nils-herrmann/clean_scirepeval huggingface dataset:
An efficient audio fingerprint search algorithm for music retrieval

The conventional audio fingerprinting system by Haitsma uses a lookup table to identify the candidate songs in the database, which contains the sub-fingerprints of songs, and searches the candidates to find a song whose bit error rate is the lowest. However, this approach has a drawback that the number of database accesses increases dramatically, especially when the database contains a large number of songs or when a matching sub-fingerprint is not found in the lookup table due to a heavily degraded input signal. In this paper, a novel search method is proposed to overcome these difficulties. The proposed method partitions each song found from the lookup table into blocks, assigns a weight to each block, and uses the weight as a search priority to speed up the search process while reducing the number of database accesses. Various results from our experiment show the significant improvement in search speed while maintaining the search accuracy comparable to the conventional method.
Edge Computing – EDGE 2018

Edge computing systems (Cloudlet, Fog Computing, Multi-access Edge Computing) provide numerous benefits to information technology: reduced latency, improved bandwidth, battery lifetime, etc. Despite all the benefits, edge computing systems have several issues that could significantly reduce the performance of certain applications. Indeed, current edge computing technologies do not assure ultra-low latency for real-time applications and they encounter overloading issues for data processing. To solve the aforementioned issues, we propose Home Edge Computing (HEC): a new three-tier edge computing architecture that provides data storage and processing in close proximity to the users. The term “Home” in Home Edge Computing does not restrain our work to the homes of the users, we take into account other places where the users could connect to the Internet such as: companies, shopping malls, hospitals, etc. Our three-tier architecture is composed of a Home Server, an Edge Server and a Central Cloud which we also find in traditional edge computing architectures. The Home Server is located within the vicinities of the users which allow the achievement of ultra-low latency for applications that could be processed by the said server; this also help reduce the amount of data that could be treated in the Edge Server and the Central Cloud. We demonstrate the validity of our architecture by leveraging the EdgeCloudSim simulation platform. The results of the simulation show that our proposal can, in fact, help achieve ultra-low latency and reduce overloading issues.
A trust model-based Bayesian decision theory in large scale Internet of Things

In addressing the growing problem of security of Internet of Things, we present, from a statistical decision point of view, a naval approach for trust-based access control using Bayesian decision theory. We build a trust model, TrustBayes which represents a trust level for identity management in IoT. TrustBayes model is be applied to address access control on uncertainty environment where identities are not known in advance. The model consists of EX (Experience), KN (Knowledge) and RC (Recommendation) values which is be obtained in measurement while a IoT device requests to access a resource. A decision will be taken based model parameters and be computed using Bayesian decision rules. To evaluate our a trust model, we do a statistical analysis and simulate it using OMNeT++ to investigate battery usage. The simulation result shows that the Bayesian decision theory approach for trust based access control guarantees scalability and it is energy efficient as increasing number of devices and not affecting the functioning and performance.
Linking organizational culture , structure , strategy , and organizational effectiveness : Mediating role of knowledge management

a Human Resource Development, Department of Counseling, Adult and Higher Education, Northern Illinois University, Gabel Hall 201E, Northern Illinois University, DeKalb, IL 60115, United States b Department of Human Resources and Organizational Behavior, Tsinghua University, Mailing Address: Haidian District, Beijing, 100087 China c Department of Work and Human Resource Education, Texas A&M University, United States
Energy-based Models for Video Anomaly Detection

