nils-herrmann/clean_scirepeval huggingface dataset:
Virtualization at the network edge: A performance comparison

The rapid growth of the Internet of Things (IoT) is driving the evolution of Cloud technologies towards resource distribution across multi-clouds and the inclusion of various heterogeneous devices. IoT specific applications may require the deployment of gateways at the network edge to enable its interaction with physical sensors, pre-processing data from these sensors, and synchronizing it with the cloud. The orchestration, deployment, and maintenance of the software running on the gateways in large-scale deployments is known to be challenging. In this paper we analyze two virtualization techniques commonly used for deployment and management of applications at the cloud, but on the context of IoT and network edge. A study was conducted to evaluate the performance of the container-based approach compared to a hypervisor-based virtualization when running on devices typically used at the network edge. The study was performed through the execution of several synthetic benchmarks providing an insight in the performance overhead introduced by Docker containers (lightweight-virtualization) and KVM VMs (hypervisor-virtualization) running at network edge devices.
TOWARDS AUTOMATED DEM GENERATION FROM HIGH RESOLUTION STEREO SATELLITE IMAGES

High resolution stereo satellite imagery is well suited for the creation of digital surface models (DSM). In this paper, a system for highly automated DSM and orthoimage generation based on CARTOSAT-1 imagery is presented. The proposed system processes photometrically corrected level-1 stereo scenes using the rational polynomial coefficients (RPC) universal sensor model. The RPC are derived from orbit and attitude information and have a much lower accuracy than the ground resolution of approximately 2.5 m. Ground control points are used to estimate affine RPC correction. Accurate GCP are not always available, especially for remote areas and large scale reconstruction. In this paper, GCP are automatically derived from lower resolution reference images (Landsat ETM+ Geocover and SRTM DSM). It is worthwhile to note that SRTM has a much higher lateral accuracy than the Landsat ETM+ mosaic, which limits the accuracy of both DSM and orthorectified images. Thus, affine RPC correction parameters are estimated by aligning a preliminary DSM to the SRTM DSM, resulting in significantly improved geolocation of both DSM and orthoimages. Robust stereo matching and outlier removal techniques and prior information such as cloud masks are used during this process. DSM with a grid spacing of 10 m are generated for 9 CARTOSAT-1 scenes in Catalonia. Checks against independent ground truth indicate a lateral error of 3-4 meters and a height accuracy of 4-5 meters. Independently processed scenes align at subpixel level and are well suited for mosaicing.
Inferring the score of a tennis match from in-play betting exchange markets

Over the past few years, betting exchanges have attracted a large variety of customers ranging from casual human participants to sophisticated automated trading agents. Professional tennis matches give rise to a number of betting exchange markets, which vary in liquidity. A problem faced by participants in tennis-related betting exchange markets is the lack of a reliable, low-latency and low-cost point-to-point score feed. Using existing quantitative tennis models, this paper demonstrates that it is possible to infer the score of a tennis match solely from live Match Odds betting exchange market data, assuming it has sufficient liquidity. By comparing the implied odds generated by our quantitative model during play with market data retrieved from the betting exchange, we devise an algorithm that detects when a point is scored by either player. This algorithm is further refined by identifying scenarios where false positives or misses occur and heuristically correcting for them. Testing this algorithm using live matches, we demonstrate that this idea is not only feasible but in fact is also capable of deducing the score of entire sets with few errors. While errors are still common and can lead to a derailment of the detection algorithm, with more work as well as improved data collection, the system has the potential of becoming a precise tool for score inference.
FRIENDSHIPS IN ONLINE PEER-TO-PEER LENDING : PIPES , PRISMS , AND RELATIONAL HERDING 1

This paper investigates how friendship relationships act as pipes, prisms, and herding signals in a large online, peer-to-peer (P2P) lending site. By analyzing decisions of lenders, we find that friends of the borrower, especially close offline friends, act as financial pipes by lending money to the borrower. On the other hand, the prism effect of friends’ endorsements via bidding on a loan negatively affects subsequent bids by third parties. However, when offline friends of a potential lender, especially close friends, place a bid, a relational herding effect occurs as potential lenders are likely to follow their offline friends with a bid.
Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation

In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking. Our proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and to recover missed detections. Furthermore, target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e., during the tracking process. Our approach is effective even when such cues fail to identify or follow the target due to occlusions or object-to-object interactions. We also propose to learn the weights of these two tracking cues to handle the difficult situations, such as severe occlusions and object-to-object interactions effectively. Our method has been validated on several public datasets and the experimental results show that it outperforms several state-of-the-art tracking methods.
Development of support system for forward tilting of the upper body

We have been developing a wearable robot that directly and physically supports human movement. A ldquomuscle suitrdquo for the arm that will provide muscular support for the paralyzed or those otherwise unable to move unaided is one example. Whereas low back pain is one of the most serious issue for the manual worker and in this paper, we focus on developing the support system for forward tilting of the upper body. In this paper, the concept and mechanical structure of the support system is shown. From the experimental result to keep forward tilting posture with load indicates our system is very effective for keeping forward tilting posture and it becomes clear that more than 60% reduction of muscular power is possible.
All-Digital Background Calibration of a Successive Approximation ADC Using the “Split ADC” Architecture

The “split ADC” architecture enables fully digital calibration and correction of nonlinearity errors due to capacitor mismatch in a successive approximation (SAR) ADC. The die area of a single ADC design is split into two independent halves, each converting the same input signal. Total area and power is unchanged, resulting in minimal increase in analog complexity. For each conversion, the half-sized ADCs generate two independent outputs which are digitally corrected using estimates of capacitor mismatch errors for each ADC. The ADC outputs are averaged to produce the ADC output code. The difference of the two outputs is used in a background calibration algorithm which estimates the error in the correction parameters. Any nonzero difference drives an LMS feedback loop toward zero difference which can only occur when the average error in each correction parameter is zero. A novel segmentation and shuffling scheme in the SAR capacitive DAC enables background calibration for a wide range of input signals including dc. Simulation of a 16 bit 1 Msps SAR ADC in 180 nm CMOS shows calibration convergence within 200 000 samples.
Risk, reward, and economic decision making in aging.

Older adults' decision quality is considered to be worse than that of younger adults. This age-related difference is often attributed to reductions in risk tolerance. Little is known about the circumstances that affect older adults' decisions and whether risk attitudes directly influence economic decisions. We measure the influence of risk attitudes on age-related differences in decision making in both nonsocial and social contexts.



Risk attitudes and economic decision making were measured in 30 healthy older adults and 29 healthy younger adults.



Older adults report being less impulsive, sensation seeking and risk tolerant than younger adults. Age did not affect a measure of nonsocial economic decision making. Older adults were more likely to reject unfair divisions of money during an economic social-bargaining game and more likely to make equitable divisions of money during social-giving game. These age-related differences were determined in part by individuals' self-reported risk taking.



We conclude that age-related differences in decision making are domain specific and that some social economic decision making is influenced by risk attitudes. Older adults are more risk avoidant, but this does not alter their willingness to wait for reward in a nonsocial context. Perceiving more risk is associated with an unwillingness to accept an unfair offer in social economic contexts and ultimately leads to poorer outcomes for older adults.
$\mathsf {pSCAN}$ : Fast and Exact Structural Graph Clustering

We study the problem of structural graph clustering, a fundamental problem in managing and analyzing graph data. Given an undirected unweighted graph, structural graph clustering is to assign vertices to clusters, and to identify the sets of hub vertices and outlier vertices as well, such that vertices in the same cluster are densely connected to each other while vertices in different clusters are loosely connected. In this paper, we develop a new two-step paradigm for scalable structural graph clustering based on our three observations. Then, we present a <inline-formula> <tex-math notation="LaTeX">$\mathsf {pSCAN}$</tex-math><alternatives> <inline-graphic xlink:href="chang-ieq2-2618795.gif"/></alternatives></inline-formula> approach, within the paradigm, aiming to reduce the number of structural similarity computations, and propose optimization techniques to speed up checking whether two vertices are structure-similar. <inline-formula><tex-math notation="LaTeX">$\mathsf {pSCAN}$ </tex-math><alternatives><inline-graphic xlink:href="chang-ieq3-2618795.gif"/></alternatives></inline-formula> outputs exactly the same clusters as the existing approaches <inline-formula><tex-math notation="LaTeX">$\mathsf {SCAN}$ </tex-math><alternatives><inline-graphic xlink:href="chang-ieq4-2618795.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathsf {SCAN\text{++}}$</tex-math><alternatives> <inline-graphic xlink:href="chang-ieq5-2618795.gif"/></alternatives></inline-formula>, and we prove that <inline-formula><tex-math notation="LaTeX">$\mathsf {pSCAN}$</tex-math><alternatives> <inline-graphic xlink:href="chang-ieq6-2618795.gif"/></alternatives></inline-formula> is worst-case optimal. Moreover, we propose efficient techniques for updating the clusters when the input graph dynamically changes, and we also extend our techniques to other similarity measures, e.g., Jaccard similarity. Performance studies on large real and synthetic graphs demonstrate the efficiency of our new approach and our dynamic cluster maintenance techniques. Noticeably, for the twitter graph with 1 billion edges, our approach takes 25 minutes while the state-of-the-art approach cannot finish even after 24 hours.
Carbon: architectural support for fine-grained parallelism on chip multiprocessors

