nils-herrmann/clean_scirepeval huggingface dataset:
Is criminal behavior a central component of psychopathy? Conceptual directions for resolving the debate.

The development of the Psychopathy Checklist-Revised (PCL-R; R. D. Hare, 2003) has fueled intense clinical interest in the construct of psychopathy. Unfortunately, a side effect of this interest has been conceptual confusion and, in particular, the conflating of measures with constructs. Indeed, the field is in danger of equating the PCL-R with the theoretical construct of psychopathy. A key point in the debate is whether criminal behavior is a central component, or mere downstream correlate, of psychopathy. In this article, the authors present conceptual directions for resolving this debate. First, factor analysis of PCL-R items in a theoretical vacuum cannot reveal the essence of psychopathy. Second, a myth about the PCL-R and its relation to violence must be examined to avoid the view that psychopathy is merely a violent variant of antisocial personality disorder. Third, a formal, iterative process between theory development and empirical validation must be adopted. Fundamentally, constructs and measures must be recognized as separate entities, and neither reified. Applying such principles to the current state of the field, the authors believe the evidence favors viewing criminal behavior as a correlate, not a component, of psychopathy.
Automated API Property Inference Techniques

Frameworks and libraries offer reusable and customizable functionality through Application Programming Interfaces (APIs). Correctly using large and sophisticated APIs can represent a challenge due to hidden assumptions and requirements. Numerous approaches have been developed to infer properties of APIs, intended to guide their use by developers. With each approach come new definitions of API properties, new techniques for inferring these properties, and new ways to assess their correctness and usefulness. This paper provides a comprehensive survey of over a decade of research on automated property inference for APIs. Our survey provides a synthesis of this complex technical field along different dimensions of analysis: properties inferred, mining techniques, and empirical results. In particular, we derive a classification and organization of over 60 techniques into five different categories based on the type of API property inferred: unordered usage patterns, sequential usage patterns, behavioral specifications, migration mappings, and general information.
A Consistent Metric for Performance Evaluation of Multi-Object Filters

The concept of a miss-distance, or error, between a reference quantity and its estimated/controlled value, plays a fundamental role in any filtering/control problem. Yet there is no satisfactory notion of a miss-distance in the well-established field of multi-object filtering. In this paper, we outline the inconsistencies of existing metrics in the context of multi-object miss-distances for performance evaluation. We then propose a new mathematically and intuitively consistent metric that addresses the drawbacks of current multi-object performance evaluation metrics.
Adaptive Deep Brain Stimulation In Advanced Parkinson Disease

Brain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.



We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.



Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.



BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD.
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples

Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attacks can severely undermine DNN systems, particularly in security-sensitive settings. It was observed that an adversary could easily generate adversarial samples by making a small perturbation on irrelevant feature dimensions that are unnecessary for the current classification task. To overcome this problem, we introduce a defensive mechanism called DeepCloak. By identifying and removing unnecessary features in a DNN model, DeepCloak limits the capacity an attacker can use generating adversarial samples and therefore increase the robustness against such inputs. Comparing with other defensive approaches, DeepCloak is easy to implement and computationally efficient. Experimental results show that DeepCloak can increase the performance of DNN models against adversarial samples.
Hopelessness Depression : A Theory-Based Subtype of Depression

We present a revision of the 1978 reformulated theory of helplessness and depression and call it the hopelessness theory of depression. Although the 1978 reformulation has generated a vast amount of empirical work on depression over the past 10 years and recently has been evaluated as a model of depression, we do not think that it presents a clearly articulated theory of depression. We build on the skeletal logic of the 1978 statement and (a) propose a hypothesized subtype of depression— hopelessness depression, (b) introduce hopelessness as a proximal sufficient cause of the symptoms of hopelessness depression, (c) deemphasize causal attributions because inferred negative consequences and inferred negative characteristics about the self are also postulated to contribute to the formation of hopelessness and, in turn, the symptoms of hopelessness depression, and (d) clarify the diathesisstress and causal mediation components implied, but not explicitly articulated, in the 1978 statement. We report promising findings for the hopelessness theory and outline the aspects that still need to be tested.
Part 5: Machine Translation Evaluation Chapter 5.1 Introduction

