nils-herrmann/clean_scirepeval huggingface dataset:
Assessing the origins, evolution and prospects of the literature on dynamic capabilities: A bibliometric analysis

The purpose of this study is to serve as orientation and guidance to academics that are starting or currently developing their research within the field of dynamic capabilities, in order to enhance their knowledge about which are the key scientific journals, authors and articles shaping this topic. This paper presents a bibliometric analysis on dynamic capabilities, making use of the Web of Science database to perform it. This analysis comprises fundamental issues such as (i) the number of studies published per year, (ii) the countries with the highest rate of productivity, (iii) the most prolific and influential authors, (iv) assessment of studies citing dynamic capabilities, and (v) the most productive journals on dynamic capabilities and recent studies on this topic. Results reveal an exponential growth in the number of publications on dynamic capabilities for the 2000–2012 period. Although, since 2012 this growth has decelerated, the number of publications on this topic remains noteworthy. This study brings useful information for those academics and practitioners attempting to analyze and deepen within this particular field of research, at the same time that provides some insights concerning the future development and progress of the dynamic capabilities topic in the management, business and economics academic literature.
Radiation Effects in Pinned Photodiode CMOS Image Sensors: Pixel Performance Degradation Due to Total Ionizing Dose

Several Pinned Photodiode (PPD) CMOS Image Sensors (CIS) are designed, manufactured, characterized and exposed biased to ionizing radiation up to 10 kGy(SiO2 ). In addition to the usually reported dark current increase and quantum efficiency drop at short wavelengths, several original radiation effects are shown: an increase of the pinning voltage, a decrease of the buried photodiode full well capacity, a large change in charge transfer efficiency, the creation of a large number of Total Ionizing Dose (TID) induced Dark Current Random Telegraph Signal (DC-RTS) centers active in the photodiode (even when the Transfer Gate (TG) is accumulated) and the complete depletion of the Pre-Metal Dielectric (PMD) interface at the highest TID leading to a large dark current and the loss of control of the TG on the dark current. The proposed mechanisms at the origin of these degradations are discussed. It is also demonstrated that biasing (i.e., operating) the PPD CIS during irradiation does not enhance the degradations compared to sensors grounded during irradiation.
Grand challenges in altmetrics: heterogeneity, data quality and dependencies

With increasing uptake among researchers, social media are finding their way into scholarly communication and, under the umbrella term altmetrics, are starting to be utilized in research evaluation. Fueled by technological possibilities and an increasing demand to demonstrate impact beyond the scientific community, altmetrics have received great attention as potential democratizers of the scientific reward system and indicators of societal impact. This paper focuses on the current challenges for altmetrics. Heterogeneity, data quality and particular dependencies are identified as the three major issues and discussed in detail with an emphasis on past developments in bibliometrics. The heterogeneity of altmetrics reflects the diversity of the acts and online events, most of which take place on social media platforms. This heterogeneity has made it difficult to establish a common definition or conceptual framework. Data quality issues become apparent in the lack of accuracy, consistency and replicability of various altmetrics, which is largely affected by the dynamic nature of social media events. Furthermore altmetrics are shaped by technical possibilities and are particularly dependent on the availability of APIs and DOIs, strongly dependent on data providers and aggregators, and potentially influenced by the technical affordances of underlying platforms.
Dynamic Network Surgery for Efficient DNNs

Deep learning has become a ubiquitous technology to improve machine intelligence. However, most of the existing deep models are structurally very complex, making them difficult to be deployed on the mobile platforms with limited computational power. In this paper, we propose a novel network compression method called dynamic network surgery, which can remarkably reduce the network complexity by making on-the-fly connection pruning. Unlike the previous methods which accomplish this task in a greedy way, we properly incorporate connection splicing into the whole process to avoid incorrect pruning and make it as a continual network maintenance. The effectiveness of our method is proved with experiments. Without any accuracy loss, our method can efficiently compress the number of parameters in LeNet-5 and AlexNet by a factor of 108× and 17.7× respectively, proving that it outperforms the recent pruning method by considerable margins. Code and some models are available at https://github.com/yiwenguo/Dynamic-Network-Surgery.
USING DATA MINING FOR BANK DIRECT MARKETING : AN APPLICATION OF THE CRISP-DM METHODOLOGY

The increasingly vast number of marketing campaigns over time has reduced its effect on the general public. Furthermore, economical pressures and competition has led marketing managers to invest on directed campaigns with a strict and rigorous selection of contacts. Such direct campaigns can be enhanced through the use of Business Intelligence (BI) and Data Mining (DM) techniques. This paper describes an implementation of a DM project based on the CRISP-DM methodology. Real-world data were collected from a Portuguese marketing campaign related with bank deposit subscription. The business goal is to find a model that can explain success of a contact, i.e. if the client subscribes the deposit. Such model can increase campaign efficiency by identifying the main characteristics that affect success, helping in a better management of the available resources (e.g. human effort, phone calls, time) and selection of a high quality and affordable set of potential buying customers.
Crawling the Hidden Web

