nils-herrmann/clean_scirepeval huggingface dataset:
Aggregating inconsistent information: Ranking and clustering

We address optimization problems in which we are given contradictory pieces of input information and the goal is to find a globally consistent solution that minimizes the extent of disagreement with the respective inputs. Specifically, the problems we address are rank aggregation, the feedback arc set problem on tournaments, and correlation and consensus clustering. We show that for all these problems (and various weighted versions of them), we can obtain improved approximation factors using essentially the same remarkably simple algorithm. Additionally, we almost settle a long-standing conjecture of Bang-Jensen and Thomassen and show that unless NP⊆BPP, there is no polynomial time algorithm for the problem of minimum feedback arc set in tournaments.
Object Recognition in Multi-View Dual Energy X-ray Images

Object recognition in X-ray images is an interesting application of machine vision that can help reduce the workload of human operators of X-ray scanners at security checkpoints. However, automatic inspection systems using machine vision techniques are not yet commonplace for generic threat detection in X-ray images. Moreover, this problem has not been well explored by machine vision community due to the lack of publicly available X-ray image datasets. This paper aims to fill in this gap. We first present a comprehensive evaluation of image classification and object detection in X-ray images using standard local features in a BoW framework with (structural) SVMs. Then, we extend the features to utilize the extra information available in dual energy X-ray images. Finally, we propose a multi-view branch-and-bound algorithm for multiview object detection. Through extensive experiments on three object categories (laptops, guns, bottles), we show that the classification and detection performance substantially improves with the extended features and multiple views.
Exploring the Hidden Dimension in Graph Processing

Task partitioning of a graph-parallel system is traditionally considered equivalent to the graph partition problem. Such equivalence exists because the properties associated with each vertex/edge are normally considered indivisible. However, this assumption is not true for many Machine Learning and Data Mining (MLDM) problems: instead of a single value, a vector of data elements is defined as the property for each vertex/edge. This feature opens a new dimension for task partitioning because a vertex could be divided and assigned to different nodes. To explore this new opportunity, this paper presents 3D partitioning, a novel category of task partition algorithms that significantly reduces network traffic for certain MLDM applications. Based on 3D partitioning, we build a distributed graph engine CUBE. Our evaluation results show that CUBE outperforms state-of-the-art graph-parallel system PowerLyra by up to 4.7× (up to 7.3× speedup against PowerGraph).
Serious games - engaging training solutions: A research and development project for supporting training needs

Addresses for correspondence: Dr Sara de Freitas, London Knowledge Lab, Birkbeck College, University of London, 23–29 Emerald Street, London WC1N 3QS. UK.  2117;  2754;  sara@lkl.ac.uk. Steve Jarvis, Vega Group PLC, 2 Falcon Way, Shire Park, Welwyn Garden City, Herts AL7 1TW, UK. Tel: +44 (0)1707 362602; Fax: +44 (0)1707 393909; email: steve.jarvis@vega.co.uk
Big Data Processing in Cloud Computing Environments

With the rapid growth of emerging applications like social network analysis, semantic Web analysis and bioinformatics network analysis, a variety of data to be processed continues to witness a quick increase. Effective management and analysis of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing technics from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including cloud computing platform, cloud architecture, cloud database and data storage scheme. Following the Map Reduce parallel processing framework, we then introduce Map Reduce optimization strategies and applications reported in the literature. Finally, we discuss the open issues and challenges, and deeply explore the research directions in the future on big data processing in cloud computing environments.
Learning Affinity via Spatial Propagation Networks

In this paper, we propose spatial propagation networks for learning the affinity matrix for vision tasks. We show that by constructing a row/column linear propagation model, the spatially varying transformation matrix exactly constitutes an affinity matrix that models dense, global pairwise relationships of an image. Specifically, we develop a three-way connection for the linear propagation model, which (a) formulates a sparse transformation matrix, where all elements can be outputs from a deep CNN, but (b) results in a dense affinity matrix that effectively models any task-specific pairwise similarity matrix. Instead of designing the similarity kernels according to image features of two points, we can directly output all the similarities in a purely data-driven manner. The spatial propagation network is a generic framework that can be applied to many affinity-related tasks, such as image matting, segmentation and colorization, to name a few. Essentially, the model can learn semantically-aware affinity values for high-level vision tasks due to the powerful learning capability of deep CNNs. We validate the framework on the task of refinement of image segmentation boundaries. Experiments on the HELEN face parsing and PASCAL VOC-2012 semantic segmentation tasks show that the spatial propagation network provides a general, effective and efficient solution for generating high-quality segmentation results.
E-Government and Developing Countries: An Overview

