nils-herrmann/clean_scirepeval huggingface dataset:
Rethink fronthaul for soft RAN

In this article we discuss the design of a new fronthaul interface for future 5G networks. The major shortcomings of current fronthaul solutions are first analyzed, and then a new fronthaul interface called next-generation fronthaul interface (NGFI) is proposed. The design principles for NGFI are presented, including decoupling the fronthaul bandwidth from the number of antennas, decoupling cell and user equipment processing, and focusing on high-performancegain collaborative technologies. NGFI aims to better support key 5G technologies, in particular cloud RAN, network functions virtualization, and large-scale antenna systems. NGFI claims the advantages of reduced bandwidth as well as improved transmission efficiency by exploiting the tidal wave effect on mobile network traffic. The transmission of NGFI is based on Ethernet to enjoy the benefits of flexibility and reliability. The major impact, challenges, and potential solutions of Ethernet-based fronthaul networks are also analyzed. Jitter, latency, and time and frequency synchronization are the major issues to overcome.
Reducing Patient Mortality in Hospitals : The Role of Human Resource

Developing effective health care organizations is increasingly complex as a result of demographic changes, globalization and developments in medicine. This study examines the potential contribution of organizational behavior theory and research by investigating the relationship between systems of human resource management (HRM) practices and effectiveness of patient care in hospitals. Relatively little research has been conducted to explore these issues in health care settings. In a sample of 52 hospitals in England, we examine the relationship between the HRM system and health care outcome. Specifically, we study the association between high performance HRM policies and practices and standardized patient mortality rates. The research reveals that, after controlling for prior mortality and other potentially confounding factors such as the ratio of doctors to patients, greater use of a complementary set of HRM practices has a statistically and practically significant relationship with patient mortality. The findings suggest that managers and policy makers should focus sharply on improving the functioning of relevant HR management systems in health care organizations as one important means by which to improve patient care.
Direct Causality Detection via the Transfer Entropy Approach

The detection of direct causality, as opposed to indirect causality, is an important and challenging problem in root cause and hazard propagation analysis. Several methods provide effective solutions to this problem when linear relationships between variables are involved. For nonlinear relationships, currently only overall causality analysis can be conducted, but direct causality cannot be identified for such processes. In this paper, we describe a direct causality detection approach suitable for both linear and nonlinear connections. Based on an extension of the transfer entropy approach, a direct transfer entropy (DTE) concept is proposed to detect whether there is a direct information flow pathway from one variable to another. Especially, a differential direct transfer entropy concept is defined for continuous random variables, and a normalization method for the differential direct transfer entropy is presented to determine the connectivity strength of direct causality. The effectiveness of the proposed method is illustrated by several examples, including one experimental case study and one industrial case study.
Unsupervised Learning of Image Manifolds by Semidefinite Programming

Can we detect low dimensional structure in high dimensional data sets of images? In this paper, we propose an algorithm for unsupervised learning of image manifolds by semidefinite programming. Given a data set of images, our algorithm computes a low dimensional representation of each image with the property that distances between nearby images are preserved. More generally, it can be used to analyze high dimensional data that lies on or near a low dimensional manifold. We illustrate the algorithm on easily visualized examples of curves and surfaces, as well as on actual images of faces, handwritten digits, and solid objects.
Vehicle-to-Vehicle Communication: Fair Transmit Power Control for Safety-Critical Information

Direct radio-based vehicle-to-vehicle communication can help prevent accidents by providing accurate and up-to-date local status and hazard information to the driver. In this paper, we assume that two types of messages are used for traffic safety-related communication: 1) Periodic messages (ldquobeaconsrdquo) that are sent by all vehicles to inform their neighbors about their current status (i.e., position) and 2) event-driven messages that are sent whenever a hazard has been detected. In IEEE 802.11 distributed-coordination-function-based vehicular networks, interferences and packet collisions can lead to the failure of the reception of safety-critical information, in particular when the beaconing load leads to an almost-saturated channel, as it could easily happen in many critical vehicular traffic conditions. In this paper, we demonstrate the importance of transmit power control to avoid saturated channel conditions and ensure the best use of the channel for safety-related purposes. We propose a distributed transmit power control method based on a strict fairness criterion, i.e., distributed fair power adjustment for vehicular environments (D-FPAV), to control the load of periodic messages on the channel. The benefits are twofold: 1) The bandwidth is made available for higher priority data like dissemination of warnings, and 2) beacons from different vehicles are treated with ldquoequal rights,rdquo and therefore, the best possible reception under the available bandwidth constraints is ensured. We formally prove the fairness of the proposed approach. Then, we make use of the ns-2 simulator that was significantly enhanced by realistic highway mobility patterns, improved radio propagation, receiver models, and the IEEE 802.11p specifications to show the beneficial impact of D-FPAV for safety-related communications. We finally put forward a method, i.e., emergency message dissemination for vehicular environments (EMDV), for fast and effective multihop information dissemination of event-driven messages and show that EMDV benefits of the beaconing load control provided by D-FPAV with respect to both probability of reception and latency.
Biometric Gait Authentication Using Accelerometer Sensor

