nils-herrmann/clean_scirepeval huggingface dataset:
The perfect solution for detecting sarcasm in tweets #not

To avoid a sarcastic message being understood in its unintended literal meaning, in microtexts such as messages on Twitter.com sarcasm is often explicitly marked with the hashtag ‘#sarcasm’. We collected a training corpus of about 78 thousand Dutch tweets with this hashtag. Assuming that the human labeling is correct (annotation of a sample indicates that about 85% of these tweets are indeed sarcastic), we train a machine learning classifier on the harvested examples, and apply it to a test set of a day’s stream of 3.3 million Dutch tweets. Of the 135 explicitly marked tweets on this day, we detect 101 (75%) when we remove the hashtag. We annotate the top of the ranked list of tweets most likely to be sarcastic that do not have the explicit hashtag. 30% of the top-250 ranked tweets are indeed sarcastic. Analysis shows that sarcasm is often signalled by hyperbole, using intensifiers and exclamations; in contrast, non-hyperbolic sarcastic messages often receive an explicit marker. We hypothesize that explicit markers such as hashtags are the digital extralinguistic equivalent of nonverbal expressions that people employ in live interaction when conveying sarcasm.
A 34 MB/s MLC Write Throughput 16 Gb NAND With All Bit Line Architecture on 56 nm Technology

A 16 Gb 4-state MLC NAND flash memory augments the sustained program throughput to 34 MB/s by fully exercising all the available cells along a selected word line and by using additional performance enhancement modes. The same chip operating as an 8 Gb SLC device guarantees over 60 MB/s programming throughput. The newly introduced all bit line (ABL) architecture has multiple advantages when higher performance is targeted and it was made possible by adopting the ldquocurrent sensingrdquo (as opposed to the mainstream ldquovoltage sensingrdquo) technique. The general chip architecture is presented in contrast to a state of the art conventional circuit and a double size data buffer is found to be necessary for the maximum parallelism attained. Further conceptual changes designed to counterbalance the area increase are presented, hierarchical column architecture being of foremost importance. Optimization of other circuits, such as the charge pump, is another example. Fast data access rate is essential, and ways of boosting it are described, including a new redundancy scheme. ABL contribution to energy saving is also acknowledged.
Sample-based informationl-theoretic stochastic optimal control

Many Stochastic Optimal Control (SOC) approaches rely on samples to either obtain an estimate of the value function or a linearisation of the underlying system model. However, these approaches typically neglect the fact that the accuracy of the policy update depends on the closeness of the resulting trajectory distribution to these samples. The greedy operator does not consider such closeness constraint to the samples. Hence, the greedy operator can lead to oscillations or even instabilities in the policy updates. Such undesired behaviour is likely to result in an inferior performance of the estimated policy. We reuse inspiration from the reinforcement learning community and relax the greedy operator used in SOC with an information theoretic bound that limits the `distance' of two subsequent trajectory distributions in a policy update. The introduced bound ensures a smooth and stable policy update. Our method is also well suited for model-based reinforcement learning, where we estimate the system dynamics model from data. As this model is likely to be inaccurate, it might be dangerous to exploit the model greedily. Instead, our bound ensures that we generate new data in the vicinity of the current data, such that we can improve our estimate of the system dynamics model. We show that our approach outperforms several state of the art approaches on challenging simulated robot control tasks.
Hierarchical face parsing via deep learning

This paper investigates how to parse (segment) facial components from face images which may be partially occluded. We propose a novel face parser, which recasts segmentation of face components as a cross-modality data transformation problem, i.e., transforming an image patch to a label map. Specifically, a face is represented hierarchically by parts, components, and pixel-wise labels. With this representation, our approach first detects faces at both the part- and component-levels, and then computes the pixel-wise label maps (Fig.1). Our part-based and component-based detectors are generatively trained with the deep belief network (DBN), and are discriminatively tuned by logistic regression. The segmentators transform the detected face components to label maps, which are obtained by learning a highly nonlinear mapping with the deep autoencoder. The proposed hierarchical face parsing is not only robust to partial occlusions but also provide richer information for face analysis and face synthesis compared with face keypoint detection and face alignment. The effectiveness of our algorithm is shown through several tasks on 2, 239 images selected from three datasets (e.g., LFW , BioID [13] and CUFSF [29]).
K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods

In this paper, we present "k-means+ID3", a method to cascade k-means clustering and the ID3 decision tree learning methods for classifying anomalous and normal activities in a computer network, an active electronic circuit, and a mechanical mass-beam system. The k-means clustering method first partitions the training instances into k clusters using Euclidean distance similarity. On each cluster, representing a density region of normal or anomaly instances, we build an ID3 decision tree. The decision tree on each cluster refines the decision boundaries by learning the subgroups within the cluster. To obtain a final decision on classification, the decisions of the k-means and ID3 methods are combined using two rules: 1) the nearest-neighbor rule and 2) the nearest-consensus rule. We perform experiments on three data sets: 1) network anomaly data (NAD), 2) Duffing equation data (DED), and 3) mechanical system data (MSD), which contain measurements from three distinct application domains of computer networks, an electronic circuit implementing a forced Duffing equation, and a mechanical system, respectively. Results show that the detection accuracy of the k-means+ID3 method is as high as 96.24 percent at a false-positive-rate of 0.03 percent on NAD; the total accuracy is as high as 80.01 percent on MSD and 79.9 percent on DED
Porosity prediction of plain weft knitted fabrics

Wearing comfort of clothing is dependent on air permeability, moisture absorbency and wicking properties of fabric, which are related to the porosity of fabric. In this work, a plug-in is developed using Python script and incorporated in Abaqus/CAE for the prediction of porosity of plain weft knitted fabrics. The Plug-in is able to automatically generate 3D solid and multifilament weft knitted fabric models and accurately determine the porosity of fabrics in two steps. In this work, plain weft knitted fabrics made of monofilament, multifilament and spun yarn made of staple fibers were used to evaluate the effectiveness of the developed plug-in. In the case of staple fiber yarn, intra yarn porosity was considered in the calculation of porosity. The first step is to develop a 3D geometrical model of plain weft knitted fabric and the second step is to calculate the porosity of the fabric by using the geometrical parameter of 3D weft knitted fabric model generated in step one. The predicted porosity of plain weft knitted fabric is extracted in the second step and is displayed in the message area. The predicted results obtained from the plug-in have been compared with the experimental results obtained from previously developed models; they agreed well.
Comparison of clinical versus ultrasound-determined synovitis in juvenile idiopathic arthritis.

To compare clinical evaluation and ultrasonography (US) in the assessment of joint synovitis in children with juvenile idiopathic arthritis (JIA).



Thirty-two patients underwent clinical evaluation of 52 joints by 2 pediatric rheumatologists. Joints were assessed for swelling, tenderness/pain on motion, and restricted motion. The same joints were scanned independently by an experienced sonographer for synovial hyperplasia, joint effusion, and power Doppler (PD) signal.



In total, 1,664 joints were assessed both clinically and with US. On clinical examination, 98 joints (5.9%) were swollen, 59 joints (3.5%) were tender, and 40 joints (2.4%) had restricted motion. On US evaluation, 125 joints (7.5%) had synovial hyperplasia, 153 joints (9.2%) had joint effusion, and 53 joints (3.2%) had PD signal. A total of 104 (6.3%) and 167 (10%) joints had clinical and US synovitis, respectively. Of the 1,560 clinically normal joints, 86 (5.5%) had subclinical synovitis (i.e., had synovitis on US). US led to classifying 5 patients as having polyarthritis who were classified as having oligoarthritis or were found to have no synovitis on clinical evaluation. US variables were moderately correlated with clinical measures of joint swelling, but poorly correlated with those of joint tenderness/pain on motion and restricted motion. Overall, correlations were lower for PD signal than for synovial hyperplasia and joint effusion.