Automated detection of abnormalities in data has been studied in research area in recent years because of its diverse applications in practice including video surveillance, industrial damage detection and network intrusion detection. However, building an effective anomaly detection system is a non-trivial task since it requires to tackle challenging issues of the shortage of annotated data, inability of defining anomaly objects explicitly and the expensive cost of feature engineering procedure. Unlike existing appoaches which only partially solve these problems, we develop a unique framework to cope the problems above simultaneously. Instead of hanlding with ambiguous definition of anomaly objects, we propose to work with regular patterns whose unlabeled data is abundant and usually easy to collect in practice. This allows our system to be trained completely in an unsupervised procedure and liberate us from the need for costly data annotation. By learning generative model that capture the normality distribution in data, we can isolate abnormal data points that result in low normality scores (high abnormality scores). Moreover, by leverage on the power of generative networks, i.e. energy-based models, we are also able to learn the feature representation automatically rather than replying on hand-crafted features that have been dominating anomaly detection research over many decades. We demonstrate our proposal on the specific application of video anomaly detection and the experimental results indicate that our method performs better than baselines and are comparable with state-of-the-art methods in many benchmark video anomaly detection datasets.
New Algorithms for SIMD Alignment

Optimizing programs for modern multiprocessor or vector platforms is a major important challenge for compilers today. In this work, we focus on one challenging aspect: the SIMD ALIGNMENT problem. Previously, only heuristics were used to solve this problem, without guarantees on the number of shifts in the obtained solution. We study two interesting and realistic special cases of the SIMD ALIGNMENT problem and present two novel and efficient algorithms that provide optimal solutions for these two cases. The new algorithms employ dynamic programming and a MIN-CUT/MAX-FLOW algorithm as subroutines. We also discuss the relation between the SIMD ALIGNMENT problem and the MULTIWAY CUT and NODE MULTIWAY CUT problems; and we show how to derive an approximated solution to the SIMD ALIGNMENT problem based on approximation algorithms to these two known problems.
Anti-hyperglycemic activity of an aqueous extract from flower buds of Cleistocalyx operculatus (Roxb.) Merr and Perry.

A screening of 5 plants used for making drinks in Vietnam revealed a Cleistocalyx operculatus (Roxb.) Merr and Perry flower bud extract to have the highest inhibitory activity against the alpha-glucosidase enzyme. The anti-hyperglycemic effects of an aqueous extract from flower buds of Cleistocalyx operculatus (CO), a commonly used material for drink preparation in Vietnam, were therefore investigated in vitro and in vivo. In vitro, the CO extract inhibited the rat-intestinal maltase and sucrase activities, with IC50 values of 0.70 and 0.47 mg/ml, respectively. These values are lower than those for a guava leaf extract (GE; IC50 0.97 and 1.28 mg/ml, respectively). Postprandial blood glucose testing of normal mice and STZ-induced diabetic rats by maltose loading (2 g/kg body weight (bw)) showed that the blood glucose reduction with CO (500 mg/kg bw) was slightly less than that with acarbose (25 mg/kg bw) but was more potent than that with GE (500 mg/kg bw). In an 8-week experiment, the blood glucose level of STZ diabetic rats treated with 500 mg of CO/kg bw/day was markedly decreased in comparison with that of non-treated diabetic rats. Consequently, CO is considered to be a promising material for preventing and treating diabetes.
Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis

In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data.
Hyperparameter Optimization: A Spectral Approach

We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters. The algorithm — an iterative application of compressed sensing techniques for orthogonal polynomials — requires only uniform sampling of the hyperparameters and is thus easily parallelizable. Experiments for training deep neural networks on Cifar-10 show that compared to state-of-the-art tools (e.g., Hyperband and Spearmint), our algorithm finds significantly improved solutions, in some cases better than what is attainable by hand-tuning. In terms of overall running time (i.e., time required to sample various settings of hyperparameters plus additional computation time), we are at least an order of magnitude faster than Hyperband and Bayesian Optimization. We also outperform Random Search 8×. Our method is inspired by provably-efficient algorithms for learning decision trees using the discrete Fourier transform. We obtain improved sample-complexty bounds for learning decision trees while matching state-of-the-art bounds on running time (polynomial and quasipolynomial, respectively). ar X iv :1 70 6. 00 76 4v 4 [ cs .L G ] 2 0 Ja n 20 18
Do "Also-Viewed" Products Help User Rating Prediction?