This paper presents a biometric user authentication based on a person’s gait. Unlike most previous gait recognition approaches, which are based on machine vision techniques, in our approach gait patterns are extracted from a physical device attached to the lower leg. From the output of the device accelerations in three directions: vertical, forward-backward, and sideways motion of the lower leg are obtained. A combination of these accelerations is used for authentication. Applying two different methods, histogram similarity and cycle length, equal error rates (EER) of 5% and 9% were achieved, respectively.
Two-Switch Voltage Equalizer Using an LLC Resonant Inverter and Voltage Multiplier for Partially Shaded Series-Connected Photovoltaic Modules

Various kinds of differential power processing converters and voltage equalizers have been proposed for series-connected photovoltaic (PV) modules to prevent negative influences of partial shading, such as significant reduction in power generation and the occurrence of multiple maximum power points (MPPs), including local and global MPPs, that hinders and confuses MPP tracking algorithms to operate properly. However, since conventional topologies are based on multiple individual dc-dc converters, the required switch count increases proportionally to the number of modules connected in series, increasing the complexity. A two-switch voltage equalizer using an LLC resonant inverter and voltage multiplier is proposed in this paper. The circuitry can be dramatically simplified compared with conventional topologies due to the two-switch configuration. Detailed operation analyses for the LLC resonant inverter and voltage multiplier are separately performed. Experimental equalization tests emulating partially shaded conditions were performed for four PV modules connected in series. With the proposed voltage equalizer, local MPPs successfully disappeared, and extractable maximum power increased compared with those without equalization, demonstrating the effectiveness and performance of the proposed voltage equalizer.
Improving Interaction in HMD-Based Vehicle Simulators through Real Time Object Reconstruction

Bringing real objects into the virtual world has been shown to increase usability and presence in virtual reality applications. This paper presents a system to generate a real time virtual reconstruction of real world user interface elements for use in a head mounted display based driving simulator. Our system uses sensor fusion algorithms to combine data from depth and color cameras to generate an accurate, detailed, and fast rendering of the user's hands while using the simulator. We tested our system and show in our results that the inclusion of the participants real hands, the wheel, and the shifter in the virtual environment increases the immersion, presence, and usability of the simulation. Our system can also be used to bring other real objects into the virtual world, especially when accuracy, detail, and real time updates are desired.
Project Aura: Toward Distraction-Free Pervasive Computing

A s the effects of Moore's law cause computing systems to become cheaper and more plentiful, a new problem arises: increasingly, the bottleneck in computing is not its disk capacity, processor speed, or communication bandwidth, but rather the limited resource of human attention. Human attention refers to a user's ability to attend to his or her primary tasks, ignoring system-generated distractions such as poor performance and failures. By exploiting plentiful computing resources to reduce user distraction, Project Aura is creating a system whose effectiveness is considerably greater than that of other systems today. Aura is specifically intended for pervasive computing environments involving wireless communication , wearable or handheld computers, and smart spaces. Human attention is an especially scarce resource in such environments, because the user is often preoccupied with walking, driving, or other real-world interactions. In addition, mobile computing poses difficult challenges such as intermittent and variable-bandwidth connectivity, concern for battery life, and the client resource constraints that weight and size considerations impose. To accomplish its ambitious goals, research in Aura spans every system level: from the hardware, through the operating system, to applications and end users. Underlying this diversity of concerns, Aura applies two broad concepts. First, it uses proactivity, which is a system layer's ability to anticipate requests from a higher layer. In today's systems , each layer merely reacts to the layer above it. Second, Aura is self-tuning: layers adapt by observing the demands made on them and adjusting their performance and resource usage characteristics accordingly. Currently, system-layer behavior is relatively static. Both of these techniques will help lower demand for human attention. To illustrate the kind of world we are trying to create, we present two hypothetical Aura scenarios. Although these might seem far-fetched today, they represent the kind of scenarios we expect to make commonplace through our research. In the first scenario, Jane is at Gate 23 in the Pitts-burgh airport, waiting for her connecting flight. She has edited many large documents and would like to use her wireless connection to email them. Unfortunately , bandwidth is miserable because many passengers at Gates 22 and 23 are surfing the Web. Aura observes that, at the current bandwidth, Jane won't be able to finish sending her documents before her flight departs. Consulting the airport's wireless network bandwidth service and flight schedule service, Aura discovers that wireless bandwidth is excellent at Gate 15, and that there are …
Threatening faces and social anxiety: a literature review.

