nils-herrmann/clean_scirepeval huggingface dataset:
A two-stage short-term load forecasting approach using temperature daily profiles estimation

Electrical load forecasting plays an important role in the regular planning of power systems, in which load is influenced by several factors that must be analysed and identified prior to modelling in order to ensure better and instant load balancing between supply and demand. This paper proposes a two-stage approach for short-term electricity load forecasting. In the first stage, a set of day classes of load profiles are identified using K-means clustering algorithm alongside daily temperature estimation profiles. The proposed estimation method is particularly useful in case of lack of historical regular temperature data. While in the second stage, the stacked denoising autoencoders approach is used to build regression models able to forecast each day type independently. The obtained models are trained and evaluated using hourly electricity power data offered by Algeria’s National Electricity and Gas Company. Several models are investigated to substantiate the accuracy and effectiveness of the proposed approach.
A modified antipodal Vivaldi antenna (AVA) with elliptical slotting edge (ESE) for ultra-wideband (UWB) applications

As the demand of UWB system increases nowadays, several types of hybrid technique on antenna design are studied, modified, and proposed for UWB applications. In this work, an enhanced performance of modified antipodal Vivaldi antenna (AVA) for targeting ultra-wideband (UWB) frequency range between 2.17 GHz and 10.6 GHz has been proposed. This proposed UWB antenna is using FR-4 substrate with dielectric constant, εr = 4.4 and the electrical conductivity tangent loss, tanΔ = 0.019. This antenna design is based on the modified from the dual exponential tapered slot antenna (DETSA) with varying of the exponential flares design for obtain the best return loss of the antenna within the frequency bandwidth of the UWB. It has two exponential flares that follow the characteristic of exponential curve. Next, an elliptical slotting edge (ESE) with fixed horizontal radius and varied vertical radius is implemented to the antenna which effectively increases the performance of the antenna at lower frequencies. These modifications are done without changing the antenna size, which is 70 mm × 90 mm in dimension, which is approximately 0.5λ × 0.6λ, where λ is the wavelength of 2 GHz. The effects of the parameters of the antenna characteristic are also studied. In this case, the parametric study on varying distance between elliptical slots, Ks. It shows that, the higher value of Ks will effect to shift to the resonant frequency and increase the return loss of the antenna. At 9.104 GHz of resonant frequencies, it shows of the best return loss of 48.809 dB. Rather than that, the side lobe levels of the radiation pattern of the antenna are reduced in the lower frequencies.
Mostly-Optimistic Concurrency Control for Highly Contended Dynamic Workloads on a Thousand Cores

Future servers will be equipped with thousands of CPU cores and deep memory hierarchies. Traditional concurrency control (CC) schemes -both optimistic and pessimistic -slow down orders of magnitude in such environments for highly contended workloads. Optimistic CC (OCC) scales the best for workloads with few conflicts, but suffers from clobbered reads for high conflict workloads. Although pessimistic locking can protect reads, it floods cache-coherence backbones in deep memory hierarchies and can also cause numerous deadlock aborts. External Posting Date: July 19, 2016 [Fulltext] Internal Posting Date: July 19, 2016 [Fulltext] Approved for External Publication  Copyright 2016 Hewlett Packard Enterprise Development LP Mostly-Optimistic Concurrency Control for Highly Contended Dynamic Workloads on a Thousand Cores (Extended Version) Tianzheng Wang University of Toronto tzwang@cs.toronto.edu Hideaki Kimura Hewlett Packard Labs hideaki.kimura@hpe.com
Management Information Systems Research: What's There in a Methodology?

Management information systems (MIS) is both a young and unique field, constantly experiencing rapid change and turmoil. Consequently, MIS research faces dual changes of rigor and relevance. Many research methodologies exist that provide various combinations of rigor and relevance. The MIS researcher selects a methodology based on several factors including rigor, relevance, subject area, and personal preferences. In this article, we examine thirteen different methodologies as used by seven leading MIS journals during a recent five-year period. The results of this extensive analysis yielded some interesting results. Survey methodology consistently ranks at the top; while frameworks and conceptual models, lab experiments, and case studies also found significant use among the MIS community. Several trends were observed, one of them being a greater use of the case study method and other qualitative technologies over the years. Clear patterns also emerged based on the journal itself and the subject areas. At a macro level, this information should help authors in the choice of appropriate methodologies to use in specific subject areas and in targeting manuscripts to appropriate journals. It would also be helpful to journal editors in assessing the type of research and methods being used across journals and subjects, and whether they need to make any changes in the emphases of their own journals.
Hard Drive Failure Prediction Using Big Data

