nils-herrmann/clean_scirepeval huggingface dataset:
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes

The detection of abnormal behaviours in crowded scenes has to deal with many challenges. This paper presents an efficient method for detection and localization of anomalies in videos. Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes. High performance in terms of speed and accuracy is achieved by investigating the cascaded detection as a result of reducing computation complexities. This FCN-based architecture addresses two main tasks, feature representation and cascaded outlier detection. Experimental results on two benchmarks suggest that detection and localization of the proposed method outperforms existing methods in terms of accuracy.
Web-based Nominal Group Technique Decision Making Tool Using Blockchain

The overall interest of modern managers is to find effective group decision-making methods when members with diverse backgrounds and perspectives have to solve problems together. Nominal Group Technique is used as one of the methods for group decision making. However, the Nominal Group Technique is not easy to ensure anonymity in a face-to-face environment, and there is no clear evidence to support the judgment of results after voting. In addition, it is difficult to trust the data because the integrity of the data generated by the voting result is not ensured. In this paper, propose a web-based Nominal Group Technique using blockchain. Web-based Nominal Group Technique can construct an agent - based model and request the blockchain network when data management is needed to guarantee the integrity of the data. In addition, the anonymity of all members is ensured through the web chat function, and the voting results can be visualized using the Priority Graph Tool, which is implemented on the web during the group decision making.
NIMA: Neural Image Assessment

Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications, such as evaluating image capture pipelines, storage techniques, and sharing media. Despite the subjective nature of this problem, most existing methods only predict the mean opinion score provided by data sets, such as AVA and TID2013. Our approach differs from others in that we predict the distribution of human opinion scores using a convolutional neural network. Our architecture also has the advantage of being significantly simpler than other methods with comparable performance. Our proposed approach relies on the success (and retraining) of proven, state-of-the-art deep object recognition networks. Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline. All this is done without need for a “golden” reference image, consequently allowing for single-image, semantic- and perceptually-aware, no-reference quality assessment.
Obtaining accurate trajectories from repeated coarse and inaccurate location logs

Context awareness is a key property for enabling context aware services. For a mobile device, the user's location or trajectory is one of the crucial contexts. One common challenge for detecting location or trajectory by mobile devices is to manage the tradeoff between accuracy and power consumption. Typical approaches are (1) controlling the frequency of usage of sensors and (2) sensor fusion technique. The algorithm proposed in this paper takes a different approach to improve the accuracy by merging repeatedly measured coarse and inaccurate location data from cell tower. The experimental result shows that the mean error distance between the detected trajectory and the ground truth is improved from 44m to 10.9m by merging data from 41 days' measurement.
mmWave communications for 5G: implementation challenges and advances

The requirement of the fifth generation (5G) wireless communication for high throughput motivates the wireless industry to use the mmWave (millimeter wave) communications for its wide bandwidth advantage. To compensate the heavy path loss and increase the communications capacity, phased array beamforming and massive multiple-input multiple-output (MIMO) techniques are employed at both the user equipment (UE) and base stations (BS). Considering the commercial requirements, 5G mmWave large array systems should be implemented in an energy- and cost-efficient way with a small form factor. To address above issues and realize a reliable communications link, taking into account the particular characteristics of 5G mmWave systems, this paper firstly examines the design challenges and trade-offs in system implementations, then some of the design strategies are summarized. At last, recent advance in RF front-end circuits and receiver sub-systems is then highlighted.
Interacting with in-vehicle systems: understanding, measuring, and evaluating attention

In-vehicle systems research is becoming a significant field as the market for in-vehicle systems continue to grow. As a consequence, researchers are increasingly concerned with opportunities and limitations of HCI in a moving vehicle. Especially aspects of attention constitute a challenge for in-vehicle systems development. This paper seeks to remedy this by defining and exemplifying attention understandings. 100 papers were classified in a two-fold perspective; under what settings are in-vehicle systems evaluated and how is driver attention measured in regard to in-vehicle systems HCI. A breakdown of the distribution of driving settings and measures is presented and the impact of driver attention is discussed. The classification revealed that most of the studies were conducted in driving simulators and real traffic driving, while lateral and longitudinal control and eye behaviour were the most used measures.
A Fast Alignment Scheme for Automatic OCR Evaluation of Books

This paper aims to evaluate the accuracy of optical character recognition (OCR) systems on real scanned books. The ground truth e-texts are obtained from the Project Gutenberg website and aligned with their corresponding OCR output using a fast recursive text alignment scheme (RETAS). First, unique words in the vocabulary of the book are aligned with unique words in the OCR output. This process is recursively applied to each text segment in between matching unique words until the text segments become very small. In the final stage, an edit distance based alignment algorithm is used to align these short chunks of texts to generate the final alignment. The proposed approach effectively segments the alignment problem into small sub problems which in turn yields dramatic time savings even when there are large pieces of inserted or deleted text and the OCR accuracy is poor. This approach is used to evaluate the OCR accuracy of real scanned books in English, French, German and Spanish.
MOMCC: Market-oriented architecture for Mobile Cloud Computing based on Service Oriented Architecture