For online product recommendation engines, learning highquality product embedding that captures various aspects of the product is critical to improving the accuracy of user rating prediction. In recent research, in conjunction with user feedback, the appearance of a product as side information has been shown to be helpful for learning product embedding. However, since a product has a variety of aspects such as functionality and specifications, taking into account only its appearance as side information does not suffice to accurately learn its embedding. In this paper, we propose a matrix co-factorization method that leverages information hidden in the so-called “also-viewed” products, i.e., a list of products that has also been viewed by users who have viewed a target product. “Also-viewed” products reflect various aspects of a given product that have been overlooked by visually-aware recommendation methods proposed in past research. Experiments on multiple real-world datasets demonstrate that our proposed method outperforms state-of-the-art baselines in terms of user rating prediction. We also perform classification on the product embedding learned by our method, and compare it with a state-of-the-art baseline to demonstrate the superiority of our method in generating high-quality product embedding that better represents the product.
Learning Joint Multilingual Sentence Representations with Neural Machine Translation

In this paper, we use the framework of neural machine translation to learn joint sentence representations across six very different languages. Our aim is that a representation which is independent of the language, is likely to capture the underlying semantics. We define a new crosslingual similarity measure, compare up to 1.4M sentence representations and study the characteristics of close sentences. We provide experimental evidence that sentences that are close in embedding space are indeed semantically highly related, but often have quite different structure and syntax. These relations also hold when comparing sentences in different languages.
CTSiM: A Computational Thinking Environment for Learning Science through Simulation and Modeling

Computational thinking (CT) draws on fundamental computer science concepts to formulate and solve problems, design systems, and understand human behavior. CT practices (e.g., problem representation, abstraction, decomposition, simulation, verification, and prediction) are also central to the development of expertise in a variety of STEM disciplines. Exploiting this synergy between CT and STEM disciplines, we have developed CTSiM, a cross-domain, scaffolded, visual-programming and agent-based learning environment for middle school science. We present and justify the CTSiM architecture and its implementation. To identify challenges and scaffolding needs in learning with CTSiM, we present a case study describing the challenges that a highand a low-achieving student faced while working on kinematics and ecology units using CTSiM. Decreases in the number of challenges for both students over sequences of related activities illustrate the combined effectiveness of our approach. Further, the specific challenges and scaffolds identified suggest the design of an adaptive scaffolding framework to help students develop a synergistic understanding of CT and science concepts.
Epistemological perspectives on IS research: a framework for analysing and systematizing epistemological assumptions

Over the last three decades, a methodological pluralism has developed within information systems (IS) research. Various disciplines and many research communities as well, contribute to this discussion. However, working on the same research topic or studying the same phenomenon does not necessarily ensure mutual understanding. Especially within this multidisciplinary and international context, the epistemological assumptions made by different researchers may vary fundamentally. These assumptions exert a substantial impact on how concepts like validity, reliability, quality and rigour of research are understood. Thus, the extensive publication of epistemological assumptions is, in effect, almost mandatory. Hence, the aim of this paper is to develop an epistemological framework which can be used for systematically analysing the epistemological assumptions in IS research. Rather than attempting to identify and classify IS research paradigms, this research aims at a comprehensive discussion of epistemology within the context of IS. It seeks to contribute to building the basis for identifying similarities as well as differences between distinct IS approaches and methods. In order to demonstrate the epistemological framework, the consensus-oriented interpretivist approach to conceptual modelling is used as an example.
An all-inkjet printed flexible capacitor on a textile using a new poly(4-vinylphenol) dielectric ink for wearable applications

