nils-herrmann/clean_scirepeval huggingface dataset:
Interpreting and Unifying Outlier Scores

Outlier scores provided by different outlier models differ widely in their meaning, range, and contrast between different outlier models and, hence, are not easily comparable or interpretable. We propose a unification of outlier scores provided by various outlier models and a translation of the arbitrary “outlier factors” to values in the range [0, 1] interpretable as values describing the probability of a data object of being an outlier. As an application, we show that this unification facilitates enhanced ensembles for outlier detection.
The pains and pleasures of parenting: when, why, and how is parenthood associated with more or less well-being?

The relationship between parenthood and well-being has become a hot topic among scholars, media, and general public alike. The research, however, has been mixed-some studies indicate that parents are happier than nonparents, whereas others suggest the reverse. We suggest that the question of whether parents are more or less happy than their childless peers is not the most meaningful one. To reconcile the conflicting literature and expand understanding of the emotional experience of parenthood, we present a model of parents' well-being that describes why and how parents experience more or less happiness than nonparents (i.e., mediators of the link between parenthood and well-being). We then apply this model to explain when parents are more likely to experience more or less happiness (i.e., moderators of parents' well-being, such as parent age or child temperament). Supporting our model, we review 3 primary methodological approaches: studies comparing parents and nonparents, studies examining changes in well-being across the transition to parenthood, and studies comparing parents' experiences while with their children to their other daily activities. Our review suggests that the relationship between parenthood and well-being is highly complex. We propose that parents are unhappy to the extent that they encounter relatively greater negative emotions, magnified financial problems, more sleep disturbance, and troubled marriages. By contrast, when parents experience greater meaning in life, satisfaction of their basic needs, greater positive emotions, and enhanced social roles, they are met with happiness and joy.
Practical Lattice-Based Cryptography: A Signature Scheme for Embedded Systems

Nearly all of the currently used and well-tested signature schemes (e.g. RSA or DSA) are based either on the factoring assumption or the presumed intractability of the discrete logarithm problem. Further algorithmic advances on these problems may lead to the unpleasant situation that a large number of schemes have to be replaced with alternatives. In this work we present such an alternative – a signature scheme whose security is derived from the hardness of lattice problems. It is based on recent theoretical advances in lattice-based cryptography and is highly optimized for practicability and use in embedded systems. The public and secret keys are roughly 12000 and 2000 bits long, while the signature size is approximately 9000 bits for a security level of around 100 bits. The implementation results on reconfigurable hardware (Spartan/Virtex 6) are very promising and show that the scheme is scalable, has low area consumption, and even outperforms some classical schemes.
A Benchmark for Automatic Acral Melanoma Preliminary Screening

Despite recent progress in artificial intelligence and computer vision, the lack of data for acral melanoma, a particular type of skin disease under nails, makes it difficult to develop its automatic visual diagnosis system. This paper introduces a large data set of dermoscopic images for acral melanoma, which is annotated by senior dermatologists. It contains 6,066 images of two categories: subungual hematomas and other acral melanoma symptoms of malignant tendency (i.e., just bleeding under nails or more critical disorder that requires treatment). We hope this benchmark data set will encourage further research on acral melanoma recognition and will continue to maintain this data set to better serve it. We address the classification task using various computer vision algorithms from conventional techniques to cutting edge deep learning. Currently, we achieve 0.928 accuracy.
BUSINESS PROCESS REENGINEERING METHOD VERSUS KAIZEN METHOD

The essence of this paper is the comparison of the Business Process Reengineering method (BPR) and Kaizen method. The BPR method is defined by Hammer and Champy as “the fundamental reconsideration and radical redesign of organizational processes, in order to achieve drastic improvement of current performance in cost, service and speed”. At it’s turn, the Kaizen method is an management concept for incremental change. The key elements of Kaizen are quality, effort, involvement of all employees, willingness to change and communication. When BPR is compared with Kaizen method, the BPR is harder to implement, technology – oriented, enables radical change. On the other hand, Kaizen method is easier to implement, is more people – oriented and requires long term discipline.
The hippocampus as a cognitive map

