nils-herrmann/clean_scirepeval huggingface dataset:
k-Sparse Autoencoders

Recently, it has been observed that when representations are learnt in a way that encourages sparsity, improved performance is obtained on classification tasks. These methods involve combinations of activation functions, sampling steps and different kinds of penalties. To investigate the effectiveness of sparsity by itself, we propose the “k sparse autoencoder”, which is an autoencoder with linear activation function, where in hidden layers only the k highest activities are kept. When applied to the MNIST and NORB datasets, we find that this method achieves better classification results than denoising autoencoders, networks trained with dropout, and RBMs. k -sparse autoencoders are simple to train and the encoding stage is very fast, making them well-suited to large problem sizes, where conventional sparse coding algorithms cannot be applied.
Foot-and-mouth disease in Asiatic black bears (Ursus thibetanus).

Foot-and-mouth disease (FMD) is a highly contagious, debilitating, and globally significant viral disease typically affecting cloven-hoofed hosts. The diagnosis of FMD in bears in Vietnam is described. The current study describes a confirmed case of FMD in a bear species, and the clinical signs compatible with FMD in a Malayan sun bear. Thirteen Asiatic black bears (Ursus thibetanus) and 1 Malayan sun bear (Helarctos malayanus) were apparently affected. In August 2011, an adult bear became lethargic, and developed footpad vesicles. Over 15 days, 14 out of 17 bears developed similar signs; the remaining 3 co-housed bears and another 57 resident bears did not. All affected bears developed vesicles on all footpads, and most were lethargic for 24-48 hr. Nasal and oral lesions were noted in 6 and 3 cases, respectively. Within 1 month, all looked normal. Foot-and-mouth disease virus (FMDV) was detected by reverse transcription polymerase chain reaction, classified as serotype O, and isolated by virus isolation techniques. Phylogenetic analysis demonstrated clustering of 3 bear isolates, in a branch distinct from other FMDV type O isolates. The outbreak likely occurred due to indirect contact with livestock, and was facilitated by the high density of captive bears. It showed that Asiatic black bears are capable of contracting FMDV and developing clinical disease, and that the virus spreads easily between bears in close contact.
Achieving k-Anonymity by Clustering in Attribute Hierarchical Structures

Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. A more general view of k-anonymity is clustering with a constraint of the minimum number of objects in every cluster. Most existing approaches to achieving k-anonymity by clustering are for numerical (or ordinal) attributes. In this paper, we study achieving k-anonymity by clustering in attribute hierarchical structures. We define generalisation distances between tuples to characterise distortions by generalisations and discuss the properties of the distances. We conclude that the generalisation distance is a metric distance. We propose an efficient clusteringbased algorithm for k-anonymisation. We experimentally show that the proposed method is more scalable and causes significantly less distortions than an optimal global recoding k-anonymity method.
Robust identification of fuzzy duplicates

Detecting and eliminating fuzzy duplicates is a critical data cleaning task that is required by many applications. Fuzzy duplicates are multiple seemingly distinct tuples, which represent the same real-world entity. We propose two novel criteria that enable characterization of fuzzy duplicates more accurately than is possible with existing techniques. Using these criteria, we propose a novel framework for the fuzzy duplicate elimination problem. We show that solutions within the new framework result in better accuracy than earlier approaches. We present an efficient algorithm for solving instantiations within the framework. We evaluate it on real datasets to demonstrate the accuracy and scalability of our algorithm.
Yoga as an adjunctive treatment for posttraumatic stress disorder: a randomized controlled trial.

More than a third of the approximately 10 million women with histories of interpersonal violence in the United States develop posttraumatic stress disorder (PTSD). Currently available treatments for this population have a high rate of incomplete response, in part because problems in affect and impulse regulation are major obstacles to resolving PTSD. This study explored the efficacy of yoga to increase affect tolerance and to decrease PTSD symptomatology.



Sixty-four women with chronic, treatment-resistant PTSD were randomly assigned to either trauma-informed yoga or supportive women's health education, each as a weekly 1-hour class for 10 weeks. Assessments were conducted at pretreatment, midtreatment, and posttreatment and included measures of DSM-IV PTSD, affect regulation, and depression. The study ran from 2008 through 2011.



