nils-herrmann/clean_scirepeval huggingface dataset:
Exploring Inter-feature and Inter-class Relationships with Deep Neural Networks for Video Classification

Videos contain very rich semantics and are intrinsically multimodal. In this paper, we study the challenging task of classifying videos according to their high-level semantics such as human actions or complex events. Although extensive efforts have been paid to study this problem, most existing works combined multiple features using simple fusion strategies and neglected the exploration of inter-class semantic relationships. In this paper, we propose a novel unified framework that jointly learns feature relationships and exploits the class relationships for improved video classification performance. Specifically, these two types of relationships are learned and utilized by rigorously imposing regularizations in a deep neural network (DNN). Such a regularized DNN can be efficiently launched using a GPU implementation with an affordable training cost. Through arming the DNN with better capability of exploring both the inter-feature and the inter-class relationships, the proposed regularized DNN is more suitable for identifying video semantics. With extensive experimental evaluations, we demonstrate that the proposed framework exhibits superior performance over several state-of-the-art approaches. On the well-known Hollywood2 and Columbia Consumer Video benchmarks, we obtain to-date the best reported results: 65.7% and 70.6% respectively in terms of mean average precision.
Outcome after rectovaginal fascia reattachment for rectocele repair.

This study was undertaken to determine the effects of rectovaginal fascia reattachment on symptoms and vaginal topography.



Standardized preoperative and postoperative assessments of vaginal topography (the Pelvic Organ Prolapse staging system of the International Continence Society, American Urogynecologic Society, and Society of Gynecologic Surgeons) and 5 symptoms commonly attributed to rectocele were used to evaluate 66 women who underwent rectovaginal fascia reattachment for rectocele repair. All patients had abnormal fluoroscopic results with objective rectocele formation.



Seventy percent (n = 46) of the women were objectively assessed at 1 year. Preoperative symptoms included the following: protrusion, 85% (n = 39); difficult defecation, 52% (n = 24); constipation, 46% (n = 21); dyspareunia, 26% (n = 12); and manual evacuation, 24% (n = 11). Posterior vaginal topography was considered abnormal in all patients with a mean Ap point (a point located in the midline of the posterior vaginal wall 3 cm proximal to the hymen) value of -0.5 cm (range, -2 to 3 cm). Postoperative symptom resolution was as follows: protrusion, 90% (35/39; P <.0005); difficult defecation, 54% (14/24; P <.0005); constipation, 43% (9/21; P =.02); dyspareunia, 92% (11/12; P =.01); and manual evacuation, 36% (4/11; P =.125). Vaginal topography at 1 year was improved, with a mean Ap point value of -2 cm (range, -3 to 2 cm).



This technique of rectocele repair improves vaginal topography and alleviates 3 symptoms commonly attributed to rectoceles. It is relatively ineffective for relief of manual evacuation, and constipation is variably decreased.
Towards a Financial Fraud Ontology: A Legal Modelling Approach

This document discusses the status of research on detection and prevention of financial fraud undertaken as part of the IST European Commission funded FF POIROT (Financial Fraud Prevention Oriented Information Resources Using Ontology Technology) project. A first task has been the specification of the user requirements that define the functionality of the financial fraud ontology to be designed by the FF POIROT partners. It is claimed here that modeling fraudulent activity involves a mixture of law and facts as well as inferences about facts present, facts presumed or facts missing. The purpose of this paper is to explain this abstract model and to specify the set of user requirements.
Identifying the characteristics of vulnerable code changes: an empirical study

To focus the efforts of security experts, the goals of this empirical study are to analyze which security vulnerabilities can be discovered by code review, identify characteristics of vulnerable code changes, and identify characteristics of developers likely to introduce vulnerabilities. Using a three-stage manual and automated process, we analyzed 267,046 code review requests from 10 open source projects and identified 413 Vulnerable Code Changes (VCC). Some key results include: (1) code review can identify common types of vulnerabilities; (2) while more experienced contributors authored the majority of the VCCs, the less experienced contributors' changes were 1.8 to 24 times more likely to be vulnerable; (3) the likelihood of a vulnerability increases with the number of lines changed, and (4) modified files are more likely to contain vulnerabilities than new files. Knowing which code changes are more prone to contain vulnerabilities may allow a security expert to concentrate on a smaller subset of submitted code changes. Moreover, we recommend that projects should: (a) create or adapt secure coding guidelines, (b) create a dedicated security review team, (c) ensure detailed comments during review to help knowledge dissemination, and (d) encourage developers to make small, incremental changes rather than large changes.
Performance evaluation of interthread communicationmechanisms on multicore/multithreaded architectures

