nils-herrmann/clean_scirepeval huggingface dataset:
On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites

Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as MultiPlatform-LDA (MultiLDA). Instead of just merging all posts from different social media platforms into a single text collection, MultiLDA keeps one text collection for each social media platform but allowing these platforms to share a common set of topics. MultiLDA further learns the user-specific platform preferences for each topic. We evaluate MultiLDA against TwitterLDA, the state-of-the-art method for social media content modeling, on two aspects: (i) the effectiveness in modeling topics across social media platforms, and (ii) the ability to predict platform choices for each post. We conduct experiments on three real-world datasets from Twitter, Instagram and Tumblr sharing a set of common users. Our experiments results show that the MultiLDA outperforms in both topic modeling and platform choice prediction tasks. We also show empirically that among the three social media platforms, “Daily matters” and “Relationship matters” are dominant topics in Twitter, “Social gathering”, “Outing” and “Fashion” are dominant topics in Instagram, and “Music”, “Entertainment” and “Fashion” are dominant topics in Tumblr.
Practical Privacy-Preserving Medical Diagnosis Using Homomorphic Encryption

The use of remote services offered by cloud providers have been popular in the last lustrum. Services allow users to store remote files, or to analyze data for several purposes, like health-care or message analysis. However, when personal data are sent to the Cloud, users may lose privacy on the data-content, and on the other side cloud providers may use those data for their own businesses. In this paper, we present our solution to analyze users health-data directly into the Cloud while preserving users privacy. Our solution makes use of homomorphic encryption to protect users data during the analysis. In particular, we developed a mobile application that offloads users data into the Cloud, and a homomorphic encryption algorithm that processes those data without leaking any information to the Cloud provider. Performed empirical tests show that our HE algorithm is able to evaluate users data in reasonable time proving the feasibility of this emerging way of private-data evaluation.
An Optimized Priority Enabled Energy Management System for Smart Homes

With the advent of smart grid (SG) and the emergence of information and communication technology, smart meters, bidirectional communication, smart homes and storage systems the energy consumption patterns at the consumer premises have been revolutionized. Moreover, with the rise of renewable energy sources (RESs), storage systems and electric vehicles (EVs) a profound amelioration in the energy management systems has been observed. Home energy management systems (HEMSs) help to control, manage and optimize the energy in smart homes. In this paper, we present a HEMS using multi-agent system (MAS) for smart homes. The HEMS uses priority techniques with the integration of electrical supply system (ESS). Furthermore, a bio-inspired technique, binary particle swarm optimization (BPSO), is used for the optimal scheduling of appliances in a smart home. Simulation results illustrate the effectiveness of the HEMS in terms of electricity cost, demand, user comfort and peak to average ratio (PAR).
Traffic Surveillance using Multi-Camera Detection and Multi-Target Tracking

Non-intrusive video-detection for traffic flow observation and surveillance is the primary alternative to conventional inductive loop detectors. Video Image Detection Systems (VIDS) can derive traffic parameters by means of image processing and pattern recognition methods. Existing VIDS emulate the inductive loops. We propose a trajectory based recognition algorithm to expand the common approach and to obtain new types of information (e.g. queue length or erratic movements). Different views of the same area by more than one camera sensor is necessary, because of the typical limitations of single camera systems, resulting from the occlusion effect of other cars, trees and traffic signs. A distributed cooperative multi-camera system enables a significant enlargement of the observation area. The trajectories are derived from multi-target tracking. The fusion of object data from different cameras is done using a tracking method. This approach opens up opportunities to identify and specify traffic objects, their location, speed and other characteristic object information. The system provides new derived and consolidated information about traffic participants. Thus, this approach is also beneficial for a description of individual traffic participants.
PENS-wheel (one-wheeled self balancing vehicle) balancing control using PID controller

