nils-herrmann/clean_scirepeval huggingface dataset:
Location Fingerprinting With Bluetooth Low Energy Beacons

The complexity of indoor radio propagation has resulted in location-awareness being derived from empirical fingerprinting techniques, where positioning is performed via a previously-constructed radio map, usually of WiFi signals. The recent introduction of the Bluetooth Low Energy (BLE) radio protocol provides new opportunities for indoor location. It supports portable battery-powered beacons that can be easily distributed at low cost, giving it distinct advantages over WiFi. However, its differing use of the radio band brings new challenges too. In this work, we provide a detailed study of BLE fingerprinting using 19 beacons distributed around a ~600 m2 testbed to position a consumer device. We demonstrate the high susceptibility of BLE to fast fading, show how to mitigate this, and quantify the true power cost of continuous BLE scanning. We further investigate the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency. We also provide quantitative comparison with WiFi fingerprinting. Our results show advantages to the use of BLE beacons for positioning. For one-shot (push-to-fix) positioning we achieve <; 2.6 m error 95% of the time for a dense BLE network (1 beacon per 30 m2), compared to <; 4.8 m for a reduced density (1 beacon per 100 m2) and <; 8.5 m for an established WiFi network in the same area.
National drug control policy and prescription drug abuse: facts and fallacies.

In a recent press release Joseph A. Califano, Jr., Chairman and President of the National Center on Addiction and Substance Abuse at Columbia University called for a major shift in American attitudes about substance abuse and addiction and a top to bottom overhaul in the nation's healthcare, criminal justice, social service, and eduction systems to curtail the rise in illegal drug use and other substance abuse. Califano, in 2005, also noted that while America has been congratulating itself on curbing increases in alcohol and illicit drug use and in the decline in teen smoking, abuse and addition of controlled prescription drugs-opioids, central nervous system depressants and stimulants-have been stealthily, but sharply rising. All the statistics continue to show that prescription drug abuse is escalating with increasing emergency department visits and unintentional deaths due to prescription controlled substances. While the problem of drug prescriptions for controlled substances continues to soar, so are the arguments of undertreatment of pain. The present state of affairs show that there were 6.4 million or 2.6% Americans using prescription-type psychotherapeutic drugs nonmedically in the past month. Of these, 4.7 million used pain relievers. Current nonmedical use of prescription-type drugs among young adults aged 18-25 increased from 5.4% in 2002 to 6.3% in 2005. The past year, nonmedical use of psychotherapeutic drugs has increased to 6.2% in the population of 12 years or older with 15.172 million persons, second only to marijuana use and three times the use of cocaine. Parallel to opioid supply and nonmedical prescription drug use, the epidemic of medical drug use is also escalating with Americans using 80% of world's supply of all opioids and 99% of hydrocodone. Opioids are used extensively despite a lack of evidence of their effectiveness in improving pain or functional status with potential side effects of hyperalgesia, negative hormonal and immune effects, addiction and abuse. The multiple reasons for continued escalation of prescription drug abuse and overuse are lack of education among all segments including physicians, pharmacists, and the public; ineffective and incoherent prescription monitoring programs with lack of funding for a national prescription monitoring program NASPER; and a reactive approach on behalf of numerous agencies. This review focuses on the problem of prescription drug abuse with a discussion of facts and fallacies, along with proposed solutions.
Confidential Business Process Execution on Blockchain

One of the main issues in service collaborations among business partners is the possible lack of trust among them. A promising approach to cope with this issue is leveraging on blockchain technology by encoding with smart contracts the business process workflow. This brings the benefits of trust decentralization, transparency, and accountability of the service composition process. However, data in the blockchain are public, implying thus serious consequences on confidentiality and privacy. Moreover, smart contracts can access data outside the blockchain only through Oracles, which might pose new confidentiality risks if no assumptions are made on their trustworthiness. For these reasons, in this paper, we are interested in investigating how to ensure data confidentiality during business process execution on blockchain even in the presence of an untrusted Oracle.
Using energy scenarios to explore alternative energy pathways in California

