nils-herrmann/clean_scirepeval huggingface dataset:
The Yahoo! Music Dataset and KDD-Cup '11

KDD-Cup 2011 challenged the community to identify user tastes in music by leveraging Yahoo! Music user ratings. The competition hosted two tracks, which were based on two datasets sampled from the raw data, including hundreds of millions of ratings. The underlying ratings were given to four types of musical items: tracks, albums, artists, and genres, forming a four level hierarchical taxonomy. The challenge started on March 15, 2011 and ended on June 30, 2011 attracting 2389 participants, 2100 of which were active by the end of the competition. The popularity of the challenge is related to the fact that learning a large scale recommender systems is a generic problem, highly relevant to the industry. In addition, the contest drew interest by introducing a number of scientific and technical challenges including dataset size, hierarchical structure of items, high resolution timestamps of ratings, and a non-conventional ranking-based task. This paper provides the organizers’ account of the contest, including: a detailed analysis of the datasets, discussion of the contest goals and actual conduct, and lessons learned throughout the contest.
Food Image Segmentation for Dietary Assessment

The prevalence of diet-related chronic diseases strongly impacts global health and health services. Currently, it takes training and strong personal involvement to manage or treat these diseases. One way to assist with dietary assessment is through computer vision systems that can recognize foods and their portion sizes from images and output the corresponding nutritional information. When multiple food items may exist, a food segmentation stage should also be applied before recognition. In this study, we propose a method to detect and segment the food of already detected dishes in an image. The method combines region growing/merging techniques with a deep CNN-based food border detection. A semi-automatic version of the method is also presented that improves the result with minimal user input. The proposed methods are trained and tested on non-overlapping subsets of a food image database including 821 images, taken under challenging conditions and annotated manually. The automatic and semi-automatic dish segmentation methods reached average accuracies of 88% and 92%, respectively, in roughly 0.5 seconds per image.
Graphs in Scientific Visualization: A Survey

Graphs represent general node-link diagrams and have long been utilized in scientific visualization for data organization and management. However, using graphs as a visual representation and interface for navigating and exploring scientific data sets has a much shorter history yet the amount of work along this direction is clearly on the rise in recent years. In this paper, we take a holistic perspective and survey graph-based representations and techniques for scientific visualization. Specifically, we classify these representations and techniques into four categories, namely, partition-wise, relationship-wise, structure-wise, and provenance-wise. We survey related publications in each category, explaining the roles of graphs in related work and highlighting their similarities and differences. At the end, we reexamine these related publications following the graph-based visualization pipeline. We also point out research trends and remaining challenges in graph-based representations and techniques for scientific visualization.
Building and sustaining profitable customer loyalty for the 21 st century

The concept of customer loyalty is conspicuous by it’s ubiquity. Therefore, there is no surprise that it is one of the most widely studied areas by researchers and one of the most widely implemented marketing initiatives by practitioners. This article draws upon past research to review important findings related to customer behavior and attitude in the context of customer loyalty. Further, research related to linking loyalty to profitability and forward looking metric such as the customer lifetime value is reviewed to propose a conceptual framework for building and sustaining loyalty and profitability simultaneously at individual customer level. A two-tiered rewards structure is presented as a means for marketers to operationalize the framework. The conceptual framework hopes to serve as a platform to understand the evolving dominant logic of loyalty programs for building and sustaining loyalty in the twenty first century as well as induce further research in that direction.
Review of Active Power Decoupling Topologies in Single-Phase Systems

Active power decoupling methods are developed to deal with the inherent ripple power at twice the grid frequency in single-phase systems generally by adding active switches and energy storage units. They have obtained a wide range of applications, such as photovoltaic (PV) systems, light-emitting diodes (LEDs) drivers, fuel cell (FC) power systems, and electric vehicle (EV) battery chargers, etc. This paper provides a comprehensive review of active power decoupling circuit topologies. They are categorized into two groups in terms of the structure characteristics: independent and dependent decoupling circuit topologies. The former operates independently with the original converter, and the latter, however, shares the power semiconductor devices with the original converter partially and even completely. The development laws for the active power decoupling topologies are revealed from the view of “duality principle,” “switches sharing,” and “differential connection.” In addition, the exceptions and special cases are also briefly introduced. This paper is targeted to help researchers, engineers, and designers to construct some new decoupling circuit topologies and properly select existing ones according to the specific application.
Design equations for tapered microstrip-to-Substrate Integrated Waveguide transitions

