Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimizing Delay in NFV 5G Networks by Means of Flexible Split Selection and Scheduling

It is well known that network function virtualization will be a key enabler to meet the stringent requirements of 5G networks. However, fully centralized approaches, such as Cloud Radio Access Network (C-RAN), might not be feasible, considering the particular needs of the fronthaul links and the large cost of implementing such architectural shift. In this sense, flexible functional split brings a practical solution, which trades off performance and practicability. In spite of the growing interest in flexible functional split, little attention has been paid to the interaction of split selection and scheduling. In this paper, we analyze joint strategies that minimize traffic delay. We compare the global optimum solution with partial optimizations, that can be more suitable in practical implementations, using different scenarios. According to our results, fixed scheduling behaves alike the global optimum in heterogeneous RAN scenarios. Furthermore, we observe that it is usually better to optimize the split degree for fixed scheduling setups, than deciding a scheduling policy for a particular split configuration.
A New Design Method for PI-PD Control of Unstable Fractional-Order System with Time Delay

In this paper, a practical PI-PD controller parameter tuning method is proposed, which uses the incenter of the triangle and the Fermat point of the convex polygon to optimize the PI-PD controller. Combined with the stability boundary locus method, the PI-PD controller parameters that can ensure stability for the unstable fractional-order system with time delay are obtained. Firstly, the parameters of the inner-loop PD controller are determined by the centre coordinates of the CSR in the kd−kf plane. Secondly, a new graphical method is used to calculate the parameters of the PI controller, in which Fermat points in the CSR of (kp−ki) plane are selected. Furthermore, the method is extended to uncertain systems, and the PI-PD controller parameters are obtained by using the proposed method through common stable region of all stable regions. The proposed graphical method not only ensures the stability of the closed-loop system but also avoids the complicated optimization calculations. The superior control performance of this method is illustrated by simulation.
User Association With Mode Selection in LWA-Based Multi-RAT HetNet

Restrained by the long term evolution (LTE) limited network capacity, WiFi technology is considered as one of the promising solutions to leverage the traffic load and enhance the LTE capacity. Exploiting both the licensed and unlicensed spectrum was the motivated key to standardize the LTE-wireless local area network (WLAN) aggregation (LWA) technology by 3GPP in Release 13. In this paper, we consider the user association problem in LWA-based Multiple Radio Access Technologies (Multi-RAT) Heterogeneous Networks (HetNet) in which three transmission modes are available (LTE, WiFi, and aggregation mode) and the user needs to select not only the wireless node that will associate with it, but also the used transmission mode. For this, a new user association algorithm that considers the joint node and mode selection is proposed in this paper. This association process is formulated as an optimization problem with the aim to maximize total network throughput. To solve this problem, a one-to-many matching game-based association algorithm is designed, where each user is matched to the best transmission mode/node according to well-developed utility function that considers the achieved data rate of each user as well as the proportional fairness among users. Simulation results have shown that our proposed algorithm outperforms comparable association techniques such as WLAN first, LTE first, and LTE-W in terms of system throughput, outage probability and fairness between users.
OTT_A_220698 8139..8149

1Department of Respiration, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, People’s Republic of China; 2Department of Tumor Biological Treatment, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, People’s Republic of China; 3Jiangsu Engineering Research Center for Tumor Immunotherapy, Changzhou, Jiangsu 213003, People’s Republic of China; 4Institute of Cell Therapy, Soochow University, Changzhou, Jiangsu 213003, People’s Republic of China; 5Stony Brook University, Stony Brook, NY 11794, USA
Towards More Practical Spacing for Smartphone Touch GUI Objects Accompanied by Distractors

To achieve better touch GUI designs, researchers have studied optimal target margins, but state-of-the-art results on this topic have been observed for only middle-size touchscreens. In this study, to better determine more practical target arrangements for smartphone GUIs, we conducted four experiments to test the effects of gaps among targets on touch pointing performance. Two lab-based and two crowd-based experiments showed that wider gaps tend to help users reduce the task completion time and the rate of accidental taps on unintended items. For 1D and 2D tasks, the results of the lab-based study with a one-handed thumb operation style showed that 4-mm gaps were the smallest sizes to remove negative effects of surrounding items. The results of the crowd-based study, however, indicated no specific gaps to completely remove the negative effects.
MicroLapse: Measuring Workers' Leniency to Prediction Errors of Microtasks' Working Times