This paper reports a flexible capacitor for wearable applications which has been all-inkjet printed on a standard 65/35 polyester cotton textile using a new poly(4-vinylphenol) (PVP) dielectric material. Capacitors form the basis of a variety of sensors, such as for proximity and touch, as well as electronic circuits. This paper reports a general fabrication printing process to realize capacitors on textiles. The parallel plate capacitor design uses a combination of heat curable silver ink and a new UV curable dielectric ink based on PVP, printed on to the textile. This new inkjet printable dielectric ink is ultra-violet (UV) cured for 100 seconds to achieve a thin dielectric film suitable for a textile based capacitor. After printing, the dielectric properties were measured and the cross-sectional film structure was observed using an SEM. The inkjet printed PVP film on the textile exhibited good insulating behavior and similar flexibility to the original textile.
Generalized Displacement Maps

In this paper, we introduce a real-time algorithm to render the rich visual effects of general non-height-field geometric details, known as mesostructure. Our method is based on a five-dimensional generalized displacement map (GDM) that represents the distance of solid mesostructure along any ray cast from any point within a volumetric sample. With this GDM information, we propose a technique that computes mesostructure visibility jointly in object space and texture space which enables both control of texture distortion and efficient computation of texture coordinates and shadowing. GDM can be rendered with either local or global illumination as a per-pixel process in graphics hardware to achieve real-time rendering of general mesostructure.
Testing Unconstrained Optimization Software

Much of the testing of optimization software is inadequate because the number of test functmns is small or the starting points are close to the solution. In addition, there has been too much emphasm on measurmg the efficmncy of the software and not enough on testing reliability and robustness. To address this need, we have produced a relatwely large but easy-to-use collection of test functions and designed gmdelines for testing the reliability and robustness of unconstrained optimization software.
Neural networks for computer-aided diagnosis in medicine: A review

This survey makes an overview of the most recent applications on the neural networks for the computer-aided medical diagnosis (CAMD) over the past decade. CAMD can facilitate the automation of decision making, extraction and visualization of complex characteristics for clinical diagnosis purposes. Over the past decade, neural networks have attained considerable research interest and are widely employed to complex CAMD systems in diverse clinical application domains, such as detecting diseases, classification of diseases, testing the compatibility of new drugs, etc. Overall, this paper reviews the state-of-the-art of neural networks for CAMD. It helps the readers understand the topic of neural networks for CAMD by summarizing the findings addressed in recent academic papers as well as presenting a few open issues of developing the research on this topic.
Classification and compensation of amplitude imbalance and imperfect quadrature in resolver signals

This paper proposes a detection and compensation algorithm of two non-ideal output signals of a resolver to reduce position error. Practically, a resolver generates position errors because of the amplitude imbalance and imperfect quadrature between the two output signals of the resolver. In this paper, a digital signal processor (DSP) based resolver-to-digital converter (RDC) is used to reconstruct the two output signals of the resolver. The proposed algorithm does not need any additional hardware. Furthermore, this algorithm can be applied to both the steady and transient states. The simulation results verify the effectiveness of the proposed algorithm.
Deep Learning-Based Classification of Hyperspectral Data

Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the hyperspectral data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introduced into hyperspectral data classification for the first time. First, we verify the eligibility of stacked autoencoders by following classical spectral information-based classification. Second, a new way of classifying with spatial-dominated information is proposed. We then propose a novel deep learning framework to merge the two features, from which we can get the highest classification accuracy. The framework is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression. Specifically, as a deep learning architecture, stacked autoencoders are aimed to get useful high-level features. Experimental results with widely-used hyperspectral data indicate that classifiers built in this deep learning-based framework provide competitive performance. In addition, the proposed joint spectral-spatial deep neural network opens a new window for future research, showcasing the deep learning-based methods' huge potential for accurate hyperspectral data classification.
New classes of miniaturized planar Marchand baluns

Three new classes of miniaturized Marchand balun are defined based on the synthesis of filter prototypes. They are suitable for mixed lumped-distributed planar realizations with small size resulting from transmission-line resonators being a quarter-wavelength long at frequencies higher than the passband center frequency. Each class corresponds to an S-plane bandpass prototype derived from the specification of transmission zero locations. A tunable 50:100-/spl Omega/ balun is realized at 1 GHz to demonstrate the advantages of the approach presented here.