nils-herrmann/clean_scirepeval huggingface dataset:
Experience report on the FIDES reliability prediction method

This paper examines predicted failure rates for three circuit card assemblies (CCA's) using the new FIDES prediction methodology. These results are compared to observed field failure rates, as well as predicted failure rates derived from MIL-HDBK 217, Reliability Prediction of Electronic Equipment, and the PRISM/spl reg/ prediction software tool that become satisfactory. The FIDES prediction methodology, implemented in Excel workbooks, is easy to use and according to our preliminary study, appears to be an accurate prediction tool.
Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing

Machine Learning has been the quintessential solution for many AI problems, but learning models are heavily dependent on specific training data. Some learning models can be incorporated with prior knowledge using a Bayesian setup, but these learning models do not have the ability to access any organized world knowledge on demand. In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks. Our aim is to develop a deep learning model that can extract relevant prior support facts from knowledge graphs depending on the task using attention mechanism. We introduce a convolutionbased model for learning representations of knowledge graph entity and relation clusters in order to reduce the attention space. We show that the proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. Using this method we show significant improvement in performance for text classification with 20Newsgroups (News20) & DBPedia datasets, and natural language inference with Stanford Natural Language Inference (SNLI) dataset. We also demonstrate that a deep learning model can be trained with substantially less amount of labeled training data, when it has access to organized world knowledge in the form of a knowledge base.
Pore pressure and poroelasticity effects in Coulomb stress analysis of earthquake interactions

Pore pressure changes are rigorously included in Coulomb stress calculations for fault interaction studies. These are considered changes under undrained conditions for analyzing very short term postseismic response. The assumption that pore pressure is proportional to faultnormal stress leads to the widely used concept of an effective friction coefficient. We provide an exact expression for undrained fault zone pore pressure changes to evaluate the validity of that concept. A narrow fault zone is considered whose poroelastic parameters are different from those in the surrounding medium, which is assumed to be elastically isotropic. We use conditions for mechanical equilibrium of stress and geometric compatibility of strain to express the effective normal stress change within the fault as a weighted linear combination of mean stress and faultnormal stress changes in the surroundings. Pore pressure changes are determined by fault-normal stress changes when the shear modulus within the fault zone is significantly smaller than in the surroundings but by mean stress changes when the elastic mismatch is small. We also consider an anisotropic fault zone, introducing a Skempton tensor for pore pressure changes. If the anisotropy is extreme, such that fluid pressurization under constant stress would cause expansion only in the fault-normal direction, then the effective friction coefficient concept applies exactly. We finally consider moderately longer timescales than those for undrained response. A sufficiently permeable fault may come to local pressure equilibrium with its surroundings even while that surrounding region may still be undrained, leading to pore pressure change determined by mean stress changes in those surroundings.
Integrated Position Estimation Using Aerial Image Sequences

This paper presents an integrated system for navigation parameter estimation using sequential aerial images, where navigation parameters represent the position and velocity information of an aircraft for autonomous navigation. The proposed integrated system is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values decreases the reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in a large position error. Therefore, absolute position estimation is required to compensate for the position error generated in relative position estimation. Absolute position estimation algorithms by image matching and digital elevation model (DEM) matching are presented. In image matching, a robust-oriented Hausdorff measure (ROHM) is employed, whereas in DEM matching the algorithm using multiple image pairs is used. Experiments with four real aerial image sequences show the effectiveness of the proposed integrated position estimation algorithm.
Audio event and scene recognition: A unified approach using strongly and weakly labeled data

In this paper we propose a novel learning framework called Supervised and Weakly Supervised Learning where the goal is to learn simultaneously from weakly and strongly labeled data. Strongly labeled data can be simply understood as fully supervised data where all labeled instances are available. In weakly supervised learning only data is weakly labeled which prevents one from directly applying supervised learning methods. Our proposed framework is motivated by the fact that a small amount of strongly labeled data can give considerable improvement over only weakly supervised learning. The primary problem domain focus of this paper is acoustic event and scene detection in audio recordings. We first propose a naive formulation for leveraging labeled data in both forms. We then propose a more general framework for Supervised and Weakly Supervised Learning (SWSL). Based on this general framework, we propose a graph based approach for SWSL. Our main method is based on manifold regularization on graphs in which we show that the unified learning can be formulated as a constraint optimization problem which can be solved by iterative concave-convex procedure (CCCP). Our experiments show that our proposed framework can address several concerns of audio content analysis using weakly labeled data.
Enhanced K-Mean Clustering Algorithm for Liver Image Segmentation to Extract Cyst Region

