Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Online Market Mechanism for Resource Leasing in Cloud Radio Access Networks

This work studies the emerging C-RAN market in a 5G wireless network where mobile operators lease computation and communication resources from the tower company to serve wireless users. We propose an online C-RAN auction where each mobile operator bids for three types of resources in a future time window: wireless spectrum at base stations (BSs), front-haul link capacities, and mobile BS instances at the mobile cloud. We target an online C-RAN auction that executes in polynomial time, elicits truthful bids from mobile operators, and maximizes the social welfare of the C-RAN eco-system with both spectrum cost at BSs and server cost at the mobile cloud considered. We show how the marriage of (i) a new Fenchel dual approach to convex optimization with (ii) the posted pricing framework for online auction design can help achieve the three goals simultaneously, and evaluate the efficiency of our online C-RAN auction through both theoretical analysis and empirical studies.
Coupled Modeling of Permanent Magnet Generator Cooling using CFD

The aim of this paper is to show the complex analysis of electric machine cooling, using a modern method based on computational fluid dynamics (CFD) and finite elements method (FEM). The main advantage of this solution are lower computational demands compared with commonly used approach based only on CFD analysis. This solution shows using of two simulations based on a coupled one-way solution. First, airflow in the machine was solved to determine velocities in the inner space of the machine. Then thermal analysis was done with respect to results from the flow analysis that was done in previous step. An experimental measurement was performed for verification of this approach and for identification of thermal properties of the machine. The goal is to assess the ventilation impact on the thermal properties of individual parts of the electric machine.
Deep Medical Entity Recognition for Swedish and Spanish

Clinical texts, although challenging to process, are rich in valuable information, and named entity recognition is an important element in any system designed to extract relevant information from such texts. Recently, improved performance for named entity recognition has been achieved through deep learning methods, and here, a recurrent neural network is evaluated for medical named entity recognition in clinical texts in two different languages, Spanish and Swedish. An important factor for any machine learning model is the input representation, how the features are preprocessed and presented to the model. Therefore, a number of different embeddings derived from large corpora of clinical texts, and several combination strategies for embeddings have been evaluated for this task. Combining a bidirectional LSTM with embeddings derived from words and lemmas gave an improvement in performance with over three points in average F-measure over using only shallow learning methods for both languages, while at the same time reducing the dependency on external resources and feature engineering, showing this approach to be suitable for medical named entity recognition. An average F-measure of 74.87 is obtained for Spanish using lemma embeddings and of 76.04 for Swedish when concatenated lemma and word embeddings are used.
Efficient beamforming scheme in distributed massive MIMO system

Massive MIMO is a multi-user MIMO where the number of antennas and the number of scheduled users can be very large. Effective transmission strategies in such systems is of great importance as the complexity burden over the shoulder of the system can be too high to take due to the large dimensions of the system. In a distributed massive MIMO system, where antennas’ links are capacity constrained, calculating the precoding vector and compression separately can be a waste of calculating hardwares. We propose a simple beamforming-compression scheme based on an iterative algorithm which can efficiently achieve the performance of the well-known linear precoders with much less complexity.
2D UAV Path Planning with Radar Threatening Areas using Simulated Annealing Algorithm for Event Detection

Path Planning for Unmanned Aerial Vehicles (UAVs) can be used for many purposes. However, the problem becomes more and more complex when dealing with a large number of points to visit for detecting and catching different type of events and simple threat avoidance such as Radar Areas. In the literature different type of algorithms (especially evolutionary algorithms) are preferred. In this project, Simulated Annealing (SA) Algorithm is used for solving the path planning problem. Firstly, problem is converted to a part of Travelling Salesman Problem (TSP), and then the solutions are optimized with the 2-Opt approach and other simple algorithms. The code is implemented in MATLAB by using its visualization. Circular avoidance approach is developed and applied with the Simulated Annealing in order to escape from circular radar threats. Tests have been made to observe the results of SA algorithm and radar threats avoidance approaches, where the results show that after a period of time, SA algorithm gives acceptable solutions with the capacities of escaping from radar area threats. Where SA algorithm gives better solutions in less period of time when there are no radar threats. Experimental results depicted that the proposed model can result in an acceptable solution for UAVs in sufficient execution time. This model can be used as an alternative solution to the similar evolutionary algorithms.
Versatile Communication Optimization for Deep Learning by Modularized Parameter Server