This paper develops and analyzes four energy scenarios for California that are both exploratory and quantitative. The businessas-usual scenario represents a pathway guided by outcomes and expectations emerging from California’s energy crisis. Three alternative scenarios represent contexts where clean energy plays a greater role in California’s energy system: Split Public is driven by local and individual activities; Golden State gives importance to integrated state planning; Patriotic Energy represents a national drive to increase energy independence. Future energy consumption, composition of electricity generation, energy diversity, and greenhouse gas emissions are analyzed for each scenario through 2035. Energy savings, renewable energy, and transportation activities are identified as promising opportunities for achieving alternative energy pathways in California. A combined approach that brings together individual and community activities with state and national policies leads to the largest energy savings, increases in energy diversity, and reductions in greenhouse gas emissions. Critical challenges in California’s energy pathway over the next decades identified by the scenario analysis include dominance of the transportation sector, dependence on fossil fuels, emissions of greenhouse gases, accounting for electricity imports, and diversity of the electricity sector. The paper concludes with a set of policy lessons revealed from the California energy scenarios.
Interdisciplinary management of anterior dental esthetics.

Dental esthetics has become a popular topic among all disciplines in dentistry. When a makeover is planned for the esthetic appearance of a patient's teeth, the clinician must have a logical diagnostic approach that results in the appropriate treatment plan. With some patients, the restorative dentist cannot accomplish the correction alone but may require the assistance of other dental disciplines.



This article describes an interdisciplinary approach to the diagnosis and management of anterior dental esthetics. The authors practice different disciplines in dentistry: restorative care, orthodontics and periodontics. However, for more than 20 years, this team has participated in an interdisciplinary dental study group that focuses on a wide variety of dental problems. One such area has been the analysis of anterior dental esthetic problems requiring interdisciplinary correction. This article will describe a unique approach to interdisciplinary dental diagnosis, beginning with esthetics but encompassing structure, function and biology to achieve an optimal result.



If a clinician uses an esthetically based approach to the diagnosis of anterior dental problems, then the outcome of the esthetic treatment plan will be enhanced without sacrificing the structural, functional and biological aspects of the patient's dentition.
E-learning services acceptance in higher educational institutes: A case study in Brunei

This research studies on the e-learning services acceptance in higher education institutions in Brunei Darussalam. This research has seven hypotheses, relating to independent, intermediary, and dependent variables. The independent variables include lecturer’s characteristics, design of learning contents, teaching materials, and playfulness; while the intermediary variables are perceived benefits and perceived ease of use. On the other hand, the dependent variable in this research is the intention to use e-learning. Multiple regression analysis were conducted to test the hypotheses proposed. Data analysis from this research has confirmed that the lecturer’s characteristics, teaching materials, perceived ease of use and the intention to use e-learning corresponds to the perceived benefits. Meanwhile, the design of learning content and the intention to use e-learning are positively related to the perceived ease of use and lastly, the variable playfulness is positively related to the intention to use e-learning.
A survey on mobile edge computing

Mobile Edge Computing is an emerging technology that provides cloud and IT services within the close proximity of mobile subscribers. Traditional telecom network operators perform traffic control flow (forwarding and filtering of packets), but in Mobile Edge Computing, cloud servers are also deployed in each base station. Therefore, network operator has a great responsibility in serving mobile subscribers. Mobile Edge Computing platform reduces network latency by enabling computation and storage capacity at the edge network. It also enables application developers and content providers to serve context-aware services (such as collaborative computing) by using real time radio access network information. Mobile and Internet of Things devices perform computation offloading for compute intensive applications, such as image processing, mobile gaming, to leverage the Mobile Edge Computing services. In this paper, some of the promising real time Mobile Edge Computing application scenarios are discussed. Later on, a state-of-the-art research efforts on Mobile Edge Computing domain is presented. The paper also presents taxonomy of Mobile Edge Computing, describing key attributes. Finally, open research challenges in successful deployment of Mobile Edge Computing are identified and discussed.
Attack vulnerability of complex networks.