Pens-Wheel is an electric vehicle which uses one wheel that able to balance itself so the rider does not fall forward or backward while riding it. This vehicle uses one brushless DC motor as actuator which capable to rotate in both directions symmetrically. The vehicle uses a combination of accelerometer and gyroscope contained in IMU (Inertial Measurement Unit) for balancing sensor. The controlled motion on the vehicle occurs only on the x-axis (pitch angle), in forward and backward directions. The PID (Proportional Integral Derivative) control algorithm is used to maintain the balance and movement of the vehicle. From the simulation and application in the real vehicle, the use of PID control is capable driving the vehicle in maintaining the balance condition within ±10° tilt angle boundary on flat surface, bumpy road, and inclining road up to 15° slope.
Towards Knowledge-Driven Annotation

While the Web of data is attracting increasing interest and rapidly growing in size, the major support of information on the surface Web are still multimedia documents. Semantic annotation of texts is one of the main processes that are intended to facilitate meaning-based information exchange between computational agents. However, such annotation faces several challenges such as the heterogeneity of natural language expressions, the heterogeneity of documents structure and context dependencies. While a broad range of annotation approaches rely mainly or partly on the target textual context to disambiguate the extracted entities, in this paper we present an approach that relies mainly on formalized-knowledge expressed in RDF datasets to categorize and disambiguate noun phrases. In the proposed method, we represent the reference knowledge bases as co-occurrence matrices and the disambiguation problem as a 0-1 Integer Linear Programming (ILP) problem. The proposed approach is unsupervised and can be ported to any RDF knowledge base. The system implementing this approach, called KODA, shows very promising results w.r.t. state-of-the-art annotation tools in cross-domain experimentations.
SK-LSH: An Efficient Index Structure for Approximate Nearest Neighbor Search

Approximate Nearest Neighbor (ANN) search in high dimensional space has become a fundamental paradigm in many applications. Recently, Locality Sensitive Hashing (LSH) and its variants are acknowledged as the most promising solutions to ANN search. However, state-of-the-art LSH approaches suffer from a drawback: accesses to candidate objects require a large number of random I/O operations. In order to guarantee the quality of returned results, sufficient objects should be verified, which would consume enormous I/O cost. To address this issue, we propose a novel method, called SortingKeys-LSH (SK-LSH), which reduces the number of page accesses through locally arranging candidate objects. We firstly define a new measure to evaluate the distance between the compound hash keys of two points. A linear order relationship on the set of compound hash keys is then created, and the corresponding data points can be sorted accordingly. Hence, data points that are close to each other according to the distance measure can be stored locally in an index file. During the ANN search, only a limited number of disk pages among few index files are necessary to be accessed for sufficient candidate generation and verification, which not only significantly reduces the response time but also improves the accuracy of the returned results. Our exhaustive empirical study over several real-world data sets demonstrates the superior efficiency and accuracy of SK-LSH for the ANN search, compared with state-of-the-art methods, including LSB, C2LSH and CK-Means.
Ozy: A General Orchestration Container

Service-Oriented Computing is a paradigm that uses services as building blocks for building distributed applications. The primary motivation for orchestrating services in the cloud used to be distributed business processes, which drove the standardization of the Business Process Execution Language (BPEL) and its central notion that a service is a business process. In recent years, there has been a transition towards other motivations for orchestrating services in the cloud, e.g., XaaS, RMAD. Although it is theoretically possible to make all of those services into WSDL/SOAP services, it would be too complicated and costly for industry adoption. Therefore, the central notion that a service is a business process is too restrictive. Instead, we view a service as a technology neutral, loosely coupled, location transparent procedure. With these ideas in mind, we introduce a new approach to services orchestration: Ozy, a general orchestration container. We define this new approach in terms of existing technology, and we show that the Ozy container relaxes many traditional constraints and allows for simpler, more feature-rich applications.
An innovative cost-effective smart meter with embedded non intrusive load monitoring

Most of the time, domestic energy usage is invisible to the users. Households have only a vague idea about the amount of energy they are using for different purposes during the day, and the electric bill is usually the only monthly/bimonthly feedback. Making the energy consumption promptly visible can make the difference and foster a really efficient usage of the energy in a city. The emerging smart metering infrastructure has the potential to address some of these goals, by providing both households and electric utilities useful information about energy consumption, with a detailed breakdown to single appliance contribution. This paper presents a novel ultra-low-cost smart metering system capable of providing domestic AC load identification, by implementing both basic/typical smart meter measurement and a simple yet effective load disaggregation algorithm, which can detect and monitor domestic loads starting from a single point of measurement.
On-line Fault Detection of Sensor Measurements