This paper presents design equations for the microstrip-to-Substrate Integrated Waveguide (SIW) transition. The transition is decomposed in two distinct parts: the microstrip taper and the microstrip-to-SIW step. Analytical equations are used for the microstrip taper. As for the step, the microstrip is modeled by an equivalent transverse electromagnetic (TEM) waveguide. An equation relating the optimum microstrip width to the SIW width is derived using a curve fitting technique. It is shown that when the step is properly sized, it provides a return loss superior to 20 dB. Three design examples are presented using different substrate permittivity and frequency bands between 18 GHz and 75 GHz. An experimental verification is also presented. The presented technique allows to design transitions covering the complete single-mode SIW bandwidth.
Can Accurate Predictions Improve Video Streaming in Cellular Networks?

Existing video streaming algorithms use various estimation approaches to infer the inherently variable bandwidth in cellular networks, which often leads to reduced quality of experience (QoE). We ask the question: "If accurate bandwidth prediction were possible in a cellular network, how much can we improve video QoE?". Assuming we know the bandwidth for the entire video session, we show that existing streaming algorithms only achieve between 69%-86% of optimal quality. Since such knowledge may be impractical, we study algorithms that know the available bandwidth for a few seconds into the future. We observe that prediction alone is not sufficient and can in fact lead to degraded QoE. However, when combined with rate stabilization functions, prediction outperforms existing algorithms and reduces the gap with optimal to 4%. Our results lead us to believe that cellular operators and content providers can tremendously improve video QoE by predicting available bandwidth and sharing it through APIs.
Wideband In-Phase and Out-of-Phase Balanced Power Dividing and Combining Networks

Two new wideband in-phase and out-of-phase balanced power dividing/combining networks are proposed in this paper. Based on matrix transformation, the differential-mode and common-mode equivalent circuits of the two wideband in-phase and out-of-phase networks can be easily deduced. A patterned ground-plane technique is used to realize the strong coupling of the shorted coupled lines for the differential mode. Two planar wideband in-phase and out-of-phase balanced networks with bandwidths of 55.3% and 64.4% for the differential mode with wideband common-mode suppression are designed and fabricated. The theoretical and measured results agree well with each other and show good in-band performances.
On the local minima free condition of backpropagation learning

It is shown that if there are P noncoincident input patterns to learn and a two-layered feedforward neural network having P-1 sigmoidal hidden neuron and one dummy hidden neuron is used for the learning, then any suboptimal equilibrium point of the corresponding error surface is unstable in the sense of Lyapunov. This result leads to a sufficient local minima free condition for the backpropagation learning.
Secure set intersection cardinality with application to association rule mining

There has been concern over the apparent conflict between privacy and data mining. There is no inherent conflict, as most types of data mining produce summary results that do not reveal information about individuals. The process of data mining may use private data, leading to the potential for privacy breaches. Secure Multiparty Computation shows that results can be produced without revealing the data used to generate them. The problem is that general techniques for secure multiparty computation do not scale to data-mining size computations. This paper presents an efficient protocol for securely determining the size of set intersection, and shows how this can be used to generate association rules where multiple parties have different (and private) information about the same set of individuals.
Algorithms for Spatial Outlier Detection

A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. One drawback of existing methods is that normal objects tend to be falsely detected as spatial outliers when their neighborhood contains true spatial outliers. In this paper, we propose a suite of spatial outlier detection algorithms to overcome this disadvantage. We formulate the spatial outlier detection problem in a general way and design algorithms which can accurately detect spatial outliers. In addition, using a real-world census data set, we demonstrate that our approaches can not only avoid detecting false spatial outliers but also find true spatial outliers ignored by existing methods.
The EM Side – Channel ( s ) : Attacks and Assessment Methodologies

We present a systematic investigation of the leakage of compromising information via electromagnetic (EM) emanations from chipcards and other devices. This information leakage differs substantially from and is more powerful than the leakage from other conventional side-channels such as timing and power. EM emanations are shown to consist of a multiplicity of compromising signals, each leaking somewhat different information. Our experimental results confirm that these signals could individually contain enough leakage to break cryptographic implementations and to defeat countermeasures against other side-channels such as power. Using techniques from Signal Detection Theory, we also show that generalized and far more devastating attacks can be constructed from an effective pooling of leakages from multiple signals derived from EM emanations. The magnitude of EM exposure demands a leakage assessment methodology whose correctness can be rigorously proved. We define a model that completely and quantitatively bounds the information leaked from multiple (or all available) EM side-channel signals in CMOS devices and use that to develop a practical assessment methodology for devices such as chipcards.
Intermediation in a Sharing Economy: Insurance, Moral Hazard, and Rent Extraction

