nils-herrmann/clean_scirepeval huggingface dataset:
Unifying Logical and Statistical AI

Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Learning algorithms are based on the voted perceptron, pseudo-likelihood and inductive logic programming. Markov logic has been successfully applied to a wide variety of problems in natural language understanding, vision, computational biology, social networks and others, and is the basis of the open-source Alchemy system.
PEBIL: Efficient static binary instrumentation for Linux

Binary instrumentation facilitates the insertion of additional code into an executable in order to observe or modify the executable's behavior. There are two main approaches to binary instrumentation: static and dynamic binary instrumentation. In this paper we present a static binary instrumentation toolkit for Linux on the x86/x86_64 platforms, PEBIL (PMaC's Efficient Binary Instrumentation Toolkit for Linux). PEBIL is similar to other toolkits in terms of how additional code is inserted into the executable. However, it is designed with the primary goal of producing efficient-running instrumented code. To this end, PEBIL uses function level code relocation in order to insert large but fast control structures. Furthermore, the PEBIL API provides tool developers with the means to insert lightweight hand-coded assembly rather than relying solely on the insertion of instrumentation functions. These features enable the implementation of efficient instrumentation tools with PEBIL. The overhead introduced for basic block counting by PEBIL is an average of 65% of the overhead of Dyninst, 41% of the overhead of Pin, 15% of the overhead of DynamoRIO, and 8% of the overhead of Valgrind.
Krill-eye : Superposition compound eye for wide-angle imaging via GRIN lenses

We propose a novel wide angle imaging system inspired by compound eyes of animals. Instead of using a single lens, well compensated for aberration, we used a number of simple lenses to form a compound eye which produces practically distortion-free, uniform images with angular variation. The images formed by the multiple lenses are superposed on a single surface for increased light efficiency. We use GRIN (gradient refractive index) lenses to create sharply focused images without the artifacts seen when using reflection based methods for X-ray astronomy. We show the theoretical constraints for forming a blur-free image on the image sensor, and derive a continuum between 1 ∶ 1 flat optics for document scanners and curved sensors focused at infinity. Finally, we show a practical application of the proposed optics in a beacon to measure the relative rotation angle between the light source and the camera with ID information.
Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration

This article expands on research that has been done to develop a recurrent neural network (RNN) capable of predicting aircraft engine vibrations using long short-term memory (LSTM) neurons. LSTM RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, making this approach ungeneralizable across multiple engines. In initial work, multiple LSTM RNN architectures were proposed, evaluated and compared. This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network. A parallelized version of the ACO neuroevolution algorithm has been developed and the evolved LSTM RNNs were compared to the previously used fixed topology. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, evolving 1000 different LSTM cell structures using 168 cores over 4 days. The new evolved LSTM cells showed an improvement of 1.35%, reducing prediction error from 5.51% to 4.17% when predicting excessive engine vibrations 10 seconds in the future, while at the same time dramatically reducing the number of weights from 21,170 to 11,810.
Design of planar dual-band multi-way power dividers

This paper presents planar multi-way bagley polygon (BP) power dividers (PDs) that can operate at two arbitrary frequencies. Design equations are derived mathematically, and practical issues in microstrip manufacture are discussed. 3-way and 5-way dual-band BP PDs are fabricated and measured for verification purpose. Good agreement between simulation and measurement results proves the validity of the design method. The presented PDs exhibit wide bandwidths and 180° phase shifts between output ports. Furthermore, simple layouts lead to convenient design procedure and easy fabrication.
Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models

Words are polysemous. However, most approaches to representation learning for lexical semantics assign a single vector to every surface word type. Meanwhile, lexical ontologies such as WordNet provide a source of complementary knowledge to distributional information, including a word sense inventory. In this paper we propose two novel and general approaches for generating sense-specific word embeddings that are grounded in an ontology. The first applies graph smoothing as a postprocessing step to tease the vectors of different senses apart, and is applicable to any vector space model. The second adapts predictive maximum likelihood models that learn word embeddings with latent variables representing senses grounded in an specified ontology. Empirical results on lexical semantic tasks show that our approaches effectively captures information from both the ontology and distributional statistics. Moreover, in most cases our sense-specific models outperform other models we compare against.
Shame Proneness and Guilt Proneness : Toward the Further Understanding of Reactions to Public and Private Transgressions

