focuses on assessing the suitability of audio descriptors for the study of similarity in world music corpora. An evaluation strategy is designed to capture challenges in the automatic processing of world music recordings and different state-of-the-art descriptors are assessed. Following this evaluation, three approaches to audio feature extraction are considered, each addressing a different research question. First, a study of singing style similarity is presented. Singing is one of the most common forms of musical expression and it has played an important role in the oral transmission of world music. Hand-designed pitch descriptors are used to model aspects of the singing voice and clustering methods reveal singing style similarities in world music. Second, a study on music dissimilarity is performed. While musical exchange is evident in the history of world music it might be possible that some music cultures have resisted external musical influence. Low-level audio features are combined with machine learning methods to find music examples that stand out in a world music corpus, and geographical patterns are examined. The last study models music similarity using descriptors learned automatically with deep neural networks. It focuses on identifying music examples that appear to be similar in their audio content but share no (obvious) geographical or cultural links in their metadata. Unexpected similarities modelled in this way uncover possible hidden links between world music cultures. This research investigates whether automatic computational analysis can uncover meaningful similarities between recordings of world music. Applications derive musicological insights from one of the largest world music corpora studied so far. Computational analysis as proposed in this thesis advances the state-of-the-art in the study of world music and expands the knowledge and understanding of musical exchange in the world.
Investigation of fascial treatment effectiveness on pain, flexibility, functional level, and kinesiophobia in patients with chronic low back pain

Introduction. Fascial manipulations have received attention in the treatment of chronic low back pain (CLBP). The purpose of this study is to investigate the effectiveness of fascial manipulation on pain, flexibility, functional level, and kinesiophobia in patients with CLBP. Methods. overall, 55 individuals with a history of CLBP, without neurological deficits, lumbar disc herniation, lumbarization, spondylosis, or lumbar surgical history were included in the study. The participants were randomly divided into 2 groups depending on the patient protocol numbers. Subjects with odd protocol numbers were assigned to the study group (fascial manipulation) and those with even numbers to the control group (conventional physiotherapy). Both groups were assessed before and after the treatment in terms of pain, flexibility, functional level, and kinesiophobia. Results. There were significant differences after both treatment methods in terms of pain, functional level, and flexibility (p < 0.05). However, pain levels decreased more statistically significantly in the fascial manipulation group than in the control group (p < 0.05). No significant difference was observed between the groups in terms of functional level, flexibility, or kinesiophobia after the treatment (p > 0.05). Conclusions. We stated that fascial manipulation was more effective on pain than conventional physiotherapy methods (hot pack, microwave diathermy, interferential flow-vacuum applications, posterior pelvic tilt and abdominal exercises, stretching hip flexors and lumbar extensors) in patients with CLBP.
How Can IoT Improve the Life-quality of Diabetes Patients?

The Internet of things (IoT) is revolutionizing several industries with unprecedented possibilities and opportunities. One of the industries that has a critical direct impact on humans, is the healthcare industry. While IoT technologies can be seen in medical supply chains within the healthcare sector, however, IoT technologies are still not widely adopted in other areas and services within the healthcare sector. The number of patients living with diabetes has increased drastically over the past four decades and is expected to increase even more over the next ones. The disease currently has no cure, and if the patients are not monitored and get the right treatment at the right time, it might end a patient’s life. The IoT is said to be a game-changer for the healthcare industry and the aim of this paper is to review and investigate how IoT technologies and solutions can improve the quality of life and aid the people living with the chronic disease. Our findings suggest that IoT can aid in patient monitoring, including the continuous glucose monitoring, and also can assist in providing a healthier lifestyle for patients through activity and diet tracking. In addition, we have identified key challenges for IoT adoptions and implementations in healthcare within the scope of this research.
Classification of OpenCL Kernels for accelerating Java Multi-agent Simulation

Java-based multi-agent simulation (MAS) can be offloaded to graphical processing units (GPU) and other OpenCL accelerators to achieve many hundred-fold speedups. However, the performance gain from the accelerated code depends strongly on whether the computation (kernels) have been scheduled to the appropriate devices. Thus, accelerating Java MAS may not lead to a sustainable speedup. This paper proposes a method for a kernel classifier to specify suitable devices to execute OpenCL kernels. The classifier can identify suitable OpenCL devices for kernels based on the static and dynamic characteristics of the code of the kernels. Kernels are grouped by their suitability for particular devices using the multiclass support virtual machine technique. After that, kernels are scheduled to an appropriate task queue. Kernel scheduling based on the proposed technique is compared against the firstcome-first-serve (FCFS) technique and against oracle scheduling when handling eight kernels. Our results show that, using the proposed method, all kernels finished execution 45 percent sooner than using the FCFS technique. However, the overall execution time was 22.5 percent longer than with oracle scheduling. Our results seem to confirm that kernel classification techniques might contribute towards sustainable high performance in accelerated Java-based MAS models.
Medical Image Segmentation: Energy Minimization and Deformable Models