The three major solutions for increasing the nominal performance of a CPU are: multiplying the number of cores per socket, expanding the embedded cache memories and use multi-threading to reduce the impact of the deep memory hierarchy. Systems with tens or hundreds of hardware threads, all sharing a cache coherent UMA or NUMA memory space, are today the de-facto standard. While these solutions can easily provide benefits in a multi-program environment, they require recoding of applications to leverage the available parallelism. Threads must synchronize and exchange data, and the overall performance is heavily in influenced by the overhead added by these mechanisms, especially as developers try to exploit finer grain parallelism to be able to use all available resources.
Design and simulation of 16 Bit UART Serial Communication Module Based on VHDL

This paper describes the design of UART (universal asynchronous receiver transmitter) based on VHDL. As UART is consider as a low speed, low cost data exchange between computer and peripherals.[1].To overcome the problem of low speed data transmission , a 16 bit UART is proposed in this paper. It works on 9600bps baud rate. This will result in increased the speed of UART.Whole design is simulated with Xilinx ISE8.2i software and results are completely consistent with UART protocol.  Baud rate generator, HDL, ISE8.2i, Receiver, Serial communication, Transmitter, Xilinx.
5.75 to 44Gb/s quarter rate CDR with data rate selection in 90nm bulk CMOS

This paper presents a quarter rate clock/data recovery (CDR) circuit for plesiochronous serial I/O-links. This 2x-oversampled phase-tracking CDR, implemented in 90 nm bulk CMOS technology, covers the whole range of data rates from 5.75 to 44 Gb/s thanks to a data rate selection logic. A bit error rate <10-12 was verified up to 38 Gb/s using a 27-1 PRBS pattern. The CDR is able to track a maximum frequency deviation of plusmn615 ppm between incoming data and reference clock.
Deep feature learning and selection for activity recognition

Human physical activity recognition from sensor data is a growing area of research due to the widespread adoption of sensor-rich wearable and smart devices. The growing interest resulted in several formulations with multiple proposals for each of them. This paper is interested in activity recognition from short sequences of sensor readings. Traditionally, solutions to this problem have relied on handcrafted features and feature selection from large predefined feature sets. More recently, deep methods have been employed to provide an end-to-end classification system for activity recognition with higher accuracy at the expense of much slower performance. This paper proposes a middle ground in which a deep neural architecture is employed for feature learning followed by traditional feature selection and classification. This approach is shown to outperform state-of-the-art systems on six out of seven experiments using publicly available datasets.
Repair of full-thickness alar defects.

A basal cell carcinoma can invade the cartilage in the nasal ala and can reach the nasal vestibule skin. A full-thickness reconstruction of the nasal ala is required to repair the tissue damage after tumor removal. We combined an auricular composite graft with a nasolabial flap to repair the defects in the nasal ala.



Eleven patients with full-thickness infiltration of the nasal ala by basal cell carcinoma were included in the study. The outer defect area after tumor removal ranged from 1.5 × 2.0 to 2.0 × 2.5 cm(2). Skin-cartilage composite grafts 1.5 × 1.5 to 1.5 × 2.0 cm(2) in size were harvested from the helix at the top of the ear. The composite graft was used as a support scaffold to reconstruct the defects. A nasolabial flap was used to cover the cartilage.



All cases were treated successfully. No flap necrosis, skin color changes, or blistering occurred. The shape of the flap was stable, and the skin color was consistent with that of the surrounding tissue. The reconstructed nasal ala was symmetrical.



Combining an auricular composite graft with a nasolabial flap can repair defects up to 2.0 × 2.5 cm(2) in size. The reconstructed nasal ala matches the original skin color and maintains the appropriate shape.
aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis

This paper describes the system with which we participated in SemEval-2016 Task 4 (Sentiment Analysis in Twitter) and specifically the Message Polarity Classification subtask. Our system is a weighted ensemble of two systems. The first one is based on a previous sentiment analysis system and uses manually crafted features. The second system of our ensemble uses features based on word embeddings. Our ensemble was ranked 5th among 34 teams. The source code of our system is publicly available.
Learning Face Age Progression: A Pyramid Architecture of GANs

The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. In this paper, we present a novel generative adversarial network based approach. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. Further, to generate more lifelike facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects in a finer manner. The proposed method is applicable to diverse face samples in the presence of variations in pose, expression, makeup, etc., and remarkably vivid aging effects are achieved. Both visual fidelity and quantitative evaluations show that the approach advances the state-of-the-art.
Towards an optimized model of incident ticket correlation