Deep learning has become one of the most promising approaches to solve the artificial intelligence problems. Training large-scale deep learning models efficiently is challenging. A widely used approach to accelerate the training process is by distributing the computation across multiple nodes with a centralized parameter server. To overcome the communication overhead caused by exchanging information between workers and the parameter server, three types of optimization methods are adopted – data placement, consistency control, and compression.In this paper, we proposed modularized parameter server, an architecture composed of key components that can be overridden without much effort. This allows developers to easily incorporate optimization techniques in the training process instead of using ad-hoc ways in existing systems. With this platform, the users can analyze different combinations of techniques and develop new optimization algorithms. The experiment results show that, compared with Google’s distributed TensorFlow, our distributed training system based on the proposed modularized parameter server can achieve near-linear speedup for computing and reduce half of the training time by combining multiple optimization techniques while maintaining the convergent accuracy.
Implementation of a Capless-LdIn An Energy Harvesting Chain for Aircraft Applications

This paper deals with a power recovery system to reduce cabling mass, focused on development of a power conversion chain to harvest energy from Data lines, that is used eventually to supply the aircraft sensors or wireless monitoring, this proposed harvests energy interface from data CAN bus without disturbing the data transmission. The solution includes a fast energy-harvesting Chain, which is composed by fast analog schemes that based on a fully integrated LDO, to make the aircraft sensors self-powered. The regulation system of the power management block is designed in 180 nm CMOS technology. The simulation results prove the advantage of the proposed architecture.
Experimental Analysis of Cost-Effective Mobile Sensing Technologies for Activity Analytics in Elderly Care

Advancements in sensor technology has provided new ways for researchers to monitor the elderly in uncontrolled environments. Sensors have become smaller, cheaper and can now be worn on the body. Smart phones are also more common in the average household and can provide some analysis of behaviour. Because of this, researchers are able to monitor behaviours in a more natural setting, which can produce useful data. For those suffering with a mental illness, this is important as it allows for continuous, non-invasive monitoring in order to diagnose symptoms from different behaviours. However, issues with the sensors and the involvement of human factors are challenges that need to be addressed. These challenges must be taken into consideration in addition to the behavioural symptoms of Dementia that can appear in the elderly. The application of sensor technologies can aid in improving the quality of life of an elderly person with Dementia and monitor the progression of the disease through behavioural analysis. This paper will provide an experiment protocol that can be used to monitor those with mild cognitive impairment in a natural environment. We will also provide data and results from an initial experiment and discuss our plans for future experimentation.
MetaFlow: an interactive user-friendly workflow for automated analysis of whole genome shotgun sequencing metagenomic data

Metagenomics is a rapidly emerging field that is concerned with the study of microbial communities “microbiomes” on both levels of taxonomic classification and functional annotation. Targeted amplicon (16S rRNA) and whole genome shotgun (WGS) sequencing are the two main sequencing strategies in metagenomics. As amplicon sequencing provides a cheap way to classify the composition of a microbial community, it lacks the ability to identify microbial genes and annotate its corresponding functions. On the other hand, WGS sequencing allows further investigation of the complete genomes with all associated genetic information, and hence, gives another dimension for functional analysis, however, these mentioned advantages are not in favor of sequencing cost, duration of the analysis, and required computational resources. Currently, most tools for WGS microbiome data analysis are commandline based, which limits its use to only experienced bioinformaticians, due to its complexity during installation, usage and fine tuning numerous parameters. Moreover, current tools required for different steps of microbiome data analysis are platform specific and not user-friendly for biologists without previous deep knowledge in command line and installation/configuration troubleshooting abilities. In this paper, we present MetaFlow; a comprehensive automatic platform-independent workflow with a friendly graphical user interface (GUI), for interactive analysis of shotgun metagenomic data. MetaFlow will facilitate the analysis of WGS metagenomic data on a personal laptop, more smoothly, rapidly in an automatic, comprehensive and interactive way for biologists and microbiologists.
Fuzzy Constrained Min-Max Model Predictive Control Research of Networked System via Piecewise Lyapunov Functions

This paper is mainly introduced with the fuzzy constrained Min-Max MPC of networked control systems, in fact Markovian data loss and quantization are applied in this system. The central idea of this article is to devise state feedback control law based on the fuzzy model to predict the minimize and the worst case objective function, therefore the optimal transient control performance could be obtained. Contemporaryly, the influences of Markovian packet data loss and quantization on the system performance are considered. In contrasted with common Lyapunov functions, piecewise Lyapunov functions are applied to reduce the conservatism of system. The simulation upshots are effectiveness.
Collaborative Human-Robot Motion Generation Using LSTM-RNN