The vision of augmenting computing capabilities of mobile devices, especially smartphones with least cost is likely transforming to reality leveraging cloud computing. Cloud exploitation by mobile devices breeds a new research domain called Mobile Cloud Computing (MCC). However, issues like portability and interoperability should be addressed for mobile augmentation which is a non-trivial task using component-based approaches. Service Oriented Architecture (SOA) is a promising design philosophy embraced by mobile computing and cloud computing communities to stimulate portable, complex application using prefabricated building blocks called Services. Utilizing distant cloud resources to host and run Services is hampered by long WAN latency. Exploiting mobile devices in vicinity alleviates long WAN latency, while creates new set of issues like Service publishing and discovery as well as clientserver security, reliability, and Service availability. In this paper, we propose a market-oriented architecture based on SOA to stimulate publishing, discovering, and hosting Services on nearby mobiles, which reduces long WAN latency and creates a business opportunity that encourages mobile owners to embrace Service hosting. Group of mobile phones simulate a nearby cloud computing platform. We create new role of Service host by enabling unskilled mobile owners/users to host Services developed by skilled developers. Evidently, Service availability, reliability, and Service-oriented mobile application portability will increase towards green ubiquitous computing in our mobile cloud infrastructure.
A three-component scattering model for polarimetric SAR data

An approach has been developed that involves the fit of a combination of three simple scattering mechanisms to polarimetric SAR observations. The mechanisms are canopy scatter from a cloud of randomly oriented dipoles, evenor double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants and Bragg scatter from a moderately rough surface. This composite scattering model is used to describe the polarimetric backscatter from naturally occurring scatterers. The model is shown to describe the behavior of polarimetric backscatter from tropical rain forests quite well by applying it to data from NASA/Jet Propulsion Laboratory’s (JPL’s) airborne polarimetric synthetic aperture radar (AIRSAR) system. The model fit allows clear discrimination between flooded and nonflooded forest and between forested and deforested areas, for example. The model is also shown to be usable as a predictive tool to estimate the effects of forest inundation and disturbance on the fully polarimetric radar signature. An advantage of this model fit approach is that the scattering contributions from the three basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. Furthermore, it is shown that the contributions of the three scattering mechanisms to the HH, HV, and VV backscatter can be calculated from the model fit. Finally, this model fit approach is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem.
Semantically Enriched Machine Learning Approach to Filter YouTube Comments for Socially Augmented User Models

Social media are media for social interaction that allow creating and exchanging user-generated content. The massive social content can provide rich resources for deriving social profiles that can augment user models and improve adaptation in traditional applications. However, potentially valuable social contributions can be buried within highly noisy content that is irrelevant or spam. This paper sketches a research roadmap toward augmenting user models with key user characteristics derived from social content. It then focuses on the first step: identifying and filtering noisy content to create data corpus about a specific activity. A novel, semantically enriched machine learning approach to filter the noisy content from social media is described. This is applied to a specific social source and activity: public comments on YouTube job interview videos. A potential application of the approach to augment user models in simulated experiential learning environments is discussed.
Human and organizational factors in computer and information security: Pathways to vulnerabilities

The purpose of this study was to identify and describe how human and organizational factors may be related to technical computer and information security (CIS) vulnerabilities. A qualitative study of CIS experts was performed, which consisted of 2, 5-member focus groups sessions. The participants in the focus groups each produced a causal network analysis of human and organizational factors pathways to types of CIS vulnerabilities. Findings suggested that human and organizational factors play a significant role in the development of CIS vulnerabilities and emphasized the relationship complexities among human and organizational factors. The factors were categorized into 9 areas: external influences, human error, management, organization, performance and resource management, policy issues, technology, and training. Security practitioners and management should be aware of the multifarious roles of human and organizational factors and CIS vulnerabilities and that CIS vulnerabilities are not the sole result of a technological problem or programming mistake. The design and management of CIS systems need an integrative, multi-layered approach to improve CIS performance (suggestions for analysis provided). a 2009
An elaborate data set on human gait and the effect of mechanical perturbations

Here we share a rich gait data set collected from fifteen subjects walking at three speeds on an instrumented treadmill. Each trial consists of 120 s of normal walking and 480 s of walking while being longitudinally perturbed during each stance phase with pseudo-random fluctuations in the speed of the treadmill belt. A total of approximately 1.5 h of normal walking (>5000 gait cycles) and 6 h of perturbed walking (>20,000 gait cycles) is included in the data set. We provide full body marker trajectories and ground reaction loads in addition to a presentation of processed data that includes gait events, 2D joint angles, angular rates, and joint torques along with the open source software used for the computations. The protocol is described in detail and supported with additional elaborate meta data for each trial. This data can likely be useful for validating or generating mathematical models that are capable of simulating normal periodic gait and non-periodic, perturbed gaits.
Analysis of SDN Contributions for Cloud Computing Security