A threatening facial expression is a potent social sign of hostility or dominance. During the past 20 years, photographs of threatening faces have been increasingly included as stimuli in studies with socially anxious participants, based on the hypothesis that a threatening face is especially salient to people with fears of social interaction or negative evaluation. The purpose of this literature review is to systematically evaluate the accumulated research and suggest possible avenues for further research. The main conclusion is that photographs of threatening faces engage a broad range of perceptual processes in socially anxious participants, particularly when exposure times are very short.
Agile estimation using people and project related factors

The key measures of any project about its perfectness are ability to complete in the scheduled time and within the estimated cost. Cost and effort are important factors whenever a project is developed. The estimation of cost and effort is a difficult task in Agile environment. It has been observed that the current Agile methods mostly depends on an expert opinion, planning poker and historical data of project for estimation of cost, size, effort and duration. In absence of the historical data and experts the previous methods are not efficient. So there is need of an algorithmic method, which can calculate cost and effort of the project. In this direction S.Bhalero, Maya Ingel  has considered some project-related factors that calculate the size as well as duration of the project. However, several other project as well as people related factors may affect the Agile environment. In this work an Algorithmic estimation method is being proposed considering various factors thereby estimating the more accurate release date, cost, effort and duration for the project. The effectiveness and feasibility of the proposed algorithm has been shown by considering three cases in which different levels of factors are taken and compared.
In-class distractions: The role of Facebook and the primary learning task

While laptops and other Internet accessible technologies facilitate student learning in the classroom, they also increase opportunities for interruptions from off-task social networking sites such as Facebook (FB). A small number of correlational studies have suggested that FB has a detrimental effect on learning performance, however; these studies had neglected to investigate student-engagement in the primary learning task and how this affects task-switching to goal-irrelevant FB intrusions (distractions); and how purposeful deployment of attention to FB (goal-relevant interruptions) affect lecture comprehension on such tasks. This experiment fills a gap in the literature by manipulating lecture interest-value and controls for duration of FB exposure, time of interruption, FB material and the order of FB posts. One hundred and fifty participants were randomly allocated to one of six conditions: (A) no FB intrusions, high-interest (HI) lecture; (B) no FB intrusions, low-interest (LI) lecture (C) goal-relevant FB intrusions, HI lecture (D) goal-relevant FB intrusions, LI lecture (E) goal-irrelevant FB intrusions, HI lecture (F) goal-irrelevant FB intrusions, LI lecture. As predicted, participants were more susceptible to FB distractions when the primary learning task was of low-interest. The study also found that goal-relevant FB intrusions significantly reduced HI lecture comprehension compared to the control condition (A). The results highlight the need for recourses that will help educators increase student engagement with their learning task. Implications for future research are discussed.
Review of pricing models for grid & cloud computing

Distributed system resources have become prevalent in ICT departments to lessen the burden of huge expenses incurred by very expensive storage computer systems. Add to this the continuous introduction and ever-growing evolution of simple to complex applications, the demand to access huge quantities of data, intensive computations, powerful simulations, maintaining and offering system resources and middleware infrastructure services the need to do all of this at an affordable and reasonable price is crucial. Distributed grid and cloud computing resources are currently considered to be one of the best technology options to provide this. They have many similar features and functions, and both of them are classed as distributed systems. They are capable of offering unaffordable resources and services at a reasonable price in a mass marketplace. The big question is: what is a reasonable price? How is pricing modeled and on what kind of economic principles is it based? Much of the issues surrounding these questions are very complex in themselves. This paper provides a comparative review of grid and cloud computing economic and pricing models from which appropriate tariffs and charging models can be chosen to meet particular business objectives. The actual choice depends on many other factors like enterprise regulations, tax laws, service level agreements and return on investments, are very important but outside the scope of this paper. In this paper we give the basic core principles and a comparative review of the latest and most appropriate economic and pricing models applicable to grid and cloud computing in order to propose better models for the future.
A method of requirements elicitation and analysis for Global Software Development

