nils-herrmann/clean_scirepeval huggingface dataset:
Adversarial Personalized Ranking for Recommendation

Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) - the most widely used model in recommendation - as a demonstration, we show that optimizing it with BPR leads to a recommender model that is not robust. In particular, we find that the resultant model is highly vulnerable to adversarial perturbations on its model parameters, which implies the possibly large error in generalization. To enhance the robustness of a recommender model and thus improve its generalization performance, we propose a new optimization framework, namely Adversarial Personalized Ranking (APR). In short, our APR enhances the pairwise ranking method BPR by performing adversarial training. It can be interpreted as playing a minimax game, where the minimization of the BPR objective function meanwhile defends an adversary, which adds adversarial perturbations on model parameters to maximize the BPR objective function. To illustrate how it works, we implement APR on MF by adding adversarial perturbations on the embedding vectors of users and items. Extensive experiments on three public real-world datasets demonstrate the effectiveness of APR - by optimizing MF with APR, it outperforms BPR with a relative improvement of 11.2% on average and achieves state-of-the-art performance for item recommendation. Our implementation is available at: \urlhttps://github.com/hexiangnan/adversarial_personalized_ranking.
Distributed Internet-Based Load Altering Attacks Against Smart Power Grids

With the increase in use of information technology in advanced demand side management and given the growth in power consumption in the computation and communications sectors, a new class of cyberintrusion plans is emerging that aims to alter the load through the Internet and by means of automatic and distributed software intruding agents. These attacks work by compromising direct load control command signals, demand side management price signals, or cloud computation load distribution algorithms to affect the load at the most crucial locations in the grid in order to cause circuit overflow or other malfunctions and damage the power system equipment. To gain insights into these less-examined yet important intrusion strategies, in this paper, we identify a variety of practical loads that can be volnurable to Internet-based load altering attacks. In addition, we overview a collection of defense mechanisms that can help in blocking these attacks or minimizing the damage caused by them. Our simulation results based on the standard setting in the IEEE 24-bus Reliability Test System show that our proposed cost-efficent load protection strategy can significantly reduce the cost of load protection while it guarantees that no Internet-based load altering attack may overload the power distribution system.
Floating-point sparse matrix-vector multiply for FPGAs

Large, high density FPGAs with high local distributed memory bandwidth surpass the peak floating-point performance of high-end, general-purpose processors. Microprocessors do not deliver near their peak floating-point performance on efficient algorithms that use the Sparse Matrix-Vector Multiply (SMVM) kernel. In fact, it is not uncommon for microprocessors to yield only 10--20% of their peak floating-point performance when computing SMVM. We develop and analyze a scalable SMVM implementation on modern FPGAs and show that it can sustain high throughput, near peak, floating-point performance. For benchmark matrices from the Matrix Market Suite we project 1.5 double precision Gflops/FPGA for a single Virtex II 6000-4 and 12 double precision Gflops for 16 Virtex IIs (750Mflops/FPGA).
Detecting Novel Attacks by Identifying Anomalous Network Packet Headers

We describe a simple and eff icient network intrusion detection algorithm that detects novel attacks by flagging anomalous field values in packet headers at the data link, network, and transport layers. In the 1999 DARPA off-line intrusion detection evaluation test set (Lippmann et al. 2000), we detect 76% of probes and 48% of denial of service attacks (at 10 false alarms per day). When this system is merged with the 18 systems in the original evaluation, the average detection rate for attacks of all types increases from 61% to 65%. We investigate the effect on performance when attack free training data is not available.
Real-time shading-based refinement for consumer depth cameras

We present the first real-time method for refinement of depth data using shape-from-shading in general uncontrolled scenes. Per frame, our real-time algorithm takes raw noisy depth data and an aligned RGB image as input, and approximates the time-varying incident lighting, which is then used for geometry refinement. This leads to dramatically enhanced depth maps at 30Hz. Our algorithm makes few scene assumptions, handling arbitrary scene objects even under motion. To enable this type of real-time depth map enhancement, we contribute a new highly parallel algorithm that reformulates the inverse rendering optimization problem in prior work, allowing us to estimate lighting and shape in a temporally coherent way at video frame-rates. Our optimization problem is minimized using a new regular grid Gauss-Newton solver implemented fully on the GPU. We demonstrate results showing enhanced depth maps, which are comparable to offline methods but are computed orders of magnitude faster, as well as baseline comparisons with online filtering-based methods. We conclude with applications of our higher quality depth maps for improved real-time surface reconstruction and performance capture.
An Image-based Deep Spectrum Feature Representation for the Recognition of Emotional Speech