This paper, first analysis the performance of image segmentation techniques; K-mean clustering algorithm and region growing for cyst area extraction from liver images, then enhances the performance of K-mean by post-processing. The K-mean algorithm makes the clusters effectively. But it could not separate out the desired cluster (cyst) from the image. So, to enhance its performance for cyst region extraction, morphological opening-by-reconstruction is applied on the output of K-mean clustering algorithm. The results are presented both qualitatively and quantitatively, which demonstrate the superiority of enhanced K-mean as compared to standard K-mean and region growing algorithm.
RESPARC: A reconfigurable and energy-efficient architecture with Memristive Crossbars for deep Spiking Neural Networks

Neuromorphic computing using post-CMOS technologies is gaining immense popularity due to its promising abilities to address the memory and power bottlenecks in von-Neumann computing systems. In this paper, we propose RESPARC - a reconfigurable and energy efficient architecture built-on Memristive Crossbar Arrays (MCA) for deep Spiking Neural Networks (SNNs). Prior works were primarily focused on device and circuit implementations of SNNs on crossbars. RESPARC advances this by proposing a complete system for SNN acceleration and its subsequent analysis. RESPARC utilizes the energy-efficiency of MCAs for inner-product computation and realizes a hierarchical reconfigurable design to incorporate the data-flow patterns in an SNN in a scalable fashion. We evaluate the proposed architecture on different SNNs ranging in complexity from 2k-230k neurons and 1.2M-5.5M synapses. Simulation results on these networks show that compared to the baseline digital CMOS architecture, RESPARC achieves 500x (15x) efficiency in energy benefits at 300x (60x) higher throughput for multi-layer perceptrons (deep convolutional networks). Furthermore, RESPARC is a technology-aware architecture that maps a given SNN topology to the most optimized MCA size for the given crossbar technology.
Development of a robot finger for five-fingered hand using ultrasonic motors

A robot finger is developed for five-fingered robot hand having equal number of DOF to human hand. The robot hand is driven by a new method proposed by authors using ultrasonic motors and elastic elements. The method utilizes restoring force of elastic element as driving power for grasping an object, so that the hand can perform the soft and stable grasping motion with no power supply. In addition, all the components are placed inside the hand thanks to the ultrasonic motors with compact size and high torque at low speed. Applying the driving method to multi-DOF mechanism, a robot index finger is designed and implemented. It has equal number of joints and DOF to human index finger, and it is also equal in size to the finger of average adult male. The performance of the robot finger is confirmed by fundamental driving test.
A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy

This paper considers the problem of secure data aggregation (mainly summation) in a distributed setting, while ensuring differential privacy of the result. We study secure multiparty addition protocols using well known security schemes: Shamir’s secret sharing, perturbation-based, and various encryptions. We supplement our study with our new enhanced encryption scheme EFT, which is efficient and fault tolerant.Differential privacy of  the final result is achieved by either distributed Laplace or Geometric mechanism (respectively DLPA or DGPA), while approximated differential privacy is achieved by diluted mechanisms.  Distributed random noise is generated collectively by all participants, which draw random variables from one of several distributions: Gamma, Gauss, Geometric, or their diluted versions.  We introduce a new distributed privacy mechanism with noise drawn from the Laplace distribution, which achieves smaller redundant noise with efficiency.  We compare complexity and security characteristics of the protocols with different differential privacy mechanisms and security schemes.  More importantly, we implemented all protocols and present an experimental comparison on their performance and scalability in a real distributed environment.  Based on the evaluations, we identify our security scheme and Laplace DLPA as the most efficient for secure distributed data aggregation with differential privacy.
A New Dynamic Hash Algorithm in Digital Signature

This paper presents adoption of a new hash algorithm in digital signature. Digital signature presents a technique to endorse the content of the message. This message has not been altered throughout the communication process. Due to this, it increased the receiver confidence that the message was unchanged. If the message is digitally signed, any changes in the message will invalidate the signature. The comparison of digital signature between Rivest, Shamir and Adleman (RSA) algorithms are summarized. The finding reveals that previous algorithms used large file sizes. Finally the new encoding and decoding dynamic hash algorithm is proposed in a digital signature. The proposed algorithm had reduced significantly the file sizes (8 bytes) during the transferring message.
Coupled Support Vector Machines for Supervised Domain Adaptation

