nils-herrmann/clean_scirepeval huggingface dataset:
Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition

Social behavior and many cultural etiquettes are influenced by gender. There are numerous potential applications of automatic face gender recognition such as human-computer interaction systems, content based image search, video surveillance and more. The immense increase of images that are uploaded online has fostered the construction of large labeled datasets. Recently, impressive progress has been demonstrated in the closely related task of face verification using deep convolutional neural networks. In this paper we explore the applicability of deep convolutional neural networks on gender classification by fine-tuning a pretrained neural network. In addition, we explore the performance of dropout support vector machines by training them on the deep features of the pretrained network as well as on the deep features of the fine-tuned network. We evaluate our methods on the color FERET data collection and the recently constructed Adience data collection. We report cross-validated performance rates on each dataset. We further explore generalization capabilities of our approach by conducting cross-dataset tests. It is demonstrated that our fine-tuning method exhibits state-of-the-art performance on both datasets.
Learning Where to Attend with Deep Architectures for Image Tracking

We discuss an attentional model for simultaneous object tracking and recognition that is driven by gaze data. Motivated by theories of perception, the model consists of two interacting pathways, identity and control, intended to mirror the what and where pathways in neuroscience models. The identity pathway models object appearance and performs classification using deep (factored)-restricted Boltzmann machines. At each point in time, the observations consist of foveated images, with decaying resolution toward the periphery of the gaze. The control pathway models the location, orientation, scale, and speed of the attended object. The posterior distribution of these states is estimated with particle filtering. Deeper in the control pathway, we encounter an attentional mechanism that learns to select gazes so as to minimize tracking uncertainty. Unlike in our previous work, we introduce gaze selection strategies that operate in the presence of partial information and on a continuous action space. We show that a straightforward extension of the existing approach to the partial information setting results in poor performance, and we propose an alternative method based on modeling the reward surface as a gaussian process. This approach gives good performance in the presence of partial information and allows us to expand the action space from a small, discrete set of fixation points to a continuous domain.
Understanding Student Motivation, Behaviors and Perceptions in MOOCs

Massive Open Online Courses (MOOCs) have recently experienced rapid development and garnered significant attention from various populations. Despite the wide recognition of MOOCs as an important opportunity within educational practices, there are still many questions as to how we might satisfy students' needs, as evidenced by very high dropout rates. Researchers lack a solid understanding of what student needs are being addressed by MOOCs, and how well MOOCs now address (or fail to address) these needs. To help in building such an understanding, we conducted in-depth interviews probing student motivations, learning perceptions and experiences towards MOOCs, paying special attention to the MOOC affordances and experiences that might lead to high drop rates. Our study identified learning motivations, learning patterns, and a number of factors that appear to influence student retention. We proposed that the issue of retention should be addressed from two perspectives: retention as a problem but also retention as an opportunity.
Cross Sentence Inference for Process Knowledge

For AI systems to reason about real world situations, they need to recognize which processes are at play and which entities play key roles in them. Our goal is to extract this kind of rolebased knowledge about processes, from multiple sentence-level descriptions. This knowledge is hard to acquire; while semantic role labeling (SRL) systems can extract sentence level role information about individual mentions of a process, their results are often noisy and they do not attempt create a globally consistent characterization of a process. To overcome this, we extend standard within sentence joint inference to inference across multiple sentences. This cross sentence inference promotes role assignments that are compatible across different descriptions of the same process. When formulated as an Integer Linear Program, this leads to improvements over within-sentence inference by nearly 3% in F1. The resulting role-based knowledge is of high quality (with a F1 of nearly 82).
A Humanized Version of Foxp2 Affects Cortico-Basal Ganglia Circuits in Mice

It has been proposed that two amino acid substitutions in the transcription factor FOXP2 have been positively selected during human evolution due to effects on aspects of speech and language. Here, we introduce these substitutions into the endogenous Foxp2 gene of mice. Although these mice are generally healthy, they have qualitatively different ultrasonic vocalizations, decreased exploratory behavior and decreased dopamine concentrations in the brain suggesting that the humanized Foxp2 allele affects basal ganglia. In the striatum, a part of the basal ganglia affected in humans with a speech deficit due to a nonfunctional FOXP2 allele, we find that medium spiny neurons have increased dendrite lengths and increased synaptic plasticity. Since mice carrying one nonfunctional Foxp2 allele show opposite effects, this suggests that alterations in cortico-basal ganglia circuits might have been important for the evolution of speech and language in humans.
Crossmodal processing and sensory substitution: Is "seeing" with sound and touch a form of perception or cognition?