In recent years, IT Service Management (ITSM) has become one of the most researched areas of IT.  Management are two of the Service Operation processes in the IT Infrastructure Library (ITIL). These two processes aim to recognize, log, isolate and correct errors which occur in the environment and disrupt the delivery of services.  Management form the basis of the tooling provided by an Incident Ticket Systems (ITS).
Contactless fingerprint enhancement via intrinsic image decomposition and guided image filtering

Although contactless fingerprint images are rarely affected by skin conditions and finger pressure in comparison with touch-based fingerprint images, they are usually noisy and suffer from low ridge-valley contrast. This paper proposes a robust contactless fingerprint enhancement method based on intrinsic image decomposition and guided image filtering. In order to strengthen the contrast of ridge and valley, intrinsic image decomposition is firstly performed on the observed fingerprint image. Then, the obtained intrinsic fingerprint image is used as the guided image to filter the observed fingerprint image, which can efficiently eliminate noise while preserving the ridge-valley information. Finally, an improved Gabor-based contextual filter is adopted to further enhance the fingerprint image quality. Experimental results of minutiae extraction based on the enhanced fingerprint image demonstrate the validity of the proposed method.
Soliton-like thermophoresis of graphene wrinkles.

We studied the thermophoretic motion of wrinkles formed in substrate-supported graphene sheets by nonequilibrium molecular dynamics simulations. We found that a single wrinkle moves along applied temperature gradient with a constant acceleration that is linearly proportional to temperature deviation between the heating and cooling sides of the graphene sheet. Like a solitary wave, the atoms of the single wrinkle drift upwards and downwards, which prompts the wrinkle to move forwards. The driving force for such thermophoretic movement can be mainly attributed to a lower free energy of the wrinkle back root when it is transformed from the front root. We establish a motion equation to describe the soliton-like thermophoresis of a single graphene wrinkle based on the Korteweg-de Vries equation. Similar motions are also observed for wrinkles formed in a Cu-supported graphene sheet. These findings provide an energy conversion mechanism by using graphene wrinkle thermophoresis.
AceMap: A Novel Approach towards Displaying Relationship among Academic Literatures

A large number of papers are being published every year, which makes it difficult for researchers to grasp the relationship among the scientific literatures and the big picture of academic fields. The new challenges have thus been raised, such as analyzing the complicated citation and author network, mining valuable scientific knowledge, and visualizing big scholarly data. The existing academic systems, such as Google Scholar and DBLP have mainly adopted text-based methods, while some other systems make attempts to better navigate the literatures, for example, AMiner and Science Navigation Map. Although these systems show improvements, they fail to present the academic data in a holistic way, and also have limited functions. Therefore, we need to develop new tools which can realize more modules and further explore the academic literatures.
Kinematic and Dynamic Analyses of the Orthoglide 5-axis

This paper deals with the kinematic and dynamic analyses of the Orthoglide 5-axis, a five-degree-of-freedom manipulator. It is derived from two manipulators: i) the Orthoglide 3-axis; a three dof translational manipulator and ii) the Agile eye; a parallel spherical wrist. First, the kinematic and dynamic models of the Orthoglide 5-axis are developed. The geometric and inertial parameters of the manipulator are determined by means of a CAD software. Then, the required motors performances are evaluated for some test trajectories. Finally, the motors are selected in the catalogue from the previous results.
Outlier Detection Using Replicator Neural Networks

We consider the problem of finding outliers in large multivariate databases. Outlier detection can be applied during the data cleansing process of data mining to identify problems with the data itself, and to fraud detection where groups of outliers are often of particular interest. We use replicator neural networks (RNNs) to provide a measure of the outlyingness of data records. The performance of the RNNs is assessed using a ranked score measure. The effectiveness of the RNNs for outlier detection is demonstrated on two publicly available databases.
Simulating fault injection on disk arrays

We present an application for the simulation of errors in storage systems. The software is completely parameterizable in order to simulate different types of disk errors and disk array configurations. It can be used to verify and optimize error correction schemes for storage. Realistic simulation of disk errors is a complex task as many test rounds need to be performed in order to characterize the performance of an algorithm based on highly sporadic errors under a large variety of parameters. The software allows different levels of abstraction to perform quick tests for rough estimations as well as detailed configurations for more realistic but complex simulation runs. We believe that this simulation software is the first one that is able to cover a complete range of disk error types in many commonly used disk array configurations.
Radar resource management: Dynamic programming and dynamic finite state machines