E-governance is more than just a government website on the Internet. The strategic objective of e-governance is to support and simplify governance for all parties; government, citizens and businesses. The use of ICTs can connect all three parties and support processes and activities. In other words, in e-governance electronic means support and stimulate good governance. Therefore, the objectives of e-governance are similar to the objectives of good governance. Good governance can be seen as an exercise of economic, political, and administrative authority to better manage affairs of a country at all levels. It is not difficult for people in developed countries to imagine a situation in which all interaction with government can be done through one counter 24 hours a day, 7 days a week, without waiting in lines. However to achieve this same level of efficiency and flexibility for developing countries is going to be difficult. The experience in developed countries shows that this is possible if governments are willing to decentralize responsibilities and processes, and if they start to use electronic means. This paper is going to examine the legal and infrastructure issues related to e-governance from the perspective of developing countries. Particularly it will examine how far the developing countries have been successful in providing a legal framework.
Adaptive data rate control in low power wide area networks for long range IoT services

Internet of Things (IoT) technologies can provide various intelligent services by collecting information from objects. To collect information, Wireless Sensor Networks (WSNs) are exploited. The Low Power Wide Area Network (LPWAN), one type of WSN, has been designed for long-range IoT services. It consumes low power and uses a low data rate for data transmission. The LPWAN includes several communication standards, and Long Range Wide Area Network (LoRaWAN) is the representative standard of the LPWAN. LoRaWAN provides several data rates for transmission and enables adaptive data rate control in order to maintain network connectivity. In the LoRaWAN, the wireless condition is considered by the reception status of the acknowledgement (ACK) message, and adaptive data rate control is performed according to the wireless condition. Because the judgment of the wireless condition by the reception status of ACK messages does not reflect congestion, adaptive data rate control can lead to inefficiency in data transmission. For efficient data transmission in long-range IoT services, this paper proposes a congestion classifier using logistic regression and modified adaptive data rate control. The proposed scheme controls the data rate according to the congestion estimation. Through extensive analysis, we show the proposed scheme’s efficiency in data transmission.
Markerless Motion Capture with unsynchronized moving cameras

In this work we present an approach for markerless motion capture (MoCap) of articulated objects, which are recorded with multiple unsynchronized moving cameras. Instead of using fixed (and expensive) hardware synchronized cameras, this approach allows us to track people with off-the-shelf handheld video cameras. To prepare a sequence for motion capture, we first reconstruct the static background and the position of each camera using Structure-from-Motion (SfM). Then the cameras are registered to each other using the reconstructed static background geometry. Camera synchronization is achieved via the audio streams recorded by the cameras in parallel. Finally, a markerless MoCap approach is applied to recover positions and joint configurations of subjects. Feature tracks and dense background geometry are further used to stabilize the MoCap. The experiments show examples with highly challenging indoor and outdoor scenes.
Unsupervised Feature Learning for RGB-D Image Classification

Motivated by the success of Deep Neural Networks in computer vision, we propose a deep Regularized Reconstruction Independent Component Analysis network (RICA) for RGB-D image classification. In each layer of this network, we include a RICA as the basic building block to determine the relationship between the gray-scale and depth images corresponding to the same object or scene. Implementing commonly used local contrast normalization and spatial pooling, we gradually enhance our network to be resilient to local variance resulting in a robust image representation for RGB-D image classification. Moreover, compared with conventional handcrafted feature-based RGB-D image representation, the proposed deep RICA is a feedforward network. Hence, it is more efficient for image representation. Experimental results on three publicly available RGB-D datasets demonstrate that the proposed method consistently outperforms the state-of-the-art conventional, manually designed RGB-D image representation confirming its effectiveness for RGB-D image classification.
Environmental Impacts of Distributed Manufacturing from 3-D Printing of Polymer Components and Products