We found that subclinical synovitis as detected by US is common in children with JIA. This finding may have important implications for patient classification and may affect the choice of the optimal therapeutic strategy in individual patients.
A Survey of LIDAR Technology and its Use in Spacecraft Relative Navigation

This paper provides a survey of modern LIght Detection And Ranging (LIDAR) sensors from a perspective of how they can be used for spacecraft relative navigation. In addition to LIDAR technology commonly used in space applications today (e.g. scanning, flash), this paper reviews emerging LIDAR technologies gaining traction in other non-aerospace fields. The discussion will include an overview of sensor operating principles and specific pros/cons for each type of LIDAR. This paper provides a comprehensive review of LIDAR technology as applied specifically to spacecraft relative navigation.
A high speed, 500 frames/s, 1024/spl times/1024 CMOS active pixel sensor

The paper presents a high-speed (500 f/s) large-format 1 K/spl times/1 K 8 bit 3.3 V CMOS active pixel sensor (APS) with 1024 ADCs integrated on chip. The sensor achieves an extremely high output data rate of over 500 Mbytes per second and a low power dissipation of 350 mW at the 66 MHz master clock rate. Principal architecture and circuit solutions allowing such a high throughput are discussed along with preliminary results of the chip characterization.
Appendiceal Mucocele and Pseudomyxoma Peritonei; The Clinical Boundaries of a Subtle Disease

Male, 70 • Male, 84.


FINAL DIAGNOSIS
Appendiceal mucocele and pseudomyxoma peritonei.


SYMPTOMS
-.



-.


 PROCEDURE
-.


SPECIALTY
Surgery.



Rare disease.



Mucocele of the appendix is an uncommon cystic lesion characterized by distension of the appendiceal lumen with mucus. Most commonly, it is the result of epithelial proliferation, but it can also be caused by inflammation or obstruction of the appendix. When an underlying mucinous cystadenocarcinoma exists, spontaneous or iatrogenic rupture of the mucocele can lead to mucinous intraperitoneal ascites, a syndrome known as pseudomyxoma peritonei.



We report 2 cases that represent the clinical extremities of this heterogeneous disease; an asymptomatic mucocele of the appendix in a 70-year-old female and a case of pseudomyxoma peritonei in an 84-year-old male. Subsequently, we review the current literature focusing to the optimal management of both conditions.



Mucocele of the appendix is a rare disease, usually diagnosed on histopathologic examination of appendectomized specimens. Due to the existing potential for malignant transformation and pseudomyxoma peritonei caused by rupture of the mucocele, extensive preoperative evaluation and thorough intraoperative gastrointestinal and peritoneal examination is required.
Evaluation of the desensitizing effect of Gluma Dentin Bond on teeth prepared for complete-coverage restorations.

This clinical trial assessed the ability of Gluma Dentin Bond to inhibit dentinal sensitivity in teeth prepared to receive complete cast restorations. Twenty patients provided 76 teeth for the study. Following tooth preparation, dentinal surfaces were coated with either sterile water (control) or two 30-second applications of Gluma Dentin Bond (test) on either intact or removed smear layers. Patients were recalled after 14 days for a test of sensitivity of the prepared dentin to compressed air, osmotic stimulus (saturated CaCl2 solution), and tactile stimulation via a scratch test under controlled loads. A significantly lower number of teeth responded to the test stimuli for both Gluma groups when compared to the controls (P less than .01). No difference was noted between teeth with smear layers intact or removed prior to treatment with Gluma.
Fog Networking: An Overview on Research Opportunities