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Design-Space Exploration in MDE: An Initial Pattern Catalogue

A designer often has to evaluate alternative designs during the development of a system. A multitude of Design-Space Exploration (DSE) techniques exist in the literature. Integration of these techniques into the modelling paradigm is needed when a model-driven engineering approach is used for designing systems. To a greater or lesser extent, the integration of those different DSE techniques share characteristics with each other. Inspired by software design patterns, we introduce an initial pattern catalogue to categorise the embedding of different DSE techniques in an MDE context. We demonstrate their use by a literature survey and discuss the consequences of each pattern.
The inconspicuous penis in children

The term 'inconspicuous penis' refers to a group of anatomical abnormalities in which the penis looks smaller than is expected. Micropenis can be defined as 'true micropenis'—which results from a defect in the hypothalamic–pituitary–gonadal axis—and 'micropenis secondary to congenital anatomical anomalies of the surrounding and overlying structures'—also known as 'concealed penis'. The different forms of concealed penis include webbed penis, congenital megaprepuce and partially hidden penis caused by prepubic adiposity. This disorder can also have iatrogenic causes resulting from adhesions that are secondary to circumcision—this type of concealed penis is known as 'trapped penis'. However, in both groups, micropenis is defined as a stretched penile length that is at least 2.5 SD below the mean for the patient's age, but without any other penile defects. Patients with true micropenis can be managed with testosterone, which has demonstrated good penile elongation results in the long term. Surgery also has a pivotal role in reconstruction for elongating the penis and for correction of anatomical abnormalities in concealed penis.
CONIKS: Bringing Key Transparency to End Users

Public keys must be distributed securely even in the presence of attackers. This is known as the Public Key Infrastructure problem or PKI. This problem arises in applications such as secure messaging and web surfing/https where a man in the middle can intercept public keys and return erroneous public keys. Secure communication today depends on placing trust in a few centralized certificate authorities via SSL/TLS protocols depicted below for completeness. However, an attacker can take advantage of the following weaknesses.
Rey-Osterrieth Complex Figure - copy and immediate recall: Normative data for the Latin American Spanish speaking adult population.

To generate normative data on the Rey-Osterrieth Complex Figure Test (ROCF) across 11 countries in Latin America, with country-specific adjustments for gender, age, and education, where appropriate.



The sample consisted of 3,977 healthy adults who were recruited from Argentina, Bolivia, Chile, Cuba, El Salvador, Guatemala, Honduras, Mexico, Paraguay, Peru, and, Puerto Rico. Each subject was administered the ROCF as part of a larger neuropsychological battery. A standardized five-step statistical procedure was used to generate the norms.



The final multiple linear regression models explained 7-34% of the variance in ROCF copy scores and 21-41% of the variance in immediate recall scores. Although t-tests showed significant differences between men and women on ROCF copy and immediate recall scores, none of the countries had an effect size larger than 0.3. As a result, gender-adjusted norms were not generated.



The present study is the first to create norms for the ROCF in Latin America. As a result, this study will have important implications for the formation and practice of neuropsychology in this region.
Recursive Neural Networks for Learning Logical Semantics

Supervised recursive neural network models (RNNs) for sent ence meaning have been successful in an array of sophisticated language tasks , but it remains an open question whether they can learn compositional semantic gra mm rs that support logical deduction. We address this question directly by for the first time evaluating whether each of two classes of neural model — plain RNNs and re cursive neural tensor networks (RNTNs) — can correctly learn relationship such as entailment and contradiction between pairs of sentences, where we have generated controlled data sets of sentences from a logical grammar. Our first exper iment evaluates whether these models can learn the basic algebra of logical r elations involved. Our second and third experiments extend this evaluation to c omplex recursive structures and sentences involving quantification. We find t hat he plain RNN achieves only mixed results on all three experiments, where as the stronger RNTN model generalizes well in every setting and appears capable of learning suitable representations for natural language logical inference.
SICE: a hardware-level strongly isolated computing environment for x86 multi-core platforms

