nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of visible and infra-red imagery for face recognition

This paper presents initial results in a study comparing the eflectiveness of visible and infra-red ( IR) imagery for detecting and recognizing faces in areas where personnel identification i s critical, (e.g., airports and secure buildings). W e compare the effectiveness of visible versus IR imagery by running three face recognition algorithms on a database of images collected for this study. There are both I R and visible images for each person in the database collected using the same scenarios. W e used three very different feature-extraction and decision-making algorithms for our study to insure that the comparisons would not depend on a particular processing technique. W e also present recognition results when visible and infra-red decision metrics are fused. The recognition results show that both visible and IR imagery perform similarly across algorithms and that fusion of IR and visible imagery is a viable means of enhancing performance beyond that of either acting alone. W e examine the relative importance of different regions of the face for recognition. W e also discuss practical issues of implementation, along with plans for the next phase of the study, face detection in an uncontrolled environment. Preliminary face detection results are presented.
Learning Continuous Semantic Representations of Symbolic Expressions

Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning. As a step in this direction, we propose a new architecture, called neural equivalence networks, for the problem of learning continuous semantic representations of algebraic and logical expressions. These networks are trained to represent semantic equivalence, even of expressions that are syntactically very different. The challenge is that semantic representations must be computed in a syntax-directed manner, because semantics is compositional, but at the same time, small changes in syntax can lead to very large changes in semantics, which can be difficult for continuous neural architectures. We perform an exhaustive evaluation on the task of checking equivalence on a highly diverse class of symbolic algebraic and boolean expression types, showing that our model significantly outperforms existing architectures.
Detecting true lies: police officers' ability to detect suspects' lies.

Ninety-nine police officers, not identified in previous research as belonging to groups that are superior in lie detection, attempted to detect truths and lies told by suspects during their videotaped police interviews. Accuracy rates were higher than those typically found in deception research and reached levels similar to those obtained by specialized lie detectors in previous research. Accuracy was positively correlated with perceived experience in interviewing suspects and with mentioning cues to detecting deceit that relate to a suspect's story. Accuracy was negatively correlated with popular stereotypical cues such as gaze aversion and fidgeting. As in previous research, accuracy and confidence were not significantly correlated, but the level of confidence was dependent on whether officers judged actual truths or actual lies and on the method by which confidence was measured.
Property Testing For Differential Privacy

We consider the problem of property testing for differential privacy: with black-box access to a purportedly private algorithm, can we verify its privacy guarantees? In particular, we show that any privacy guarantee that can be efficiently verified is also efficiently breakable in the sense that there exist two databases between which we can efficiently distinguish. We give lower bounds on the query complexity of verifying pure differential privacy, approximate differential privacy, random pure differential privacy, and random approximate differential privacy. We also give algorithmic upper bounds. The lower bounds obtained in the work are infeasible for the scale of parameters that are typically considered reasonable in the differential privacy literature, even when we suppose that the verifier has access to an (untrusted) description of the algorithm. A central message of this work is that verifying privacy requires compromise by either the verifier or the algorithm owner. Either the verifier has to be satisfied with a weak privacy guarantee, or the algorithm owner has to compromise on side information or access to the algorithm.
Joint Entity Recognition and Disambiguation

Extracting named entities in text and linking extracted names to a given knowledge base are fundamental tasks in applications for text understanding. Existing systems typically run a named entity recognition (NER) model to extract entity names first, then run an entity linking model to link extracted names to a knowledge base. NER and linking models are usually trained separately, and the mutual dependency between the two tasks is ignored. We propose JERL, Joint Entity Recognition and Linking, to jointly model NER and linking tasks and capture the mutual dependency between them. It allows the information from each task to improve the performance of the other. To the best of our knowledge, JERL is the first model to jointly optimize NER and linking tasks together completely. In experiments on the CoNLL’03/AIDA data set, JERL outperforms state-of-art NER and linking systems, and we find improvements of 0.4% absolute F1 for NER on CoNLL’03, and 0.36% absolute precision@1 for linking on AIDA.
T 2009 : 06 ISSN : 1100-3154 A literature survey of active machine learning in the context of natural language processing