The past 15 years have seen the rise of the Cloud, along with rapid increase in Internet backbone traffic and more sophisticated cellular core networks. There are three different types of “Clouds:” (1) data center, (2) backbone IP network and (3) cellular core network, responsible for computation, storage, communication and network management. Now the functions of these three types of Clouds are “descending” to be among or near the end users, i.e., to the edge of networks, as “Fog.”
Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates

In this paper, we describe a phenomenon, which we named “super-convergence”, where neural networks can be trained an order of magnitude faster than with standard training methods. The existence of super-convergence is relevant to understanding why deep networks generalize well. One of the key elements of super-convergence is training with one learning rate cycle and a large maximum learning rate. A primary insight that allows super-convergence training is that large learning rates regularize the training, hence requiring a reduction of all other forms of regularization in order to preserve an optimal regularization balance. We also derive a simplification of the Hessian Free optimization method to compute an estimate of the optimal learning rate. Experiments demonstrate super-convergence for Cifar-10/100, MNIST and Imagenet datasets, and resnet, wide-resnet, densenet, and inception architectures. In addition, we show that super-convergence provides a greater boost in performance relative to standard training when the amount of labeled training data is limited. The architectures to replicate this work will be made available upon publication.
A 3-D flowering simulation based on botany characteristics and random generation algorithm

In recent years, virtual reality is experiencing a rapid development, which is also applied in plant morphology simulation. With a variety dynamic process of blooming, you are not only able to create a virtual landscape but to decorate a virtual space. In the internet, people can enjoy flowering anytime and anywhere to promote flower exhibitions and business. This paper focuses on the botanical characteristics of blooming process and creates a vivid effect based on Bezier curves and surfaces theory.
adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs

Recurrent Neural Networks (RNNs) are powerful models that a chieve exceptional performance on several pattern recognition problem s. However, the training of RNNs is a computationally difficult task owing to the wellknown “vanishing/exploding” gradient problem. Algorithms proposed for training RNNs either exploit no (or limited) curvature information and have chea p per-iteration complexity, or attempt to gain significant curvature informati on at the cost of increased per-iteration cost. The former set includes diagonally-sc aled first-order methods such as ADAGRAD and ADAM , while the latter consists of second-order algorithms like Hessian-Free Newton and K-FAC. In this paper, we presentADAQN, a stochastic quasi-Newton algorithm for training RNNs. Our approach retains a low per-iteration cost while allowing for non-diagonal sca ling through a stochastic L-BFGS updating scheme. The method uses a novel L-BFGS scali ng initialization scheme and is judicious in storing and retaining L-BFGS curvature pairs. We present numerical experiments on two language modeling tas ks nd show that ADAQN is competitive with popular RNN training algorithms.
Generic Advice: On the Combination of AOP with Generative Programming in AspectC++

Besides object-orientation, generic types or templates and aspectoriented programming (AOP) gain increasing popularity as they provide additional dimensions of decomposition. Most modern programming languages like Ada, Eiffel, and C++ already have built-in support for templates. For Java and C# similar extensions will be available in the near future. Even though promising, the combination of aspects with generic and generative programming is still a widely unexplored field. This paper presents our extensions to the AspectC++ language, an aspect-oriented C++ derivate. By these extensions aspects can now affect generic code and exploit the potentials of generic code and template metaprogramming in their implementations. This allows aspects to inject template metaprograms transparently into the component code. A case study demonstrates that this feature enables the development of highly expressive and efficient generic aspect implementations in AspectC++. A discussion whether these concepts are applicable in the context of other aspect-oriented language extensions like AspectJ rounds up our contribution.
An improved unified scalable radix-2 Montgomery multiplier

This paper describes an improved version of the Tenca-Koc unified scalable radix-2 Montgomery multiplier with half the latency for small and moderate precision operands and half the queue memory requirement. Like the Tenca-Koc multiplier, this design is reconfigurable to accept any input precision in either GF(p) or GF(2/sup n/) up to the size of the on-chip memory. An FPGA implementation can perform 1024-bit modular exponentiation in 16 ms using 5598 4-input lookup tables, making it the fastest unified scalable design yet reported.
Agnostic active learning