Current-day crawlers retrieve content only from the publicly indexable Web, i.e., the set of web pages reachable purely by following hypertext links, ignoring search forms and pages that require authorization or prior registration. In particular, they ignore the tremendous amount of high quality content “hidden” behind search forms, in large searchable electronic databases. In this paper, we provide a framework for addressing the problem of extracting content from this hidden Web. At Stanford, we have built a task-specific hidden Web crawler called the Hidden Web Exposer (HiWE). We describe the architecture of HiWE and present a number of novel techniques that went into its design and implementation. We also present results from experiments we conducted to test and validate our techniques.
Ten-year evaluation of removable partial dentures: survival rates based on retreatment, not wearing and replacement.

From a group of 1480 patients, 1036 were treated with metal frame removable partial dentures (RPDs) at least 5 years before this analysis. Of those, 748 patients who wore 886 RPDs were followed up between 5 and 10 years; 288 patients dropped out. The 748 patients in the study groups were wearing 703 conventionally designed metal frame RPDs and 183 RPDs with attachments. When dropout patients and patients who remained in the study were compared, no differences were shown in the variables analyzed, which indicated that the dropouts did not bias the results. Survival rates of the RPDs were calculated by different failure criteria. Taking abutment retreatment as failure criterion, 40% of the conventional RPDs survived 5 years and more than 20% survived 10 years. In RPDs with attachments crowning abutments seemed to retard abutment retreatment. Fracture of the metal frame was found in 10% to 20% of the RPDs after 5 years and in 27% to 44% after 10 years. Extension base RPDs needed more adjustments of the denture base than did tooth-supported base RPDs. Taking replacement or not wearing the RPD as failure criteria, the survival rate was 75% after 5 years and 50% after 10 years (half-life time). The treatment approach in this study was characterized by a simple design of the RPD and regular surveillance of the patient in a recall system.
Multi-Grained Chinese Word Segmentation

Traditionally, word segmentation (WS) adopts the single-granularity formalism, where a sentence corresponds to a single word sequence. However, Sproat et al. (1996) show that the inter-nativespeaker consistency ratio over Chinese word boundaries is only 76%, indicating single-grained WS (SWS) imposes unnecessary challenges on both manual annotation and statistical modeling. Moreover, WS results of different granularities can be complementary and beneficial for high-level applications. This work proposes and addresses multi-grained WS (MWS). First, we build a large-scale pseudo MWS dataset for model training and tuning by leveraging the annotation heterogeneity of three SWS datasets. Then we manually annotate 1,500 test sentences with true MWS annotations. Finally, we propose three benchmark approaches by casting MWS as constituent parsing and sequence labeling. Experiments and analysis lead to many interesting findings.
Graph Classification with 2 D Convolutional Neural Networks

Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations. Convolutional Neural Networks (CNNs) offer a very appealing alternative, but processing graphs with CNNs is not trivial. To address this challenge, many sophisticated extensions of CNNs have recently been introduced. In this paper, we reverse the problem: rather than proposing yet another graph CNNmodel, we introduce a novel way to represent graphs as multi-channel image-like structures that allows them to be handled by vanilla 2D CNNs. Experiments reveal that our method is more accurate than state-of-the-art graph kernels and graph CNNs on 4 out of 6 real-world datasets (with and without continuous node attributes), and close elsewhere. Our approach is also preferable to graph kernels in terms of time complexity. Code and data are publicly available1.
Compact Generalized Non-local Network

The non-local module [27] is designed for capturing long-range spatio-temporal dependencies in images and videos. Although having shown excellent performance, it lacks the mechanism to model the interactions between positions across channels, which are of vital importance in recognizing fine-grained objects and actions. To address this limitation, we generalize the non-local module and take the correlations between the positions of any two channels into account. This extension utilizes the compact representation for multiple kernel functions with Taylor expansion that makes the generalized non-local module in a fast and low-complexity computation flow. Moreover, we implement our generalized non-local method within channel groups to ease the optimization. Experimental results illustrate the clear-cut improvements and practical applicability of the generalized non-local module on both fine-grained object recognition and video classification. Code is available at: https://github.com/KaiyuYue/cgnl-network.pytorch.
Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review.

The aim of the study was to present a systematic review of studies that investigate the effects of robot-assisted therapy on motor and functional recovery in patients with stroke.