We design a general framework named Hdoctor for hard drive failure prediction. Hdoctor leverages the power of big data to achieve a significant improvement comparing to all previous researches that used sophisticated machine learning algorithms. Hdoctor exhibits a series of engineering innovations: (1) constructing time dependent features to characterize the Self-Monitoring, Analysis and Reporting Technology (SMART) value transitions during disk failures, (2) combining features to enable the model to learn the correlation among different SMART attributes, (3) regarding circumstance data such as cluster workload, temperature, humidity, location as related features. Meanwhile, Hdoctor collects/labels samples and updates model automatically, and works well for all kinds of disk failure prediction in our intelligent data center. In this work, we use Hdoctor to collect 74,477,717 training records from our clusters involving 220,022 disks. By training a simple and scalable model, our system achieves a detection rate of 97.82%, with a false alarm rate (FAR) of 0.3%, which hugely outperforms all previous algorithms. In addition, Hdoctor is an excellent indicator for how to predict different hardware failures efficiently under various circumstances.
A new non-invasive voltage measurement method for wireless analysis of electrical parameters and power quality

The analysis of voltage and current waveforms is important for estimating the quality of the electric power system. In this paper we propose a non-invasive approach for measuring the voltage waveform of conductive elements such as wires or cables. This non-intrusive sensor does not require any electrical contact to the mains electricity, which makes the measurement much safer and easy. The voltage is measured by exploiting coupling-capacitance elements. With this approach, the harmonic content and the amplitude of the voltage signal is evaluated and transmitted. We also present a wireless sensor network designed to implement the proposed non-invasive voltage measurement method. Each node features a low-power, 32-bit microcontroller and a wireless transceiver to send data via the IEEE 802.15.4 standard. Finally, we compare the proposed non-intrusive method with measurements using the direct access to the wire and we demonstrate that we achieve high accuracy with a maximum error lower than 1%.
An innovative ontology-driven system supporting personnel selection: the OntoHR case

This paper describes the initial development of an HRM system that aims to decrease the gap between higher vocational education and the labour market for a specific job in the ICT sector. This paper focuses specifically on the delineation of a process model and the selection of a suitable job role (information system analyst) that is valid across organisational and cultural boundaries. The process model implies various applied uses of this ontology-based system, including mapping qualifications in vocational education to current and valid job roles, testing and evaluating the student applicant on the basis of labour market driven competencies, and providing ad-hoc support to educational institutions by elucidating the weaknesses of particular VET curricula.
Sorting And Indexing With Partitioned B-Trees

Partitioning within a B-tree, based on an artificial leading key column and combined with online reorganization, can be exploited during external merge sort for accurate deep read-ahead and dynamic resource allocation, during index creation for a reduced delay until the first query can search the new index, during data loading for streaming integration of new data into a fully indexed database, and for miscellaneous other operations. Despite improving multiple fundamental database operations using a single basic mechanism, the proposal offers these benefits without requiring data structures or algorithms not yet supported in modern relational database management systems. While some of the ideas discussed here have been touched upon elsewhere, the focus here is on re-thinking the relationship between sorting and B-trees more thoroughly, on exploiting this relationship to simplify and unify data structures and algorithms, and on gathering comprehensive lists of issues and benefits.
Maps of the brain.

We review recent developments in brain mapping and computational anatomy that have greatly expanded our ability to analyze brain structure and function. The enormous diversity of brain maps and imaging methods has spurred the development of population-based digital brain atlases. These atlases store information on how the brain varies across age and gender, across time, in health and disease, and in large human populations. We describe how brain atlases, and the computational tools that align new datasets with them, facilitate comparison of brain data across experiments, laboratories, and from different imaging devices. The major methods are presented for the construction of probabilistic atlases, which store information on anatomic and functional variability in a population. Algorithms are reviewed that create composite brain maps and atlases based on multiple subjects. We show that group patterns of cortical organization, asymmetry, and disease-specific trends can be resolved that may not be apparent in individual brain maps. Finally, we describe the creation of four-dimensional (4D) maps that store information on the dynamics of brain change in development and disease. Digital atlases that correlate these maps show considerable promise in identifying general patterns of structural and functional variation in human populations, and how these features depend on demographic, genetic, cognitive, and clinical parameters.
Modeling and Formalization of Fuzzy Finite Automata for Detection of Irregular Fire Flames