SICE is a novel framework to provide hardware-level isolation and protection for sensitive workloads running on x86 platforms in compute clouds. Unlike existing isolation techniques, SICE does not rely on any software component in the host environment (i.e., an OS or a hypervisor). Instead, the security of the isolated environments is guaranteed by a trusted computing base that only includes the hardware, the BIOS, and the System Management Mode (SMM). SICE provides fast context switching to and from an isolated environment, allowing isolated workloads to time-share the physical platform with untrusted workloads. Moreover, SICE supports a large range (up to 4GB) of isolated memory. Finally, the most unique feature of SICE is the use of multicore processors to allow the isolated environments to run concurrently and yet securely beside the untrusted host. We have implemented a SICE prototype using an AMD x86 hardware platform. Our experiments show that SICE performs fast context switching (67 microseconds) to and from the isolated environment and that it imposes a reasonable overhead (3% on all but one benchmark) on the operation of an isolated Linux virtual machine. Our prototype demonstrates that, subject to a careful security review of the BIOS software and the SMM hardware implementation, current hardware architecture already provides abstractions that can support building strong isolation mechanisms using a very small SMM software foundation of about 300 lines of code.
Neural Approaches to Conversational AI

This tutorial surveys neural approaches to conversational AI that were developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) social bots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and challenges we are facing, using specific systems and models as case studies.
AQuPR: Attention based Query Passage Retrieval

Search queries issued over the Web increasingly look like questions, especially as the domain becomes more specific. Finding good response to such queries amounts to finding relevant passages from Web documents. Traditional information retrieval based Web search still matches the query to the words in the entire document. With the advent of machine reading comprehension techniques, Web search is moving more towards identifying the best sentence / group of sentences in the document. We present AQuPR an A ttention based Qu ery P assage R etrieval system to find human acceptable answer containing passages to technology queries issued over the Web. We train character level embeddings for the query and passage pairs, train a deep recurrent network with a novel simplified attention mechanism and incorporate additional signals present in Web documents to improve the performance of such a system. We collect a database of human issued queries along with their answer passages and learn an end to end system to enable automated query resolution. We present results for answering human issued search queries which show considerable promise against basic versions of current generation question answering systems.
Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings

Neural networks achieve the state-of-the-art in image classification tasks. However, they can encode spurious variations or biases that may be present in the training data. For example, training an age predictor on a dataset that is not balanced for gender can lead to gender biased predicitons (e.g. wrongly predicting that males are older if only elderly males are in the training set). We present two distinct contributions: 1) An algorithm that can remove multiple sources of variation from the feature representation of a network. We demonstrate that this algorithm can be used to remove biases from the feature representation, and thereby improve classification accuracies, when training networks on extremely biased datasets. 2) An ancestral origin database of 14,000 images of individuals from East Asia, the Indian subcontinent, sub-Saharan Africa, and Western Europe. We demonstrate on this dataset, for a number of facial attribute classification tasks, that we are able to remove racial biases from the network feature representation.
Historical Document Layout Analysis Competition

This paper presents an objective comparative evaluation of layout analysis methods for scanned historical documents. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2011 and the International Workshop on Historical Document Imaging and Processing (HIP2011), presenting the results of the evaluation of four submitted methods. A commercial state-of-the-art system is also evaluated for comparison. Two scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and the other evaluating the whole pipeline of segmentation and region classification (with a text extraction goal). The results indicate that there is a convergence to a certain methodology with some variations in the approach. However, there is still a considerable need to develop robust methods that deal with the idiosyncrasies of historical documents.
Can Parenting Styles Affect the Children ’ s Development of Narcissism ? A Systematic Review

The aim of this paper is to define whether different types of parenting styles (and which ones) affect the child's development in the direction of narcissism, through a systematic review of the studies on the subject in the literature, considering only research published from the Nineties to today. The ten studies considered in this review are representative of the main approaches used to investigate the association between parenting and the emergence of narcissistic features in children. These studies have used different research methods, operationalizing the concept of parenting in diversified ways and showing sensitivity to the multidimensionality of the construct of narcissism. The results of these studies allow us to say that types of positive parenting are more associated in general with the development of healthy narcissistic tendencies, compatible with the normal physical, mental and adaptive child's development.
Happy families: a twin study of humour.