On-line fault detection in sensor networks is of paramount importance due to the convergence of a variety of challenging technological, application, conceptual, and safety related factors. We introduce a taxonomy for classi£cation of faults in sensor networks and the £rst on-line model-based testing technique. The approach is generic in the sense that it can be applied on an arbitrary system of heterogeneous sensors with an arbitrary type of fault model, while it provides a ¤exible tradeoff between accuracy and latency. The key idea is to formulate on-line testing as a set of instances of a non-linear function minimization and consequently apply nonparametric statistical methods to identify the sensors that have the highest probability to be faulty. The optimization is conducted using the Powell nonlinear function minimization method. The effectiveness of the approach is evaluated in the presence of random noise using a system of light sensors.
A convolutional neural network for search term detection

Pathfinding in hospitals is challenging for patients, visitors, and even employees. Many people have experienced getting lost due to lack of clear guidance, large footprint of hospitals, and confusing array of hospital wings. In this paper, we propose Halo; An indoor navigation application based on voice-user interaction to help provide directions for users without assistance of a localization system. The main challenge is accurate detection of origin and destination search terms. A custom convolutional neural network (CNN) is proposed to detect origin and destination search terms from transcription of a submitted speech query. The CNN is trained based on a set of queries tailored specifically for hospital and clinic environments. Performance of the proposed model is studied and compared with Levenshtein distance-based word matching.
Davydov's procedure for the treatment of neovaginal prolapse in Rokitansky syndrome.

A 19-year-old woman with Rokitansky syndrome with neovaginal prolapse following self-dilation showed vaginal vault eversion of a 4- × 3-cm neovagina. A modified Davydov procedure was performed. No complications occurred. Vaginoscopy after 6 months showed an iodine-positive 8- × 3-cm neovagina. Functional results were assessed with the Female Sexual Function Index.
A novel tri-state boost PFC converter with fast dynamic performance

Dynamic response of boost power factor correction (PFC) converter operating in continuous-conduction mode (CCM) is heavily influenced by low-bandwidth of voltage control loop. A novel tri-state boost PFC converter operating in pseudo-continuous-conduction mode (PCCM) is proposed in this paper. An additional degree of control-freedom introduced by a freewheeling switching control interval helps to achieve PFC control. A simple and fast voltage control loop can be used to maintain a constant output voltage. Furthermore, compared with boost PFC converter operating in conventional discontinuous-conduction mode (DCM), boost PFC converter operating in PCCM demonstrates greatly improved current handling capability with reduced current and voltage ripples. Analytical and simulation results of the tri-state boost PFC converter have been presented and compared with those of the boost PFC converter operating in conventional CCM and DCM. Simulation results show excellent dynamic performance of the tri-state boost PFC converter.
Video anomaly detection based on local statistical aggregates

Anomalies in many video surveillance applications have local spatio-temporal signatures, namely, they occur over a small time window or a small spatial region. The distinguishing feature of these scenarios is that outside this spatio-temporal anomalous region, activities appear normal. We develop a probabilistic framework to account for such local spatio-temporal anomalies. We show that our framework admits elegant characterization of optimal decision rules. A key insight of the paper is that if anomalies are local optimal decision rules are local even when the nominal behavior exhibits global spatial and temporal statistical dependencies. This insight helps collapse the large ambient data dimension for detecting local anomalies. Consequently, consistent data-driven local empirical rules with provable performance can be derived with limited training data. Our empirical rules are based on scores functions derived from local nearest neighbor distances. These rules aggregate statistics across spatio-temporal locations & scales, and produce a single composite score for video segments. We demonstrate the efficacy of our scheme on several video surveillance datasets and compare with existing work.
Domain Attention with an Ensemble of Experts