Fire-flame detection using a video camera is difficult because a flame has irregular characteristics, i.e., vague shapes and color patterns. Therefore, in this paper, we propose a novel fire-flame detection method using fuzzy finite automata (FFA) with probability density functions based on visual features, thereby providing a systemic approach to handling irregularity in computational systems and the ability to handle continuous spaces by combining the capabilities of automata with fuzzy logic. First, moving regions are detected via background subtraction, and the candidate flame regions are then identified by applying flame color models. In general, flame regions have a continuous irregular pattern; therefore, probability density functions are generated for the variation in intensity, wavelet energy, and motion orientation and applied to the FFA. The proposed algorithm is successfully applied to various fire/non-fire videos, and its detection performance is better than that of other methods.
An Intelligent Load Management System With Renewable Energy Integration for Smart Homes

Demand side management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via home energy management systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, evolutionary algorithms-based (binary particle swarm optimization, genetic algorithm, and cuckoo search) DSM model for scheduling the appliances of residential users is presented. The model is simulated in time of use pricing environment for three cases: 1) traditional homes; 2) smart homes; and 3) smart homes with renewable energy sources. Simulation results show that the proposed model optimally schedules the appliances resulting in electricity bill and peaks reductions.
Deep generative-contrastive networks for facial expression recognition

As the expressive depth of an emotional face differs with individuals or expressions, recognizing an expression using a single facial image at a moment is difficult. A relative expression of a query face compared to a reference face might alleviate this difficulty. In this paper, we propose to utilize contrastive representation that embeds a distinctive expressive factor for a discriminative purpose. The contrastive representation is calculated at the embedding layer of deep networks by comparing a given (query) image with the reference image. We attempt to utilize a generative reference image that is estimated based on the given image. Consequently, we deploy deep neural networks that embed a combination of a generative model, a contrastive model, and a discriminative model with an end-to-end training manner. In our proposed networks, we attempt to disentangle a facial expressive factor in two steps including learning of a generator network and a contrastive encoder network. We conducted extensive experiments on publicly available face expression databases (CK+, MMI, Oulu-CASIA, and in-the-wild databases) that have been widely adopted in the recent literatures. The proposed method outperforms the known state-of-the art methods in terms of the recognition accuracy.
Using a Graph-based Coherence Model in Document-Level Machine Translation

Although coherence is an important aspect of any text generation system, it has received little attention in the context of machine translation (MT) so far. We hypothesize that the quality of document-level translation can be improved if MT models take into account the semantic relations among sentences during translation. We integrate the graph-based coherence model proposed by Mesgar and Strube (2016) with Docent1 (Hardmeier et al., 2012; Hardmeier, 2014) a document-level machine translation system. The application of this graph-based coherence modeling approach is novel in the context of machine translation. We evaluate the coherence model and its effects on the quality of the machine translation. The result of our experiments shows that our coherence model slightly improves the quality of translation in terms of the average Meteor score.
The Research of PHP Development Framework Based on MVC Pattern

PHP is one of the leading web development languages, however, the development model of existing PHP organizes without a structure, which mixes the code of data access, the processing of business logic , and web presentation layer together, as a relult, it brought about many problems in the web applications , meanwhile, it could not meet the rapid development of web apply any more. In this paper, a implementation of PHP based on MVC design patterns - FDF framework was provided for PHP developers, which can offer a framework for web applications, separate the data, view and control of web applications, afford to achieve loose coupling, thereby enhanced the efficiency, reliability, maintainability and scalability of application development.
A Kernel Approach to Comparing Distributions

We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert Space. We apply this technique to construct a two-sample test, which is used for determining whether two sets of observations arise from the same distribution. We use this test in attribute matching for databases using the Hungarian marriage method, where it performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks

