nils-herrmann/clean_scirepeval huggingface dataset:
Ecological correlations and the behavior of individuals.

AN INDIVIDUAL CORRELATION is a correlation in which the statistical object or thing described is indivisible. The correlation between color and illiteracy for persons in the United States, shown later in Table I, is an individual correlation, because the kind of thing described is an indivisible unit, a person. In an individual correlation the variables are descriptive properties of individuals, such as height, income, eye color, or race, and not descriptive statistical constants such as rates or means. In an ecological correlation the statistical object is a group of persons. The correlation between the percentage of the population which is Negro and the percentage of the population which is illiterate for the 48 states, shown later as Figure 2, is an ecological correlation. The thing described is the population of a state, and not a single individual. The variables are percentages, descriptive properties of groups, and not descriptive properties of individuals. Ecological correlations are used in an impressive number of quantitative sociological studies, some of which by now have attained the status of classics: Cowles’ ‘‘Statistical Study of Climate in Relation to Pulmonary Tuberculosis’’; Gosnell’s ‘‘Analysis of the 1932 Presidential Vote in Chicago,’’ Factorial and Correlational Analysis of the 1934 Vote in Chicago,’’ and the more elaborate factor analysis in Machine Politics; Ogburn’s ‘‘How women vote,’’ ‘‘Measurement of the Factors in the Presidential Election of 1928,’’ ‘‘Factors in the Variation of Crime Among Cities,’’ and Groves and Ogburn’s correlation analyses in American Marriage and Family Relationships; Ross’ study of school attendance in Texas; Shaw’s Delinquency Areas study of the correlates of delinquency, as well as The more recent analyses in Juvenile Delinquency in Urban Areas; Thompson’s ‘‘Some Factors Influencing the Ratios of Children to Women in American Cities, 1930’’; Whelpton’s study of the correlates of birth rates, in ‘‘Geographic and Economic Differentials in Fertility;’’ and White’s ‘‘The Relation of Felonies to Environmental Factors in Indianapolis.’’ Although these studies and scores like them depend upon ecological correlations, it is not because their authors are interested in correlations between the properties of areas as such. Even out-and-out ecologists, in studying delinquency, for example, rely primarily upon data describing individuals, not areas. In each study which uses ecological correlations, the obvious purpose is to discover something about the behavior of individuals. Ecological correlations are used simply because correlations between the properties of individuals are not available. In each instance, however, the substitution is made tacitly rather than explicitly. The purpose of this paper is to clarify the ecological correlation problem by stating, mathematically, the exact relation between ecological and individual correlations, and by showing the bearing of that relation upon the practice of using ecological correlations as substitutes for individual correlations.
The metabolic cost of neural information

We derive experimentally based estimates of the energy used by neural mechanisms to code known quantities of information. Biophysical measurements from cells in the blowfly retina yield estimates of the ATP required to generate graded (analog) electrical signals that transmit known amounts of information. Energy consumption is several orders of magnitude greater than the thermodynamic minimum. It costs 104 ATP molecules to transmit a bit at a chemical synapse, and 106 - 107 ATP for graded signals in an interneuron or a photoreceptor, or for spike coding. Therefore, in noise-limited signaling systems, a weak pathway of low capacity transmits information more economically, which promotes the distribution of information among multiple pathways.
A New ZVS-PWM Full-Bridge Converter

A full-bridge converter which employs a coupled inductor to achieve zero-voltage switching of the primary switches in the entire line and load range is described. Because the coupled inductor does not appear as a series inductance in the load current path, it does not cause a loss of duty cycle or severe voltage ringing across the output rectifier. The operation and performance of the proposed converter is verified on a 670-W prototype.
Preconditioned Stochastic Gradient Descent

Stochastic gradient descent (SGD) still is the workhorse for many practical problems. However, it converges slow, and can be difficult to tune. It is possible to precondition SGD to accelerate its convergence remarkably. But many attempts in this direction either aim at solving specialized problems, or result in significantly more complicated methods than SGD. This paper proposes a new method to adaptively estimate a preconditioner, such that the amplitudes of perturbations of preconditioned stochastic gradient match that of the perturbations of parameters to be optimized in a way comparable to Newton method for deterministic optimization. Unlike the preconditioners based on secant equation fitting as done in deterministic quasi-Newton methods, which assume positive definite Hessian and approximate its inverse, the new preconditioner works equally well for both convex and nonconvex optimizations with exact or noisy gradients. When stochastic gradient is used, it can naturally damp the gradient noise to stabilize SGD. Efficient preconditioner estimation methods are developed, and with reasonable simplifications, they are applicable to large-scale problems. Experimental results demonstrate that equipped with the new preconditioner, without any tuning effort, preconditioned SGD can efficiently solve many challenging problems like the training of a deep neural network or a recurrent neural network requiring extremely long-term memories.
Anatomy of an extremely fast LVCSR decoder