Although additive layer manufacturing is well established for rapid prototyping the low throughput and historic costs have prevented mass-scale adoption. The recent development of the RepRap, an open source self-replicating rapid prototyper, has made low-cost 3-D printers readily available to the public at reasonable prices (<$1,000). The RepRap (Prusa Mendell variant) currently prints 3-D objects in a 200x200x140 square millimeters build envelope from acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA). ABS and PLA are both thermoplastics that can be injection-molded, each with their own benefits, as ABS is rigid and durable, while PLA is plant-based and can be recycled and composted. The melting temperature of ABS and PLA enable use in low-cost 3-D printers, as these temperature are low enough to use in melt extrusion in the home, while high enough for prints to retain their shape at average use temperatures. Using 3-D printers to manufacture provides the ability to both change the fill composition by printing voids and fabricate shapes that are impossible to make using tradition methods like injection molding. This allows more complicated shapes to be created while using less material, which could reduce environmental impact. As the open source 3-D printers continue to evolve and improve in both cost and performance, the potential for economically-viable distributed manufacturing of products increases. Thus, products and components could be customized and printed on-site by individual consumers as needed, reversing the historical trend towards centrally mass-manufactured and shipped products. Distributed manufacturing reduces embodied transportation energy from the distribution of conventional centralized manufacturing, but questions remain concerning the potential for increases in the overall embodied energy of the manufacturing due to reduction in scale. In order to quantify the environmental impact of distributed manufacturing using 3-D printers, a life cycle analysis was performed on a plastic juicer. The energy consumed and emissions produced from conventional large-scale production overseas are compared to experimental measurements on a RepRap producing identical products with ABS and PLA. The results of this LCA are discussed in relation to the environmental impact of distributed manufacturing with 3-D printers and polymer selection for 3-D printing to reduce this impact. The results of this study show that distributed manufacturing uses less energy than conventional manufacturing due to the RepRap's unique ability to reduce fill composition. Distributed manufacturing also has less emissions than conventional manufacturing when using PLA and when using ABS with solar photovoltaic power. The results of this study indicate that opensource additive layer distributed manufacturing is both technically viable and beneficial from an ecological perspective.
Dynamic analysis and state estimation for wearable robotic limbs subject to human-induced disturbances

We present the Supernumerary Robotic Limbs (SRL), a wearable robot designed to assist human workers with additional arms and legs attached to the wearer's body. The SRL can work closely with the wearer by holding an object, positioning a workpiece, operating a powered tool, securing the human body, and more. Although the SRL has the potential to provide the wearer with greater strength, higher accuracy, flexibility, and dexterity, its control performance is hindered by unpredictable disturbances due to involuntary motions of the wearer, which include postural sway and physiological tremor. This paper presents 1) a Kalman filter approach to estimate the state of the SRL despite the involuntary wearer's motion, and 2) a method for improving the accuracy and stabilizing the human body and the SRL. The dynamics of the human-SRL system are analyzed, including human-induced disturbance models based on biomechanics literature. A discrete Kalman filter is constructed and its performance is evaluated in terms of error covariance. A “bracing” technique is then introduced to suppress the human-induced disturbances; one robotic limb grasps an environment structure and uses it as a support to attenuate the disturbances. We show how bracing can be used to shape the stiffness parameters at the robot base. This in turn allows to enhance state estimation accuracy in the areas of the workspace where the user needs assistance.
A User-Intelligent Adaptive Learning Model for Learning Management System Using Data Mining and Artificial Intelligence

Entire world is revolving towards digital space as a result of the Internet and other emerging web technologies are helping the society to reach the universe. ICT and e-learning are growing radically fast and have captured a major role in higher educational systems. To develop the coast-to-coast purposes, institutions are changing the teaching style from chalk and talk to Learning Management System (LMS), called e-learning systems. Impact of e-learning introduces various delivery methods for teachers and different platforms to learn for learners. Teaching through LMS is most challenging because the difference in the learning styles and the nature of the course. Materials used for delivering the course are mostly static for all types of learners. Students are learning from the LMS tools, but not most effectively. To overcome this issues, the author(s) are recommending a new user intelligent adaptive learning model to be used in LMS. This allows the model to identify the learners learning style and endorses the appropriate learning materials for the learners. This method of delivering will be effective and efficient for different LMS learners.
Efficient Anytime Density-based Clustering

Many clustering algorithms suffer from scalability problems on massive datasets and do not support any user interaction during runtime. To tackle these problems, anytime clustering algorithms are proposed. They produce a fast approximate result which is continuously refined during the further run. Also, they can be stopped or suspended anytime and provide an answer. In this paper, we propose a novel anytime clustering algorithm based on the density-based clustering paradigm. Our algorithm called A-DBSCAN is applicable to very high dimensional databases such as time series, trajectory, medical data, etc. The general idea of our algorithm is to use a sequence of lower-bounding functions (LBs) of the true similarity measure to produce multiple approximate results of the true density-based clusters. ADBSCAN operates in multiple levels w.r.t. the LBs and is mainly based on two algorithmic schemes: (1) an efficient distance upgrade scheme which restricts distance calculations to core-objects at each level of the LBs; (2) a local reclustering scheme which restricts update operations to the relevant objects only. Extensive experiments demonstrate that A-DBSCAN acquires very good clustering results at very early stages of execution thus saves a large amount of computational time. Even if it runs to the end, A-DBSCAN is still orders of magnitude faster than DBSCAN.
RDBMS, NoSQL, Hadoop: A Performance-Based Empirical Analysis