The evaluation of machine translation (MT) systems is a vital field of research, both for determining the effectiveness of existing MT systems and for optimizing the performance of MT systems. This part describes a range of different evaluation approaches used in the GALE community and introduces evaluation protocols and methodologies used in the program. We discuss the development and use of automatic, human, task-based and semi-automatic (human-in-the-loop) methods of evaluating machine translation, focusing on the use of a human-mediated translation error rate HTER as the evaluation standard used in GALE. We discuss the workflow associated with the use of this measure, including post editing, quality control, and scoring. We document the evaluation tasks, data, protocols, and results of recent GALE MT Evaluations. In addition, we present a range of different approaches for optimizing MT systems on the basis of different measures. We outline the requirements and specific problems when using different optimization approaches and describe how the characteristics of different MT metrics affect the optimization. Finally, we describe novel recent and ongoing work on the development of fully automatic MT evaluation metrics that can have the potential to substantially improve the effectiveness of evaluation and optimization of MT systems. Progress in the field of machine translation relies on assessing the quality of a new system through systematic evaluation, such that the new system can be shown to perform better than pre-existing systems. The difficulty arises in the definition of a better system. When assessing the quality of a translation, there is no single correct answer; rather, there may be any number of possible correct translations. In addition, when two translations are only partially correct but in different ways it is difficult to distinguish quality. Moreover, quality assessments may be dependent on the intended use for the translation, e.g., the tone of a translation may be crucial in some applications, but irrelevant in other applications. Traditionally, there are two paradigms of machine translation evaluation: (1) Glass Box evaluation, which measures the quality of a system based upon internal system properties, and (2) Black Box evaluation, which measures the quality of a system based solely upon its output, without respect to the internal mechanisms of the translation system. Glass Box evaluation focuses upon an examination of the system's linguistic
Physically-based facial modelling, analysis, and animation

We develop a new 3D hierarchical model of the human face. The model incorporates a physically-based approximation to facial tissue and a set of anatomically-motivated facial muscle actuators. Despite its sophistication, the model is efficient enough to produce facial animation at interactive rates on a high-end graphics workstation. A second contribution of this paper is a technique for estimating muscle contractions from video sequences of human faces performing expressive articulations. These estimates may be input as dynamic control parameters to the face model in order to produce realistic animation. Using an example, we demonstrate that our technique yields sufficiently accurate muscle contraction estimates for the model to reproduce expressions from dynamic images of faces.
Deep points consolidation

In this paper, we present a consolidation method that is based on a new representation of 3D point sets. The key idea is to augment each surface point into a deep point by associating it with an inner point that resides on the meso-skeleton, which consists of a mixture of skeletal curves and sheets. The deep points representation is a result of a joint optimization applied to both ends of the deep points. The optimization objective is to fairly distribute the end points across the surface and the meso-skeleton, such that the deep point orientations agree with the surface normals. The optimization converges where the inner points form a coherent meso-skeleton, and the surface points are consolidated with the missing regions completed. The strength of this new representation stems from the fact that it is comprised of both local and non-local geometric information. We demonstrate the advantages of the deep points consolidation technique by employing it to consolidate and complete noisy point-sampled geometry with large missing parts.
CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors

Representing and summarizing human behaviors with rich contexts facilitates behavioral sciences and user-oriented services. Traditional behavioral modeling represents a behavior as a tuple in which each element is one contextual factor of one type, and the tensor-based summaries look for high-order dense blocks by clustering the values (including timestamps) in each dimension. However, the human behaviors are multicontextual and dynamic: (1) each behavior takes place within multiple contexts in a few dimensions, which requires the representation to enable non-value and set-values for each dimension; (2) many behavior collections, such as tweets or papers, evolve over time. In this paper, we represent the behavioral data as a two-level matrix (temporal-behaviors by dimensional-values) and propose a novel representation for behavioral summary called Tartan that includes a set of dimensions, the values in each dimension, a list of consecutive time slices and the behaviors in each slice. We further develop a propagation method CatchTartan to catch the dynamic multicontextual patterns from the temporal multidimensional data in a principled and scalable way: it determines the meaningfulness of updating every element in the Tartan by minimizing the encoding cost in a compression manner. CatchTartan outperforms the baselines on both the accuracy and speed. We apply CatchTartan to four Twitter datasets up to 10 million tweets and the DBLP data, providing comprehensive summaries for the events, human life and scientific development.
RMSProp and equilibrated adaptive learning rates for non-convex optimization

