Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Learning-Based Approach to Power Minimization in Multi-Carrier NOMA With SWIPT

Simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) are promising technologies for future fifth generation and beyond wireless networks due to their potential capabilities in energy-efficient and spectrum-efficient system designs, respectively. In this paper, the joint downlink resource allocation problem for a SWIPT-enabled MC-NOMA system with time switching-based receivers is investigated, where pattern division multiple access (PDMA) technique is employed. We focus on minimizing the total transmit power of the system while satisfying the quality-of-service requirements of each user in terms of data rate and harvested power. The corresponding optimization problem is a non-convex and a mixed integer programming problem which is difficult to solve. Different from the conventional iterative searching-based algorithms, we propose an efficient deep learning-based approach to determine an approximated optimal solution. Specifically, we employ a typical class of deep learning model, namely, deep belief network (DBN), where the detailed procedure of the developed approach consists of three parts, i.e., data preparation, training, and running. The simulation results demonstrate that the proposed DBN-based approach can achieve similar performance of power consumption to the exhaustive search method. Furthermore, the results also confirm that MC-NOMA with PDMA outperforms MC-NOMA with sparse code multiple access, single-carrier non-orthogonal multiple access, and orthogonal frequency division multiple access in terms of power consumption in SWIPT-enabled systems.
An Adaptive Namespace Management for Ultra Large Storage Systems

Existing distributed storage frameworks for the most part neglect to offer a sufficient ability for the semantic questions. Since the genuine esteem or worth of information intensely relies upon how proficiently semantic pursuit can be done on the information in (near- ) real-time, vast parts of information wind up with their qualities being lost or essentially diminished because of the information staleness. With a specific end goal to completely assess the framework execution, we actualize all segments and functionalities of FAST in a model framework. The model framework is utilized to assess a utilization instance of close constant information examination of computerized pictures. We gather a major and genuine picture set that comprises of more than 60 million pictures (more than 200 TB storage limit) taken of a best traveler spot amid an occasion. Utilizing this genuine picture dataset as a contextual investigation, we assess the execution of FAST of finding missing youngsters from the picture dataset and contrast it and the cutting edge plans. The contextual investigation assessment exhibits the proficiency and adequacy of FAST in the execution changes and vitality reserve funds.
Joint Optimization of Communication and Traffic Efficiency in Vehicular Networks

Consider single-cell downlink vehicular networks, where a base station (BS) employing massive multiple-input multiple-output simultaneously transmits information to multiple vehicles on its covered road. Taking into account both the traffic and communication performance, the Flow rate of vehicles to the Power consumption of the BS Ratio (FPR) is defined as a comprehensive metric to represent the number of vehicles supported under limited transmit power. The objective of this paper is to maximize the FPR while guaranteeing the information rate requirements of the vehicles. We first derive the average power consumption of the BS with respect to vehicle density, based on which the FPR is established by using a flow rate function that quantifies the number of passed vehicles per time unit in terms of traffic density. Then, the optimal vehicle density is given in order to maximize the FPR. Simulation results indicate that the proposed scheme can significantly improve the power efficiency of the vehicular networks.
Damping Control of Sloshing in Liquid Container in Cart With Active Vibration Reducer: The Case of a Curved Path on a Horizontal Plane

This paper proposes a control system for an active vibration reducer to damp sloshing in a liquid container transferred by a cart. The active vibration reducer is installed on the cart, and has a parallel linkage mechanism with six degrees of freedom. It tilts to horizontally shift the liquid container to damp the sloshing generated as the container is moved along a path. Only (1, 1)-mode sloshing is modeled as the dominant mode in the control system. The reducer is controlled by a frequency-dependent optimal servo system adapted to prevent the control system from destabilizing due to the unmodeled sloshing mode. The control gain in the optimal servo system is determined using a genetic algorithm (GA), where the amplitude of sloshing is considered for the fitness of the GA. The cart was driven along a curved path on a horizontal plane in an experiment to test the proposed control system, and exhibited satisfactory damping performance.
Modelo de proceso para la evaluación y selección de soluciones software de gestión logística