The outputs of the higher layers of deep pre-trained convolutional neural networks (CNNs) have consistently been shown to provide a rich representation of an image for use in recognition tasks. This study explores the suitability of such an approach for speech-based emotion recognition tasks. First, we detail a new acoustic feature representation, denoted as deep spectrum features, derived from feeding spectrograms through a very deep image classification CNN and forming a feature vector from the activations of the last fully connected layer. We then compare the performance of our novel features with standardised brute-force and bag-of-audio-words (BoAW) acoustic feature representations for 2- and 5-class speech-based emotion recognition in clean, noisy and denoised conditions. The presented results show that image-based approaches are a promising avenue of research for speech-based recognition tasks. Key results indicate that deep-spectrum features are comparable in performance with the other tested acoustic feature representations in matched for noise type train-test conditions; however, the BoAW paradigm is better suited to cross-noise-type train-test conditions.
Learning ReLUs via Gradient Descent

In this paper we study the problem of learning Rectified Linear Units (ReLUs) which are functions of the form x ↦ max(0, ⟨w,x⟩) with w ∈ R denoting the weight vector. We study this problem in the high-dimensional regime where the number of observations are fewer than the dimension of the weight vector. We assume that the weight vector belongs to some closed set (convex or nonconvex) which captures known side-information about its structure. We focus on the realizable model where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to a planted weight vector. We show that projected gradient descent, when initialized at 0, converges at a linear rate to the planted model with a number of samples that is optimal up to numerical constants. Our results on the dynamics of convergence of these very shallow neural nets may provide some insights towards understanding the dynamics of deeper architectures.
Real-Time Brazilian License Plate Detection and Recognition Using Deep Convolutional Neural Networks

Automatic License Plate Recognition (ALPR) is an important task with many applications in Intelligent Transportation and Surveillance systems. As in other computer vision tasks, Deep Learning (DL) methods have been recently applied in the context of ALPR, focusing on country-specific plates, such as American or European, Chinese, Indian and Korean. However, either they are not a complete DL-ALPR pipeline, or they are commercial and utilize private datasets and lack detailed information. In this work, we proposed an end-to-end DL-ALPR system for Brazilian license plates based on state-of-the-art Convolutional Neural Network architectures. Using a publicly available dataset with Brazilian plates, the system was able to correctly detect and recognize all seven characters of a license plate in 63.18% of the test set, and 97.39% when considering at least five correct characters (partial match). Considering the segmentation and recognition of each character individually, we are able to segment 99% of the characters, and correctly recognize 93% of them.
Energy-balance studies reveal associations between gut microbes, caloric load, and nutrient absorption in humans.

Studies in mice indicate that the gut microbiome influences both sides of the energy-balance equation by contributing to nutrient absorption and regulating host genes that affect adiposity. However, it remains uncertain as to what extent gut microbiota are an important regulator of nutrient absorption in humans.



With the use of a carefully monitored inpatient study cohort, we tested how gut bacterial community structure is affected by altering the nutrient load in lean and obese individuals and whether their microbiota are correlated with the efficiency of dietary energy harvest.



We investigated dynamic changes of gut microbiota during diets that varied in caloric content (2400 compared with 3400 kcal/d) by pyrosequencing bacterial 16S ribosomal RNA (rRNA) genes present in the feces of 12 lean and 9 obese individuals and by measuring ingested and stool calories with the use of bomb calorimetry.



The alteration of the nutrient load induced rapid changes in the gut microbiota. These changes were directly correlated with stool energy loss in lean individuals such that a 20% increase in Firmicutes and a corresponding decrease in Bacteroidetes were associated with an increased energy harvest of ≈150 kcal. A high degree of overfeeding in lean individuals was accompanied by a greater fractional decrease in stool energy loss.