Working time estimation is known to be helpful for allowing crowd workers to select lucrative microtasks. We previously proposed a machine learning method for estimating the working times of microtasks, but a practical evaluation was not possible because it was unclear what errors would be problematic for workers across different scales of microtask working times. In this study, we formulate MicroLapse, a function that expresses a maximal error in working time prediction that workers can accept for a given working time length. We collected 60,760 survey answers from 660 Amazon Mechanical Turk workers to formulate MicroLapse. Our evaluation of our previous method based on MicroLapse demonstrated that our working time prediction method was fairly successful for shorter microtasks, which could not have been concluded in our previous paper.
Performance Evaluation of Urban Rail Traffic Management Techniques. (Évaluation de Performances pour les Techniques de Régulation du Trafic Ferroviaire Urbain)

Urban rail traffic is subject to numerous disrupting events that drift it from its nominal behavior. In order to minimize the impact of these disturbances, rail operators rely on a set of techniques. Despite their efficiency, performances of theses techniques are rarely well studied, nor are they of proven optimality; a direct consequence of them being empirically built. It is in this particular context that comes our work to provide solutions that allow for the evaluation of such techniques and for the comparison of their relative performances in various scenarios. The proposed approach is based on variants of Petri nets as models, and on the Monte-Carlo method for the simulation of their execution. This combination has led to the development of SIMSTORS, a tool for the simulation of urban rail systems, and more generally, stochastic systems under dynamic rescheduling. Additionally, this thesis addresses the question of timetable realizability; that is whether or not a given timetable is indeed realizable by a system for which it was built. Indeed, a timetable is meant to drive the behavior of a system but there is no guarantee as to its realizability. We therefore propose a method for the verification of the realizability of timetables with a strictly positive probability.
Towards Identification of Packaged Products via Computer Vision: Convolutional Neural Networks for Object Detection and Image Classification in Retail Environments

Identification of packaged products in retail environments still relies on barcodes, requiring active user input and limited to one product at a time. Computer vision (CV) has already enabled many applications, but has so far been under-discussed in the retail domain, albeit allowing for faster, hands-free, more natural human-object interaction (e.g. via mixed reality headsets). To assess the potential of current convolutional neural network (CNN) architectures to reliably identify packaged products within a retail environment, we created and open-source a dataset of 300 images of vending machines with 15k labeled instances of 90 products. We assessed observed accuracies from transfer learning for image-based product classification (IC) and multi-product object detection (OD) on multiple CNN architectures, and the number of images instances required per product to achieve meaningful predictions. Results show that as little as six images are enough for 90% IC accuracy, but around 30 images are needed for 95% IC accuracy. For simultaneous OD, 42 instances per product are necessary and far more than 100 instances to produce robust results. Thus, this study demonstrates that even in realistic, fast-paced retail environments, image-based product identification provides an alternative to barcodes, especially for use-cases that do not require perfect 100% accuracy.
SMETO: Stable Matching for Energy-Minimized Task Offloading in Cloud-Fog Networks

In order to minimize the total energy consumption of a cloud-fog network, one of the most essential challenges is the assignment of subtasks from the task node (TN) to suitable fog nodes (FNs). In this paper, we apply a many-to-one matching to deal with this problem. Specifically, we first introduce two concepts, Service Efficiency (SE) and Energy Efficiency (EE), as the indexes of the preference list (PL) of TNs and FNs, respectively. Then a stable matching algorithm for energy-minimized task offloading (SMETO) is proposed, which is comprised of two key components: (i) Deferred Acceptance Algorithm Based On Energy Efficiency (EEDA) and (ii) Energy-Minimized Task Allocation (EMTA). Algorithm (i) is an iterative procedure that matches TNs and helpers based on PLs. Algorithm (ii) minimizes the energy consumption in the network by allocating the subtasks to helpers according to the result of matching. Finally, numerical simulations indicate the stability and energy-minimization of our proposed SMETO.
Teaching software‐defined network security through malicious tenant detection

Software‐Defined Networking (SDN) has radically changed how we manage our network, increasing flexibility, and enabling network programmability. Providing security for tenants is one of the most significant issues in SDN. In this paper, our goal is to introduce with an educational use case, four types of attack vectors associated with malicious tenants that seriously challenge the SDN foundation. We also provide suggestions to teach how to investigate the mitigation of these techniques against the attacks. Our solution, to be tested in the classroom, introduce an approach that utilizes several concepts, such as a topological graph to detect malicious tenants. Unlike other methods to teach how to secure SDNs, our proposed approach teaches how to detect and secure a system without requiring any changes to the underlying protocols.
The Coerciveness of the Primary Key