We state and analyze the first active learning algorithm which works in the presence of arbitrary forms of noise. The algorithm, A2 (for Agnostic Active), relies only upon the assumption that the samples are drawn i.i.d. from a fixed distribution. We show that A2 achieves an exponential improvement (i.e., requires only O (ln 1/ε) samples to find an ε-optimal classifier) over the usual sample complexity of supervised learning, for several settings considered before in the realizable case. These include learning threshold classifiers and learning homogeneous linear separators with respect to an input distribution which is uniform over the unit sphere.
Decoy Password Vaults: At Least as Hard as Steganography?

Cracking-resistant password vaults have been recently proposed with the goal of thwarting offline attacks. This requires the generation of synthetic password vaults that are statistically indistinguishable from real ones. In this work, we establish a conceptual link between this problem and steganography, where the stego objects must be undetectable among cover objects. We compare the two frameworks and highlight parallels and differences. Moreover, we transfer results obtained in the steganography literature into the context of decoy generation. Our results include the infeasibility of perfectly secure decoy vaults and the conjecture that secure decoy vaults are at least as hard to construct as secure steganography.
SWARM: An Actuated Wearable for Mediating Affect

We present SWARM, a wearable affective technology designed to help a user to reflect on their own emotional state, modify their affect, and interpret the emotional states of others. SWARM aims for a universal design (inclusive of people with various disabilities), with a focus on modular actuation components to accommodate users' sensory capabilities and preferences, and a scarf form-factor meant to reduce the stigma of accessible technologies through a fashionable embodiment. Using an iterative, user-centered approach, we present SWARM's design. Additionally, we contribute findings for communicating emotions through technology actuations, wearable design techniques (including a modular soft circuit design technique that fuses conductive fabric with actuation components), and universal design considerations for wearable technology.
Polarity Inducing Latent Semantic Analysis

Existing vector space models typically map synonyms and antonyms to similar word vectors, and thus fail to represent antonymy. We introduce a new vector space representation where antonyms lie on opposite sides of a sphere: in the word vector space, synonyms have cosine similarities close to one, while antonyms are close to minus one. We derive this representation with the aid of a thesaurus and latent semantic analysis (LSA). Each entry in the thesaurus – a word sense along with its synonyms and antonyms – is treated as a “document,” and the resulting document collection is subjected to LSA. The key contribution of this work is to show how to assign signs to the entries in the co-occurrence matrix on which LSA operates, so as to induce a subspace with the desired property. We evaluate this procedure with the Graduate Record Examination questions of (Mohammed et al., 2008) and find that the method improves on the results of that study. Further improvements result from refining the subspace representation with discriminative training, and augmenting the training data with general newspaper text. Altogether, we improve on the best previous results by 11 points absolute in F measure.
Understanding Graph-Based Trust Evaluation in Online Social Networks: Methodologies and Challenges

Online Social Networks (OSNs) are becoming a popular method of meeting people and keeping in touch with friends. OSNs resort to trust evaluation models and algorithms to improve service quality and enhance user experiences. Much research has been done to evaluate trust and predict the trustworthiness of a target, usually from the view of a source. Graph-based approaches make up a major portion of the existing works, in which the trust value is calculated through a trusted graph (or trusted network, web of trust, or multiple trust chains). In this article, we focus on graph-based trust evaluation models in OSNs, particularly in the computer science literature. We first summarize the features of OSNs and the properties of trust. Then we comparatively review two categories of graph-simplification-based and graph-analogy-based approaches and discuss their individual problems and challenges. We also analyze the common challenges of all graph-based models. To provide an integrated view of trust evaluation, we conduct a brief review of its pre- and postprocesses (i.e., the preparation and validation of trust models, including information collection, performance evaluation, and related applications). Finally, we identify some open challenges that all trust models are facing.