In Study 1, participants completed five extant shame and guilt proneness inventories based on different theoretical conceptions of the difference between shame and guilt. Factor analyses revealed that despite very different theoretical distinctions, the shame proneness subscales loaded on one factor, and the guilt proneness subscales loaded on one factor. In Study 2, we altered scale items so that hypothetical transgressions were committed in either public or private, and likelihood response options were either typical of a “shame-prone response” (negative selfevaluation; avoidance behavior) or a “guilt-prone response” (negative behavior-evaluation; approach behavior). Our findings indicate that shame and guilt proneness can be measured both by responses to transgressions (e.g., negative self-evaluation and avoidance responses vs. negative behavior-evaluation and approach responses) and the situational context in which the transgression occurs (e.g., public vs. private). We provide recommendations regarding optimal measurement of shame and guilt proneness.
Securing Bitcoin wallets via a new DSA / ECDSA threshold signature scheme

The Bitcoin ecosystem has suffered frequent thefts and losses affecting both businesses and individuals. Due to the irreversibility, automation, and pseudonymity of transactions, Bitcoin currently lacks support for the sophisticated internal control systems deployed by modern businesses to deter fraud. To address this problem, we present the first threshold signature scheme compatible with Bitcoin’s ECDSA signatures and show how distributed Bitcoin wallets can be built using this primitive. For businesses, we show how our distributed wallets can be used to systematically eliminate single points of failure at every stage of the flow of bitcoins through the system. For individuals, we design, implement, and evaluate a two-factor secure Bitcoin wallet.
Cultural Congruence and Infusion Nursing Practice.

The importance of cultural competence in every nursing practice setting in today's world cannot be understated. Unconscious bias can have detrimental effects on therapeutic relationships and health outcomes. Nursing models of cultural competence by Purnell, Leininger, and Campinha-Bacote are reviewed. The Kleinman Model and LEARN Model offer questions and guidelines to facilitate assessment of patients' understanding of illness and treatment. The Infusion Nursing Standards of Practice contains elements of diversity and cultural competence throughout. Self-reflection of one's own values, beliefs, biases, and practice as an infusion nurse will promote the development of cultural competence.
An Attention-based Regression Model for Grounding Textual Phrases in Images

Grounding, or localizing, a textual phrase in an image is a challenging problem that is integral to visual language understanding. Previous approaches to this task typically make use of candidate region proposals, where end performance depends on that of the region proposal method and additional computational costs are incurred. In this paper, we treat grounding as a regression problem and propose a method to directly identify the region referred to by a textual phrase, eliminating the need for external candidate region prediction. Our approach uses deep neural networks to combine image and text representations and refines the target region with attention models over both image subregions and words in the textual phrase. Despite the challenging nature of this task and sparsity of available data, in evaluation on the ReferIt dataset, our proposed method achieves a new state-of-the-art in performance of 37.26% accuracy, surpassing the previously reported best by over 5 percentage points. We find that combining image and text attention models and an image attention area-sensitive loss function contribute to substantial improvements.
Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty

Data mining (DM) methodology has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the data used by researchers. This study has proposed a new procedure, based on expanded RFM model by including one additional parameter, joining WRFM-based method to K-means algorithm applied in DM with K-optimum according to Davies– Bouldin Index, and then classifying customer product loyalty in under B2B concept. The developed methodology has been implemented for SAPCO Co. in Iran. The result shows a tremendous capability to the firm to assess his customer loyalty in marketing strategy designed by this company in comparing with random selection commonly used by most companies in Iran.
Impact of single-microphone dereverberation on DNN-based meeting transcription systems

Over the past few decades, a range of front-end techniques have been proposed to improve the robustness of automatic speech recognition systems against environmental distortion. While these techniques are effective for small tasks consisting of carefully designed data sets, especially when used with a classical acoustic model, there has been limited evidence that they are useful for a state-of-the-art system with large scale realistic data. This paper focuses on reverberation as a type of distortion and investigates the degree to which dereverberation processing can improve the performance of various forms of acoustic models based on deep neural networks (DNNs) in a challenging meeting transcription task using a single distant microphone. Experimental results show that dereverberation improves the recognition performance regardless of the acoustic model structure and the type of the feature vectors input into the neural networks, providing additional relative improvements of 4.7% and 4.1% to our best configured speaker-independent and speaker-adaptive DNN-based systems, respectively.
Tri-Party Deep Network Representation