Cloud infrastructures are composed fundamentally of computing, storage, and networking resources. In regards to network, Software-Defined Networking (SDN) has become one of the most important architectures for the management of networks that require frequent re-policing or re-configurations. Considering the already known security issues of Cloud Computing, SDN helps to give fast answers to emerging threats, but also introduces new vulnerabilities related to its own architecture. In this paper, we analyze recent security proposals derived from the use of SDN, and elaborate on whether it helps to improve trust, security and privacy in Cloud Computing. Moreover, we discuss security concerns introduced by the SDN architecture and how they could compromise Cloud services. Finally, we explore future security perspectives with regard to leveraging SDN benefits and mitigating its security issues.
Object Detection in Video with Spatiotemporal Sampling Networks

We propose a Spatiotemporal Sampling Network (STSN) that uses deformable convolutions across time for object detection in videos. Our STSN performs object detection in a video frame by learning to spatially sample features from the adjacent frames. This naturally renders the approach robust to occlusion or motion blur in individual frames. Our framework does not require additional supervision, as it optimizes sampling locations directly with respect to object detection performance. Our STSN outperforms the state-of-the-art on the ImageNet VID dataset and compared to prior video object detection methods it uses a simpler design, and does not require optical flow data for training.
Enhanced Twitter Sentiment Analysis by Using Feature Selection and Combination

Tweet sentiment analysis is an important research topic. An accurate and timely analysis report could give good indications on the general public's opinions. After reviewing the current research, we identify the need of effective and efficient methods to conduct tweet sentiment analysis. This paper aims to achieve a high level of performance for classifying tweets with sentiment information. We propose a feasible solution which improves the level of accuracy with good time efficiency. Specifically, we develop a novel feature combination scheme which utilizes the sentiment lexicons and the extracted tweet unigrams of high information gain. We evaluate the performance of six popular machine learning classifiers among which the Naive Bayes Multinomial (NBM) classifier achieves the accuracy rate of 84.60% and takes a few minutes to complete classifying thousands of tweets.
Human Activity Recognition Using Recurrent Neural Networks

Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.
Analysis of n-Dimensional Quadtrees using the Hausdorff Fractal Dimension

There is mounting evidence [Man77, SchSI] that real datasets are statistically self-similar, and thus, ‘fractal’. This is an important insight since it permits a compact statistical description of spatial datasets; subsequently, as we show, it also forms the basis for the theoretical analysis of spatial access methods, without using the typical, but unrealistic, uniformity assumption. In this paper, we focus on the estimation of the number of quadtree blocks that a real, spatial dataset will require. Using the the wellknown Hausdorff fractal dimension, we derive some closed formulas which allow us to predict the number of quadtree blocks, given some few parameters. Using our formulas, it is possible to predict the space overhead and the response time of linear quadtrees/z-ordering [OM88], which are widely used in practice. In order to verify our analytical model, we performed *This work was partially supported by the National Science Foundation under Grants No. CDR8803012, EEC-94-02384, IRI-8958546 and IRI-9205273), with matching funds from Empress Software Inc. and Thinking Machines Inc. Some of the work was performed while he was visiting AT&T Bell Labor+ tories, Murray Hill, NJ. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.  experimental investigation using several real datasets coming from different domains. In these experiments, we found that our analytical model agrees well with our experiments as well as with older empirical observations on 2-d [Gae95b] and 3-d [ACF+94] data.
Classification of learning outcomes : evidence from the computer games literature

Following up on an earlier issue of The Curriculum Journal (Vol. 16, No. 1), this article focuses on learning outcomes in the context of video games. Learning outcomes are viewed from two theoretical frameworks: Kirkpatrick’s levels of evaluation and the CRESST model of learning. These are used to analyse the outcomes claimed in journal articles that report empirical work, indicating the usefulness of the frameworks, and the necessity to consider the role of affective learning. The article ends with some comments on the relationship of instructional design to effective games and learning outcomes.
Being Innovative About Service Innovation: Service, Design and Digitalization

Moving beyond questions about “services” and “the services economy,” this panel considers fresh ways of thinking about service innovation in the era of pervasive digitization. Panelists will argue that our understanding of digital services and products is radically transformed if we consider all exchanges to be service-for-service exchanges in which customers and suppliers co-create value in exchange networks. Innovation can then be understood as the continual process of breaking down knowledge (information) and reintegrating it to create new knowledge-based resources. Pervasive digitalization and generative digital platforms are revolutionizing service exchange possibilities. Value exchanges nonetheless occur within contexts that are material and social, tangible and tacit. The dynamics of these dimensions of service exchange challenge our concepts and methods for designing for service. Representing different approaches and disciplines, panelists will share their views on how the IS field might rethink service innovation, design and digitalization.
Face Alignment via Regressing Local Binary Features

This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.
ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

Chemogenomics data generally refers to the activity data of chemical compounds on an array of protein targets and represents an important source of information for building in silico target prediction models. The increasing volume of chemogenomics data offers exciting opportunities to build models based on Big Data. Preparing a high quality data set is a vital step in realizing this goal and this work aims to compile such a comprehensive chemogenomics dataset. This dataset comprises over 70 million SAR data points from publicly available databases (PubChem and ChEMBL) including structure, target information and activity annotations. Our aspiration is to create a useful chemogenomics resource reflecting industry-scale data not only for building predictive models of in silico polypharmacology and off-target effects but also for the validation of cheminformatics approaches in general.