These results show that the nutrient load is a key variable that can influence the gut (fecal) bacterial community structure over short time scales. Furthermore, the observed associations between gut microbes and nutrient absorption indicate a possible role of the human gut microbiota in the regulation of the nutrient harvest.
Mechanical construction and analysis of an axial flux segmented armature torus machine

This paper deals with the mechanical construction and static strength analysis of an axial flux permanent magnet machine with segmented armature torus topology, which consists of two external rotors and an inner stator. In order to conduct the three dimensional magnetic flux, the soft magnetic composites is used to manufacture the stator segments and the rotor yoke. On the basis of the detailed electromagnetic analysis, the main geometric dimensions of the machine are determined, which is also the precondition of the mechanical construction. Through the application of epoxy with high thermal conductivity and high mechanical strength, the independent segments of the stator are bounded together with the liquid-cooling system, which makes a high electrical load possible. Due to the unavoidable errors in the manufacturing and montage, there might be large force between the rotors and the stator. Thus, the rotor is held with a rotor carrier made from aluminum alloy with high elastic modulus and the form of the rotor carrier is optimized, in order to reduce the axial deformation. In addition, the shell and the shaft are designed and the choice of bearings is discussed. Finally, the strain and deformation of different parts are analyzed with the help of finite element method to validate the mechanical construction.
Characteristics of high performing testers: a case study

We studied what are the characteristics of high performing software testers in the industry.  We conducted an exploratory case study, collecting data through recorded interviews of one development manager and three testers in each of the three companies, analysis of the defect database, and informal communication within our research partnership with the companies.  We found that experience, reflection, motivation and personal characteristics were the top level themes. Experience related to the domain, e.g. processes of the customer, and on the other hand, specialized technical skills, e.g. performance testing, were seen more important than skills of test case design and test planning.
Which cultural values matter to business process management?: Results from a global Delphi study

Business Process Management (BPM) requires a holistic perspective that includes managing the culture of an organization to achieve objectives of efficient and effective business processes. Still, the specifics of a BPM-supportive organizational culture have not been examined so far. Thus, the purpose of our paper is to identify the characteristics of a cultural setting supportive of BPM objectives.  We examine the constituent values of a BPM-supportive cultural setting through a global Delphi study with BPM experts from academia and practice and explore these values in a cultural value framework.  We empirically identify and define four key cultural values supporting BPM, viz., customer orientation, excellence, responsibility, and teamwork. We discuss the relationships between these values and identify a particular challenge in managing these seemingly competing values.  The identification and definition of these values represents a first step towards the operationalization (and empirical analysis) of what has been identified as the concept of BPM culture, i.e. a culture supportive of achieving BPM objectives.  Identifying these cultural values provides the basis for developing an instrument that can measure how far an existing cultural context is supportive of BPM. This, in turn, is fundamental for identifying measures towards achieving a BPM culture as a necessary, yet not sufficient means to obtain BPM success.  We examine which cultural values create an environment receptive for BPM and, thus, specify the important theoretical construct BPM culture. In addition, we raise awareness for realizing these values in a BPM context.
Reusability and maintainability metrics for object-oriented software

In this paper, a quality model for object-oriented software and an automated metric tool, Reconfigurable Automated Metrics for Object-Oriented Software (RAMOOS) are proposed. The quality model is targeted at the maintainability and reusability aspects of software which can be effectively predicted from the source code. RAMOOS assists users in applying customized quality model during the development of software. In the beginning of adopting RAMOOS, a user may need to use his intuition to select or modify a system-recommended metric model to fit his specific software project needs. If the initial metrics do not meet the expectation, the user can retrive the saved intermediate results and perform further modification to the metric model. The verified model can then be applied to future similar projects.
Thermal-Visible Video Fusion for Moving Target Tracking and Pedestrian Classification

The paper presents a fusion-tracker and pedestrian classifier for color and thermal cameras. The tracker builds a background model as a multi-modal distribution of colors and temperatures. It is constructed as a particle filter that makes a number of informed reversible transformations to sample the model probability space in order to maximize posterior probability of the scene model. Observation likelihoods of moving objects account their 3D locations with respect to the camera and occlusions by other tracked objects as well as static obstacles. After capturing the coordinates and dimensions of moving objects we apply a pedestrian classifier based on periodic gait analysis. To separate humans from other moving objects, such as cars, we detect, in human gait, a symmetrical double helical pattern, that can then be analyzed using the Frieze Group theory. The results of tracking on color and thermal sequences demonstrate that our algorithm is robust to illumination noise and performs well in the outdoor environments.
A generalized confusion matrix for assessing area estimates from remotely sensed data

The formulation of a generalized area-based confusion matrix for exploring the accuracy of area estimates is presented. The generalized confusion matrix is appropriate for both traditional classi cation algorithms and sub-pixel area estimation models. An error matrix, derived from the generalized confusion matrix, allows the accuracy of maps generated using area estimation models to be assessed quantitatively and compared to the accuracies obtained from traditional classi cation techniques. The application of this approach is demonstrated for an area estimation model applied to Landsat data of an urban area of the United Kingdom.
Redundancy Resolution of the Human Arm and an Upper Limb Exoskeleton