Proc 2016; 1–27  To perform requirements elicitation and analysis, effective communication and collaboration between stakeholders are necessary. Global Software Development (GSD), where software teams are located in different parts of the world, has become increasingly popular. However, geographical distance, cultural diversity, differences in time zones, and language barriers create difficulties for GSD stakeholders in engaging in effective communication. Taking into consideration the factors involved in GSD, previous research showed that the ways by which requirements are gathered and analyzed for collocated software development cannot be used effectively for GSD. Thus, in this paper, we present a method of requirements elicitation and analysis for GSD. The method consists of 4 stages: (1) data collection; (2) educating stakeholders about GSD issues; (3) post‐education assessment; and (4) requirements elicitation and analysis. Past researchers used student groups in a university environment to play the roles of stakeholders in experiments in GSD studies. Likewise, we preliminarily validate our method by applying it to a case study of an online shopping system, where the roles of client, requirements engineer, project analyst, and designers were played by a group of students.
Tensor Decompositions, State of the Art and Applications

In this paper, we present a partial survey of the tools borrowed from tensor algebra, which have been utilized recently in Statistics and Signal Processing. It is shown why the decompositions well known in linear algebra can hardly be extended to tensors. The concept of rank is itself difficult to define, and its calculation raises difficulties. Numerical algorithms have nevertheless been developed, and some are reported here, but their limitations are emphasized. These reports hopefully open research perspectives for enterprising readers.
Duplicate Reduction in Graph Mining: Approaches, Analysis, and Evaluation

At the core of graph mining lies independent expansion of substructures where a substructure (also referred to as a subgraph) independently grows into a number of larger substructures in each iteration. Such an independent expansion, invariably, leads to the generation of duplicates. In the presence of graph partitions, duplicates are generated both within and across partitions. Eliminating these duplicates (for correctness) not only incurs generation and storage cost but also additional computation for its elimination. Our primary aim is to design techniques to reduce generating duplicate substructures as we show that they cannot be eliminated. This paper introduces three constraint-based optimization techniques, each significantly improving the overall mining cost by reducing the number of duplicates generated. These alternatives provide flexibility to choose the right technique based on graph properties. We establish theoretical correctness of each technique as well as its analysis with respect to graph characteristics such as degree, number of unique labels, and label distribution. We also investigate the applicability of their combination for improvements in duplicate reduction. Finally, we discuss the effects of the constraints with respect to the partitioning schemes used in graph mining. Our experiments demonstrate significant benefits of these constraints in terms of storage, computation, and communication cost (specific to partitioned approaches) across graphs with varied characteristics.
Cloud Performance Modeling with Benchmark Evaluation of Elastic Scaling Strategies

In this paper, we present generic cloud performance models for evaluating Iaas, PaaS, SaaS, and mashup or hybrid clouds. We test clouds with real-life benchmark programs and propose some new performance metrics. Our benchmark experiments are conducted mainly on IaaS cloud platforms over scale-out and scale-up workloads. Cloud benchmarking results are analyzed with the efficiency, elasticity, QoS, productivity, and scalability of cloud performance. Five cloud benchmarks were tested on Amazon IaaS EC2 cloud: namely YCSB, CloudSuite, HiBench, BenchClouds, and TPC-W. To satisfy production services, the choice of scale-up or scale-out solutions should be made primarily by the workload patterns and resources utilization rates required. Scaling-out machine instances have much lower overhead than those experienced in scale-up experiments. However, scaling up is found more cost-effective in sustaining heavier workload. The cloud productivity is greatly attributed to system elasticity, efficiency, QoS and scalability. We find that auto-scaling is easy to implement but tends to over provision the resources. Lower resource utilization rate may result from auto-scaling, compared with using scale-out or scale-up strategies. We also demonstrate that the proposed cloud performance models are applicable to evaluate PaaS, SaaS and hybrid clouds as well.
Properties and Therapeutic Application of Bromelain: A Review

Bromelain belongs to a group of protein digesting enzymes obtained commercially from the fruit or stem of pineapple. Fruit bromelain and stem bromelainare prepared differently and they contain different enzymatic composition. "Bromelain" refers usually to the "stem bromelain." Bromelain is a mixture of different thiol endopeptidases and other components like phosphatase, glucosidase, peroxidase, cellulase, escharase, and several protease inhibitors. In vitro and in vivo studies demonstrate that bromelain exhibits various fibrinolytic, antiedematous, antithrombotic, and anti-inflammatory activities. Bromelain is considerably absorbable in the body without losing its proteolytic activity and without producing any major side effects. Bromelain accounts for many therapeutic benefits like the treatment of angina pectoris, bronchitis, sinusitis, surgical trauma, and thrombophlebitis, debridement of wounds, and enhanced absorption of drugs, particularly antibiotics. It also relieves osteoarthritis, diarrhea, and various cardiovascular disorders. Bromelain also possesses some anticancerous activities and promotes apoptotic cell death. This paper reviews the important properties and therapeutic applications of bromelain, along with the possible mode of action.