The brain has evolved in this multisensory context to perceive the world in an integrated fashion. Although there are good reasons to be skeptical of the influence of cognition on perception, here we argue that the study of sensory substitution devices might reveal that perception and cognition are not necessarily distinct, but rather continuous aspects of our information processing capacities.
Agency Play: Dimensions of Agency for Interactive Narrative Design

Agency in interactive narrative is often narrowly understood as a user’s freedom to either perform virtually embodied actions or alter the mechanics of narration at will, followed by an implicit assumption of “the more agency the better.” This paper takes notice of a broader range of agency phenomena in interactive narrative and gaming that may be addressed by integrating accounts of agency from diverse fields such as sociology of science, digital media studies, philosophy, and cultural theory. The upshot is that narrative agency is contextually situated, distributed between the player and system, and mediated through user interpretation of system behavior and system affordances for user actions. In our new and developing model of agency play, multiple dimensions of agency can be tuned during story execution as a narratively situated mechanism to convey meaning. More importantly, we propose that this model of variable dimensions of agency can be used as an expressive theoretical tool for interactive narrative design. Finally, we present our current interactive narrative work under development as a case study for how the agency play model can be deployed expressively.
Top-N Recommendation on Graphs

Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix. The proposed method constructs a new representation which preserves affinity and structure information in the user-item rating matrix and then performs recommendation task. To capture proximity information about users and items, two graphs are constructed. Manifold learning idea is used to constrain the new representation to be smooth on these graphs, so as to enforce users and item proximities. Our model is formulated as a convex optimization problem, for which we need to solve the well known Sylvester equation only. We carry out extensive empirical evaluations on six benchmark datasets to show the effectiveness of this approach.
Morphological typology of languages for IR

This paper presents a morphological classification of languages from the IR perspective. Linguistic typology research has shown that the morphological complexity of each language of the world can be described by two variables, index of synthesis and index of fusion. These variables provide a theoretical basis for IR research handling morphological issues. A common theoretical framework is needed in particular due to the increasing significance of cross-language retrieval research and CLIR systems processing different languages. The paper elaborates the linguistic morphological typology for the purposes of IR research. It is studied how the indices of synthesis and fusion could be used as practical tools in monoand cross-lingual IR research. The need for semantic and syntactic typologies is discussed. The paper also reviews studies done in different languages on the effects of morphology and stemming in IR.
An Analysis of Data Mining Techniques in Aggregation with Real Time Dataset for the Prediction of Heart Disease

Data is a collection of facts and figures available in huge volume which needs to be organized into meaningful way to get useful information out of it. Data mining helps to get useful patterns out of big data. Healthcare field has wide scope for research since vast amount of unattended data is available which needs to be investigated for the benefit of human health. A big data related with heart disease can be investigated by applying different data mining techniques and come up with predictive model that will help patients to diagnose the disease at early stage. Most of the studies have stated that Decision Tree is an efficient technique to identify useful statistics out of available data. Accuracy of result set can be enhanced further using Genetic Algorithm in conjunction with data mining techniques. Real time dataset can help to understand the significant trends in heart disease which could play instrumental role in prediction of heart disease. The aim of study is to revise the existing findings, analyze and co-relate heart disease data, find better solution and minimize unwanted medical costs for heart disease diagnosis and save many lives in right time.
Affective Interactions Using Virtual Reality: The Link between Presence and Emotions

Many studies showed the ability of movies and imagery techniques to elicit emotions. Nevertheless, it is less clear how to manipulate the content of interactive media to induce specific emotional responses. In particular, this is true for the emerging medium virtual reality (VR), whose main feature is the ability to induce a feeling of "presence" in the computer-generated world experienced by the user. The main goal of this study was to analyze the possible use of VR as an affective medium. Within this general goal, the study also analyzed the relationship between presence and emotions. The results confirmed the efficacy of VR as affective medium: the interaction with "anxious" and "relaxing" virtual environments produced anxiety and relaxation. The data also showed a circular interaction between presence and emotions: on one side, the feeling of presence was greater in the "emotional" environments; on the other side, the emotional state was influenced by the level of presence. The significance of these results for the assessment of affective interaction is discussed.
Bilingualism provides a neural reserve for aging populations