Resumen. Frecuentemente las empresas invierten gran cantidad de tiempo, esfuerzo y dinero en encontrar soluciones que les permitan reformular sus procesos de negocio para mantenerse a la vanguardia del progreso y alcanzar mayor rentabilidad económica ante las constantes e imprevisibles fluctuaciones del mercado. La oferta de nuevas tecnologías y herramientas disponibles para tal fin es sumamente amplia, y la tarea de escoger la alternativa que mejor se adapte a las necesidades de cada empresa envuelve un gran desafío. Si bien la tendencia del mercado en estos últimos años se ha visto inclinada hacia la implementación de Sistemas de Gestión ERP, la elección de la solución más adecuada no es una tarea sencilla y en la actualidad se continúan detectando múltiples casos de insatisfacción en su implantación. Es en este contexto, en el cual se propone la definición de un procedimiento para evaluar y seleccionar un Sistema de Gestión acotando el ámbito de implantación al de las empresas ocupadas en la Gestión Logística, más conocidas como Operadoras Logísticas.
Elastic Nonnegative Matrix Factorization

Nonnegative Matrix Factorization factors a large matrix into smaller nonnegative components. Non-negative models are often more amenable to interpretation vis-a-vis standard principal component analysis where negative entries may not correspond to any physical process. Non-negative matrix factorization has been used widely in a variety of data mining settings, in a multitude of disciplines, including the famous Netflix challenge. Finding optimum solution to these problems are intractable. Iterative updates are widely used to find a local optima in most algorithms, but many of these updates are unstable and lack strong convergence guarantees, including several well known ones. We provide a technique that guarantees convergence even under distributed updates (with mild restrictions). Moreover there has been extensive research in many variants of non-negative matrix factorization in the presence of noise. None of these robust algorithms have guaranteed convergence to even a local optima. We provide an algorithm that adheres to the general analysis technique and addresses noise and contamination. The algorithm is implemented in an elastic distributed system and provides a better quality of factorization than many alternative approaches on a variety of datasets
Context-based BSM Aggregation for Broad-Scale Applications in Vehicular Networks

In the U.S, the Basic Safety Message (BSM), commonly known as heartbeat message, has been proposed as the primary message set used by the connected vehicles’ safety applications to constantly exchange data within the vehicular ad hoc networks (VANETs). BSM is communicated around 10 times per second through Dedicated Short-Range Communications (DSRC) to be exclusively used in the safety context. Once the BSM is used, it becomes obsolete and is destroyed. Nevertheless, albeit the BSM has been primarily developed to be used in the safety context, it has a capacious field of use. Other connected vehicle non-safety applications, like efficiency, environment, weather and mobility, may also use the data in the message. A wide range of BSM data elements are required by an important bunch of non-safety applications. Leastwise, core data elements of the BSM part one if cached, grouped and sent otherwise, they can solely and adequately provide the vehicle-based information needed for broad-scale applications. In this paper, we propose an Application-Level Flextime Aggregation (ALFA) scheme, allowing BSMs to be cached on-board the vehicle, intelligently aggregated according to different contexts and then sent in new message containing stored snapshots for every stretch of roadway. These new messages could be sent to vehicles and infrastructures, via Wi-Fi, digital cellular, or over a combination of all to provide broad-scale applications with needed information. The main novelty of our ALFA scheme is its contribution to change the way active safety transportation data (BSM) is treated, making it more valued, resulting in reliable and useful data source, and an efficient transportation network scaling to large areas. After a study case relevant to the vehicles mobility, obtained results showed that ALFA enhances the BSM’ usefulness and provide mobility applications with useful input data.
Oral Epithelial Dysplasia Computer Aided Diagnostic Approach

The main purpose of this research is to establish a Computer Aided Diagnostic (CAD) approach for the detection and classification of Oral Epithelial Dysplasia. The disturbances that occur in the epithelial layers is diagnosed as premalignant dysplasia. The epithelial dysplasia diagnosis, in-terms of accuracy, is pathologically difficult and contributes to main challenges to oral pathologists due to the multiple dysplastic criteria of the disease such as the loss of polarity of the basal cells and other cellular and nuclear changes. A new approach has been developed based on different selections and magnifications of stained microscopic images. The approach extracts a set of features that would automatically diagnose the image supplying its condition and the category it has reached so far. The resulted analysis from our research will enable the pathologists in classifying cells abnormalities. Feature extracted using Oriented FAST and Rotated BRIEF (ORB) algorithm with the Support Vector Machine (SVM) as a classification algorithm. The proposed approach achieved 92.8% of accuracy in classification Oral Epithelial Dysplasia. The system was trained and tested on a total of forty-six cases of magnification 100× levels of 70% and 30% respectively. This research presents for the first time a diagnostic approach for grading oral epithelial dysplasia according to sixteen extracted features with the given experimented accuracy rates on different magnification levels.
Generating Unified Candidate Skill Graph for Career Path Recommendation