We propose a deep learning based method for fast and responsive human-robot handovers that generate robot motion according to human motion observations. Our method learns an offline human-robot interaction model through a Recurrent Neural Network with Long Short-Term Memory units (LSTM-RNN). The robot uses the learned network to respond appropriately to novel online human motions. Our method is tested both on pre-recorded data and real-world human-robot handover experiments. Our method achieves robot motion accuracies that outperform the baseline. In addition, our method demonstrates a strong ability to adapt to changes in velocity of human motions.
The perceived usability of automated testing tools for mobile applications

Mobile application development is a fast-emerging area in software development.The testing of mobile applications is very significant and there are a lot of tools available for testing such applications particularly for Android and iOS.This paper presents the most frequently used automated testing tools for mobile applications.In this study, we found that Android app developers used automated testing tools such as JUnit, MonkeyTalk, Robotium, Appium, and Robolectric.However, they often prefer to test their apps manually, whereas Windows apps developers prefer to use in-house tools such as Visual Studio and Microsoft Test Manager.Both Android and Windows apps developers face many challenges such as time constraints, compatibility issues, lack of exposure, and cumbersome tools, etc. Software testing tools are the key assets of a project that can help improve productivity and software quality.A survey method was used to assess the perceived usability of automated testing tools using forty (40) respondents as participants.The result indicates that JUnit has the highest perceived usability.The study’s result will profit practitioners and researchers in the research and development of usable automated testing tools for mobile applications.
Skill Transfer for Mediated Interaction Learning

We investigate the effectiveness of transfer learning for accelerating shallow and deep reinforcement learning of “mediated interaction tasks”. In these tasks, the desired effects cannot be created through direct interaction, but instead require the learner to discover how to exert suitable effects on the target object through involving a suitable “mediator object”. We focus on the case where transfer learning is applied to generalize experiences from source tasks that are solvable through direct, unmediated interaction, to target tasks that require mediated interaction for their solution. We find that transfer learning employed in this context leads to a significant acceleration of learning. A refinement of the basic transfer learning strategy that is motivated from the principle of scaffolding in psychology leads to further improvements, totalling to an overall speed-up factor of almost one order of magnitude for a reinforcement learner solving an “extension-of-reach” task in a 2D world with simulated physics.
Proactive Risk Assessment Based on Attack Graphs: An Element of the Risk Management Process on System, Enterprise and National Level

The paper discusses graph based risk assessment approach to the evaluation of computer systems' security. The method is based on MulVAL attack graph generation tool, adapted for risk calculation, generating possible attack paths leading to crucial assets of the audited IT system. It uses information from the security audits (hosts vulnerabilities) and detailed topology information. The authors present the approach to standard IT system security evaluation and risk assessment as well as advantages of the graph – based method. As the follow up, high level risk assessment of a broader, multi-domain national level environment is proposed.
Performance Analysis of Mixed-ADC Massive MIMO Systems Over Spatially Correlated Channels

This paper analyzes the uplink performance of multiuser massive multi-input-multi-output systems with spatially correlated channels by using a mixed analog-to-digital converter (ADC) architecture, where the base station (BS) is equipped with two resolution levels’ ADC. More specifically, the exponential correlation matrix model is used for modeling spatially correlated channels. Meanwhile, the additive quantized noise model and the maximum ratio combining technique are used at the BS receiver. Thereby, closed-form approximations for the achievable rate are derived under perfect and imperfect channel state information (CSI), respectively. In addition, we have studied the tradeoff between achievable rate and energy efficiency. Then, the influence of physical parameters on the corresponding results is analyzed, including user transmit power, the number of BS antennas, spatial correlation coefficient, CSI errors, and ADC quantization resolution. The numerical results show that as the spatial correlation coefficient increases, the achievable rate is almost constant at the low signal-to-noise-ratio (SNR) stage, but the achievable rate is gradually reduced to near zero at the high SNR stage. Furthermore, in terms of the tradeoff between achievable rate and energy efficiency, the performance of the two-level ADC architecture is significantly better than the uniform ADC architecture.
Stochastic Processors on FPGAs to Compute Sensor Data Towards Fault-Tolerant IoT Systems