It has been postulated that bilingualism may act as a cognitive reserve and recent behavioral evidence shows that bilinguals are diagnosed with dementia about 4-5 years later compared to monolinguals. In the present study, we investigated the neural basis of these putative protective effects in a group of aging bilinguals as compared to a matched monolingual control group. For this purpose, participants completed the Erikson Flanker task and their performance was correlated to gray matter (GM) volume in order to investigate if cognitive performance predicts GM volume specifically in areas affected by aging. We performed an ex-Gaussian analysis on the resulting RTs and report that aging bilinguals performed better than aging monolinguals on the Flanker task. Bilingualism was overall associated with increased GM in the ACC. Likewise, aging induced effects upon performance correlated only for monolinguals to decreased gray matter in the DLPFC. Taken together, these neural regions might underlie the benefits of bilingualism and act as a neural reserve that protects against the cognitive decline that occurs during aging.
High-Efficiency Dual-Band On-Chip Rectenna for 35- and 94-GHz Wireless Power Transmission in 0.13-$\mu{\hbox {m}}$ CMOS Technology

This paper proposes a high-efficiency dual-band on-chip rectifying antenna (rectenna) at 35 and 94 GHz for wireless power transmission. The rectenna is designed in slotline (SL) and finite-width ground coplanar waveguide (FGCPW) transmission lines in a CMOS 0.13-μm process. The rectenna comprises a high gain linear tapered slot antenna (LTSA), an FGCPW to SL transition, a bandpass filter, and a full-wave rectifier. The LTSA achieves a VSWR=2 fractional bandwidth of 82% and 41%, and a gain of 7.4 and 6.5 dBi at the frequencies of 35 and 94 GHz. The measured power conversion efficiencies are 53% and 37% in free space at 35 and 94 GHz, while the incident radiation power density is 30 mW/cm2 . The fabricated rectenna occupies a compact size of 2.9 mm2.
Receptive vocabulary differences in monolingual and bilingual children.

Studies often report that bilingual participants possess a smaller vocabulary in the language of testing than monolinguals, especially in research with children. However, each study is based on a small sample so it is difficult to determine whether the vocabulary difference is due to sampling error. We report the results of an analysis of 1,738 children between 3 and 10 years old and demonstrate a consistent difference in receptive vocabulary between the two groups. Two preliminary analyses suggest that this difference does not change with different language pairs and is largely confined to words relevant to a home context rather than a school context.
Object-Centric Spatial Pooling for Image Classification

Spatial pyramid matching (SPM) based pooling has been the dominant choice for state-of-art image classification systems. In contrast, we propose a novel object-centric spatial pooling (OCP) approach, following the intuition that knowing the location of the object of interest can be useful for image classification. OCP consists of two steps: (1) inferring the location of the objects, and (2) using the location information to pool foreground and background features separately to form the image-level representation. Step (1) is particularly challenging in a typical classification setting where precise object location annotations are not available during training. To address this challenge, we propose a framework that learns object detectors using only image-level class labels, or so-called weak labels. We validate our approach on the challenging PASCAL07 dataset. Our learned detectors are comparable in accuracy with stateof-the-art weakly supervised detection methods. More importantly, the resulting OCP approach significantly outperforms SPM-based pooling in image classification.
Learning to Generate the "Unseen" via Part Synthesis and Composition

Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks. A reoccurring challenge is how to sample a rich variety of data from the entire target distribution, rather than only from the distribution of the training data. In other words, we would like the generative model to go beyond the observed training samples and learn to also generate “unseen” data. In our work, we present a generative neural network for shapes that is based on a part-based prior, where the key idea is for the network to synthesize shapes by varying both the shape parts and their compositions. Treating a shape not as an unstructured whole, but as a (re-)composable set of deformable parts, adds a combinatorial dimension to the generative process to enrich the diversity of the output, encouraging the generator to venture more into the “unseen”. We show that our part-based model generates richer variety of feasible shapes compared with a baseline generative model. To this end, we introduce two quantitative metrics to evaluate the ingenuity of the generative model and assess how well generated data covers both the training data and unseen data from the same target distribution.
Online Deep Learning: Learning Deep Neural Networks on the Fly

Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open challenge of “Online Deep Learning” (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is significantly more challenging since the optimization of the DNN objective function is non-convex, and regular backpropagation does not work well in practice, especially for online learning settings. In this paper, we present a new online deep learning framework that attempts to tackle the challenges by learning DNN models of adaptive depth from a sequence of training data in an online learning setting. In particular, we propose a novel Hedge Backpropagation (HBP) method for online updating the parameters of DNN effectively, and validate the efficacy of our method on large-scale data sets, including both stationary and concept drifting scenarios.
Utopias and Dystopias as Cybernetic Information Systems: Envisioning the Posthuman Neuropolity

While it is possible to understand utopias and dystopias as particular kinds of sociopolitical systems, in this text we argue that utopias and dystopias can also be understood as particular kinds of information systems in which data is received, stored, generated, processed, and transmitted by the minds of human beings that constitute the system’s ‘nodes’ and which are connected according to specific network topologies. We begin by formulating a model of cybernetic information-processing properties that characterize utopias and dystopias. It is then shown that the growing use of neuroprosthetic technologies for human enhancement is expected to radically reshape the ways in which human minds access, manipulate, and share information with one another; for example, such technologies may give rise to posthuman ‘neuropolities’ in which human minds can interact with their environment using new sensorimotor capacities, dwell within shared virtual cyberworlds, and link with one another to form new kinds of social organizations, including hive minds that utilize communal memory and decision-making. Drawing on our model, we argue that the dynamics of such neuropolities will allow (or perhaps even impel) the creation of new kinds of utopias and dystopias that were previously impossible to realize. Finally, we suggest that it is important that humanity begin thoughtfully exploring the ethical, social, and political implications of realizing such technologically enabled societies by studying neuropolities in a place where they have already been ‘pre-engineered’ and provisionally exist: in works of audiovisual science fiction such as films, television series, and role-playing games
The Petrolingual Ligament: The Anatomy and Surgical Exposure of the Posterolateral Landmark of The Cavernous Sinus

The petrolingual ligament is the posteroinferior attachment of the lateral wall of the cavernous sinus, where the internal carotid artery enters the cavernous sinus. The petrous segment of the internal carotid artery finishes and the cavernous segment begins at the superior margin of this ligament. The ligament is surgically important due to its identification as a landmark for dissection of the internal carotid artery during the approaches to posterolateral intracavernous and extracavernous lesions. It can be well exposed after mobilization of the gasserian ganglion, or after the trigeminal root and ganglion have been split along the junction of V2 and V3 (the transtrigeminal approach). The petrolingual ligament was studied in five cadaveric head specimens from ten sides. The size of the ligament was measured, and its anatomical, clinical and surgical importance is discussed.
Mutual Fund Performance : Luck or Skill ?

Should individuals include actively managed mutual funds in their investment portfolios? They should if and only if the result of active management is superior performance due to skill. This paper employs a previously ignored statistical technique to detect whether skill drives the superior performance of some mutual funds. This technique, the generalized binomial distribution, models a sequence of n Bernoulli events in which the result of each event is either success or failure (successive quarters during which funds outperform or do not outperform the market). Results display a statistically significant proportion of mutual funds, though small in number, outperform their peers on a risk–adjusted basis and do so as a result of skill, not luck. This result signifies the rationality of entrusting one’s wealth to successful and skillfully managed mutual funds. Hence, a well–designed portfolio that includes actively managed funds may trump a wholly passive index fund strategy.
Recommendation Systems: A Probabilistic Analysis

A recommendation system tracks past actions of a group of users to make recommendations to individual members of the group. The growth of computer-mediated marketing and commerce has led to increased interest in such systems. We introduce a simple analytical framework for recommendation systems, including a basis for defining the utilit y of such a system. We perform probabilistic analyses of algorithmic methods within this framework. These analyses yield insights into how much utility can be derived from the memory of past actions and on how this memory can be exploited.
Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection

In this paper, we present the system we have used for the Implicit WASSA 2018 Implicit Emotion Shared Task. The task is to predict the emotion of a tweet of which the explicit mentions of emotion terms have been removed. The idea is to come up with a model which has the ability to implicitly identify the emotion expressed given the context words. We have used a Gated Recurrent Neural Network (GRU) and a Capsule Network based model for the task. Pre-trained word embeddings have been utilized to incorporate contextual knowledge about words into the model. GRU layer learns latent representations using the input word embeddings. Subsequent Capsule Network layer learns high-level features from that hidden representation. The proposed model managed to achieve a macro-F1 score of 0.692.