"How should I progress in my career? " is an important question that every working professional seeks answer multiple times during her career. Given the amount of career position data of individuals available online, personalized career path recommendation systems that could mine and recommend the most relevant career paths for a user are on the rise. However, such recommendation systems typically are only effective within a single organization where there are standardized job roles. At an industry sector level such as Information Technology or across such different industry sectors (such as retail, insurance, health care), mining and recommending the most relevant career paths for a user is still an unsolved research challenge. Towards addressing this problem, we propose a system that leverages the notion of skills to construct skill graphs that can form the basis for career path recommendations. We perceive skills are more amenable for career path standardizations across the organizations. Our proposed system ingests a users profile (in a pdf, word format or other public and shared data sources) and leverages an Open IE pipeline to extract education and experiences. Subsequently, the extracted entities are mapped as specific skills that are expressed in the form of a novel unified skill graph. We believe that such skill graphs which capture both spatial and temporal relationships aid in generating precise career path recommendations. An evaluation of our current skill extraction model with an industrial scale dataset yielded a precision and recall of 80.54% and 86.44% respectively.
Tracking Aspects in News Documents

In this investigation, we discuss aspect tracking, i.e., how to identify tracking story-lines of document topics. Because a huge amount of fragment information occurs, it is difficult to see what they mean and how they go within topics by hand. Here we are addressing this type of problem using stochastic models. Our basic idea is that we consider state transitions as internal structure of stories based on HMM and we extract some story-lines as aspects of topics by probabilistic likelihood. We will use KL divergence to screen topics. Also, we discuss topic detection, i.e., how to construct clusters of events to discuss the same topic.
An Efficient Construction Method for Ultra-Reliable Finite Length Analog Fountain Codes

Ultra-reliable low latency communication (URLLC) is defined by the the third generation partnership project (3GPP) for the fifth generation (5G) mobile systems. In order to realize the stringent requirements of ultra-high reliability and low latency, the data block length needs to be small. However, most of the state-of-art channel codes are designed and optimized for long block length. Analog fountain code (AFC) is a capacity-approaching self-adaptive code on wireless channels with linear encoding/decoding complexity. In this paper, we study the optimization of AFC in the finite length regime. We first analyze the effect of average degree on decoding performance of AFC in the finite length regime. Then, based on the characteristic of weight coefficient in AFC, we proposed a joint design of weight optimization (WO) progressive edge growth (PEG)-AFC algorithm to improve the decoding performance in the finite length regime, named PEG-AFC algorithm. Simulation results show that the proposed PEG-AFC algorithm can achieve lower bit error rate (BER) in a wide range of signal to noise ratio (SNR).
Acute Hepatic Porphyrias: Review and Recent Progress

The acute hepatic porphyrias (AHPs) are a group of four inherited diseases of heme biosynthesis that present with episodic, acute neurovisceral symptoms. The four types are 5‐aminolevulinic acid (ALA) dehydratase deficiency porphyria, acute intermittent porphyria, hereditary coproporphyria, and variegate porphyria. Their diagnoses are often missed or delayed because the clinical symptoms mimic other more common disorders. Recent results indicate that acute intermittent porphyria, the most severe of the more common types of AHP, is more prevalent than previously thought, occurring in about 1 in 1600 Caucasians, but with low clinical penetrance (approximately 2%‐3%). Here we provide an updated review of relevant literature and discuss recent and emerging advances in treatment of these disorders. Symptomatic attacks occur primarily in females between 14 and 45 years of age. AHP is diagnosed by finding significantly elevated levels of porphyrin precursors ALA and porphobilinogen in urine. Acute attacks should be treated promptly with intravenous heme therapy to avoid the development of potentially irreversible neurologic sequelae. All patients should be counseled about avoiding potential triggers for acute attacks and monitored regularly for the development of long‐term complications. Their first‐degree relatives should undergo targeted gene testing. Patients who suffer recurrent acute attacks can be particularly challenging to manage. Approximately 20% of patients with recurrent symptoms develop chronic and ongoing pain and other symptoms. We discuss newer treatment options in development, including small interfering RNA, to down‐regulate ALA synthase‐1 and/or wild‐type messenger RNA of defective genes delivered selectively to hepatocytes for these patients. We expect that the newer treatments will diminish and perhaps obviate the need for liver transplantation as treatment of these inborn metabolic disorders.
JobSense: A Data-Driven Career Knowledge Exploration Framework and System