Parameter-specific adaptive learning rate methods are computationally efficient ways to reduce the ill-conditioning problems encountered when training large deep networks. Following recent work that strongly suggests that most of the critical points encountered when training such networks are saddle points, we find how considering the presence of negative eigenvalues of the Hessian could help us design better suited adaptive learning rate schemes. We show that the popular Jacobi preconditioner has undesirable behavior in the presence of both positive and negative curvature, and present theoretical and empirical evidence that the socalled equilibration preconditioner is comparatively better suited to non-convex problems. We introduce a novel adaptive learning rate scheme, called ESGD, based on the equilibration preconditioner. Our experiments show that ESGD performs as well or better than RMSProp in terms of convergence speed, always clearly improving over plain stochastic gradient descent.
Unsupervised Cross-Domain Word Representation Learning

Meaning of a word varies from one domain to another. Despite this important domain dependence in word semantics, existing word representation learning methods are bound to a single domain. Given a pair of source-target domains, we propose an unsupervised method for learning domain-specific word representations that accurately capture the domainspecific aspects of word semantics. First, we select a subset of frequent words that occur in both domains as pivots. Next, we optimize an objective function that enforces two constraints: (a) for both source and target domain documents, pivots that appear in a document must accurately predict the co-occurring non-pivots, and (b) word representations learnt for pivots must be similar in the two domains. Moreover, we propose a method to perform domain adaptation using the learnt word representations. Our proposed method significantly outperforms competitive baselines including the state-of-theart domain-insensitive word representations, and reports best sentiment classification accuracies for all domain-pairs in a benchmark dataset.
Intellectual disability and its relationship to autism spectrum disorders.

Intellectual disability (ID) and autism spectrum disorders (ASDs) covary at very high rates. Similarly, greater severity of one of these two disorders appears to have effects on the other disorder on a host of factors. A good deal of research has appeared on the topic with respect to nosology, prevalence, adaptive functioning, challenging behaviors, and comorbid psychopathology. The purpose of this paper was to provide a critical review and status report on the research published on these topics. Current status and future directions for better understanding these two covarying disorders was reviewed along with a discussion of relevant strengths and weaknesses of the current body of research.
Parameter Generation Methods With Rich Context Models for High-Quality and Flexible Text-To-Speech Synthesis

In this paper, we propose parameter generation methods using rich context models as yet another hybrid method combining Hidden Markov Model (HMM)-based speech synthesis and unit selection synthesis. Traditional HMM-based speech synthesis enables flexible modeling of acoustic features based on a statistical approach. However, the speech parameters tend to be excessively smoothed. To address this problem, several hybrid methods combining HMM-based speech synthesis and unit selection synthesis have been proposed. Although they significantly improve quality of synthetic speech, they usually lose flexibility of the original HMM-based speech synthesis. In the proposed methods, we use rich context models, which are statistical models that represent individual acoustic parameter segments. In training, the rich context models are reformulated as Gaussian Mixture Models (GMMs). In synthesis, initial speech parameters are generated from probability distributions over-fitted to individual segments, and the speech parameter sequence is iteratively generated from GMMs using a parameter generation method based on the maximum likelihood criterion. Since the basic framework of the proposed methods is still the same as the traditional framework, the capability of flexibly modeling acoustic features remains. The experimental results demonstrate: (1) the use of approximation with a single Gaussian component sequence yields better synthetic speech quality than the use of EM algorithm in the proposed parameter generation method, (2) the state-based model selection yields quality improvements at the same level as the frame-based model selection, (3) the use of the initial parameters generated from the over-fitted speech probability distributions is very effective to further improve speech quality, and (4) the proposed methods for spectral and F0 components yields significant improvements in synthetic speech quality compared with the traditional HMM-based speech synthesis.
Design of E-shaped Microstrip Antenna And Parameters estimation using ANN – A review