The human arm has 7 degrees of freedom (DOF) while only 6 DOF are required to position the wrist and orient the palm. Thus, the inverse kinematics of an human arm has a nonunique solution. Resolving this redundancy becomes critical as the human interacts with a wearable robot and the inverse kinematics solution of these two coupled systems must be identical to guarantee an seamless integration. The redundancy of the arm can be formulated by defining the swivel angle, the rotation angle of the plane defined by the upper and lower arm around a virtual axis that connects the shoulder and wrist joints. Analyzing reaching tasks recorded with a motion capture system indicates that the swivel angle is selected such that when the elbow joint is flexed, the palm points to the head. Based on these experimental results, a new criterion is formed to resolve the human arm redundancy. This criterion was implemented into the control algorithm of an upper limb 7-DOF wearable robot. Experimental results indicate that by using the proposed redundancy resolution criterion, the error between the predicted and the actual swivel angle adopted by the motor control system is less then 5°.
Architectural geometry

Around 2005 it became apparent in the geometry processing community that freeform architecture contains many problems of a geometric nature to be solved, and many opportunities for optimization which however require geometric understanding. This area of research, which has been called architectural geometry, meanwhile contains a great wealth of individual contributions which are relevant in various fields. For mathematicians, the relation to discrete differential geometry is significant, in particular the integrable system viewpoint. Besides, new application contexts have become available for quite some old-established concepts. Regarding graphics and geometry processing, architectural geometry yields interesting new questions but also new objects, e.g. replacing meshes by other combinatorial arrangements. Numerical optimization plays a major role but in itself would be powerless without geometric understanding. Summing up, architectural geometry has become a rewarding field of study. We here survey the main directions which have been pursued, we show real projects where geometric considerations have played a role, and we outline open problems which we think are significant for the future development of both theory and practice of architectural geometry.
From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation

Competitions for self-driving cars facilitated the development and research in the domain of autonomous vehicles towards potential solutions for the future mobility.  Miniature vehicles can bridge the gap between simulation-based evaluations of algorithms relying on simplified models, and those time-consuming vehicle tests on real-scale proving grounds.  This article combines findings from a systematic literature review, an in-depth analysis of results and technical concepts from contestants in a competition for self-driving miniature cars, and experiences of participating in the 2013 competition for self-driving cars.  A simulation-based development platform for real-scale vehicles has been adapted to support the development of a self-driving miniature car. Furthermore, a standardized platform was designed and realized to enable research and experiments in the context of future mobility solutions.  A clear separation between algorithm conceptualization and validation in a model-based simulation environment enabled efficient and riskless experiments and validation. The design of a reusable, low-cost, and energy-efficient hardware architecture utilizing a standardized software/hardware interface enables experiments, which would otherwise require resources like a large real-scale
The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions.

To test the feasibility of creating a valid and reliable checklist with the following features: appropriate for assessing both randomised and non-randomised studies; provision of both an overall score for study quality and a profile of scores not only for the quality of reporting, internal validity (bias and confounding) and power, but also for external validity.



A pilot version was first developed, based on epidemiological principles, reviews, and existing checklists for randomised studies. Face and content validity were assessed by three experienced reviewers and reliability was determined using two raters assessing 10 randomised and 10 non-randomised studies. Using different raters, the checklist was revised and tested for internal consistency (Kuder-Richardson 20), test-retest and inter-rater reliability (Spearman correlation coefficient and sign rank test; kappa statistics), criterion validity, and respondent burden.



The performance of the checklist improved considerably after revision of a pilot version. The Quality Index had high internal consistency (KR-20: 0.89) as did the subscales apart from external validity (KR-20: 0.54). Test-retest (r 0.88) and inter-rater (r 0.75) reliability of the Quality Index were good. Reliability of the subscales varied from good (bias) to poor (external validity). The Quality Index correlated highly with an existing, established instrument for assessing randomised studies (r 0.90). There was little difference between its performance with non-randomised and with randomised studies. Raters took about 20 minutes to assess each paper (range 10 to 45 minutes).



This study has shown that it is feasible to develop a checklist that can be used to assess the methodological quality not only of randomised controlled trials but also non-randomised studies. It has also shown that it is possible to produce a checklist that provides a profile of the paper, alerting reviewers to its particular methodological strengths and weaknesses. Further work is required to improve the checklist and the training of raters in the assessment of external validity.