A database of articles published up to October 2006 was compiled using the following Medline key words: cerebral vascular accident, cerebral vascular disorders, stroke, paresis, hemiplegia, upper extremity, arm, and robot. References listed in relevant publications were also screened. Studies that satisfied the following selection criteria were included: (1) patients were diagnosed with cerebral vascular accident; (2) effects of robot-assisted therapy for the upper limb were investigated; (3) the outcome was measured in terms of motor and/or functional recovery of the upper paretic limb; and (4) the study was a randomized clinical trial (RCT). For each outcome measure, the estimated effect size (ES) and the summary effect size (SES) expressed in standard deviation units (SDU) were calculated for motor recovery and functional ability (activities of daily living [ADLs]) using fixed and random effect models. Ten studies, involving 218 patients, were included in the synthesis. Their methodological quality ranged from 4 to 8 on a (maximum) 10-point scale.



Meta-analysis showed a nonsignificant heterogeneous SES in terms of upper limb motor recovery. Sensitivity analysis of studies involving only shoulder-elbow robotics subsequently demonstrated a significant homogeneous SES for motor recovery of the upper paretic limb. No significant SES was observed for functional ability (ADL).



As a result of marked heterogeneity in studies between distal and proximal arm robotics, no overall significant effect in favor of robot-assisted therapy was found in the present meta-analysis. However, subsequent sensitivity analysis showed a significant improvement in upper limb motor function after stroke for upper arm robotics. No significant improvement was found in ADL function. However, the administered ADL scales in the reviewed studies fail to adequately reflect recovery of the paretic upper limb, whereas valid instruments that measure outcome of dexterity of the paretic arm and hand are mostly absent in selected studies. Future research into the effects of robot-assisted therapy should therefore distinguish between upper and lower robotics arm training and concentrate on kinematical analysis to differentiate between genuine upper limb motor recovery and functional recovery due to compensation strategies by proximal control of the trunk and upper limb.
The decision-making process.

Talk with faculty members who are best acquainted with the field(s) of study that interest you. • Talk with professionals in the occupations you wish to enter about the types of degrees and/or credentials they hold or recommend for entry or advancement in field. • Browse program websites or look through catalogs and graduate school reference books (some of these are available in the CSC Resource Library) and determine prerequisites, length and scope of program, etc. • Narrow down your choices based on realistic assessment of your strengths and the programs, which will meet your needs.
Economics of Air Pollution in Malaysia

Air pollutants can be either gases or aerosols with particles or liquid droplets suspended in the air. They change the natural composition of the atmosphere, can be harmful to humans and other living species and can cause damage to natural water bodies and the land. Anthropogenic specifically due to the human causes that in this study, it has been identified that Population, Gross Domestic Product (GDP) and Manufacturing Industry adaptive from IPAT Model is the major contributors to the emission of carbon dioxide. The time series data gained of carbon emission from the years 1970 to 2011 to explain the trend. The Command and Control (CAC) and Economic Incentive (EI) approaches being recommended to assist the government monitoring the air pollution trend in Malaysia
Designing Highly Reliable Fiducial Markers

Fiducial markers are artificial landmarks added to a scene to facilitate locating point correspondences between images, or between images and a known model. Reliable fiducials solve the interest point detection and matching problems when adding markers is convenient. The proper design of fiducials and the associated computer vision algorithms to detect them can enable accurate pose detection for applications ranging from augmented reality, input devices for HCI, to robot navigation. Marker systems typically have two stages, hypothesis generation from unique image features and verification/identification. A set of criteria for high robustness and practical use are identified and then optimized to produce the ARTag fiducial marker system. An edge-based method robust to lighting and partial occlusion is used for the hypothesis stage, and a reliable digital coding system is used for the identification and verification stage. Using these design criteria large gains in performance are achieved by ARTag over conventional ad hoc designs.
A method for studying the generalized slowing hypothesis in children with specific language impairment.

The present work was conducted to demonstrate a method that could be used to assess the hypothesis that children with specific language impairment (SLI) often respond more slowly than unimpaired children on a range of tasks. The data consisted of 22 pairs of mean response times (RTs) obtained from previously published studies; each pair consisted of a mean RT for a group of children with SLI for an experimental condition and the corresponding mean RT for a group of children without SLI. If children with SLI always respond more slowly than unimpaired children and by an amount that does not vary across tasks, then RTs for children with SLI should increase linearly as a function of RTs for age-matched control children without SLI. This result was obtained and is consistent with the view that differences in processing speed between children with and without SLI reflect some general (i.e., non-task specific) component of cognitive processing. Future applications of the method are suggested.
Practical UC-Secure Delegatable Credentials with Attributes and Their Application to Blockchain