We report in detail the decoding strategy that we used for the past two Darpa Rich Transcription evaluations (RT’03 and RT’04) which is based on finite state automata (FSA). We discuss the format of the static decoding graphs, the particulars of our Viterbi implementation, the lattice generation and the likelihood evaluation.  Experimental results are given on the EARS database (English conversational telephone speech) with emphasis on our faster than real-time system.
Multiple Instance Learning Convolutional Neural Networks for object recognition

Convolutional Neural Networks (CNN) have demonstrated its successful applications in computer vision, speech recognition, and natural language processing. For object recognition, CNNs might be limited by its strict label requirement and an implicit assumption that images are supposed to be target-object-dominated for optimal solutions. However, the labeling procedure, necessitating laying out the locations of target objects, is very tedious, making high-quality large-scale dataset prohibitively expensive. Data augmentation schemes are widely used when deep networks suffer the insufficient training data problem. All the images produced through data augmentation share the same label, which may be problematic since not all data augmentation methods are label-preserving. In this paper, we propose a weakly supervised CNN framework named Multiple Instance Learning Convolutional Neural Networks (MILCNN) to solve this problem. We apply MILCNN framework to object recognition and report state-of-the-art performance on three benchmark datasets: CIFAR10, CIFAR100 and ILSVRC2015 classification dataset.
Re-evaluating Automatic Summarization with BLEU and 192 Shades of ROUGE

We provide an analysis of current evaluation methodologies applied to summarization metrics and identify the following areas of concern: (1) movement away from evaluation by correlation with human assessment; (2) omission of important components of human assessment from evaluations, in addition to large numbers of metric variants; (3) absence of methods of significance testing improvements over a baseline. We outline an evaluation methodology that overcomes all such challenges, providing the first method of significance testing suitable for evaluation of summarization metrics. Our evaluation reveals for the first time which metric variants significantly outperform others, optimal metric variants distinct from current recommended best variants, as well as machine translation metric BLEU to have performance on-par with ROUGE for the purpose of evaluation of summarization systems. We subsequently replicate a recent large-scale evaluation that relied on, what we now know to be, suboptimal ROUGE variants revealing distinct conclusions about the relative performance of state-of-the-art summarization systems.
New Robotics: Design Principles for Intelligent Systems

New robotics is an approach to robotics that, in contrast to traditional robotics, employs ideas and principles from biology. While in the traditional approach there are generally accepted methods (e.g., from control theory), designing agents in the new robotics approach is still largely considered an art. In recent years, we have been developing a set of heuristics, or design principles, that on the one hand capture theoretical insights about intelligent (adaptive) behavior, and on the other provide guidance in actually designing and building systems. In this article we provide an overview of all the principles but focus on the principles of ecological balance, which concerns the relation between environment, morphology, materials, and control, and sensory-motor coordination, which concerns self-generated sensory stimulation as the agent interacts with the environment and which is a key to the development of high-level intelligence. As we argue, artificial evolution together with morphogenesis is not only nice to have but is in fact a necessary tool for designing embodied agents.
Tangible user interface for chemistry education: comparative evaluation and re-design

Augmented Chemistry (AC) is an application that utilizes a tangible user interface (TUI) for organic chemistry education. The empirical evaluation described in this paper compares learning effectiveness and user acceptance of AC versus the more traditional ball-and-stick model (BSM). Learning effectiveness results were almost the same for both learning environments. User preference and rankings, using NASA-TLX and SUMI, showed more differences and it was therefore decided to focus mainly on improving these aspects in a re-design of the AC system. For enhanced interaction, keyboard-free system configuration, and internal/external database (DB) access, a graphical user interface (GUI) has been incorporated into the TUI. Three-dimensional (3D) rendering has also been improved using shadows and related effects, thereby enhancing depth perception. The re-designed AC system was then compared to the old system by means of a small qualitative user study. This user study showed an improvement in subjective opinions a out the system's ease of use and ease of learning.
Longitudinal neuroanatomical changes determined by deformation-based morphometry in a mouse model of Alzheimer's disease