We study the response of complex networks subject to attacks on vertices and edges. Several existing complex network models as well as real-world networks of scientific collaborations and Internet traffic are numerically investigated, and the network performance is quantitatively measured by the average inverse geodesic length and the size of the largest connected subgraph. For each case of attacks on vertices and edges, four different attacking strategies are used: removals by the descending order of the degree and the betweenness centrality, calculated for either the initial network or the current network during the removal procedure. It is found that the removals by the recalculated degrees and betweenness centralities are often more harmful than the attack strategies based on the initial network, suggesting that the network structure changes as important vertices or edges are removed. Furthermore, the correlation between the betweenness centrality and the degree in complex networks is studied.
Direct Torque Control of Doubly Star Induction Motor Using Fuzzy Logic Speed Controller

Received Nov 26, 2017 Revised Jan 22, 2018 Accepted Feb 25, 2018 This paper presents the simulation of the control of doubly star induction motor using Direct Torque Control (DTC) based on Proportional and Integral controller (PI) and Fuzzy Logic Controller (FLC). In addition, the work describes a model of doubly star induction motor in α-β reference frame theory and its computer simulation in MATLAB/SIMULINK®.The structure of the DTC has several advantages such as the short sampling time required by the TC schemes makes them suited to a very fast flux and torque controlled drives as well as the simplicity of the control algorithm.the generalpurpose induction drives in very wide range using DTC because it is the excellent solution. The performances of the DTC with a PI controller and FLC are tested under differents speeds command values and load torque.
Tree Edit Distance Learning via Adaptive Symbol Embeddings

Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as molecular structures, abstract syntax trees of computer programs, or syntax trees of natural language, by learning the cost function of an edit distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree. However, learning such costs directly may yield an edit distance which violates metric axioms, is challenging to interpret, and may not generalize well. In this contribution, we propose a novel metric learning approach for trees which we call embedding edit distance learning (BEDL) and which learns an edit distance indirectly by embedding the tree nodes as vectors, such that the Euclidean distance between those vectors supports class discrimination. We learn such embeddings by reducing the distance to prototypical trees from the same class and increasing the distance to prototypical trees from different classes. In our experiments, we show that BEDL improves upon the state-of-the-art in metric learning for trees on six benchmark data sets, ranging from computer science over biomedical data to a natural-language processing data set containing over 300,000 nodes.
Better knowledge with social media? Exploring the roles of social capital and organizational knowledge management

The purpose of this paper is to explore social media’s impact on organizational knowledge quality through the theoretical lens of social capital and resource exchange.  Theory-confirming, quantitative study using panel data collected through web-based survey Findings – The results show that while social media affect structural capital and cognitive capital directly, it only affects relational capital indirectly through structural and cognitive capital. Moreover, overall social media and the enhanced social capital do help promote organizational efforts in knowledge management, which subsequently leads to higher level of organizational knowledge quality.  All survey respondents were from the U.S., which may limit the generalizability of the findings. The authors also call for more research in establishing the time sequence in the proposed causal relations and in the individual level mechanism through which social media promotes organizational knowledge quality.
A Survey on Smart Grid Potential Applications and Communication Requirements

Information and communication technologies (ICT) represent a fundamental element in the growth and performance of smart grids. A sophisticated, reliable and fast communication infrastructure is, in fact, necessary for the connection among the huge amount of distributed elements, such as generators, substations, energy storage systems and users, enabling a real time exchange of data and information necessary for the management of the system and for ensuring improvements in terms of efficiency, reliability, flexibility and investment return for all those involved in a smart grid: producers, operators and customers. This paper overviews the issues related to the smart grid architecture from the perspective of potential applications and the communications requirements needed for ensuring performance, flexible operation, reliability and economics.
Home-based exercise therapy in patients awaiting liver transplantation: protocol for an observational feasibility trial

Liver disease is the third most common cause of premature mortality in the UK. Liver failure accelerates frailty, resulting in skeletal muscle atrophy, functional decline and an associated risk of liver transplant waiting list mortality. However, there is limited research investigating the impact of exercise on patient outcomes pre and post liver transplantation. The waitlist period for patients listed for liver transplantation provides a unique opportunity to provide and assess interventions such as prehabilitation.



This study is a phase I observational study evaluating the feasibility of conducting a randomised control trial (RCT) investigating the use of a home-based exercise programme (HBEP) in the management of patients awaiting liver transplantation. Twenty eligible patients will be randomly selected from the Queen Elizabeth University Hospital Birmingham liver transplant waiting list. Participants will be provided with an individually tailored 12-week HBEP, including step targets and resistance exercises. Activity trackers and patient diaries will be provided to support data collection. For the initial 6 weeks, telephone support will be given to discuss compliance with the study intervention, achievement of weekly targets, and to address any queries or concerns regarding the intervention. During weeks 6-12, participants will continue the intervention without telephone support to evaluate longer term adherence to the study intervention. On completing the intervention, all participants will be invited to engage in a focus group to discuss their experiences and the feasibility of an RCT.