Chip multiprocessors (CMPs) are now commonplace, and the number of cores on a CMP is likely to grow steadily. However, in order to harness the additional compute resources of a CMP, applications must expose their thread-level parallelism to the hardware. One common approach to doing this is to decompose a program into parallel "tasks" and allow an underlying software layer to schedule these tasks to different threads. Software task scheduling can provide good parallel performance as long as tasks are large compared to the software overheads.
 We examine a set of applications from an important emerging domain: Recognition, Mining, and Synthesis (RMS). Many RMS applications are compute-intensive and have abundant thread-level parallelism, and are therefore good targets for running on a CMP. However, a significant number have small tasks for which software task schedulers achieve only limited parallel speedups.
 We propose Carbon, a hardware technique to accelerate dynamic task scheduling on scalable CMPs. Carbon has relatively simple hardware, most of which can be placed far from the cores. We compare Carbon to some highly tuned software task schedulers for a set of RMS benchmarks with small tasks. Carbon delivers significant performance improvements over the best software scheduler: on average for 64 cores, 68% faster on a set of loop-parallel benchmarks, and 109% faster on aset of task-parallel benchmarks.
A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy

This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 fractional Fourier entropy (FRFE) features were extracted from each brain image. Next, we submit those features to a multi-layer perceptron (MLP) classifier. Two improvements were proposed for MLP. One improvement is the pruning technique that determines the optimal hidden neuron number. We compared three pruning techniques: dynamic pruning (DP), Bayesian detection boundaries (BDB), and Kappa coefficient (KC). The other improvement is to use the adaptive real-coded biogeography-based optimization (ARCBBO) to train the biases and weights of MLP. The experiments showed that the proposed FRFE + KC-MLP + ARCBBO achieved an average accuracy of 99.53 % based on 10 repetitions of K-fold cross validation, which was better than 11 recent PBDS methods.
Detecting abnormal events on binary sensors in smart home environments

With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies.
Local Probabilistic Models for Link Prediction

One of the core tasks in social network analysis is to predict the formation of links (i.e. various types of relationships) over time. Previous research has generally represented the social network in the form of a graph and has leveraged topological and semantic measures of similarity between two nodes to evaluate the probability of link formation. Here we introduce a novel local probabilistic graphical model method that can scale to large graphs to estimate the joint co-occurrence probability of two nodes. Such a probability measure captures information that is not captured by either topological measures or measures of semantic similarity, which are the dominant measures used for link prediction. We demonstrate the effectiveness of the co-occurrence probability feature by using it both in isolation and in combination with other topological and semantic features for predicting co-authorship collaborations on real datasets.
Once for All: A Two-Flow Convolutional Neural Network for Visual Tracking

The main challenges of visual object tracking arise from the arbitrary appearance of the objects that need to be tracked. Most existing algorithms try to solve this problem by training a new model to regenerate or classify each tracked object. As a result, the model needs to be initialized and retrained for each new object. In this paper, we propose to track different objects in an object-independent approach with a novel two-flow convolutional neural network (YCNN). The YCNN takes two inputs (one is an object image patch, the other is a larger searching image patch), then outputs a response map which predicts how likely and where the object would appear in the search patch. Unlike the object-specific approaches, the YCNN is actually trained to measure the similarity between the two image patches. Thus, this model will not be limited to any specific object. Furthermore, the network is end-to-end trained to extract both shallow and deep dedicated convolutional features for visual tracking. And once properly trained, the YCNN can be used to track all kinds of objects without further training and updating. As a result, our algorithm is able to run at a very high speed of 45 frames-per-second. The effectiveness of the proposed algorithm can also be proved by the experiments on two popular data sets: OTB-100 and VOT-2014.
Real-time detection of students' emotional states in the classroom