Human activity understanding with 3D/depth sensors has received increasing attention in multimedia processing and interactions. This work targets on developing a novel deep model for automatic activity recognition from RGB-D videos. We represent each human activity as an ensemble of cubic-like video segments, and learn to discover the temporal structures for a category of activities, i.e. how the activities to be decomposed in terms of classification. Our model can be regarded as a structured deep architecture, as it extends the convolutional neural networks (CNNs) by incorporating structure alternatives. Specifically, we build the network consisting of 3D convolutions and max-pooling operators over the video segments, and introduce the latent variables in each convolutional layer manipulating the activation of neurons. Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i.e. the network configuration is allowed to be partially activated during inference. For model training, we propose an EM-type optimization method that iteratively (i) discovers the latent structure by determining the decomposed actions for each training example, and (ii) learns the network parameters by using the back-propagation algorithm. Our approach is validated in challenging scenarios, and outperforms state-of-the-art methods. A large human activity database of RGB-D videos is presented in addition.
Consumer RGB-D Cameras and their Applications

In November 2010, Microsoft released the Kinect RGB-D sensor as a new Natural User Interface (NUI) for its XBOX 360 gaming platform. The Kinect, like other RGB-D sensors, provides color information as well as the estimated depth for each pixel. At $150, the Kinect is an order of magnitude cheaper than similar sensors that had existed before it. This has dramatically reinvigorated interest in RGB-D sensors and their applications in areas such as Natural User Interfaces, reconstruction and virtual reality, or 3D mapping.
An integrated full-bridge Class-DE ultrasound transducer driver for HIFU applications

This paper presents an integrated MRI-compatible full-bridge Class-DE ultrasound transducer driver design for HIFU applications. This design includes a digital logic unit to individually control the phase and duty ratio of each transducer when the driver is used in an array configuration. This work also shows the performance of the proposed driver with three different ultrasonic transducers. Simulation results predict at least 89% efficiency for the two transducers that support ideal Class-DE operation without matching networks. The proposed driver has been implemented by using AMS AG H35 CMOS process. The total die area of the driver is 3 mm2.
A model to support sign language content development for digital television

This paper proposes a model to support sign language content development and deployment in digital television scenarios. The model objective is to overcome traditional video limitations on the production of signed content and improve accessibility on digital television. Previous work on sign and gesture recognition, sign notation systems and avatar animation are reviewed. Sign communication requirements are investigated and adequate algorithms are identified to enable sign language processing and transmission. An application interface is proposed in order to access the model functionalities. The final discussion lists the benefits and limitations of the proposed model.
Songle: A Web Service for Active Music Listening Improved by User Contributions

This paper describes a public web service for active music listening,Songle, that enriches music listening experiences by using music-understanding technologies based on signal processing. Although various research-level interfaces and technologies have been developed, it has not been easy to get people to use them in everyday life. Songle serves as a showcase to demonstrate how people can benefit from music-understanding technologies by enabling people to experience active music listening interfaces on the web. Songle facilitates deeper understanding of music by visualizing music scene descriptions estimated automatically, such as music structure, hierarchical beat structure, melody line, and chords. When using music-understanding technologies, however, estimation errors are inevitable. Songle therefore features an efficient error correction interface that encourages people to contribute by correcting those errors to improve the web service. We also propose a mechanism of collaborative training for music-understanding technologies, in which corrected errors will be used to improve the music-understanding performance through machine learning techniques. We hope Songle will serve as a research platform where other researchers can exhibit results of their music-understanding technologies to jointly promote the popularization of the field of music information research.
Validating a Measurement Tool of Presen ce in Online Communities of Inquiry

This article examines work related to the development and validation of a measurement tool for the Community of Inquiry (CoI) framework in online settings. The framework consists of three elements: social presence, teaching presence and cognitive presence, each of which is integral to the instrument. The 34 item instrument, and thus framework, was tested after being administered at four institutions in the Summer of 2007. The article also includes a discussion of implications for the future use of the CoI survey and the CoI framework itself.
Chapter 3 Semantic Web Service Description

The convergence of semantic Web with service oriented computing is manifested by Semantic Web Services (SWS) technology. It addresses the major challenge of automated, interoperable and meaningful coordination of Web Services to be carried out by intelligent software agents. In this chapter, we briefly discuss prominent SWS description frameworks, that are the standard SAWSDL, OWL-S and WSML. This is complemented by main critics of Semantic Web Services, and selected references to further readings on the subject.