nils-herrmann/clean_scirepeval huggingface dataset:
Improving the Coverage and Spectral Efficiency of Millimeter-Wave Cellular Networks Using Device-to-Device Relays

The susceptibility of millimeter waveform propagation to blockages limits the coverage of millimeter-wave (mmWave) signals. To overcome blockages, we propose to leverage two-hop device-to-device (D2D) relaying. Using stochastic geometry, we derive expressions for the downlink coverage probability of relay-assisted mmWave cellular networks when the D2D links are implemented in either uplink mmWave or uplink microwave bands. We further investigate the spectral efficiency (SE) improvement in the cellular downlink, and the effect of D2D transmissions on the cellular uplink. For mmWave links, we derive the coverage probability using dominant interferer analysis while accounting for both blockages and beamforming gains. For microwave D2D links, we derive the coverage probability considering both line-of-sight and non-line-of-sight (NLOS) propagation. Numerical results show that downlink coverage and SE can be improved using two-hop D2D relaying. Specifically, microwave D2D relays achieve better coverage because D2D connections can be established under NLOS conditions. However, mmWave D2D relays achieve better coverage when the density of interferers is large because blockages eliminate interference from NLOS interferers. The SE on the downlink depends on the relay mode selection strategy, and mmWave D2D relays use a significantly smaller fraction of uplink resources than microwave D2D relays.
Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases.



To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes.



Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes.



Use of a deep learning system.



Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard.



In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images).



In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.
An Efficient and Fast Li-Ion Battery Charging System Using Energy Harvesting or Conventional Sources

This paper presents a multi-input battery charging system that is capable of increasing the charging efficiency of lithium-ion (Li-ion) batteries. The proposed battery charging system consists of three main building blocks: a pulse charger, a step-down dc–dc converter, and a power path controller. The pulse charger allows charging via a wall outlet or an energy harvesting system. It implements charge techniques that increase the battery charge efficiency of a Li-ion battery. The power path controller (PPC) functions as a power monitor and selects the optimal path for charging either via an energy harvesting system or an ac adapter. The step-down dc–dc converter provides an initial supply voltage to start up the energy harvesting system. The integrated circuit design is implemented on a CMOS 0.18 μm technology process. Experimental results verify that the proposed pulse charger reduces the charging time of 100 mAh and 45 mAh Li-ion batteries respectively by 37.35% and 15.56% and improves the charge efficiency by 3.15% and 3.27% when compared to the benchmark constant current-constant voltage charging technique. The step-down dc–dc converter has a maximum efficiency of 90% and the operation of the PPC is also verified by charging the battery via a thermoelectric energy harvesting system.
Using BPMN to Model a BPEL Process

The Business Process Modeling Notation (BPMN) has been developed to enable business user to develop readily understandable graphical representations of business processes. BPMN is also supported with appropriate graphical object properties that will enable the generation of executable BPEL. Thus, BPMN creates a standardized bridge for the gap between the business process design and process implementation. This paper presents a simple, yet instructive example of how a BPMN diagram can be used to generate a BPEL process.
Lactose digestion and the evolutionary genetics of lactase persistence

It has been known for some 40 years that lactase production persists into adult life in some people but not in others. However, the mechanism and evolutionary significance of this variation have proved more elusive, and continue to excite the interest of investigators from different disciplines. This genetically determined trait differs in frequency worldwide and is due to cis-acting polymorphism of regulation of lactase gene expression. A single nucleotide polymorphism located 13.9 kb upstream from the lactase gene (C-13910 > T) was proposed to be the cause, and the −13910*T allele, which is widespread in Europe was found to be located on a very extended haplotype of 500 kb or more. The long region of haplotype conservation reflects a recent origin, and this, together with high frequencies, is evidence of positive selection, but also means that −13910*T might be an associated marker, rather than being causal of lactase persistence itself. Doubt about function was increased when it was shown that the original SNP did not account for lactase persistence in most African populations. However, the recent discovery that there are several other SNPs associated with lactase persistence in close proximity (within 100 bp), and that they all reside in a piece of sequence that has enhancer function in vitro, does suggest that they may each be functional, and their occurrence on different haplotype backgrounds shows that several independent mutations led to lactase persistence. Here we provide access to a database of worldwide distributions of lactase persistence and of the C-13910*T allele, as well as reviewing lactase molecular and population genetics and the role of selection in determining present day distributions of the lactase persistence phenotype.
An automotive radar network based on 77 GHz FMCW sensors

Automotive radar sensors will be used in many future applications to increase comfort and safety. Adaptive cruise control (ACC), parking aid, collision warning or avoidance and pre crash are some automotive applications, which are based on high performance radar sensors. This paper describes results gained by an European Commission funded research project and a new radar network based on four individual monostatic 77 GHz FMCW radar sensors. Each sensor measures target range and velocity simultaneously and extremely accurate even in multiple target situations with a maximum range of 40 m. The azimuth angle is estimated by multilateration techniques in a radar network architecture. The system design and the signal processing of this network are described in detail in this paper. Due to the high range resolution performance of each radar sensor ordinary targets cannot be considered any longer as point but as extended targets. System capability, performance and reliability can be improved essentially if the single sensor FMCW signal processing, the target position processing in the radar network and the tracking scheme are jointly optimized.
Automatic Root Cause Analysis for LTE Networks Based on Unsupervised Techniques