Information network mining often requires examination of linkage relationships between nodes for analysis. Recently, network representation has emerged to represent each node in a vector format, embedding network structure, so off-the-shelf machine learning methods can be directly applied for analysis. To date, existing methods only focus on one aspect of node information and cannot leverage node labels. In this paper, we propose TriDNR, a tri-party deep network representation model, using information from three parties: node structure, node content, and node labels (if available) to jointly learn optimal node representation. TriDNR is based on our new coupled deep natural language module, whose learning is enforced at three levels: (1) at the network structure level, TriDNR exploits inter-node relationship by maximizing the probability of observing surrounding nodes given a node in random walks; (2) at the node content level, TriDNR captures node-word correlation by maximizing the co-occurrence of word sequence given a node; and (3) at the node label level, TriDNR models label-word correspondence by maximizing the probability of word sequence given a class label. The tri-party information is jointly fed into the neural network model to mutually enhance each other to learn optimal representation, and results in up to 79% classification accuracy gain, compared to state-of-the-art methods.
FastVentricle: Cardiac Segmentation with ENet

Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that post-processing of exams is tedious. Without automation, precise assessment of cardiac function via CMR typically requires an annotator to spend tens of minutes per case manually contouring ventricular structures. Automatic contouring can lower the required time per patient by generating contour suggestions that can be lightly modified by the annotator. Fully convolutional networks (FCNs), a variant of convolutional neural networks, have been used to rapidly advance the state-of-theart in automated segmentation, which makes FCNs a natural choice for ventricular segmentation. However, FCNs are limited by their computational cost, which increases the monetary cost and degrades the user experience of production systems. To combat this shortcoming, we have developed the FastVentricle architecture, an FCN architecture for ventricular segmentation based on the recently developed ENet architecture. FastVentricle is 4× faster and runs with 6× less memory than the previous state-of-the-art ventricular segmentation architecture while still maintaining excellent clinical accuracy.
The short-form McGill pain questionnaire

A short form of the McGill Pain Questionnaire (SF-MPQ) has been developed. The main component of the SF-MPQ consists of 15 descriptors (11 sensory; 4 affective) which are rated on an intensity scale as 0 = none, 1 = mild, 2 = moderate or 3 = severe. Three pain scores are derived from the sum of the intensity rank values of the words chosen for sensory, affective and total descriptors. The SF-MPQ also includes the Present Pain Intensity (PPI) index of the standard MPQ and a visual analogue scale (VAS). The SF-MPQ scores obtained from patients in post-surgical and obstetrical wards and physiotherapy and dental departments were compared to the scores obtained with the standard MPQ. The correlations were consistently high and significant. The SF-MPQ was also shown to be sufficiently sensitive to demonstrate differences due to treatment at statistical levels comparable to those obtained with the standard form. The SF-MPQ shows promise as a useful tool in situations in which the standard MPQ takes too long to administer, yet qualitative information is desired and the PPI and VAS are inadequate.
Design and development of a low cost EMG signal acquisition system using surface EMG electrode

Electromyogram or EMG signal is a very small signal; it requires a system to enhance for display purpose or for further analysis process. This paper presents the development of low cost physiotherapy EMG signal acquisition system with two channel input. In the acquisition system, both input signals are amplified with a differential amplifier and undergo signal pre-processing to obtain the linear envelope of EMG signal. Obtained EMG signal is then digitalized and sent to the computer to be plotted.
Region-Enhanced Imaging for Sparse-Aperture Passive Radar

We present the application of a recently-developed region-enhanced synthetic aperture radar (SAR) image reconstruction technique to the problem of passive radar imaging. One goal in passive radar imaging is to form images of aircraft using signals transmitted by commercial radio and television stations, which then get reflected from the objects of interest. This involves reconstructing an image from sparse samples of its Fourier transform. Due to the sparse nature of the aperture, a conventional image formation approach based on direct Fourier transformation results in quite dramatic artifacts in the image, as compared to the case of active SAR imaging. The region-enhanced image formation method we consider appears to significantly reduce such artifacts, and preserve the features of the imaged object. Furthermore, this approach exhibits robustness to measurement noise. We demonstrate our results using data based on electromagnetic simulations.
Implementation of FC-TCR for Reactive Power Control

This paper deals with the simulation and implementation of fixed capacitor thyristor controlled reactor system(SVC) . The TCR system is simulated using MATLAB and the simulation results are presented. SVC is basically a shunt connected static var generator whose output is adjusted to exchange capacitive or inductive current so as to maintain or control specific power variable. In this paper, simple circuit model of Thyristor Controlled Reactor is modeled and simulated using MATLAB. The simulation results are presented. The current drawn by the TCR varies with the variation in the firing angle. The simulation results are compared with the theoretical results.
Performance evaluation of synchronous rectification in front-end full-bridge rectifiers