Magnetic resonance imaging (MRI) of transgenic mice has the potential to provide valuable insight into the complex mechanisms underlying Alzheimer's disease (AD). Quantification of pathological changes is typically performed using manual segmentation methods, and requires a priori hypotheses about anatomical structures for volumetric measurement. Alternatively, deformation-based morphometry (DBM) has been shown to be a powerful, automated technique for detecting anatomical differences between populations by examining the deformation fields used to nonlinearly warp MR images. In this multiple timepoint, in vivo study, we have applied an automated, unbiased technique for the creation of a nonlinear, population-specific reference space from which robust DBM analysis can be performed. A general, linear mixed-effects model framework was developed to follow the evolution of structural changes in mouse brain from 2.5 to 9 months of age, and to examine neuroanatomical differences between a transgenic (TG) APP/PS1 murine model of AD and wild-type (WT) littermates. Morphometric abnormalities in the TG group were localized to regions of the hippocampus, cortex, olfactory bulbs, stria terminalis, brain stem, cerebellum, and ventricles. Although volumetric reductions were detected in TG mice, no general brain atrophy was found, suggesting a developmental, rather than a degenerative, pathological process. Finally, we established a strong correlation between a DBM summary measure and manually segmented volumes for each image in the dataset. These results support the utility of DBM to study longitudinal morphological changes in mouse models of central nervous system diseases in an automated and exploratory fashion.
Modeling Users' Preferences and Social Links in Social Networking Services: A Joint-Evolving Perspective

Researchers have long converged that the evolution of a Social Networking Service (SNS) platform is driven by the interplay between users’ preferences (reflected in user-item consumption behavior) and the social network structure (reflected in user-user interaction behavior), with both kinds of users’ behaviors change from time to time. However, traditional approaches either modeled these two kinds of behaviors in an isolated way or relied on a static assumption of a SNS. Thus, it is still unclear how do the roles of users’ historical preferences and the dynamic social network structure affect the evolution of SNSs. Furthermore, can jointly modeling users’ temporal behaviors in SNSs benefit both behavior prediction tasks? In this paper, we leverage the underlying social theories (i.e., social influence and the homophily effect) to investigate the interplay and evolution of SNSs. We propose a probabilistic approach to fuse these social theories for jointly modeling users’ temporal behaviors in SNSs. Thus our proposed model has both the explanatory ability and predictive power. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed model.
Data-driven grasping

This paper propose a novel framework for a data driven grasp planner that indexes partial sensor data into a database of 3D models with known grasps and transfers grasps from those models to novel objects. We show how to construct such a database and also demonstrate multiple methods for matching into it, aligning the matched models with the known sensor data of the object to be grasped, and selecting an appropriate grasp to use. Our approach is experimentally validated in both simulated trials and trials with robots.
Amplification Hell: Revisiting Network Protocols for DDoS Abuse

In distributed reflective denial-of-service (DRDoS) attacks, adversaries send requests to public servers (e.g., open recursive DNS resolvers) and spoof the IP address of a victim. These servers, in turn, flood the victim with valid responses and – unknowingly – exhaust its bandwidth. Recently, attackers launched DRDoS attacks with hundreds of Gb/s bandwidth of this kind. While the attack technique is well-known for a few protocols such as DNS, it is unclear if further protocols are vulnerable to similar or worse attacks. In this paper, we revisit popular UDP-based protocols of network services, online games, P2P filesharing networks and P2P botnets to assess their security against DRDoS abuse. We find that 14 protocols are susceptible to bandwidth amplification and multiply the traffic up to a factor 4670. In the worst case, attackers thus need only 0.02% of the bandwidth that they want their victim(s) to receive, enabling far more dangerous attacks than what is known today. Worse, we identify millions of public hosts that can be abused as amplifiers. We then analyze more than 130 real-world DRDoS attacks. For this, we announce bait services to monitor their abuse and analyze darknet as well as network traffic from large ISPs. We use traffic analysis to detect both, victims and amplifiers, showing that attackers already started to abuse vulnerable protocols other than DNS. Lastly, we evaluate countermeasures against DRDoS attacks, such as preventing spoofing or hardening protocols and service configurations. We shows that carefully-crafted DRDoS attacks may evade poorly-designed rate limiting solutions. In addition, we show that some attacks evade packet-based filtering techniques, such as port-, contentor length-based filters.
CULTURE AND ENTREPRENEURIAL POTENTIAL : A NINE COUNTRY STUDY OF LOCUS OF CONTROL AND INNOVATIVENESS

EXECUTIVE SUMMARY Entrepreneurship research has identified a number of personal characteristics believed to be instrumental in motivating entrepreneurial behavior. Two frequently cited personal traits associated with entrepreneurial potential are internal locus of control and innovativeness. Internal locus of control has been one of the most studied psychological traits in entrepreneurship research, while innovative activity is explicit in Schumpeter’s description of the entrepreneur. Entrepreneurial traits have been studied extensively in the United States. However, crosscultural studies and studies in non-U.S. contexts are rare and in most cases limited to comparisons between one or two countries or cultures. Thus the question is raised: do entrepreneurial traits vary systematically across cultures and if so, why? Culture, as the underlying system of values peculiar to a specific group or society, shapes the development of certain personality traits and motivates individuals in a society to engage in behaviors that may not be evident in other societies. Hofstede’s (1980) extensive culture study, leading to the development of four culture dimensions, provide a clear articulation of differences between countries in values, beliefs,
Modeling of multiversion concurrency control system using Event-B