The increase in the size and complexity of current cellular networks is complicating their operation and maintenance tasks. While the end-to-end user experience in terms of throughput and latency has been significantly improved, cellular networks have also become more prone to failures. In this context, mobile operators start to concentrate their efforts on creating self-healing networks, i.e., those networks capable of troubleshooting in an automatic way, making the network more reliable and reducing costs. In this paper, an automatic diagnosis system based on unsupervised techniques for Long-Term Evolution (LTE) networks is proposed. In particular, this system is built through an iterative process, using self-organizing maps (SOMs) and Ward's hierarchical method, to guarantee the quality of the solution. Furthermore, to obtain a number of relevant clusters and label them properly from a technical point of view, an approach based on the analysis of the statistical behavior of each cluster is proposed. Moreover, with the aim of increasing the accuracy of the system, a novel adjustment process is presented. It intends to refine the diagnosis solution provided by the traditional SOM according to the so-called silhouette index and the most similar cause on the basis of the minimum Xth percentile of all distances. The effectiveness of the developed diagnosis system is validated using real and simulated LTE data by analyzing its performance and comparing it with reference mechanisms.
Knowledge Sharing in the Workplace: A Social Networking Site Assessment

Enterprise executives are taking note that great potential exists for enhancing knowledge sharing and linking experts within the organization when incorporating social software technologies into their operations. But there are warnings of potential negative effects if ineffective use or even misuse of these technologies occurs. There is relatively little empirical research examining the business impact of social software implementations evolving from Web 2.0 applications when considering the effects of trust on knowledge sharing. This paper attempts to fill this gap in the research by proposing a theoretical framework to study the effects of trust, risk and benefits, critical mass, and social influence on knowledge sharing intentions of employees using social media technology in the organization.
Towards Unsupervised Learning of Temporal Relations between Events

Automatic extraction of temporal relations between event pairs is an important task for several natural language processing applications such as Question Answering, Information Extraction, and Summarization. Since most existing methods are supervised and require large corpora, which for many languages do not exist, we have concentrated our efforts to reduce the need for annotated data as much as possible. This paper presents two different algorithms towards this goal. The first algorithm is a weakly supervised machine learning approach for classification of temporal relations between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then, inspired by the hypothesis of “one type of temporal relation per discourse”, it extracts useful information from a cluster of topically related documents. We show that by combining the global information of such a cluster with local decisions of a general classifier, a bootstrapping cross-document classifier can be built to extract temporal relations between events. Our experiments show that without any additional annotated data, the accuracy of the proposed algorithm is higher than that of several previous successful systems. The second proposed method for temporal relation extraction is based on the expectation maximization (EM) algorithm. Within EM, we used different techniques such as a greedy best-first search and integer linear programming for temporal inconsistency removal. We think that the experimental results of our EM based algorithm, as a first step toward a fully unsupervised temporal relation extraction method, is encouraging.
Aurora: a new model and architecture for data stream management

This paper describes the basic processing model and architecture of Aurora, a new system to manage data streams for monitoring applications. Monitoring applications differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from human operators requires one to rethink the fundamental architecture of a DBMS for this application area. In this paper, we present Aurora, a new DBMS currently under construction at Brandeis University, Brown University, and M.I.T. We first provide an overview of the basic Aurora model and architecture and then describe in detail a stream-oriented set of operators.
Weather tracking system using MQTT and SQLite

Every person is enthusiastic in accessing the information about certain place or object remotely and to make the things around them smart using new emerging technologies. This paper mainly focus an implementation of a framework of publishing data from ESP8266 to SQLite data server hosted on Raspberry pi module using MQTT Protocol. Here the sensor is assembled to ESP8266 microcontroller to read the values. A python application is developed to sense the values from sensor and to publish the data to raspberry pi. Now, ESP8266 acts as MQTT client and sensor data is sent to the MQTT broker running on Raspberry pi module. Additionally SQLite data server is also hosted on the Raspberry pi module and the data from the ESP8266 is stored in JSON format. This data from server is formatted using HTML file and the formatted data can be accessed remotely.
Developing a Research Agenda for Human-Centered Data Science