An important problem in domain adaptation is to quickly generalize to a new domain with limited supervision givenK existing domains. One approach is to retrain a global model across all K + 1 domains using standard techniques, for instance Daumé III (2009). However, it is desirable to adapt without having to reestimate a global model from scratch each time a new domain with potentially new intents and slots is added. We describe a solution based on attending an ensemble of domain experts. We assume K domainspecific intent and slot models trained on respective domains. When given domain K + 1, our model uses a weighted combination of the K domain experts’ feedback along with its own opinion to make predictions on the new domain. In experiments, the model significantly outperforms baselines that do not use domain adaptation and also performs better than the full retraining approach.
MapReduce as a programming model for association rules algorithm on Hadoop

As association rules widely used, it needs to study many problems, one of which is the generally larger and multi-dimensional datasets, and the rapid growth of the mount of data. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance inefficient. Recently the development of network and distributed technology makes cloud computing a reality in the implementation of association rules algorithm. In this paper we describe the improved Apriori algorithm based on MapReduce mode, which can handle massive datasets with a large number of nodes on Hadoop platform.
Mapping Large Spatial Flow Data with Hierarchical Clustering

It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high-level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolution as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi-resolution flow mapping. Different distance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clusters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area.
The Design Fictions of Philanthropic IT: Stuck Between an Imperfect Present and an Impossible Future

In this paper, we examine the stories about philanthropic IT that circulate via product websites, marketing materials, and third-party news articles. Through a series of product-centered case studies, we surface these texts' implicit and explicit visions about the (near) future of philanthropy. We detail their prescriptions about how, why, and in service of what ends nonprofit organizations could, should, and ought to leverage IT. We also examine their underlying assumptions about philanthropy: how social good is accomplished, how philanthropic organizations are - and might be more - effective, to whom organizations and beneficiaries should be accountable, and the terms of that accountability. Analyzing these visions as design fictions, we argue that they help cultivate unrealistic anticipatory relationships to the present and entail concomitantly unrealistic imperatives for the philanthropic sector. We conclude by arguing for the crucial role of HCI scholars in disrupting these impossible futures, and by highlighting areas needing further, re-imagined, research.
A model based path planning algorithm for self-driving cars in dynamic environment

Self-driving cars require robust and fast path planning algorithms to operate in dynamic environment In the last years, model based path planning has emerged as an effective solution to real-time path planning taking into account the kinematics of the vehicle. However, this approach only validates in simple environment such as static or single moving obstacle. In this paper, we propose a model based path planning algorithm for dynamic environment The approach generates candidate trajectories online, then evaluates and selects the most appropriate one according to real-time environment information. The selected trajectory can be directly tracked until emergency occurs and replanning starts. Thus, the planning algorithm is anytime and dynamic, and also effective in complex multi-obstacle environment Simulation results of different scenarios show the validity of the proposed algorithm.
Mnemonic Reader : Machine Comprehension with Iterative Aligning and Multi-hop Answer Pointing

Recently, several end-to-end neural models have been proposed for machine comprehension (MC) tasks. Most of these models only capture interactions between the context and the query, and utilize ”one-shot prediction” to point the boundary of answer, failing to fully understand the context and the query. In this paper, we introduce Mnemonic Reader for MC tasks, an end-to-end neural network which aims to tackle the above problem in two aspects. Firstly, we propose an iterative aligning mechanism which not only captures interactions between the context and the query but also models interactions among the context itself to obtain a fully-aware context representation. Second, we use a multi-hop answer pointer which allows the network to continue refining the predicted answer span. Extensive experiments on TriviaQA and SQuAD datasets show that our model obtains state-of-the-art results.
Approximate Arc Length Parametrization

Current approaches to compute the arc length of a parametric curve rely on table lookup schemes. We present an approximate closed-form solution to the problem of computing an arc length parametrization for any given parametric curve. Our solution outputs a one or two-span Bézier curve which relates the length of the curve to the parametric variable. The main advantage of our approach is that we obtain a simple continuous function relating the length of the curve and the parametric variable. This allows the length to be easily computed given the parametric values. Tests with our algorithm on several thousand curves show that the maximum error in our approximation is 8.7% and that the average of maximum errors is 1.9%. Our algorithm is fast enough to compute the closed-form solution in a fraction of a second. After that a user can interactively get an approximation of the arc length for an arbitrary parameter value.