Electronic intermediaries have become pervasive in sales transactions for many durables, such as cars, power tools, and apartments. Yet only recently have they successfully tackled the challenge of enabling parties to share such goods. A key impediment to sharing is a lender’s concern about damage due to unobservable actions by a renter, usually resulting in moral hazard. This paper shows how an intermediary can eliminate the moral-hazard problem by providing optimal insurance to the lender and first-best-incentives to the renter to exert care, as long as market participants are risk-neutral. The solution is illustrated for the collaborative housing market but applies in principle to any sharing market with vertically differentiated goods. A population of renters, heterogeneous both in their preferences for housing quality and with respect to the amounts of care they exert in a rental situation, faces a choice between collaborative housing and staying at a local hotel. The private hosts choose their prices strategically and the intermediary sets commission rates on both sides of the market as well as insurance terms for the rental agreement. The latter are set so as to eliminate moral hazard. The intermediary is able to extract the gains the hosts would earn compared to transacting directly. Finally, even if hotels set their prices at the outset so as to maximize collusive profits, we find that collaborative housing persists at substantial market shares, regardless of the difference between the efficiencies of hosts and hotels to reduce renters’ cost of effort. The aggregate of hosts, intermediary, and hotels benefits from (a variety in) these effort costs, which indicates that the intermediated sharing of goods is an economically viable, robust phenomenon.
Decreased attentional responsivity during sleep deprivation: orienting response latency, amplitude, and habituation.

Ever increasing societal demands for uninterrupted work are causing unparalleled amounts of sleep deprivation among workers. Sleep deprivation has been linked to safety problems ranging from medical misdiagnosis to industrial and vehicular accidents. Microsleeps (very brief intrusions of sleep into wakefulness) are usually cited as the cause of the performance decrements during sleep deprivation. Changes in a more basic physiological phenomenon, attentional shift, were hypothesized to be additional factors in performance declines. The current study examined the effects of 36 hours of sleep deprivation on the electrodermal-orienting response (OR), a measure of attentional shift or capture. Subjects were 71 male undergraduate students, who were divided into sleep deprivation and control (non-sleep deprivation) groups. The expected negative effects of sleep deprivation on performance were noted in increased reaction times and increased variability in the sleep-deprived group on attention-demanding cognitive tasks. OR latency was found to be significantly delayed after sleep deprivation, OR amplitude was significantly decreased, and habituation of the OR was significantly faster during sleep deprivation. These findings indicate impaired attention, the first revealing slowed shift of attention to novel stimuli, the second indicating decreased attentional allocation to stimuli, and the third revealing more rapid loss of attention to repeated stimuli. These phenomena may be factors in the impaired cognitive performance seen during sleep deprivation.
The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics

The reality gap, which often makes controllers evolved in simulation inefficient once transferred onto the physical robot, remains a critical issue in evolutionary robotics (ER). We hypothesize that this gap highlights a conflict between the efficiency of the solutions in simulation and their transferability from simulation to reality: the most efficient solutions in simulation often exploit badly modeled phenomena to achieve high fitness values with unrealistic behaviors. This hypothesis leads to the transferability approach, a multiobjective formulation of ER in which two main objectives are optimized via a Pareto-based multiobjective evolutionary algorithm: 1) the fitness; and 2) the transferability, estimated by a simulation-to-reality (STR) disparity measure. To evaluate this second objective, a surrogate model of the exact STR disparity is built during the optimization. This transferability approach has been compared to two reality-based optimization methods, a noise-based approach inspired from Jakobi's minimal simulation methodology and a local search approach. It has been validated on two robotic applications: 1) a navigation task with an e-puck robot; and 2) a walking task with a 8-DOF quadrupedal robot. For both experimental setups, our approach successfully finds efficient and well-transferable controllers only with about ten experiments on the physical robot.
A Privacy Preserving System for Cloud Computing

Cloud computing is changing the way that organizations manage their data, due to its robustness, low cost and ubiquitous nature. Privacy concerns arise whenever sensitive data is outsourced to the cloud. This paper introduces a cloud database storage architecture that prevents the local administrator as well as the cloud administrator to learn about the outsourced database content. Moreover, machine readable rights expressions are used in order to limit users of the database to a need-to-know basis. These limitations are not changeable by administrators after the database related application is launched, since a new role of rights editors is defined once an application is launced. Furthermore, trusted computing is applied to bind cryptographic key information to trusted states. By limiting the necessary trust in both corporate as well as external administrators and service providers, we counteract the often criticized privacy and confidentiality risks of corporate cloud computing.
Symmetry reCAPTCHA