The objective of this study was to estimate how much of an individual's appreciation of humour is influenced by genetic factors, the shared environment or the individual's unique environment. A population-based classical twin study of 127 pairs of female twins (71 monozygous (MZ) and 56 dizygous (DZ) pairs) aged 20-75 from the St Thomas' UK Adult Twin Registry elicited responses to five 'Far Side' Larson cartoons on a scale of 0-10. Within both MZ and DZ twin pairs, the tetrachoric correlations of responses to all five cartoons were significantly greater than zero. Furthermore, the correlations for MZ and DZ twins were of similar magnitude and in some cases the DZ correlation was greater than that of the MZ twins. This pattern of correlations suggests that shared environment rather then genetic effects contributes to cartoon appreciation. Multivariate model-fitting confirmed that these data were best explained by a model that allowed for the contribution of the shared environment and random environmental factors, but not genetic effects. However, there did not appear to be a general humour factor underlying responses to all five cartoons and no effect of age was seen. The shared environment, rather than genetic factors, explains the familial aggregation of humour appreciation as assessed by the specific 'off the wall' cognitive type of cartoons used in this study.
Distributed Deep Q-Learning

We propose a distributed deep learning model to successfully learn control policies directly from highdimensional sensory input using reinforcement learning. The model is based on the deep Q-network, a convolutional neural network trained with a variant of Q-learning. Its input is raw pixels and its output is a value function estimating future rewards from taking an action given a system state. To distribute the deep Q-network training, we adapt the DistBelief software framework to the context of efficiently training reinforcement learning agents. As a result, the method is completely asynchronous and scales well with the number of machines. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to achieve reasonable success on a simple game with minimal parameter tuning.
Estimating the selectivity of tf-idf based cosine similarity predicates

An increasing number of database applications today require sophisticated approximate string matching capabilities. Examples of such application areas include data integration and data cleaning. Cosine similarity has proven to be a robust metric for scoring the similarity between two strings, and it is increasingly being used in complex queries. An immediate challenge faced by current database optimizers is to find accurate and efficient methods for estimating the selectivity of cosine similarity predicates. To the best of our knowledge, there are no known methods for this problem. In this paper, we present the first approach for estimating the selectivity of tf.idf based cosine similarity predicates. We evaluate our approach on three different real datasets and show that our method often produces estimates that are within 40% of the actual selectivity.
Transfer learning for image classification with sparse prototype representations

To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful. We develop a new method for transfer learning which exploits available unlabeled data and an arbitrary kernel function; we form a representation based on kernel distances to a large set of unlabeled data points. To transfer knowledge from previous related problems we observe that a category might be learnable using only a small subset of reference prototypes. Related problems may share a significant number of relevant prototypes; we find such a concise representation by performing a joint loss minimization over the training sets of related problems with a shared regularization penalty that minimizes the total number of prototypes involved in the approximation. This optimization problem can be formulated as a linear program that can be solved efficiently. We conduct experiments on a news-topic prediction task where the goal is to predict whether an image belongs to a particular news topic. Our results show that when only few examples are available for training a target topic, leveraging knowledge learnt from other topics can significantly improve performance.
Novel intrusion prediction mechanism based on honeypot log similarity

The current network-based intrusion detection systems have a very high rate of false alarms, and this phenomena results in significant efforts to gauge the threat level of the anomalous traffic. In this paper, we propose an intrusion detection mechanism based on honeypot log similarity analysis and data mining techniques to predict and block suspicious flows before attacks occur. With honeypot logs and association rule mining, our approach can reduce the false alarm problem of intrusion detection because only suspicious traffic would be present in the honeypots. The proposed mechanism can reduce human effort, and the entire system can operate automatically. The results of our experiments indicate that the honeypot prediction system is practical for protecting assets from attacks or misuse.