The premise of a primary key is simple enough: every record or row in a table should have some number or string that can uniquely identify it. Primary keys are essential for linking data spread across database tables, and for looking up and retrieving data from specific records. Yet for an identifier that seems so straightforward and uncontroversial, we find myriad ways that this unassuming bit of infrastructure has an outsized influence in human services work. Through case studies of the organizational networks of two nonprofit human services organizations, we find that different stakeholders use variants of identifiers to support work practices that are far more complex and social than the linking of tables or the lookup of data. Yet we also find that the low-level technical properties of the primary key are often coercive, forcing end-users to work on the infrastructure's terms-influencing the nature and order of the work, creating new forms of work, and influencing the tenor of the relationships among stakeholders. The technical abstractions of the underlying infrastructure, then, start to become the de facto public policy. We conclude by offering design provocations for better supporting identification across a variety of contexts.
DroidARA: Android Application Automatic Categorization Based on API Relationship Analysis

An application (app) market with well-managed categorization will help users with app search and recommendation. Current categorization methods in app markets mainly rely on manual operation. Existing approaches for automatic Android app categorization suffer from low efficiency and low accuracy due to insufficient analysis of features, or inappropriate choice of features. This will mislead users to download unrelated apps and is not conducive to market stability maintenance. In this paper, we propose DroidARA, an efficient automatic categorization method for Android apps based on API relationships analysis. Considering that the app category can be characterized by API relationships which represent the combinations and links among APIs, we design a complete system to generate API call graphs, extract the API relationships information, transform them into feature vectors and train the classifier. Firstly, the API calls are obtained through static analysis to generate API call graphs that contain the relationships among APIs. A novel matrix structure as well as related algorithm are designed to extract the API relationships information from API call graphs. After that, the matrix is transformed into vector according to two feature selection methods we designed for strengthening the use of effective information in the API relationships. A convolutional neural network (CNN) model is then trained with labeled samples of such feature vectors. To validate the feasibility of DroidARA, we conduct several categorization experiments on 19949 real apps of Google Play. The results demonstrate that DroidARA can achieve an average 88.9% accuracy in categorizing the apps into 24 categories, which outperforms existing methods by 18.5%.
Information/Systems and Quantitative Core Courses in AACSB Accredited Business Schools

: During the 1994 academic year, a survey was conducted of institutions accredited by the American Assembly of Collegiate $chools of Business (MCSB) . The purpose of the survey was to determine the nature and characteristics of the quantitative and Information Systems core courses. The results, which were contrasted to those of a similar survey done in 1987, provide a snapshot of the current status of these core classes, reveal evolutionary changes, and portend possible future trends.
Low-Complexity Multi-User MIMO Algorithms for mmWave WLANs

Very high throughput and high-efficiency wireless local area networks (WLANs) have become essential for today’s significant global Internet traffic and the expected significant global increase of public WiFi hotspots. Total Internet traffic is predicted to expand 3.7fold from 2017 to 2022. In 2017, 53% of overall Internet traffic used by WiFi networks, and that number is expected to increase to 56.8% by 2022. Furthermore, 80% of overall Internet traffic is expected to be video traffic by 2022, up from 70% in 2017. WiFi networks are also expected to move towards denser deployment scenarios, such as stadiums, large office buildings, and airports, with very high data rate applications, such as ultra-high definition video wireless streaming. Thus, in order to meet the predicted growth of wireless traffic and the number of WiFi networks in the world, an efficient Internet access solution is required for the current IEEE 802.11 standards. Millimeter wave (mmWave) communication technology is expected to play a crucial role in future wireless networks with large user populations because of the large spectrum band it can provide. To further improve spectrum efficiency over mmWave bands in WLANs with large numbers of users, the IEEE 802.11ay standard was developed from the traditional IEEE 802.11ad standard, aiming to support multi-user MIMO. Propagation challenges associated with mmWave bands necessitate the use of analog beamforming (BF) technologies that employ directional transmissions to determine the optimal sector beam between a transmitter and a receiver. However, the multi-user MIMO is not exploited, since analog BF is limited to a single-user, single-transmission. The computational complexity of achieving traditional multi-user MIMO BF methods, such as full digital BF, in the mmWave systems becomes significant due to the hardware constraints. Our research focuses on how to effectively and efficiently realize multi-user MIMO transmission to improve spectrum efficiency over the IEEE 802.11ay mmWave band system while also resolving the computational complexity challenges for achieving a multi-user MIMO in mmWave systems. This thesis focuses on MAC protocol algorithms and analysis of the IEEE 802.11ay mmWave WLANs to provide multi-user MIMO support in various scenarios to improve the spectrum efficiency and system throughput. Specifically, from a downlink single-hop
Users Capacity Upper Bound of Multi-Pair Massive MIMO AF Relay System under Pilot Contamination