Today's job market sees rapid changes due to technology and business model disruptions. To fully tap on one's potential in career development, one has to acquire job and skill knowledge through working on different jobs. Another approach is to seek consultation with career coaches who are trained to offer career advice in various industry sectors. The above two approaches, nevertheless, suffer from several shortcomings. The on-the-job career development approach is highly inefficient for today's fast changing job market. The latter career coach assisted approach could help to speed up knowledge acquisition but it relies on expertise of career coaches but experienced career coaches are scarce, and they too require update of jobs and skills knowledge. Meanwhile, with wide adoption of Online Professional Net-works (OPNs) such as LinkedIn, Xing and others, publicly shared user profiles have become a treasure trove of job and skill related data. Job and skill related information is also hidden in the sea of online job posts and ads. Manually exploring and acquiring knowledge from these varieties of information are daunting and time-consuming. On the other hand, one needs substantial effort to personalize the acquired knowledge to his/her career interests. There is a dire need for a self-help tool to ease this knowledge acquisition and exploration problems. Before that, there is also a need to create and maintain a large knowledge base of these jobs, skills and careers. Our data-driven, automated knowledge acquisition and interactive exploration system, JobSense, would help users meet the above challenges. JobSense enables users at several stages of career, to explore this knowledge at ease via interactive search, easy navigation, bookmarking of information entities and personalized suggestions. Also we have introduced a career path generation module, to return relevant career paths to the users.
A New RSS Fingerprinting-Based Location Discovery Method Under Sparse Reference Point Conditions

With the increasing demand for indoor location-based services, the received signal strength fingerprinting-based localization algorithm has become a research focus due to its accuracy and low hardware requirements. However, how to achieve the accurate location discovery relies solely on the received signal strength under the sparse reference points condition, which is the main contribution of this paper. First, the Voronoi diagram is adopted to regionalize the positioning area and form a distributed signal propagation description, which can reduce the influence of environment interference. Second, aiming at the local motion tracking problem, a region-based location search model is constructed to achieve the initial position estimation and provide the motion model for the following optimization of location estimation. Third, in order to reduce the cumulative error caused by the environmental noise and the local optimum problem, the regularized particle filtering algorithm with map-correction is employed to implement the dynamic calibration of the particle updating equation. To verify the proposed algorithm, an indoor wireless experiment system is finally designed in this paper. The experiment results indicate that the proposed algorithm can increase the positioning accuracy by 28.2% compared with the fingerprinting-based localization algorithm when the RPs density is reduced to 0.2/ (0.5m*0.5m).
Voxelwise 3D Convolutional and Recurrent Neural Networks for Epilepsy and Depression Diagnostics from Structural and Functional MRI Data

In the field of psychoneurology, analysis of neuroimaging data aimed at extracting distinctive patterns of pathologies, such as epilepsy and depression, is well known to represent a challenging problem. As the resolution and acquisition rates of modern medical scanners rise, the need to automatically capture complex spatiotemporal patterns in large imaging arrays suggests using automated approaches to pattern recognition in volumetric images, such as training a classification models using deep learning. On the other hand, with typically scarce training data, the choice of a particular neural network architecture remains an unresolved issue. In this work, we evaluate off-the-shelf building blocks of deep voxelwise neural architectures with the goal of learning robust decision rules in computational psychiatry. To this end, we carry out a series of computational experiments, aiming at the recognition of epilepsy and depression on structural (3D) and functional (4D) MRI data. We discover that our investigated models perform on par with computational approaches known in literature, without the need for sophisticated preprocessing and feature extraction.
EIF-Export: An energy-interference-free data export approach for EH-WSN Networks

Different to the traditional WSN, the energy harvesting WSN(EH-WSN) harvests energy from environment that greatly prolongs its lifetime. Since the communication and routing protocols are very sensitive to energy, and the power supply of EH-WSN is intermittent, it brings quite a challenge for debugging the EH-WSN network. To avoid interfering nodes energy, EH-WSN network debugging is required of energy-interference-free, and nodes are managed to supply energy intermittently. Moreover, the debugger for traditional WSN is not available in EH-WSN networks, and the existing debugger for EH-WSN is only applicable for any single node, failure to obtain the whole information of networks. Accordingly, in this paper, we designed an energy-interference-free data export device for EH-WSN networks (EIF-Export), including hardware and software, which is possible to export the internal data of nodes without affecting nodes operation. Taking into account of the low cost of the device, it is possible to attach a EIF-Export on each of EH-WSN nodes, and the data is collected for further analysis, achieving monitoring for the whole networks. Equipped with LCD, EIF-Export displays key values directly, and the network state is clear and intuitive in visual. EIF-Export, not a debugger, is capable to debug the whole EH-WSN network by obtaining a large scale of data that debuggers are not possible to do. Experiments show that EIF-Export only consumes energy of 8.66nJ/Byte, thus this approach achieves the energy-interference-free data export basically, having no interference to nodes energy and network operation.