This.chapter. surveys. the.field. of.energy. minimization.as. it.applies. to.medical. image.segmentation. (MIS). MIS.remains. a.daunting. task.but. one.whose. solution. will. allow. for. the. automatic. extraction. of. important. structures,. organs,. and. diagnostic. features. from. medical. images,. with. applications. to. computer-aided. diagnosis,. statistical.shape. analysis,. and.medical. image.visualization. Several.classifications. of.segmentation. techniques.exist,. including.edge-,. pixel-,. and.regionbased. techniques,. in. addition. to. clustering,. and. graph-theoretic. approaches. (Pham.et.al.,.2000;.Robb,.2000;.Sonka.and.Fitzpatrick,.2000;.Yoo,.2004). However,. the.unreliability. of.traditional,. purely.pixel-based.methods. in.the. face.of. shape. variation.and. noise.has. caused.recent. trends. (Pham.et.al.,.2000). to.focus. on.incorporating. prior.knowledge. about. the. location,. intensity,. and.shape. of. the.target. anatomy. (Hamarneh.et.al.,.2001). One.type. of.approach. that.has. been.of. particular. CONTENTS
Application of the CMCDOT framework to autonomous car navigation

Perception framework CMCDOT is a Bayesian filtering system for dynamic occupation grids, allowing parallel estimation of occupation probabilities for each cell of a grid, inference of velocities, prediction of the risk of collision and association of cells belonging to the same dynamic object. It takes as input instantaneous occupation grids generated by sensor models for different sources (Lidars, stereo cameras, …). The core of the application is implemented on GPU Nvidia (Cuda), allowing real-time analysis of the local vehicle environment on embedded boards (Tegra X1, X2). Experimental set up & Demonstration The experimental platform is an electric Renault Zoe car, which has been transformed to be autonomous. It can be programmed, driven manually or driven manually with advanced driving assistance functionalities. It is equipped with lidars, camera and IMU. The projection of the car’s trajectory onto a dynamic occupancy grid computed by the CMCDOT allows the system to continuously estimate in real time the collision risk associated to each possible driving command, and then to drive the car safely. Relying on this, the demonstration consists in driving autonomously the car along a given path, while avoiding dangerous static and dynamic obstacles perceived by the onboard sensors.
A Personalized Hybrid Recommendation Procedure for Internet Shopping Support

Lately, recommender systems (RS) have offered a remarkable breakthrough to users. It lessens the user time cost thereby delivering faster and better results. After purchasing a product there are recommendations according to the different comments provided by users. Within a short span of product utilization and quality, the users receive a product recommendation. But this doesn’t work out good so as to make it much better;feedbacks, commands and reviews are fetched on the basis of in-depth commands, globally like and normal keys. Recommendation systems are crucially important for the delivery of personalized product to users. With personalized recommendation to product, users can enjoy a variety of targeted recommendations such as online product; the current paper suggests hybrid recommendation system (HRS) that makes use of rating and review to recommend any product to user. The main objective of this paper is to personalize recommendation of product that have become extremely effective revenue drivers for online shopping business. Despite the great benefits, deploying personalized recommendation services typically requires the collection of users’ personal data for processing and analytics, which undesirably makes users. To implement product recommendations following are incorporated that is retrieving personal data, Logical Language based Rule Generation (LLRG), ranking and Hybrid recommendation system. The stages in the suggested recommendation system include, Data Gathering, preprocessing, filtering and Ranking. The Ranking algorithm ranks the products in relation to the sales count. The top list displays the product having greatest count number. In the LLRG strategy, the logic rule generation methodology retrieves useful and mandatory data from reviews, commands, products original state and thereafter comes the recommendation. The HRS enforces two techniques, namely, location based and the other being heterogeneous domain based. Also the recommendations presented to the user are in context to the user’s activities, choices and conduct that are in accord with user’s personal likings and aids in decision making. When comparing the outcome, it is clear that the suggested method is superior than the traditional with regard to clarity, effective recommendation and coverage rate. It’s evaluated that Hybrid Recommendation System yields in greater results compared with rest of the existing recommendation techniques. We, also identity to some future research directions.