nils-herrmann/clean_scirepeval huggingface dataset:
IEEE Standard 1500 Compliance Verification for Embedded Cores

Core-based design and reuse are the two key elements for an efficient system-on-chip (SoC) development. Unfortunately, they also introduce new challenges in SoC testing, such as core test reuse and the need of a common test infrastructure working with cores originating from different vendors. The IEEE 1500 Standard for Embedded Core Testing addresses these issues by proposing a flexible hardware test wrapper architecture for embedded cores, together with a core test language (CTL) used to describe the implemented wrapper functionalities. Several intellectual property providers have already announced IEEE Standard 1500 compliance in both existing and future design blocks. In this paper, we address the problem of guaranteeing the compliance of a wrapper architecture and its CTL description to the IEEE Standard 1500. This step is mandatory to fully trust the wrapper functionalities in applying the test sequences to the core. We present a systematic methodology to build a verification framework for IEEE Standard 1500 compliant cores, allowing core providers and/or integrators to verify the compliance of their products (sold or purchased) to the standard.
Automatic Manga Colorization with Hint

Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014.  Mirza, Mehdi, and Simon Osindero. "Conditional generative adversarial nets."  (2014).  Arjovsky, Martin, Soumith Chintala, and Léon Bottou. "Wasserstein gan."  (2017).  Gulrajani, Ishaan, et al. "Improved training of wasserstein gans."  (2017).  Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention.  2015.  Liu, Yifan, et al. "Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks."  (2017).
MapReduce: A major step backwards

On January 8, a Database Column reader asked for our views on new distributed database research efforts, and we'll begin here with our views on MapReduce. This is a good time to discuss it, since the recent trade press has been filled with news of the revolution of so-called "cloud computing." This paradigm entails harnessing large numbers of (low-end) processors working in parallel to solve a computing problem. In effect, this suggests constructing a data center by lining up a large number of "jelly beans" rather than utilizing a much smaller number of high-end servers.
Integrating E-Commerce and Data Mining: Architecture and Challenges

We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our experience at Blue Martini Software, for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e.g., clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We conclude with a set of challenges.
FuSSO: Functional Shrinkage and Selection Operator

We present the FuSSO, a functional analogue to the LASSO, that efficiently finds a sparse set of functional input covariates to regress a real-valued response against. The FuSSO does so in a semi-parametric fashion, making no parametric assumptions about the nature of input functional covariates and assuming a linear form to the mapping of functional covariates to the response. We provide a statistical backing for use of the FuSSO via proof of asymptotic sparsistency under various conditions. Furthermore, we observe good results on both synthetic and real-world data.
Budgeted Online Collective Inference

Updating inference in response to new evidence is a fundamental challenge in artificial intelligence. Many real problems require large probabilistic graphical models, containing possibly millions of interdependent variables. For such large models, jointly updating the most likely (i.e., MAP) configuration of the variables each time new evidence is encountered can be infeasible, even if inference is tractable. In this paper, we introduce budgeted online collective inference, in which the MAP configuration of a graphical model is updated efficiently by revising the assignments to a subset of the variables while holding others fixed. The goal is to selectively update certain variables without sacrificing quality with respect to full inference. To formalize the consequences of partially updating inference, we introduce the concept of inference regret. We derive inference regret bounds for a class of graphical models with strongly-convex free energies. These theoretical insights, combined with a thorough analysis of the optimization solver, motivate new approximate methods for efficiently updating the variable assignments under a budget constraint. In experiments, we demonstrate that our algorithms can reduce inference time by 65% with accuracy comparable to full inference.
A Declarative Framework for Linking Entities

We introduce and develop a declarative framework for entity linking and, in particular, for entity resolution. As in some earlier approaches, our framework is based on a systematic use of constraints. However, the constraints we adopt are link-to-source constraints, unlike in earlier approaches where source-to-link constraints were used to dictate how to generate links. Our approach makes it possible to focus entirely on the intended properties of the outcome of entity linking, thus separating the constraints from any procedure of how to achieve that outcome. The core language consists of link-to-source constraints that specify the desired properties of a link relation in terms of source relations and built-in predicates such as similarity measures. A key feature of the link-to-source constraints is that they employ disjunction, which enables the declarative listing of all the reasons two entities should be linked. We also consider extensions of the core language that capture collective entity resolution by allowing interdependencies among the link relations.
 We identify a class of “good” solutions for entity-linking specifications, which we call maximum-value solutions and which capture the strength of a link by counting the reasons that justify it. We study natural algorithmic problems associated with these solutions, including the problem of enumerating the “good” solutions and the problem of finding the certain links, which are the links that appear in every “good” solution. We show that these problems are tractable for the core language but may become intractable once we allow interdependencies among the link relations. We also make some surprising connections between our declarative framework, which is deterministic, and probabilistic approaches such as ones based on Markov Logic Networks.
Clinical and biochemical correlates of successful semen collection for cryopreservation from 12-18-year-old patients: a single-center study of 86 adolescents.