The primary outcome measure was the Clinician-Administered PTSD Scale (CAPS). At the end of the study, 16 of 31 participants (52%) in the yoga group no longer met criteria for PTSD compared to 6 of 29 (21%) in the control group (n = 60, χ²₁ = 6.17, P = .013). Both groups exhibited significant decreases on the CAPS, with the decrease falling in the large effect size range for the yoga group (d = 1.07) and the medium to large effect size decrease for the control group (d = 0.66). Both the yoga (b = -9.21, t = -2.34, P = .02, d = -0.37) and control (b = -22.12, t = -3.39, P = .001, d = -0.54) groups exhibited significant decreases from pretreatment to the midtreatment assessment. However, a significant group × quadratic trend interaction (d = -0.34) showed that the pattern of change in Davidson Trauma Scale significantly differed across groups. The yoga group exhibited a significant medium effect size linear (d = -0.52) trend. In contrast, the control group exhibited only a significant medium effect size quadratic trend (d = 0.46) but did not exhibit a significant linear trend (d = -0.29). Thus, both groups exhibited significant decreases in PTSD symptoms during the first half of treatment, but these improvements were maintained in the yoga group, while the control group relapsed after its initial improvement.



Yoga significantly reduced PTSD symptomatology, with effect sizes comparable to well-researched psychotherapeutic and psychopharmacologic approaches. Yoga may improve the functioning of traumatized individuals by helping them to tolerate physical and sensory experiences associated with fear and helplessness and to increase emotional awareness and affect tolerance.
Advanced Wireless LAN Technologies: IEEE 802.11AC and Beyond

In 2013, global mobile data traffic grew 81% and it is projected to increase 11-fold between 2013 and 2018. Further, it is predicted that by 2018, over two-thirds of the world's mobile traffic will be video and more than half of all traffic from wireless connected devices will be offloaded to Wi-Fi networks and femto-cells . Consequently, wireless LANs need major upgrades to improve both throughput and efficiency. IEEE 802.11ac is an amendment to the 802.11 standard that was just ratified by IEEE 802.11. Promising up to gigabit data rates, many Wi-Fi products are being built based on this specification. In addition to technologies that improve throughput, IEEE 802.11ax is investigating and evaluating advanced wireless technologies that enable more efficient utilization of the existing spectrum.
Decentralizing Privacy: Using Blockchain to Protect Personal Data

The recent increase in reported incidents of surveillance and security breaches compromising users' privacy call into question the current model, in which third-parties collect and control massive amounts of personal data. Bit coin has demonstrated in the financial space that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this paper, we describe a decentralized personal data management system that ensures users own and control their data. We implement a protocol that turns a block chain into an automated access-control manager that does not require trust in a third party. Unlike Bit coin, transactions in our system are not strictly financial -- they are used to carry instructions, such as storing, querying and sharing data. Finally, we discuss possible future extensions to block chains that could harness them into a well-rounded solution for trusted computing problems in society.
Considerations for the design of exergames

Exergaming is the use of video games in an exercise activity. In this paper we consider game design for successful exergames. To do this, we review the history of exergaming and the current state of research in this field. We find that there exists some research aimed at evaluating the physical and health characteristics of exergames, but research on how to design exercise games is still in the early stages. From an analysis of this information, and drawing on established principles from sports science for the prescription of exercise programs, we then attempt to identify success factors to guide designers of exergaming systems.
A Li-Ion Battery Charger With Smooth Control Circuit and Built-In Resistance Compensator for Achieving Stable and Fast Charging

A built-in resistance compensator (BRC) technique is presented to speed up the charging time of a lithium-ion battery. A smooth control circuit (SCC) is proposed to ensure the stable transition from the constant-current (CC) to the constant-voltage (CV) stage. Due to the external parasitic resistance of the Li-ion battery-pack system, the charger circuit switches from the CC to the CV stage without fully charging the cell. The BRC technique dynamically estimates the external resistance to extend the CC stage. The experimental results show that the period of the CC stage can be extended to 40% of that of the original design. The charging time is effectively reduced.
I Forget What That's Called! Children's Online Processing of Disfluencies Depends on Speaker Knowledge.