Popular domain adaptation (DA) techniques learn a classifier for the target domain by sampling relevant data points from the source and combining it with the target data. We present a Support Vector Machine (SVM) based supervised DA technique, where the similarity between source and target domains is modeled as the similarity between their SVM decision boundaries. We couple the source and target SVMs and reduce the model to a standard single SVM. We test the Coupled-SVM on multiple datasets and compare our results with other popular SVM based DA approaches.
How to Achieve High Classification Accuracy with Just a Few Labels: A Semi-supervised Approach Using Sampled Packets

Network traffic classification, which has numerous applications from security to billing and network provisioning, has become a cornerstone of today’s computer networks. Previous studies have developed traffic classification techniques using classical machine learning algorithms and deep learning methods when large quantities of labeled data are available. However, capturing large labeled datasets is a cumbersome and time-consuming process. In this paper, we propose a semi-supervised approach that obviates the need for large labeled datasets. We first pre-train a model on a large unlabeled dataset where the input is the time series features of a few sampled packets. Then the learned weights are transferred to a new model that is re-trained on a small labeled dataset. We show that our semi-supervised approach achieves almost the same accuracy as a fully-supervised method with a large labeled dataset, though we use only 20 samples per class. In tests based on a dataset generated from the more challenging QUIC protocol, our approach yields 98% accuracy. To show its efficacy, we also test our approach on two public datasets. Moreover, we study three different sampling techniques and demonstrate that sampling packets from an arbitrary portion of a flow is sufficient for classification.
Discriminative Dictionary Learning with Pairwise Constraints

In computer vision problems such as pair matching, only binary information ‘same’ or ‘different’ label for pairs of images is given during training. This is in contrast to classification problems, where the category labels of training images are provided. We propose a unified discriminative dictionary learning approach for both pair matching and multiclass classification tasks. More specifically, we introduce a new discriminative term called ‘pairwise sparse code error’ for the discriminativeness in sparse representation of pairs of signals, and then combine it with the classification error for discriminativeness in classifier construction to form a unified objective function. The solution to the new objective function is achieved by employing the efficient feature-sign search algorithm. The learned dictionary encourages feature points from a similar pair (or the same class) to have similar sparse codes. We validate the effectiveness of our approach through a series of experiments on face verification and recognition problems.
Cancer subtype identification using deep learning approach

In this paper, a framework using deep learning approach is proposed to identify two subtypes of human colorectal carcinoma cancer. The identification process uses information from gene expression and clinical data which is obtained from data integration process. One of deep learning architecture, multimodal Deep Boltzmann Machines (DBM) is used for data integration process. The joint representation gene expression and clinical is later used as Restricted Boltzmann Machines (RBM) input for cancer subtype identification. Kaplan Meier survival analysis is employed to evaluate the identification result. The curves on survival plot obtained from Kaplan Meier analysis are tested using three statistic tests to ensure that there is a significant difference between those curves. According to Log Rank, Generalized Wilcoxon and Tarone-Ware, the two groups of patients with different cancer subtypes identified using the proposed framework are significantly different.
Peripheral Stimulation and its Effect on Perceived Spatial Scale in Virtual Environments

The following series of experiments explore the effect of static peripheral stimulation on the perception of distance and spatial scale in a typical head-mounted virtual environment. It was found that applying constant white light in an observers far periphery enabled the observer to more accurately judge distances using blind walking. An effect of similar magnitude was also found when observers estimated the size of a virtual space using a visual scale task. The presence of the effect across multiple psychophysical tasks provided confidence that a perceptual change was, in fact, being invoked by the addition of the peripheral stimulation. These results were also compared to observer performance in a very large field of view virtual environment and in the real world. The subsequent findings raise the possibility that distance judgments in virtual environments might be considerably more similar to those in the real world than previous work has suggested.
Glucocorticoid signaling defines a novel commitment state during adipogenesis in vitro.

Differentiation of 3T3-L1 preadipocytes can be induced by a 2-d treatment with a factor "cocktail" (DIM) containing the synthetic glucocorticoid dexamethasone (dex), insulin, the phosphodiesterase inhibitor methylisobutylxanthine (IBMX) and fetal bovine serum (FBS). We temporally uncoupled the activities of the four DIM components and found that treatment with dex for 48 h followed by IBMX treatment for 48 h was sufficient for adipogenesis, whereas treatment with IBMX followed by dex failed to induce significant differentiation. Similar results were obtained with C3H10T1/2 and primary mesenchymal stem cells. The 3T3-L1 adipocytes differentiated by sequential treatment with dex and IBMX displayed insulin sensitivity equivalent to DIM adipocytes, but had lower sensitivity to ISO-stimulated lipolysis and reduced triglyceride content. The nondifferentiating IBMX-then-dex treatment produced transient expression of adipogenic transcriptional regulatory factors C/EBPbeta and C/EBPdelta, and little induction of terminal differentiation factors C/EBPalpha and PPARgamma. Moreover, the adipogenesis inhibitor preadipocyte factor-1 (Pref-1) was repressed by DIM or by dex-then-IBMX, but not by IBMX-then-dex treatment. We conclude that glucocorticoids drive preadipocytes to a novel intermediate cellular state, the dex-primed preadipocyte, during adipogenesis in cell culture, and that Pref-1 repression may be a cell fate determinant in preadipocytes.
Decision-making impairments in patients with pathological gambling