In this paper, performance evaluation of synchronous rectification in front-end full-bridge rectifiers is presented. Specifically, the implementation of the full-bridge rectifier with the two diode rectifiers connected to the negative output terminal replaced by two synchronous rectifiers (SRs) and the implementation with all four diode rectifiers replaced by four SRs are considered. In both implementations, the SRs are N-channel MOSFETs controlled by sensing the line voltage. Two methods of line-voltage sensing are presented. First, direct line-voltage sensing, and second, indirect line-voltage sensing, i.e., sensing the line voltage between an input terminal and the negative output terminal. The proposed implementations also include a protection method of preventing accidental short circuit between the input or output terminals. In addition, SR power-management methods such as turning off the SRs at light loads and at no load are provided. The protection of SRs at large inrush currents is also discussed. Experimental results obtained on a 90-W (19.5-V/4.6-A) laptop adapter for the universal line voltage range (90-264 Vrms) are given. In the experimental circuit with four SRs, the efficiency improvement at 90-Vrms line voltage and full load is 1.31%.
GATA: GPS-Arduino based Tracking and Alarm system for protection of wildlife animals

In the present arena, wildlife and forest departments are facing the problem of movement of animals from forest area to residential area. The number of trees has reduced drastically from the forest that creates an unhealthy environment for animals to survive in the forest. This paper proposes a system we call GATA for tracking and alarming for the protection of Wildlife Animals. GATA combined Wireless Sensor Network (WSN)  and Global Positioning System (GPS) technologies to solve the above mentioned problem. Wild animals straying out of wildlife sanctuaries and natural parks have been tracked by auto generative location tracking and movement patterns. Automatic location and movement tracking has been implemented using GPS with the accelerometer and the WiFi shield. In the event of straying of a wild animal out of the predefined zone of sanctuary or natural reserve, an alert is sounded on a fixed base station (BS). As a prototype, we have tested this hardware on the cows, which shows that the proposed approach is very efficient in terms of flexibility and cost. This may be acting as a deterrent to various anti social activities poaching, train delays, railway accidents and danger to man due to the straying out of the animals off their habitation zone.
Applying Universal Schemas for Domain Specific Ontology Expansion

Manually created large scale ontologies are useful for organizing, searching, and repurposing content ranging from scientific papers and medical guidelines to images. However, maintenance of such ontologies is expensive. In this paper, we investigate the use of universal schemas (Riedel et al., 2013) as a mechanism for ontology maintenance. We apply this approach on top of two unique data sources: 14 million full-text scientific articles and chapters, plus a 1 million concept handcurated medical ontology. We show that using a straightforward matrix factorization algorithm one can achieve 0.7 F1 measure on a link prediction task in this environment. Link prediction results can be used to suggest new relation types and relation type synonyms coming from the literature as well as predict specific new relation instances in the ontology.
Issues in predicting and explaining usage behaviors with the technology acceptance model and the theory of planned behavior when usage is mandatory

Within certain industries there is an increasing prevalence toward computerizing work practices and mandating specific tasks performed using an Information System (IS). This trend is unlikely to diminish the need for stakeholders, involved in the implementation of an IS, to identify the determinants of successful use. Yet, to date, there is a paucity of research that has considered the issues specific to predicting and explaining user behavior in these situations. In this paper, we identify the relevant issues necessary for applying the technology acceptance model and the theory of planned behavior to the prediction and explanation of mandated IS usage, and we assess the value of these models to the task. The results of a longitudinal study conducted in a hospital setting are presented and we suggest the direction that future research might take.
Measuring cultural awareness in nursing students.

Recognizing the need for a valid and reliable way to measure outcomes of a program to promote multicultural awareness among nursing faculty and students, the authors developed a cultural awareness scale. In the first phase of the study, a scale consisting of 37 items was generated from a literature review on cultural awareness, sensitivity, and competence in nursing. A Cronbach's alpha reliability coefficient of .91 was obtained from a sample of 72 student nurses. In the second phase, the items were presented to a panel of experts in nursing and culture to determine content validity. A content validity index of .88 was calculated, and the total number of items on the scale was reduced to 36. The scale then was administered to 118 nursing students. Data from the two samples then were combined, and factor analysis was conducted to support construct validity. Cronbach's alpha for the combined samples was .82.