ETHICS AND DISSEMINATION
The protocol is approved by the National Research Ethics Service Committee North West - Greater Manchester East and Health Research Authority (REC reference: 17/NW/0120). Recruitment into the study started in April 2017 and ended in July 2017. Follow-up of participants is ongoing and due to finish by the end of 2017. The findings of this study will be disseminated through peer-reviewed publications and international presentations. In addition, the protocol will be placed on the British Liver Trust website for public access.
Dynamic video anomaly detection and localization using sparse denoising autoencoders

The emergence of novel techniques for automatic anomaly detection in surveillance videos has significantly reduced the burden of manual processing of large, continuous video streams. However, existing anomaly detection systems suffer from a high false-positive rate and also, are not real-time, which makes them practically redundant. Furthermore, their predefined feature selection techniques limit their application to specific cases. To overcome these shortcomings, a dynamic anomaly detection and localization system is proposed, which uses deep learning to automatically learn relevant features. In this technique, each video is represented as a group of cubic patches for identifying local and global anomalies. A unique sparse denoising autoencoder architecture is used, that significantly reduced the computation time and the number of false positives in frame-level anomaly detection by more than 2.5%. Experimental analysis on two benchmark data sets - UMN dataset and UCSD Pedestrian dataset, show that our algorithm outperforms the state-of-the-art models in terms of false positive rate, while also showing a significant reduction in computation time.
Pro Unity Game Development with C#

Unity am e Deelopm nt w ith C# Alan Thorn In Pro Unity Game Development with C#, Alan Thorn, author of Learn Unity for 2D` Game Development and experienced game developer, takes you through the complete C# workflow for developing a cross-platform first person shooter in Unity. C# is the most popular programming language for experienced Unity developers, helping them get the most out of what Unity offers. If you’re already using C# with Unity and you want to take the next step in becoming an experienced, professional-level game developer, this is the book you need. Whether you are a student, an indie developer, or a seasoned game dev professional, you’ll find helpful C# examples of how to build intelligent enemies, create event systems and GUIs, develop save-game states, and lots more. You’ll understand and apply powerful programming concepts such as singleton classes, component based design, resolution independence, delegates, and event driven programming.
Filter Design and Performance Evaluation for Fingerprint Image Segmentation

Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: 'true' foreground can be labeled as background and features like minutiae can be lost, or conversely 'true' background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.
TweetLID: a benchmark for tweet language identification

Language identification, as the task of determining the language a given text is written in, has progressed substantially in recent decades. However, three main issues remain still unresolved: (i) distinction of similar languages, (ii) detection of multilingualism in a single document, and (iii) identifying the language of short texts. In this paper, we describe our work on the development of a benchmark to encourage further research in these three directions, set forth an evaluation framework suitable for the task, and make a dataset of annotated tweets publicly available for research purposes. We also describe the shared task we organized to validate and assess the evaluation framework and dataset with systems submitted by seven different participants, and analyze the performance of these systems. The evaluation of the results submitted by the participants of the shared task helped us shed some light on the shortcomings of state-of-the-art language identification systems, and gives insight into the extent to which the brevity, multilingualism, and language similarity found in texts exacerbate the performance of language identifiers. Our dataset with nearly 35,000 tweets and the evaluation framework provide researchers and practitioners with suitable resources to further study the aforementioned issues on language identification within a common setting that enables to compare results with one another.
A new approach for data clustering using hybrid artificial bee colony algorithm

Data clustering is a popular data analysis technique needed in many fields. Recent years, some swarm intelligence-based approaches for clustering were proposed and achieved encouraging results. This paper presents a Hybrid Artificial Bee Colony (HABC) algorithm for data clustering. The incentive mechanism of HABC is enhancing the information exchange (social learning) between bees by functions, the proposed HABC algorithm is proved to have significant improvement over canonical ABC and several other comparison algorithms. The HABC algorithm is then employed for data clustering. Six real datasets selected from the UCI machine learning repository are used. The results show that the HABC algorithm achieved better results than other algorithms and is a competitive approach for data clustering. & 2012 Elsevier B.V. All rights reserved.