Finite state machines are a standard tool to model event-based control logic, and dynamic programming is a staple of optimal decision-making. We combine these approaches in the context of radar resource management for Naval surface warfare. There is a friendly (Blue) force in the open sea, equipped with one multi-function radar and multiple ships. The enemy (Red) force consists of missiles that target the Blue force's radar. The mission of the Blue force is to foil the enemy's threat by careful allocation of radar resources. Dynamically composed finite state machines are used to formalize the model of the battle space and dynamic programming is applied to our dynamic state machine model to generate an optimal policy. To achieve this in near-real-time and a changing environment, we use approximate dynamic programming methods. Example scenario illustrating the model and simulation results are presented.
Deep face attributes recognition using spatial transformer network

Face alignment is very crucial to the task of face attributes recognition. The performance of face attributes recognition would notably degrade if the fiducial points of the original face images are not precisely detected due to large lighting, pose and occlusion variations. In order to alleviate this problem, we propose a spatial transform based deep CNNs to improve the performance of face attributes recognition. In this approach, we first learn appropriate transformation parameters by a carefully designed spatial transformer network called LoNet to align the original face images, and then recognize the face attributes based on the aligned face images using a deep network called ClNet. To the best of our knowledge, this is the first attempt to use spatial transformer network in face attributes recognition task. Extensive experiments on two large and challenging databases (CelebA and LFWA) clearly demonstrate the effectiveness of the proposed approach over the current state-of-the-art.
Connecting the Dots: A Review of Resting Connectivity MRI Studies in Attention-Deficit/Hyperactivity Disorder

Psychopathology is increasingly viewed from a circuit perspective in which a disorder stems not from circumscribed anomalies in discrete brain regions, but rather from impairments in distributed neural networks. This focus on neural circuitry has rendered resting state functional connectivity MRI (rs-fcMRI) an increasingly important role in the elucidation of pathophysiology including attention-deficit/hyperactivity disorder (ADHD). Unlike many other MRI techniques that focus on the properties of discrete brain regions, rs-fcMRI measures the coherence of neural activity across anatomically disparate brain regions, examining the connectivity and organization of neural circuits. In this review, we explore the methods available to investigators using rs-fcMRI techniques, including a discussion of their relative merits and limitations. We then review findings from extant rs-fcMRI studies of ADHD focusing on neural circuits implicated in the disorder, especially the default mode network, cognitive control network, and cortico-striato-thalamo-cortical loops. We conclude by suggesting future directions that may help advance subsequent rs-fcMRI research in ADHD.
Morphology and the Pieces of Inflection Morris Halle and

1 Morphology with or without Affixes The last few years have seen the emergence of several clearly articulated alternative approaches to morphology. One such approach rests on the notion that only stems of the so-called lexical categories (N, V, A) are morpheme "pieces" in the traditional sense—connections between (bundles of) meaning (features) and (bundles of) sound (features). What look like affixes on this view are merely the by-product of morphophonological rules called word formation rules (WFRs) that are sensitive to features associated with the lexical categories, called lexemes. Such an amorphous or affixless theory, adumbrated by Beard (1966) and Aronoff (1976), has been articulated most notably by Anderson (1992) and in major new studies by Aronoff (1992) and Beard (1991). In contrast, Lieber (1992) has refined the traditional notion that affixes as well as lexical stems are "mor-pheme" pieces whose lexical entries relate phonological form with meaning and function. For Lieber and other "lexicalists" (see, e.g., Jensen 1990), the combining of lexical items creates the words that operate in the syntax. In this paper we describe and defend a third theory of morphology , Distributed Morphology, 1 which combines features of the affixless and the lexicalist alternatives. With Anderson, Beard, and Aronoff, we endorse the separation of the terminal elements involved in the syntax from the phonological realization of these elements. With Lieber and the lexicalists, on the other hand, we take the phonological realization of the terminal elements in the syntax to be governed by lexical (Vocabulary) entries that relate bundles of morphosyntactic features to bundles of pho-nological features. We have called our approach Distributed Morphology (hereafter DM) to highlight the fact that the machinery of what traditionally has been called morphology is not concentrated in a single component of the gram
Asymmetric Multiagent Reinforcement Learning

A novel model for asymmetric multiagent reinforcement learning is introduced in this paper. The model addresses the problem where the information states of the agents involved in the learning task are not equal; some agents (leaders) have information how their opponents (followers) will select their actions and based on this information leaders encourage followers to select actions that lead to improved payoffs for the leaders. This kind of configuration arises e.g. in semi-centralized multiagent systems with an external global utility associated to the system. We present a brief literature survey of multiagent reinforcement learning based on Markov games and then propose an asymmetric learning model that utilizes the theory of Markov games. Additionally, we construct a practical learning method based on the proposed learning model and study its convergence properties. Finally, we test our model with a simple example problem and a larger two-layer pricing application.