The microstrip antenna finds applications in varied and upcoming technologies because it of fers low profile, narrow bandwidth, high gain, and compact antenna element. The microstrip antenna have biggest disadvantage of narrow bandwidth and poor i mpedance matching. To make microstrip antenna compatible with commercial applications, the bandwi dth enhancement and impedance matching of such antennas are to be done. To improve the bandwidth t he L probe and U slot techniques are used, those ar e efficient to enhance the bandwidth. In this researc h, Microstrip patch antenna with coaxial feed technique is integrated. The impedance matching of antenna depen ds upon type and position of the feed because impedance matching of source at the feeding point o f antenna is very important for efficient operation of antenna. But it is not an easy task to achieve for a simple microstrip patch antenna. To solve this pr oblem an Artificial Neural Network (ANN) model has proposed in this research. Artificial Neural Network is highly simplified model of the structure of the biological neural network to calculate the parameters by esti mation.
Understanding the impact of video quality on user engagement

As the distribution of the video over the Internet becomes main- stream and its consumption moves from the computer to the TV screen, user expectation for high quality is constantly increasing. In this context, it is crucial for content providers to understand if and how video quality affects user engagement and how to best invest their resources to optimize video quality. This paper is a first step towards addressing these questions. We use a unique dataset that spans different content types, including short video on demand (VoD), long VoD, and live content from popular video con- tent providers. Using client-side instrumentation, we measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events.
 We quantify user engagement both at a per-video (or view) level and a per-user (or viewer) level. In particular, we find that the percentage of time spent in buffering (buffering ratio) has the largest impact on the user engagement across all types of content. However, the magnitude of this impact depends on the content type, with live content being the most impacted. For example, a 1% increase in buffering ratio can reduce user engagement by more than three minutes for a 90-minute live video event. We also see that the average bitrate plays a significantly more important role in the case of live content than VoD content.
Ontology-based decision making on uncontrolled intersections and narrow roads

Many Advanced Driver Assistance Systems (ADAS) have been developed to improve car safety. However, it is still a challenging problem to make autonomous vehicles to drive safely on urban streets such as uncontrolled intersections (without traffic lights) and narrow roads. In this paper, we introduce a decision making system that can assist autonomous vehicles at uncontrolled intersections and narrow roads. We constructed a machine understandable ontology-based Knowledge Base, which contains maps and traffic regulations. The system makes decisions in comply with traffic regulations such as Right-Of-Way rules when it receives a collision warning signal. The decisions are sent to a path planning system to change the route or stop to avoid collisions.
Effective training of convolutional neural networks for face-based gender and age prediction

Convolutional Neural Networks (CNNs) have been proven very effective for human demographics estimation by a number of recent studies. However, the proposed solutions significantly vary in different aspects leaving many open questions on how to choose an optimal CNN architecture and which training strategy to use. In this work, we shed light on some of these questions improving the existing CNN-based approaches for gender and age prediction and providing practical hints for future studies. In particular, we analyse four important factors of the CNN training for gender recognition and age estimation: (1) the target age encoding and loss function, (2) the CNN depth, (3) the need for pretraining, and (4) the training strategy: mono-task or multi-task. As a result, we design the state-of-the-art gender recognition and age estimation models according to three popular benchmarks: LFW, MORPH-II and FGNET. Moreover, our best model won the ChaLearn Apparent Age Estimation Challenge 2016 significantly outperforming the solutions of other participants.
A Novelty Detection Approach to Classification

Novelty Detection techniques are concept-learning methods that proceed by recognizing positive instances of a concept rather than diierentiating between its positive and negative instances. Novelty Detection approaches consequently require very few, if any, negative training instances. This paper presents a particular Novelty Detection approach to classiica-tion that uses a Redundancy Compression and Non-Redundancy Diierentiation technique based on the (Gluck & Myers 1993) model of the hippocampus, a part of the brain critically involved in learning and memory. In particular, this approach consists of training an autoencoder to reconstruct positive input instances at the output layer and then using this au-toencoder to recognize novel instances. Classiication is possible, after training, because positive instances are expected to be reconstructed accurately while negative instances are not. The purpose of this paper is to compare Hippo, the system that implements this technique , to C4.5 and feedforward neural network classi-cation on several applications.