This paper is a reaction to the poor performance of symmetry detection algorithms on real-world images, benchmarked since CVPR 2011. Our systematic study reveals significant difference between human labeled (reflection and rotation) symmetries on photos and the output of computer vision algorithms on the same photo set. We exploit this human-machine symmetry perception gap by proposing a novel symmetry-based Turing test. By leveraging a comprehensive user interface, we collected more than 78,000 symmetry labels from 400 Amazon Mechanical Turk raters on 1,200 photos from the Microsoft COCO dataset. Using a set of ground-truth symmetries automatically generated from noisy human labels, the effectiveness of our work is evidenced by a separate test where over 96% success rate is achieved. We demonstrate statistically significant outcomes for using symmetry perception as a powerful, alternative, image-based reCAPTCHA.
False rumors detection on Sina Weibo by propagation structures

This paper studies the problem of automatic detection of false rumors on Sina Weibo, the popular Chinese microblogging social network. Traditional feature-based approaches extract features from the false rumor message, its author, as well as the statistics of its responses to form a flat feature vector. This ignores the propagation structure of the messages and has not achieved very good results. We propose a graph-kernel based hybrid SVM classifier which captures the high-order propagation patterns in addition to semantic features such as topics and sentiments. The new model achieves a classification accuracy of 91.3% on randomly selected Weibo dataset, significantly higher than state-of-the-art approaches. Moreover, our approach can be applied at the early stage of rumor propagation and is 88% confident in detecting an average false rumor just 24 hours after the initial broadcast.
Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets

Recently, applying the novel data mining techniques for financial time-series forecasting has received much research attention. However, most researches are for the US and European markets, with only a few for Asian markets. This research applies Support-Vector Machines (SVMs) and Back Propagation (BP) neural networks for six Asian stock markets and our experimental results showed the superiority of both models, compared to the early researches.
Novel Techniques for Fraud Detection in Mobile Telecommunication Networks

This paper discusses the status of research on detection of fraud undertaken as part of the European Commission-funded ACTS ASPeCT (Advanced Security for Personal Communications Technologies) project. A first task has been the identification of possible fraud scenarios and of typical fraud indicators which can be mapped to data in toll tickets. Currently, the project is exploring the detection of fraudulent behaviour based on a combination of absolute and differential usage. Three approaches are being investigated: a rule-based approach, an approach based on neural network, where both supervised and unsupervised learning are considered. Special attention is being paid to the feasibility of the implementations.
Individualness and Determinantal Point Processes for Pedestrian Detection

In this paper, we introduce individualness of detection candidates as a complement to objectness for pedestrian detection. The individualness assigns a single detection for each object out of raw detection candidates given by either object proposals or sliding windows. We show that conventional approaches, such as non-maximum suppression, are sub-optimal since they suppress nearby detections using only detection scores. We use a determinantal point process combined with the individualness to optimally select final detections. It models each detection using its quality and similarity to other detections based on the individualness. Then, detections with high detection scores and low correlations are selected by measuring their probability using a determinant of a matrix, which is composed of quality terms on the diagonal entries and similarities on the off-diagonal entries. For concreteness, we focus on the pedestrian detection problem as it is one of the most challenging problems due to frequent occlusions and unpredictable human motions. Experimental results demonstrate that the proposed algorithm works favorably against existing methods, including non-maximal suppression and a quadratic unconstrained binary optimization based method.
Can Intelligence Explode?

The technological singularity refers to a hypothetical scenario in which technological advances virtually explode. The most popular scenario is the creation of super-intelligent algorithms that recursively create ever higher intelligences. It took many decades for these ideas to spread from science fiction to popular science magazines and finally to attract the attention of serious philosophers. David Chalmers’ (JCS 2010) article is the first comprehensive philosophical analysis of the singularity in a respected philosophy journal. The motivation of my article is to augment Chalmers’ and to discuss some issues not addressed by him, in particular what it could mean for intelligence to explode. In this course, I will (have to) provide a more careful treatment of what intelligence actually is, separate speed from intelligence explosion, compare what super-intelligent participants and classical human observers might experience and do, discuss immediate implications for the diversity and value of life, consider possible bounds on intelligence, and contemplate intelligences right at the singularity.