Certification of keys and attributes is in practice typically realized by a hierarchy of issuers. Revealing the full chain of issuers for certificate verification, however, can be a privacy issue since it can leak sensitive information about the issuer's organizational structure or about the certificate owner. Delegatable anonymous credentials solve this problem and allow one to hide the full delegation (issuance) chain, providing privacy during both delegation and presentation of certificates. However, the existing delegatable credentials schemes are not efficient enough for practical use.
 In this paper, we present the first hierarchical (or delegatable) anonymous credential system that is practical. To this end, we provide a surprisingly simple ideal functionality for delegatable credentials and present a generic construction that we prove secure in the UC model. We then give a concrete instantiation using a recent pairing-based signature scheme by Groth and describe a number of optimizations and efficiency improvements that can be made when implementing our concrete scheme. The latter might be of independent interest for other pairing-based schemes as well. Finally, we report on an implementation of our scheme in the context of transaction authentication for blockchain, and provide concrete performance figures.
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

We propose a family of learning algorithms based on a new form f regularization that allows us to exploit the geometry of the marginal distribution. We foc us on a semi-supervised framework that incorporates labeled and unlabeled data in a general-p u pose learner. Some transductive graph learning algorithms and standard methods including Suppor t Vector Machines and Regularized Least Squares can be obtained as special cases. We utilize pr op rties of Reproducing Kernel Hilbert spaces to prove new Representer theorems that provide theor e ical basis for the algorithms. As a result (in contrast to purely graph-based approaches) we ob tain a natural out-of-sample extension to novel examples and so are able to handle both transductive and truly semi-supervised settings. We present experimental evidence suggesting that our semiupervised algorithms are able to use unlabeled data effectively. Finally we have a brief discuss ion of unsupervised and fully supervised learning within our general framework.
A New High Precision Low Offset Dynamic Comparator for High Resolution High Speed ADCs

A new low offset dynamic comparator for high resolution high speed analog-to-digital application has been designed. Inputs are reconfigured from the typical differential pair comparator such that near equal current distribution in the input transistors can be achieved for a meta-stable point of the comparator. Restricted signal swing clock for the tail current is also used to ensure constant currents in the differential pairs. Simulation based sensitivity analysis is performed to demonstrate the robustness of the new comparator with respect to stray capacitances, common mode voltage errors and timing errors in a TSMC 0.18mu process. Less than 10mV offset can be easily achieved with the proposed structure making it favorable for flash and pipeline data conversion applications
Accelerating Deep Convolutional Networks using low-precision and sparsity

We explore techniques to significantly improve the compute efficiency and performance of Deep Convolution Networks without impacting their accuracy. To improve the compute efficiency, we focus on achieving high accuracy with extremely low-precision (2-bit) weight networks, and to accelerate the execution time, we aggressively skip operations on zero-values. We achieve the highest reported accuracy of 76.6% Top-1/93% Top-5 on the Imagenet object classification challenge with low-precision network while reducing the compute requirement by ∼3× compared to a full-precision network that achieves similar accuracy. Furthermore, to fully exploit the benefits of our low-precision networks, we build a deep learning accelerator core, DLAC, that can achieve up to 1 TFLOP/mm2 equivalent for single-precision floating-point operations (∼2 TFLOP/mm2 for half-precision), which is ∼5× better than Linear Algebra Core [16] and ∼4× better than previous deep learning accelerator proposal .
Attention-Based Ensemble for Deep Metric Learning

Deep metric learning aims to learn an embedding function, modeled as deep neural network. This embedding function usually puts semantically similar images close while dissimilar images far from each other in the learned embedding space. Recently, ensemble has been applied to deep metric learning to yield state-of-the-art results. As one important aspect of ensemble, the learners should be diverse in their feature embeddings. To this end, we propose an attention-based ensemble, which uses multiple attention masks, so that each learner can attend to different parts of the object. We also propose a divergence loss, which encourages diversity among the learners. The proposed method is applied to the standard benchmarks of deep metric learning and experimental results show that it outperforms the state-of-the-art methods by a significant margin on image retrieval tasks.
Mining User Reviews to Recommend Popular Dishes ∗ †

With the rapid growth of the internet as well as increasingly more accessible mobile devices, the amount of information being generated each day is enormous. We have many popular websites such as Yelp, TripAdvisor, Grubhub etc. that offer user ratings and reviews for different restaurants in the world. In most cases, though, the user is just interested in a small subset of the available information, enough to get a general overview of the restaurant and its popular dishes. In this paper, we present a way to mine user reviews to suggest popular dishes for each restaurant. Specifically, we propose a method that extracts and categorize dishes from Yelp restaurant reviews, and then ranks them to recommend the most popular dishes.