Active learning is a supervised machine learning technique in which the learner is in control of the data used for learning. That control is utilized by the learner to ask an oracle, typically a human with extensive knowledge of the domain at hand, about the classes of the instances for which the model learned so far makes unreliable predictions. The active learning process takes as input a set of labeled examples, as well as a larger set of unlabeled examples, and produces a classifier and a relatively small set of newly labeled data. The overall goal is to create as good a classifier as possible, without having to mark-up and supply the learner with more data than necessary. The learning process aims at keeping the human annotation effort to a minimum, only asking for advice where the training utility of the result of such a query is high. Active learning has been successfully applied to a number of natural language processing tasks, such as, information extraction, named entity recognition, text categorization, part-of-speech tagging, parsing, and word sense disambiguation. This report is a literature survey of active learning from the perspective of natural language processing.
Comprehending the pronouns her , him , and his : Implications for theories of referential processing q

The research investigated how comprehenders use structural information during co-reference resolution of the pronouns her, him, and his. In Experiment 1, participants read sentences containing the pronoun her in object position, functioning as SPEC or NP and preceded by one of three types of NPs in subject position (e.g., ‘‘Mary/Mark/They saw her friend/yesterday. . .’’). In Experiment 2, additional participants read similar sentences, containing the unambiguous pronouns him or his. The results of both experiments showed that the type of NP occurring in subject position influenced processing on both SPEC and NP pronouns. These results were inconsistent with claims that comprehenders consider only those antecedents that are structurally available as antecedents, following Chomsky s (1981) Binding Theory (Clifton, Kennison, & Albrecht, 1997; Nicol & Swinney, 1989). In Experiment 3, subject type did not influence processing of NP pronouns when the context contained a highly salient, structurally available antecedent. Implications for theories of referential processing are discussed. 2003 Elsevier Science (USA). All rights reserved.
A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding

Intelligent personal digital assistants (IPDAs), a popular real-life application with spoken language understanding capabilities, can cover potentially thousands of overlapping domains for natural language understanding, and the task of finding the best domain to handle an utterance becomes a challenging problem on a large scale. In this paper, we propose a set of efficient and scalable neural shortlistingreranking models for large-scale domain classification in IPDAs. The shortlisting stage focuses on efficiently trimming all domains down to a list of k-best candidate domains, and the reranking stage performs a list-wise reranking of the initial k-best domains with additional contextual information. We show the effectiveness of our approach with extensive experiments on 1,500 IPDA domains.
Physical Inactivity and Its Associated Factors among University Students

Physical inactivity has been associated with increasing prevalence and mortality of cardiovascular and other diseases. The purpose of this study is to identify if there is an association between, self–efficacy, mental health, and physical inactivity among university students. The study comprises of 202 males and 692 females age group 18-25 years drawn from seven faculties selected using a table of random numbers. Questionnaires were used for the data collection. The findings revealed that the prevalence of physical inactivity among the respondents was 41.4%. Using a univariate analysis, the study showed that there was an association between gender (female), low family income, low self-efficacy, respondents with mental health probable cases and physical inactivity (p<0.05).Using a multivariate analysis, physical inactivity was higher among females(OR = 3.72, 95% CI = 2.399-5.788), low family income (OR = 4.51, 95% CI = 3.266 – 6.241), respondents with mental health probable cases (OR = 1.58, 95% CI = 1.1362.206) and low self-efficacy for pysical activity(OR = 1.86, 95% CI = 1.350 2.578).Conclusively there is no significant decrease in physical inactivity among university students when compared with previous studies in this population, it is therefore recommended that counselling on mental health, physical activity awareness among new university students should be encouraged. Keyword:Exercise,Mental Health, Self-Efficacy,Physical Inactivity, University students
Haskell-Java-JikesRVM/ImageJ Nano-Bio Informatics Framework based Cryo-Electron Microscopy/TEM/SEM Image Processing in the Context of Haskell Image Processing Libraries

Current R&D project was the development of a software platform designed to be an advanced research testbed for the prototyping of Haskell based novel technologies in Cryo-EM Methodologies. Focused upon software architecture concepts and frameworks involving Haskell image processing libraries. Cryo-EM is an important tool to probe nano-bio systems.A number of hi-tech firms are implementing BIG-DATA analysis using Haskell especially in the domains of Pharma,Bio-informatics etc. Hence current research paper is one of the pioneering attempts made by the author to encourage advanced data analysis in the Cryo-EM domain to probe important aspects of nano-bio applications.
An Overview of Topic Discovery in Twitter Communication through Social Media Analytics

The need for automatic methods of topic discovery in the Internet grows exponentially with the amount of available textual information. Nowadays it becomes impossible to manually read even a small part of the information in order to reveal the underlying topics. Social media provide us with a great pool of user generated content, where topic discovery may be extremely useful for businesses, politicians, researchers, and other stakeholders. However, conventional topic discovery methods, which are widely used in large text corpora, face several challenges when they are applied in social media and particularly in Twitter – the most popular microblogging platform. To the best of our knowledge no comprehensive overview of these challenges and of the methods dedicated to address these challenges does exist in IS literature until now. Therefore, this paper provides an overview of these challenges, matching methods and their expected usefulness for social media analytics.
Mixed Precision Training of Convolutional Neural Networks using Integer Operations