Speech disfluencies can convey information to listeners: Adults and children predict that filled pauses (e.g., uhh) will be followed by referents that are difficult to describe or are new to the discourse. In adults, this is driven partly by an understanding that disfluencies reflect processing difficulties. This experiment examined whether 3½-year-olds' use of disfluencies similarly involves inferences about processing difficulty. Forty children were introduced to either a knowledgeable or a forgetful speaker, who then produced fluent and disfluent utterances. Children exposed to the knowledgeable speaker looked preferentially at novel, discourse-new objects during disfluent utterances. However, children who heard the forgetful speaker did not. These results suggest that, like adults, children modify their expectations about the informativeness of disfluencies on a speaker-specific basis.
Normalized Cuts and Image Segmentation

w e propose Q novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the amage data, our approach aims a t extracting the global impression of an image. We treat image segmentation QS (I graph partitioning problem and propose Q novel global criterion, the normalized cut, for segmenting the graph. The normalized cut craterion measures both the total dissimilarity between the different groups QS well as the total similarity within the groups. We show that an eficient computational technique based on a generaked eigenvalue problem can be used to optimize this criterion. w e have applied this approach to segmenting static images and found results very enco u raging.
Using randomized response techniques for privacy-preserving data mining

Privacy is an important issue in data mining and knowledge discovery. In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specially, we present a method to build decision tree classifiers from the disguised data. We conduct experiments to compare the accuracy of our decision tree with the one built from the original undisguised data. Our results show that although the data are disguised, our method can still achieve fairly high accuracy. We also show how the parameter used in the randomized response techniques affects the accuracy of the results.
Continuous deployment of software intensive products and services: A systematic mapping study

The software intensive industry is moving towards the adoption of a value-driven and adaptive real-time business paradigm. The traditional view of software as an item that evolves through releases every few months is being replaced by continuous evolution of software functionality.  This study aims to classify and analyse literature related to continuous deployment in the software domain in order to scope the phenomenon, provide an overview of its state-of-the-art, investigate the scientific evidence in the reported results and identify areas that are suitable for further research.  We conducted a systematic mapping study and classified the continuous deployment literature. The benefits and challenges related to continuous deployment were also analyzed.  The systematic mapping study includes 50 primary studies published between 2001 and 2014. An in-depth analysis of the primary studies revealed ten recurrent themes that characterize continuous deployment and provide researchers with directions for future work. In addition, a set of benefits and challenges of which practitioners may take advantage were identified.  Overall, although the topic area is very promising, it is still in its infancy, thus offering a plethora of new opportunities for both researchers and software intensive companies.
PCS/W-CDMA dual-band MMIC power amplifier with a newly proposed linearizing bias circuit

A personal communications service/wide-band code division multiple access (PCS/W-CDMA) dual-band monolithic microwave integrated circuit (MMIC) power amplifier with a single-chip MMIC and a single-path output matching network is demonstrated by adopting a newly proposed on-chip linearizer. The linearizer is composed of the base–emitter diode of an active bias transistor and a capacitor to provide an RF short at the base node of the active bias transistor. The linearizer enhances the linearity of the power amplifier effectively for both PCS and W-CDMA bands with no additional dc power consumption, and has negligible insertion power loss with almost no increase in die area. It improves the input 1-dB gain compression point by 18.5 (20) dB and phase distortion by 6.1 (12.42 ) at an output power of 28 (28) dBm for the PCS (W-CDMA) band while keeping the base bias voltage of the power amplifier as designed. A PCS and W-CDMA dual-band InGaP heterojunction bipolar transistor MMIC power amplifier with single input and output and no switch for band selection is embodied by implementing the linearizer and by designing the amplifier to have broad-band characteristics. The dual-band power amplifier exhibits an output power of 30 (28.5) dBm, power-added efficiency of 39.5% (36%), and adjacent channel power ratio of 46 ( 50) dBc at the output power of 28 (28) dBm under 3.4-V operation voltage for PCS (W-CDMA) applications.
A Practical Bayesian Framework for Backpropagation Networks

A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible (1) objective comparisons between solutions using alternative network architectures, (2) objective stopping rules for network pruning or growing procedures, (3) objective choice of magnitude and type of weight decay terms or additive regularizers (for penalizing large weights, etc.), (4) a measure of the effective number of well-determined parameters in a model, (5) quantified estimates of the error bars on network parameters and on network output, and (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian "evidence" automatically embodies "Occam's razor," penalizing overflexible and overcomplex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalization ability and the Bayesian evidence is obtained.
DEEP-SEE: Joint Object Detection, Tracking and Recognition with Application to Visually Impaired Navigational Assistance