Today, as social media sharing increases, the instant emotional state of the students changes frequently. This situation is greatly affecting the learning process as well as the motivation of the students in the classroom. Sometimes, it is insufficient for the educator to observe the emotional states of students. Therefore, an automatic system is needed that can detect and analyze the emotional states of students in the classroom. In this study, an auxiliary information system, which uses image processing and human-computer interaction, has been developed that would be used in the field of education. In this system, the dataset obtained from the students' faces was tested using various machine learning algorithms. As a result, the accuracy of this system was found as 97.15% using support vector machine. This system is aimed to direct the educator to communicate with the students and to increase their motivation when necessary.
On the Kinematic Analysis of Robotic Mechanisms

The kinematic analyses, of manipulators and other robotic devices composed of mechanical links, usually depend on the solution of sets of nonlinear equations. There are a variety of both numerical and algebraic techniques available to solve such systems of equations and to give bounds on the number of solutions. These solution methods have also led to an understanding of how special choices of the various structural parameters of a mechanism influence the number of solutions inherent to the kinematic geometry of a given structure. In this paper, results from studying the kinematic geometry of such systems are reviewed, and the three most useful solution techniques are summarized. The solution techniques are polynomial continuation, Gröbner bases, and elimination. We then discuss the results that have been obtained with these techniques in the solution of two basic problems, namely, the inverse kinematics for serial-chain manipulators, and the direct kinematics of in-parallel platform devices. inverse kinematics, direct kinematics, manipulators, in-parallel manipulators, series manipulators, polynomial continuation, characteristic polynomial
PROFITING FROM ARBITRAGE AND ODDS BIASES OF THE EUROPEAN FOOTBALL GAMBLING MARKET

A gambling market is usually described as being inefficient if there are one or more betting strategies that generate profit, at a consistent rate, as a consequence of exploiting market flaws. This paper examines the online European football gambling market based on 14 European football leagues over a period of seven years, from season 2005/06 to 2011/12 inclusive, and takes into consideration the odds provided by numerous bookmaking firms. Contrary to common misconceptions, we demonstrate that the accuracy of bookmakers' odds has not improved over this period. More importantly, our results question market efficiency by demonstrating high profitability on the basis of consistent odds biases and numerous arbitrage opportunities.
An Internet based wireless home automation system for multifunctional devices

The aim of home automation is to control home devices from a central control point. In this paper, we present the design and implementation of a low cost but yet flexible and secure Internet based home automation system. The communication between the devices is wireless. The protocol between the units in the design is enhanced to be suitable for most of the appliances. The system is designed to be low cost and flexible with the increasing variety of devices to be controlled.
A tale of two clouds: Computing on data encrypted under multiple keys

Cloud computing provides a convenient platform for big data computation such as machine learning and data mining. However, privacy conscious users often encrypt their data with their own keys before uploading them to the cloud. Existing techniques for computation on encrypted data are either in the single key setting or far from practical. In this paper, we show how two non-colluding servers can leverage proxy re-encryption to jointly compute arithmetic functions over the ciphertexts of multiple users without learning the inputs, intermediate or final results. Moreover, the computation is non-interactive to users and only requires minimal server-to-server interactions. Experimental results demonstrate that our schemes significantly improve the efficiency of outsourced computation when compared to the existing approach.
Estimating Continuous Distributions in Bayesian Classifiers

When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous vari­ ables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality as­ sumption and instead use statistical methods for nonparametric density estimation. For a naive Bayesian classifier, we present experi­ mental results on a variety of natural and ar­ tificial domains, comparing two methods of density estimation: assuming normality and modeling each conditional distribution with a single Gaussian; and using nonparamet­ ric kernel density estimation. We observe large reductions in error on several natural and artificial data sets, which suggests that kernel estimation is a useful tool for learning Bayesian models.