The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular FP16 accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a lot of research has also happened in the domain of low and mixed-precision Integer training, these works either present results for non-SOTA networks (for instance only AlexNet for ImageNet-1K), or relatively small datasets (like CIFAR-10). In this work, we train state-of-the-art visual understanding neural networks on ImageNet-1K dataset, with Integer operations on General Purpose (GP) hardware. In particular, we focus on Integer Fused-Multiply-and-Accumulate (FMA) operations which take two pairs of INT16 operands and accumulate results into an INT32 output.We propose a shared exponent representation of tensors, and develop a Dynamic Fixed Point (DFP) scheme suitable for common neural network operations. The nuances of developing an efficient integer convolution kernel is examined, including methods to handle overflow of the INT32 accumulator. We implement CNN training for ResNet-50, GoogLeNet-v1, VGG-16 and AlexNet; and these networks achieve or exceed SOTA accuracy within the same number of iterations as their FP32 counterparts without any change in hyper-parameters and with a 1.8X improvement in end-to-end training throughput. To the best of our knowledge these results represent the first INT16 training results on GP hardware for ImageNet-1K dataset using SOTA CNNs and achieve highest reported accuracy using half precision representation.
Filter-Context Dynamic Coattention Networks for Question Answering

Answering (QA) is one of the most challenging and crucial tasks in Natural Language Processing (NLP) that has a wide range of applications in various domains, such as information retrieval and entity extraction. Traditional methods involve linguistically based NLP techniques, and recent researchers apply Deep Learning on this task and have achieved promising result. In this paper, we combined Dynamic Coattention Network (DCN)  and bilateral multiperspective matching (BiMPM) model , achieved an F1 score of 63.8% and exact match (EM) of 52.3% on test set.
A Static Friction Model for Elastic-Plastic Contacting Rough Surfaces

A model that predicts the static friction for elastic-plastic contact of rough surface presented. The model incorporates the results of accurate finite element analyses elastic-plastic contact, adhesion and sliding inception of a single asperity in a statis representation of surface roughness. The model shows strong effect of the externa and nominal contact area on the static friction coefficient in contrast to the classical of friction. It also shows that the main dimensionless parameters affecting the s friction coefficient are the plasticity index and adhesion parameter. The effect of adh on the static friction is discussed and found to be negligible at plasticity index va larger than 2. It is shown that the classical laws of friction are a limiting case of present more general solution and are adequate only for high plasticity index and n gible adhesion. Some potential limitations of the present model are also discussed ing to possible improvements. A comparison of the present results with those obt from an approximate CEB friction model shows substantial differences, with the l severely underestimating the static friction coefficient.
Sustainable blockchain through proof of exercise

Cryptocurrency and blockchain technologies are recently gaining wide adoption since the introduction of Bitcoin, being distributed, authority-free, and secure. Proof of Work (PoW) is at the heart of blockchain's security, asset generation, and maintenance. Although simple and secure, a hash-based PoW like Bitcoin's puzzle is often referred to as “useless”, and the used intensive computations are considered “waste” of energy. A myriad of Proof of “something” alternatives have been proposed to mitigate energy consumption; however, they either introduced new security threats and limitations, or the “work” remained far from being really “useful”. In this work, we introduce Proof of eXercise (PoX): a sustainable alternative to PoW where an eXercise is a real world matrix-based scientific computation problem. We provide a novel study of the properties of Bitcoin's PoW, the challenges of a more “rational” solution as PoX, and we suggest a comprehensive approach for PoX.
Blockchain and Economic Development : Hype vs . Reality

Increasing attention is being paid to the potential of blockchain technology to address long-standing challenges related to economic development. Blockchain proponents argue that it will expand opportunities for exchange and collaboration by reducing reliance on intermediaries and the frictions associated with them. The purpose of this paper is to provide a clear-eyed view of the technology’s potential in the context of development. In it, we focus on identifying the questions that development practitioners should be asking technologists, and challenges that innovators must address for the technology to meet its potential.
A survey on service-oriented middleware for wireless sensor networks