In this paper, we introduce the so-called DEEP-SEE framework that jointly exploits computer vision algorithms and deep convolutional neural networks (CNNs) to detect, track and recognize in real time objects encountered during navigation in the outdoor environment. A first feature concerns an object detection technique designed to localize both static and dynamic objects without any a priori knowledge about their position, type or shape. The methodological core of the proposed approach relies on a novel object tracking method based on two convolutional neural networks trained offline. The key principle consists of alternating between tracking using motion information and predicting the object location in time based on visual similarity. The validation of the tracking technique is performed on standard benchmark VOT datasets, and shows that the proposed approach returns state-of-the-art results while minimizing the computational complexity. Then, the DEEP-SEE framework is integrated into a novel assistive device, designed to improve cognition of VI people and to increase their safety when navigating in crowded urban scenes. The validation of our assistive device is performed on a video dataset with 30 elements acquired with the help of VI users. The proposed system shows high accuracy (>90%) and robustness (>90%) scores regardless on the scene dynamics.
Visual Analysis of Public Utility Service Problems in a Metropolis

Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions.
Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction

This paper addresses the tasks of automatic seed selection for bootstrapping relation extraction, and noise reduction for distantly supervised relation extraction. We first point out that these tasks are related. Then, inspired by ranking relation instances and patterns computed by the HITS algorithm, and selecting cluster centroids using the K-means, LSA, or NMF method, we propose methods for selecting the initial seeds from an existing resource, or reducing the level of noise in the distantly labeled data. Experiments show that our proposed methods achieve a better performance than the baseline systems in both tasks.
Avatar-independent scripting for real-time gesture animation

When animation of a humanoid figure is to be generated at run-time, instead of by replaying precomposed motion clips, some method is required of specifying the avatar’s movements in a form from which the required motion data can be automatically generated. This form must be of a more abstract nature than raw motion data: ideally, it should be independent of the particular avatar’s proportions, and both writable by hand and suitable for automatic generation from higher-level descriptions of the required actions. We describe here the development and implementation of such a scripting language for the particular area of sign languages of the deaf, called SiGML (Signing Gesture Markup Language), based on the existing HamNoSys notation for sign languages. We conclude by suggesting how this work may be extended to more general animation for interactive virtual reality applications.
Applying the Clique Percolation Method to analyzing cross-market branch banking network structure: the case of Illinois

This study applies the Clique Percolation Method (CPM) to an investigation of the changing spatial organization of the Illinois cross-market branch banking network. Nonoverlapping community detection algorithms assign nodes into exclusive communities and, when results are mapped, these techniques may generate spatially disjointed geographical regions, an undesirable characteristic for geographical study. Alternative overlapping community detection algorithms allow overlapping membership where a node can be a member of different communities. Such a structure simultaneously accommodates spatial proximity and spatial separation which occur with respect to a node in relation to other nodes in the system. Applying such a structure in geographical analysis helps preserve well-established principles regarding spatial relationships within the geography discipline. The result can also be mapped for display and correct interpretation. The CPM is chosen in this study due to the complete connection within cliques which simulates the practice by banking institutions of forming highly connected networks through multi-location operations in order to diversify their business and hedge against risks. Applying the CPM helps reveal the spatial pattern of branch banking connections which would otherwise be difficult to see. However, the CPM has been shown to not be among the best performing overlapping community detection algorithms. Future research should explore other possible algorithms for detecting overlapping communities. Detecting communities in a network only reveals certain characteristics of the spatial organization of the network, rather than providing explanation of the spatial-network patterns revealed. Full interpretation of the pattern must rely on the attribute data and additional information. This may illustrate the value of an integrated approach in geographical analysis using both social network analysis and spatial analysis techniques.
Comparison of sampler and VNA based Large Signal Measurement Systems (LSNA) under CW and pulsed operation

Here we present a comparison of the Large Signal Measurements provided by both a VNA based and a sampler based LSNA system. In this study, in order to achieve a robust and reliable relative phase calibration a dual frequency phase differential method was used. Significantly this work compares measurements performed under both CW and pulse mode operation. Very similar RF I-V waveforms are achieved in both cases, verifying that both architectures can provide comparable measurements not only in CW but also under pulsed operation. The test device was a GaN on silicon carbide HFET.
A New Algorithm of Fully-automatic Red-eye Removal

the redeye effect is typically formed in consumer photos taken with a built-in camera flash. The paper presents a new algorithm of the automatic removal of redeye from digital photos. The proposed algorithm can remove red-eye automatically without manual intervention. First, Detecting faces by using the AdaBoost algorithm. Second, the red-eyes are located by using operations of segmentation, morphology and geometric constraint. Finally, correcting the located red-eyes completely. The experimental results are satisfied with high correction rates, relatively low computational complexity and robustness.