Pathological gambling (PG) is most likely associated with functional brain changes as well as neuropsychological and personality alterations. Recent research with the Iowa Gambling Task suggests decision-making impairments in PG. These deficits are usually attributed to disturbances in feedback processing and associated functional alterations of the orbitofrontal cortex. However, previous studies with other clinical populations found relations between executive (dorsolateral prefrontal) functions and decision-making using a task with explicit rules for gains and losses, the Game of Dice Task. In the present study, we assessed 25 male PG patients and 25 male healthy controls with the Game of Dice Task. PG patients showed pronounced deficits in the Game of Dice Task, and the frequency of risky decisions was correlated with executive functions and feedback processing. Therefore, risky decisions of PG patients might be influenced by both dorsolateral prefrontal and orbitofrontal cortex dysfunctions.
Investor Competence, Trading Frequency, and Home Bias

People are more willing to bet on their own judgments when they feel skillful or knowledgeable (Heath and Tversky, 1991). We investigate whether this ‘competence effect’ influences trading frequency and home bias. We find that investors who feel competent trade more often and have more internationally diversified portfolios. We also find that male investors, and investors with larger portfolios or more education, are more likely to perceive themselves as competent than are female investors, and investors with smaller portfolios or less education. Our paper also contributes to understanding the theoretical link between overconfidence and trading frequency. Existing theories on trading frequency have focused on one aspect of overconfidence, i.e., miscalibration. Our paper offers a potential mechanism for the ‘betterthan-average’ aspect of overconfidence to influence trading frequency. In the context of our paper, overconfident investors tend to perceive themselves to be more competent, and thus are more willing to act on their beliefs, leading to higher trading frequency. ______________________________________________________________________________ JEL Classification: G11, G15, F30, G12
Major recurrent aphthous ulceration: case report and review of the literature.

A 25-year-old male presented complaining of a large painful ulcer near the left tonsil persisting for two months. Biopsy, serologies and cultures were performed to rule out infectious etiology. Symptomatic treatment was prescribed and the ulcer spontaneously resolved. One year later he presented with a similar ulcer, this time near the right tonsil, persisting for three months. A diagnosis of Major Recurrent Aphthous Ulceration was made based on the history and on the exclusion of other diagnoses. Prednisone 60 mg was prescribed and the ulcer healed in one week, leaving a scar. The case illustrates an unusual presentation of aphthous disease because of the ulcer's size and duration. Major recurrent aphthous ulcers constitute 10% to 15% of recurrent aphthous ulcers. These lesions are greater than 10 mm in diameter, take 10 to 30 days to heal. Categorized as an idiopathic disease, they are painful and recur with varying frequency. It is estimated that 20% of the general population will suffer from recurrent aphthous ulcer. The major variety differs because it lasts longer, often leaves a scar, and more frequently needs systemic medication. In this case, the lesion demonstrated a good response to steroids.
Multimodal Explanations by Predicting Counterfactuality in Videos

This study addresses generating counterfactual explanations with multimodal information. Our goal is not only to classify a video into a specific category, but also to provide explanations on why it is not predicted as part of a specific class with a combination of visual-linguistic information. Requirements that the expected output should satisfy are referred to as counterfactuality in this paper: (1) Compatibility of visual-linguistic explanations, and (2) Positiveness/negativeness for the specific positive/negative class. Exploiting a spatio-temporal region (tube) and an attribute as visual and linguistic explanations respectively, the explanation model is trained to predict the counterfactuality for possible combinations of multimodal information in a posthoc manner. The optimization problem, which appears during the training/inference process, can be efficiently solved by inserting a novel neural network layer, namely the maximum subpath layer. We demonstrated the effectiveness of this method by comparison with a baseline of the actionrecognition datasets extended for this task. Moreover, we provide information-theoretical insight into the proposed method.