Wireless sensor networks (WSN) are used for many applications such as environmental monitoring, infrastructure security, healthcare applications, and traffic control. The design and development of such applications must address many challenges dictated by WSN characteristics on one hand and the targeted applications on the other. One of the emerging approaches used for relaxing these challenges is using service-oriented middleware (SOM). Service-oriented computing, in general, aims to make services available and easily accessible through standardized models and protocols without having to worry about the underlying infrastructures, development models, or implementation details. SOM could play an important role in facilitating the design, development, and implementation of service-oriented systems. This will help achieve interoperability, loose coupling, and heterogeneity support. Furthermore, SOM approaches will provision non-functional requirements like scalability, reliability, flexibility, and Quality of Service (QoS) assurance. This paper surveys the current work in SOM and the trends and challenges to be addressed when designing and developing these solutions for WSN.
Microstrip fed SIW slot antenna backed with cavity for millimeter wireless communication applications

In this paper, Proposes SIW slot antenna backed with a cavity for 57-64 GHz frequency. This frequency is used for wireless communication applications. The proposed antenna is designed by using Rogers substrate with dielectric constant of 2.2, substrate thickness is 0.381 mm and the microstrip feed is used with the input impedance of 50ohms. The structure provides 5.2GHz impedance bandwidth with a range of 57.8 to 64 GHz and matches with VSWR 2:1. The values of reflection coefficient, VSWR, gain, transmission efficiency and radiation efficiency of proposed antenna at 60GHz are −17.32dB, 1.3318, 7.19dBi, 79.5% and 89.5%.
High Performance Convolutional Neural Networks for Document Processing

Convolutional neural networks (CNNs) are well known for producing state-of-the-art recognizers for document processing . However, they can be difficult to implement and are usually slower than traditional multi-layer perceptrons (MLPs). We present three novel approaches to speeding up CNNs: a) unrolling convolution, b) using BLAS (basic linear algebra subroutines), and c) using GPUs (graphic processing units). Unrolled convolution converts the processing in each convolutional layer (both forward-propagation and back-propagation) into a matrix-matrix product. The matrix-matrix product representation of CNNs makes their implementation as easy as MLPs. BLAS is used to efficiently compute matrix products on the CPU. We also present a pixel shader based GPU implementation of CNNs. Results on character recognition problems indicate that unrolled convolution with BLAS produces a dramatic 2.4X−3.0X speedup. The GPU implementation is even faster and produces a 3.1X−4.1X speedup.
Which Visits Lead to Purchases ? Dynamic Conversion Behavior at e-Commerce Sites

This paper develops a model of conversion behavior (i.e., converting store visits into purchases) that predicts each customers probability of purchasing based on an observed history of visits and purchases. We offer an individual-level probability model that allows for consumer heterogeneity in a very flexible manner. We allow visits to play very different roles in the purchasing process. For example, some visits are motivated by a planned purchase while others are simply browsing visits. The Conversion Model in this paper has the flexibility to accommodate a number of visit-to-purchase relationships. Finally, consumers shopping behavior may evolve over time as a function of past experiences. Thus, the Conversion Model also allows for non-stationarity in behavior. Specifically, our Conversion Model decomposes an individuals purchasing conversion behavior into a visit effect and a purchasing threshold effect. Each component is allowed to vary across households as well as over time. We then apply this model to the problem of managing visitor traffic. By predicting purchasing probabilities for a given visit, the Conversion Model can identify those visits that are likely to result in a purchase. These visits should be re-directed to a server that will provide a better shopping experience while those visitors that are less likely to result in a purchase may be identified as targets for a promotion.
Scalable algorithms for scholarly figure mining and semantics

Most scholarly papers contain one or multiple figures. Often these figures show experimental results, e.g, line graphs are used to compare various methods. Compared to the text of the paper, figures and their semantics have received relatively less attention. This has significantly limited semantic search capabilities in scholarly search engines. Here, we report scalable algorithms for generating semantic metadata for figures. Our system has four sequential modules: 1. Extraction of figure, caption and mention; 2. Binary classification of figures as compound (contains sub-figures) or not; 3. Three class classification of non compound figures as line graph, bar graph or others; and 4. Automatic processing of line graphs to generate a textual summary. In each step a metadata file is generated, each having richer information than the previous one. The algorithms are scalable yet each individual step has an accuracy greater than 80%.
Packet-based communication in the cortex

Cortical circuits work through the generation of coordinated, large-scale activity patterns. In sensory systems, the onset of a discrete stimulus usually evokes a temporally organized packet of population activity lasting ∼50–200 ms. The structure of these packets is partially stereotypical, and variation in the exact timing and number of spikes within a packet conveys information about the identity of the stimulus. Similar packets also occur during ongoing stimuli and spontaneously. We suggest that such packets constitute the basic building blocks of cortical coding.