Massive MIMO relay system promises large spectral and energy efficiencies. It also allows admitting more users in the network with controlled quality of service. In this paper, we seek the answer of the following question: what is the maximum number of users that can be admitted in a two-way relay network with massive number of antennas at the relay side and subject to a minimum quality of service at the user side? In order to answer this question, we develop lower bound on the achievable user rate which we then use to bound the maximum number of the admissible users.We also study the effect of the non orthogonality of the pilot sequences on the user capacity in a small cell (dense) network and propose an interference mitigation scheme which improves the spectral efficiency. It is shown that large number of users can coexist using the proposed scheme.
Fully Dynamic Matching: Beating 2-Approximation in Δϵ Update Time

In fully dynamic graphs, we know how to maintain a 2-approximation of maximum matching extremely fast, that is, in polylogarithmic update time or better. In a sharp contrast and despite extensive studies, all known algorithms that maintain a $2-\Omega(1)$ approximate matching are much slower. Understanding this gap and, in particular, determining the best possible update time for algorithms providing a better-than-2 approximate matching is a major open question. In this paper, we show that for any constant $\epsilon > 0$, there is a randomized algorithm that with high probability maintains a $2-\Omega(1)$ approximate maximum matching of a fully-dynamic general graph in worst-case update time $O(\Delta^{\epsilon}+\text{polylog } n)$, where $\Delta$ is the maximum degree. Previously, the fastest fully dynamic matching algorithm providing a better-than-2 approximation had $O(m^{1/4})$ update-time [Bernstein and Stein, SODA 2016]. A faster algorithm with update-time $O(n^\epsilon)$ was known, but worked only for maintaining the size (and not the edges) of the matching in bipartite graphs [Bhattacharya, Henzinger, and Nanongkai, STOC 2016].
Nosocomial Acinetobacter Baumannii Bacteremia: Comparison of the Clinical Manifestations of Multiresistant Strain and Non-multiresistant Strain Infections

The purpose of this study was to compare clinical manifestations of infections caused by the multiresistant strain of Acinetobacter baumannii with that caused by the non-multiresistant strain. A total of 23 patients with nosocomial A. baumannii bacteremia treated between January 1, 1998 and April 30, 1999 were included in the study; 13 were infected with the multiresistant strain. The overall incidence of nosocomial A. baumannii bacteremia was 4.34 per 10,000 admissions. The male to female ratio was 1.3. The mean age of patients was 60.7 years old (range: 1-8 1). Fifteen patients belonged to the fatal and ultimately fatal group by the McCabe and Jackson criteria, and 17 patients required hospital stay in the intensive care unit. The main risk factors included catheter usage, previous antibiotic usage, and prior ventilator usage. Twenty-one patients had fever and two had shock at the onset of nosocomial A. baumannii bacteremia. Respiratory tract infection was the most common source of infection. The mortality rate of nosocomial A. baumannii bacteremia was 56.5% (13/23). When the clinical manifestations of patients infected with the multiresistant strain were compared to those of patients infected by the non-multiresistant strain of A. baumannii, no significant difference in the risk factors and clinical presentations could be discerned between these two groups. Nosocomial acquisition of the multiresistant A. baumannii strain was associated with excess mortality (76.9% vs. 30%, P=0.04). Early detection of multiresistant A. baumannii is important in making early and correct treatment. The development of a new diagnostic technique is needed to confront the problem of troublesome A. baumannii infection.
On the Design of Analog Fountain Codes for Short Packet Communications in 5G URLLC

Analog fountain code (AFC) is a seamless rate adaptation strategy and was recently shown to closely approach the channel capacity without the channel state information at the transmitter side. The code was mainly designed for large block lengths and therefore the design for short packet communications which is one of the communication scenarios in 5G ultra-reliable and low latency communications (URLLC), is still not clear. We tackle this problem in this work and particularly focus on the design of the precoder which is shown to significantly affects the overall performance of the AFC in the short block length regime. We consider BCH codes as the precoder, and determine the rate of the code in order to maximize the realized rate under a given block error rate constraint. Simulation is performed to investigate the effect of choices for BCH precoder on the overall performance of BCH-AFC in terms of reliability and latency. We show that by selecting the appropriate BCH code as the AFC precoder, AFC performs closely to the normal approximation benchmark. We further proposed a threshold-based decoder to reduce the decoding complexity which is of significant importance in URLLC scenarios.