The study and analysis of large and complex data sets offer a wealth of insights in a variety of applications. Computational approaches provide researchers access to broad assemblages of data, but the insights extracted may lack the rich detail that qualitative approaches have brought to the understanding of sociotechnical phenomena. How do we preserve the richness associated with traditional qualitative methods while utilizing the power of large data sets? How do we uncover social nuances or consider ethics and values in data use? These and other questions are explored by human-centered data science, an emerging field at the intersection of human-computer interaction (HCI), computer-supported cooperative work (CSCW), human computation, and the statistical and computational techniques of data science. This workshop, the first of its kind at CSCW, seeks to bring together researchers interested in human-centered approaches to data science to collaborate, define a research agenda, and form a community.
Functionality preserving shape style transfer

When geometric models with a desired combination of style and functionality are not available, they currently need to be created manually. We facilitate algorithmic synthesis of 3D models of man-made shapes which combines user-specified style, described via an exemplar shape, and functionality, encoded by a functionally different target shape. Our method automatically transfers the style of the exemplar to the target, creating the desired combination. The main challenge in performing cross-functional style transfer is to implicitly separate an object's style from its function: while stylistically the output shapes should be as close as possible to the exemplar, their original functionality and structure, as encoded by the target, should be strictly preserved. Recent literature point to the presence of similarly shaped, salient geometric elements as a main indicator of stylistic similarity between 3D shapes. We therefore transfer the exemplar style to the target via a sequence of element-level operations. We allow only compatible operations, ones that do not affect the target functionality. To this end, we introduce a cross-structural element compatibility metric that estimates the impact of each operation on the edited shape. Our metric is based on the global context and coarse geometry of evaluated elements, and is trained on databases of 3D objects. We use this metric to cast style transfer as a tabu search, which incrementally updates the target shape using compatible operations, progressively increasing its style similarity to the exemplar while strictly maintaining its functionality at each step. We evaluate our framework across a range of man-made objects including furniture, light fixtures, and tableware, and perform a number of user studies confirming that it produces convincing outputs combining the desired style and function.
Question routing in community question answering: putting category in its place

This paper investigates a ground-breaking incorporation of question category to Question Routing (QR) in Community Question Answering (CQA) services. The incorporation of question category was designed to estimate answerer expertise for routing questions to potential answerers. Two category-sensitive Language Models (LMs) were developed with large-scale real world data sets being experimented. Results demonstrated that higher accuracies of routing questions with lower computational costs were achieved, relative to traditional Query Likelihood LM (QLLM), state-of-the-art Cluster-Based LM (CBLM) and the mixture of Latent Dirichlet Allocation and QLLM (LDALM).
BRAINSUP: Brainstorming Support for Creative Sentence Generation

We present BRAINSUP, an extensible framework for the generation of creative sentences in which users are able to force several words to appear in the sentences and to control the generation process across several semantic dimensions, namely emotions, colors, domain relatedness and phonetic properties. We evaluate its performance on a creative sentence generation task, showing its capability of generating well-formed, catchy and effective sentences that have all the good qualities of slogans produced by human copywriters.
Effects of Platform and Content Attributes on Information Dissemination on We Media: A Case Study on WeChat Platform

The emergence of the WeChat has brought some new changes to people's ways of life and reading. Thanks to the WeChat Public Platform, many people from all walks of life have become We Media operators, creating their own brands and products. In a case study of WeChat Public Platform, this paper uses NLPIR, a tool for Chinese word segmentation, and SPSS, a data analysis software, to conduct an analysis of actual data captured by Web Spider about WeChat posts and the platform itself, exploring the effects of post content and platform attributes on users' reading and dissemination behaviors. Our regression analysis found that some content attributes, including parts of speech like adverb, punctuation marks such as question mark, and headline length and position, as well as platform attributes, such as the platform seniority, have significant impacts on page views; the genres of advertisement, joke and warning, among other content attributes, and follower count as a platform attribute, have a very significant effect on the dissemination index. This paper is helpful for operators to gain a better understanding of factors influencing the spread of information published by WeChat Official Accounts and providing valuable revelations on achieving fast and efficient dissemination of posts.
High-Performance Distributed ML at Scale through Parameter Server Consistency Models

As Machine Learning (ML) applications embrace greater data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands. Effective use of clusters for ML programs requires considerable expertise in writing distributed code, but existing highlyabstracted frameworks like Hadoop that pose low bar-ed frameworks like Hadoop that pose low barriers to distributed-programming have not, in practice, matched the performance seen in highly specialized and advanced ML implementations. The recent Parameter Server (PS) paradigm is a middle ground between these extremes, allowing easy conversion of single-machine parallel ML programs into distributed ones, while maintaining high throughput through relaxed “consistency models” that allow asynchronous (and, hence, inconsistent) parameter reads. However, due to insufficient theoretical study, it is not clear which of these consistency models can really ensure correct ML algorithm output; at the same time, there remain many theoreticallymotivated but undiscovered opportunities to maximize computational throughput. Inspired by this challenge, we study both the theoretical guarantees and empirical behavior of iterative-convergent ML algorithms in existing PS consistency models. We then use the gleaned insights to improve a consistency model using an “eager” PS communication mechanism, and implement it as a new PS system that enables ML programs to reach their solution more quickly.