The relational data model has been dominant and widely used since 1970. However, as the need to deal with big data grows, new data models, such as Hadoop and NoSQL, were developed to address the limitation of the traditional relational data model. As a result, determining which data model is suitable for applications has become a challenge. The purpose of this paper is to provide insight into choosing the suitable data model by conducting a benchmark using Yahoo! Cloud Serving Benchmark (YCSB) on three different database systems: (1) MySQL for relational data model, (2) MongoDB for NoSQL data model, and (3) HBase for Hadoop framework. The benchmark was conducted by running four different workloads. Each workload is executed using a different increasing operation and thread count, while observing how their change respectively affects throughput, latency, and runtime.
The Role of Interactivity in Student Satisfaction and Persistence in Online Learning

Enrollment in online courses is rapidly increasing and attrition rates remain high. This paper presents a literature review addressing the role of interactivity in student satisfaction and persistence in online learning. Empirical literature was reviewed through the lens of Bandura's social cognitive theory, Anderson's interaction equivalency theorem, and Tinto's social integration theory. Findings suggest that interactivity is an important component of satisfaction and persistence for online learners, and that preferences for types of online interactivity vary according to type of learner. Student–instructor interaction was also noted to be a primary variable in online student satisfaction and persistence.
Adaptive Rao–Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance

Particle filters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particle filter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve the efficiency and accuracy of a regular particle filter. Essentially, the distributions of the linear variables are updated analytically using a Kalman filter which is associated with each particle in a particle filtering framework. Experiments and detailed performance analysis using both simulated data and real video sequences reveal that the proposed method results in more accurate tracking than a regular particle filter
FACTORS AFFECTING INDIVIDUALS TO ADOPT MOBILE BANKING : EMPIRICAL EVIDENCE FROM THE UTAUT MODEL

Fast advances in the wireless technology and the intensive penetration of cell phones have motivated banks to spend large budget on building mobile banking systems, but the adoption rate of mobile banking is still underused than expected. Therefore, research to enrich current knowledge about what affects individuals to use mobile banking is required. Consequently, this study employs the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate what impacts people to adopt mobile banking. Through sampling 441 respondents, this study empirically concluded that individual intention to adopt mobile banking was significantly influenced by social influence, perceived financial cost, performance expectancy, and perceived credibility, in their order of influencing strength. The behavior was considerably affected by individual intention and facilitating conditions. As for moderating effects of gender and age, this study discovered that gender significantly moderated the effects of performance expectancy and perceived financial cost on behavioral intention, and the age considerably moderated the effects of facilitating conditions and perceived self-efficacy on actual adoption behavior.
Learning Spectral Mapping for Speech Dereverberation and Denoising

In real-world environments, human speech is usually distorted by both reverberation and background noise, which have negative effects on speech intelligibility and speech quality. They also cause performance degradation in many speech technology applications, such as automatic speech recognition. Therefore, the dereverberation and denoising problems must be dealt with in daily listening environments. In this paper, we propose to perform speech dereverberation using supervised learning, and the supervised approach is then extended to address both dereverberation and denoising. Deep neural networks are trained to directly learn a spectral mapping from the magnitude spectrogram of corrupted speech to that of clean speech. The proposed approach substantially attenuates the distortion caused by reverberation, as well as background noise, and is conceptually simple. Systematic experiments show that the proposed approach leads to significant improvements of predicted speech intelligibility and quality, as well as automatic speech recognition in reverberant noisy conditions. Comparisons show that our approach substantially outperforms related methods.
Memory Bounded Deep Convolutional Networks

In this work, we investigate the use of sparsity-inducing regularizers during training of Convolution Neural Networks (CNNs). These regularizers encourage that fewer connections in the convolution and fully connected layers take non-zero values and in effect result in sparse connectivity between hidden units in the deep network. This in turn reduces the memory and runtime cost involved in deploying the learned CNNs. We show that training with such regularization can still be performed using stochastic gradient descent implying that it can be used easily in existing codebases. Experimental evaluation of our approach on MNIST, CIFAR, and ImageNet datasets shows that our regularizers can result in dramatic reductions in memory requirements. For instance, when applied on AlexNet, our method can reduce the memory consumption by a factor of four with minimal loss in accuracy.