Concurrency control in a database system involves the activity of controlling the relative order of conflicting operations, thereby ensuring database consistency. Multiversion concurrency control is timestamp based protocol that can be used to schedule the operations to maintain the consistency of the databases. In this protocol each write on a data item produces a new copy (or version) of that data item while retaining the old version. A systematic approach to specification is essential for the production of any substantial system description. Formal methods are mathematical technique that provide systematic approach for building and verification of model. We have used Event-B as a formal technique for construction of our model. Event-B provides complete framework by rigorous description of problem at abstract level and discharge of proof obligations arising due to consistency checking. In this paper, we outline formal construction of model of multiversion concurrency control scheme for database transactions using Event-B.
Design of HyQ – a Hydraulically and Electrically Actuated Quadruped Robot

— A new versatile Hydraulically-powered Quadruped robot (HyQ) has been developed to serve as a platform to study not only highly dynamic motions such as running and jumping, but also careful navigation over very rough terrain. HyQ stands 1 meter tall, weighs roughly 90kg and features 12 torque-controlled joints powered by a combination of hydraulic and electric actuators. The hydraulic actuation permits the robot to perform powerful and dynamic motions that are hard to achieve with more traditional electrically actuated robots. This paper describes design and specifications of the robot and presents details on the hardware of the quadruped platform, such as the mechanical design of the four articulated legs and of the torso frame, and the configuration of the hydraulic power system. Results from the first walking experiments are presented along with test studies using a previously built prototype leg. 1 INTRODUCTION The development of mobile robotic platforms is an important and active area of research. Within this domain, the major focus has been to develop wheeled or tracked systems that cope very effectively with flat and well-structured solid surfaces (e.g. laboratories and roads). In recent years, there has been considerable success with robotic vehicles even for off-road conditions . However, wheeled robots still have major limitations and difficulties in navigating uneven and rough terrain. These limitations and the capabilities of legged animals encouraged researchers for the past decades to focus on the construction of biologically inspired legged machines. These robots have the potential to outperform the more traditional designs with wheels and tracks in terms of mobility and versatility. The vast majority of the existing legged robots have been, and continue to be, actuated by electric motors with high gear-ratio reduction drives, which are popular because of their size, price, ease of use and accuracy of control. However, electric motors produce small torques relative to their size and weight, thereby making reduction drives with high ratios essential to convert velocity into torque. Unfortunately, this approach results in systems with reduced speed capability and limited passive back-driveability and therefore not very suitable for highly dynamic motions and interactions with unforeseen terrain variance. Significant examples of such legged robots are: the biped series of HRP robots , Toyota humanoid robot , and Honda's Asimo ; and the quadruped robot series of Hirose et al. , Sony's AIBO  and Little Dog [7]. In combination with high position gain control and …
Unsupervised document structure analysis of digital scientific articles

Text mining and information retrieval in large collections of scientific literature require automated processing systems that analyse the documents’ content. However, the layout of scientific articles is highly varying across publishers, and common digital document formats are optimised for presentation, but lack structural information. To overcome these challenges, we have developed a processing pipeline that analyses the structure a PDF document using a number of unsupervised machine learning techniques and heuristics. Apart from the meta-data extraction, which we reused from previous work, our system uses only information available from the current document and does not require any pre-trained model. First, contiguous text blocks are extracted from the raw character stream. Next, we determine geometrical relations between these blocks, which, together with geometrical and font information, are then used categorize the blocks into different classes. Based on this resulting logical structure we finally extract the body text and the table of contents of a scientific article. We separately evaluate the individual stages of our pipeline on a number of different datasets and compare it with other document structure analysis approaches. We show that it outperforms a state-of-the-art system in terms of the quality of the extracted body text and table of contents. Our unsupervised approach could provide a basis for advanced digital library scenarios that involve diverse and dynamic corpora.
ClouNS - a Cloud-Native Application Reference Model for Enterprise Architects

The capability to operate cloud-native applications can generate enormous business growth and value. But enterprise architects should be aware that cloud-native applications are vulnerable to vendor lock-in. We investigated cloud-native application design principles, public cloud service providers, and industrial cloud standards. All results indicate that most cloud service categories seem to foster vendor lock-in situations which might be especially problematic for enterprise architectures. This might sound disillusioning at first. However, we present a reference model for cloud-native applications that relies only on a small subset of well standardized IaaS services. The reference model can be used for codifying cloud technologies. It can guide technology identification, classification, adoption, research and development processes for cloud-native application and for vendor lock-in aware enterprise architecture engineering methodologies.