The continuous increase in the amounts of data received at the edges of the Grid is pushing the pre-computation of sensor data at the IoT device before communicating it over the network. Moreover, in the IoT context, devices are often required to operate under heavy power and area constraints and be subjected to harsh environments. However, in this context, traditional computing paradigms struggle to provide high availability and fault-tolerance. Stochastic Computing has emerged as a competitive computing paradigm that produces fast and compact implementations of arithmetic operations, while offering high levels of parallelism, and graceful degradation when in the presence of faults. Stochastic Computing is based on the computation of pseudo-random sequences of bits, hence requiring only a single bitstream per signal. In virtue of the granularity of the bitstreams, the bit-level specification of circuits, high-performance characteristics and reconfigurable capabilities, FPGAs are often adopted to implement and test such systems. This work presents a tool that takes a high-level specification and automatically creates a complete Stochastic Computing systems capable of interfacing analog sensors directly on the FPGA, and perform computations over the stochastic bitstreams. Moreover, the presented framework is also able to generate custom stochastic processing units, perform fault-tolerance tests, and report estimates on performance, resources and power. As a proof-of-concept, this paper presents two Machine Learning applications typical in the IoT context, implementing Karhunen-Loeve Transform for data compression and Neural Networks for classification.
Semi-Passive Walk and Active Walk by One Bipedal Robot

We developed a robot which can do both of active walking (all joints are actively controlled by actuators) and semi-passive walking (hip joints are passive and spring attached). In this paper, we summarize three technologies to achieve the development. The first one is small and high-strength clutch mechanism to sustain massive weight of lifesize robot. The second one is semi-passive walk controller to consider passive joint dynamics. The last one is model parameter identification considering not only body parameters but also environment ones such as ground slope to achieve unstable motion similar to simulated result in real world.
New Trend in Video Foreground Detection Using Deep Learning

Video foreground $(FG)$ detection is one of the primitive tasks in computer vision (Cv). It is a core component in a myriad of video-based applications, like autonomous driving, activity recognition, and video surveillance. As in any other field, the deep learning (DL) has shown significant impact on video FG detection. Thus, this paper attempts to provide a fundamental study on the new strategies that harness the efficacy of deep convolutional neural networks (DCNNs) to extract video FG information. It also introduces two models and analyses their performances on various indoor and outdoor video sequences.
Documents Retrieval for Qualitative Research: Gender Discrimination Analysis

Gender discrimination is an act of exclusion or differential treatment towards a person due to its sex. This phenomenon has been studied in qualitative research by seeking to analyze and to describe the reality and context of discrimination. Qualitative researchers use a collection of documents such as surveys, interviews among another source. These large full textual documents tend to be unstructured from a Data Science point of view. These data are often complex and tend to show similar information between documents. Nevertheless, the process of selecting relevant information is manual, generating difficulties in categorizing and analyzing relevant piece of information, such as victim’s surveys. The main reason in this processing is the use of tools to simplify the task of information selection and to perform it efficiently. This article proposes two methods based on the TF-IDF measure to search documents in a corpus. Our findings show that other methods such as, LSA (Latent Semantics Analysis) and LDA (Latent Dirichlet Allocation) consume a lot of memory, and have a low effectiveness extracting meaningful words than relying on TD-IDF only. The information processed in this case is about testimonies of gender discrimination in university students in Peru.
An In-depth Comparison of Group Betweenness Centrality Estimation Algorithms

One of the important indices defined for a set of vertices in a graph is group betweenness centrality. While in recent years a number of approximate algorithms have been proposed to estimate this index, there is no comprehensive and in-depth analysis and comparison of these algorithms in the literature. In this paper, we first present a generic algorithm that is used to express different approximate algorithms in terms of probability distributions. Using this generic algorithm, we show that interestingly existing methods have the same theoretical accuracy. Then, we present an extension of distance-based sampling to group betweenness centrality, which is based on a new notion of distance between a single vertex and a set of vertices. In the end, to empirically evaluate efficiency and accuracy of different algorithms, we perform experiments over several real-world networks. Our extensive experiments reveal that those approximate algorithms that are based on shortest path sampling are orders of magnitude faster than those algorithms that are based on pair sampling, while these two types of algorithms have almost comparable empirical accuracy.
Convolutional Neural Network Based Offline Signature Verification Application

One of the most important biometric authentication technique is signature. Nowadays, there are two types of signatures, offline (static) and online (dynamic). Online signatures have higher distinctive features but offline signatures have fewer distinctive features. So offline signatures are more difficult to verify. In addition, the most important drawback of offline signatures is that they cannot be signed with the same way even by the most talented signer. This is called intra-personal variability. All these make the offline signature verification a challenging problem for researchers. In this study, we proposed a Deep Learning (DL) based offline signature verification method to prevent signature fraud by malicious people. The DL method used in the study is the Convolutional Neural Network (CNN). CNN was designed and trained separately for two different models such one Writer Dependent (WD) and the other Writer Independent (WI). The experimental results showed that WI has 62.5% of success and WD has 75% of success. It is predicted that the success of the obtained results will increase if the CNN method is supported by adding extra feature extraction methods.