Cryopreservation of semen should be offered to adults before gonadotoxic treatment. However, the experience with semen collection in adolescents is still limited. The objective of this study was to evaluate potential correlates of successful semen sampling in adolescents.



A total of 86 boys (aged 12.2-17.9 years), referred for cryopreservation of semen prior to gonadotoxic treatment were included. Age, testicular volume, diagnosis and reproductive hormones were evaluated as correlates of successful semen collection.



Median sperm concentration was 9.6 (range 0-284) million/ml. Of the 86 included boys, 76 (88.4%) had spermatozoa in their ejaculate. Of the 76 patients for whom a semen sample was obtained, 71 (93.4%) had motile spermatozoa eligible for cryopreservation. Of the 86 boys, 74 produced a semen sample by masturbation, whereas semen samples were obtained from 12 patients by penile vibration or electroejaculation. The youngest patient with an ejaculate containing motile spermatozoa was 12.2 years old, and the smallest testicular volumes in boys associated with motile spermatozoa in the ejaculate were 6-7 ml. Testicular volume correlated with sperm concentration (R = 0.283, P = 0.046), and the percentage of motile spermatozoa (R = 0.410, P = 0.003). Chronological age, but not reproductive hormones, also correlated with sperm concentration (R = 0.25, P = 0.049).



Semen was successfully collected and cryopreserved in 71 out of 86 boys and adolescents. Testicular volume, but not age or reproductive hormone levels, was indicative of successful semen collection. Regardless of their age, adolescent boys with testicular volumes of more than 5 ml should be offered semen banking prior to gonadotoxic treatment or other procedures that could potentially damage future fertility.
Deep Learning in Microscopy Image Analysis: A Survey

Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerging as a leading machine learning tool in computer vision and has attracted considerable attention in biomedical image analysis. In this paper, we provide a snapshot of this fast-growing field, specifically for microscopy image analysis. We briefly introduce the popular deep neural networks and summarize current deep learning achievements in various tasks, such as detection, segmentation, and classification in microscopy image analysis. In particular, we explain the architectures and the principles of convolutional neural networks, fully convolutional networks, recurrent neural networks, stacked autoencoders, and deep belief networks, and interpret their formulations or modelings for specific tasks on various microscopy images. In addition, we discuss the open challenges and the potential trends of future research in microscopy image analysis using deep learning.
Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation

We introduce a reinforcement learningbased approach to simultaneous machine translation—producing a translation while receiving input words— between languages with drastically different word orders: from verb-final languages (e.g., German) to verb-medial languages (English). In traditional machine translation, a translator must “wait” for source material to appear before translation begins. We remove this bottleneck by predicting the final verb in advance. We use reinforcement learning to learn when to trust predictions about unseen, future portions of the sentence. We also introduce an evaluation metric to measure expeditiousness and quality. We show that our new translation model outperforms batch and monotone translation strategies.
Towards Secure Compressive Sampling Scheme

There have been some pioneering works concerning embedding cryptographic properties in Compressive Sampli ng (CS) but it turns out that the concise linear projection encoding process makes this approach ineffective. Here we introduce a bilevel protection (BLP) model for constructing secure compr essive sampling scheme. Then we propose several techniques to esta blish secret key-related sparsifying basis and deploy them into o ur new CS model. It is demonstrated that the encoding process is simply a random linear projection, which is the same as the traditional model. However, decoding the measurements req uires the knowledge of both the key-related sensing matrix and the key-related sparsifying basis. We apply the proposed model to construct digital image ciphe r under the parallel compressive sampling reconstruction fr amework. The main properties of this cipher, such as low computational complexity, compressibility, robustness and compu tational secrecy under known/chosen plaintext attacks, are thoroug hly studied. It is shown that compressive sampling schemes base d on our BLP model is robust under various attack scenarios although the encoding process is a simple linear projection.
QoE Issues of OTT Services over 5G Network

Currently, 4G network becomes commercial in large scale around the world and the industry has started the fifth-generation mobile communication technology (5G) research. Compared to 4G network, 5G network will support larger mobility as well as higher transmission rate, higher user experience rate, energy efficiency, spectrum efficiency and so forth. All of these will boost a variety of multimedia services, especially for Over-The-Top (OTT) services. So for, OTT services have already gained great popularity and contributed to large traffic consumption, which propose a challenge for operators. As OTT services are designed to deliver over the best effort Internet, the QoE management solutions for traditional multimedia services are obsolete, which propose new challenges in QOE management aspects for network and service providers, especially for the 4G and future 5G network. This paper attempts to present the technical challenges faced by 5G network from QoE management perspective of OTT services. Our objective is to enhance the user experience of OTT services and improve network efficiency. We analysis the characteristics and QoE factors of OTT services over 5G wireless network. With the QoE factors and current QoE management situation, we summarize OTT services QoE quantification and evaluation methods, present QoE-driven radio resource management and optimization solutions. Then, we propose a framework and whole evaluation procedure which aim at obtaining the accurate user experience value as well as improving network efficiency and optimizing the user experience.
Falling Rule Lists

Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are inspired by healthcare applications where patients would be stratified into risk sets and the highest at-risk patients should be considered first. We provide a Bayesian framework for learning falling rule lists that does not rely on traditional greedy decision tree learning methods.
Mining Big Data in Real Time

Streaming data analysis in real time is becoming the fastest and most efficient way to obtain useful knowledge from what is happening now, allowing organizations to react quickly when problems appear or to detect new trends helping to improve their performance. Evolving data streams are contributing to the growth of data created over the last few years. We are creating the same quantity of data every two days, as we created from the dawn of time up until 2003. Evolving data streams methods are becoming a low-cost, green methodology for real time online prediction and analysis. We discuss the current and future trends of mining evolving data streams, and the challenges that the field will have to overcome during the next years.
International Journal of Innovative Research in Computer and Communication Engineering

Steganography means hiding the secret message within an ordinary message and extraction of it as its destination. In the texture synthesis process here re-samples smaller texture image which gives a new texture image with a similar local appearance. In the existing system, work is done for the texture synthesis process but the embedding capacity of those systems is very low. In the project introduced the method SURTDS (steganography using reversible texture synthesis) for enhancing the embedding capacity of the system by using the difference expansion method with texture synthesis. Initially, this system evaluates the binary value of the secret image and converts this value into a decimal value. The process of embedding is performed by using the difference expansion techniques. Difference expansion computes the average and difference in a patch and embedded the value one by one. This system improves the embedding capacity of the stego image. The experimental result has verified that this system improves the embedding capacity of the SURTDS is better than the existing system.
Using mobile phones for secure, distributed document processing in the developing world

Paper plays an essential role in many information ecologies in the developing world, but it can be inefficient and inflexible. We've developed an information services architecture that uses a smart phone equipped with a built-in digital camera to process augmented paper documents. The CAM document-processing framework exploits smart mobile phones' utility, usability, and growing ubiquity to link paper with modern information tools. CAM, so called because the phone's camera plays a key role in the user interface, is a three-tiered, document-based architecture for providing remote rural information services. The CAM framework comprises four components: CamForms, CamShell, CamBrowser, and CamServer.
ALS and other motor neuron diseases.

This review describes the most common motor neuron disease, ALS. It discusses the diagnosis and evaluation of ALS and the current understanding of its pathophysiology, including new genetic underpinnings of the disease. This article also covers other motor neuron diseases, reviews how to distinguish them from ALS, and discusses their pathophysiology.



In this article, the spectrum of cognitive involvement in ALS, new concepts about protein synthesis pathology in the etiology of ALS, and new genetic associations will be covered. This concept has changed over the past 3 to 4 years with the discovery of new genes and genetic processes that may trigger the disease. As of 2014, two-thirds of familial ALS and 10% of sporadic ALS can be explained by genetics. TAR DNA binding protein 43 kDa (TDP-43), for instance, has been shown to cause frontotemporal dementia as well as some cases of familial ALS, and is associated with frontotemporal dysfunction in ALS.



The anterior horn cells control all voluntary movement: motor activity, respiratory, speech, and swallowing functions are dependent upon signals from the anterior horn cells. Diseases that damage the anterior horn cells, therefore, have a profound impact. Symptoms of anterior horn cell loss (weakness, falling, choking) lead patients to seek medical attention. Neurologists are the most likely practitioners to recognize and diagnose damage or loss of anterior horn cells. ALS, the prototypical motor neuron disease, demonstrates the impact of this class of disorders. ALS and other motor neuron diseases can represent diagnostic challenges. Neurologists are often called upon to serve as a "medical home" for these patients: coordinating care, arranging for durable medical equipment, and leading discussions about end-of-life care with patients and caregivers. It is important for neurologists to be able to identify motor neuron diseases and to evaluate and treat patients affected by them.
Calibrating credit portfolio loss distributions

Determination of credit portfolio loss distributions is essential for the valuation and risk management of multi-name credit derivatives such as CDOs. The default time model has recently become a market standard approach for capturing the default correlation, which is one of the main drivers for the portfolio loss. However, the default time model yields very different default dependency compared with a continuous-time credit migration model. To build a connection between them, we calibrate the correlation parameter of a single-factor Gaussian copula model to portfolio loss distribution determined from a multi-step credit migration simulation. The deal correlation is produced as a measure of the portfolio average correlation effect that links the two models. Procedures for obtaining the portfolio loss distributions in both models are described in the paper and numerical results are presented.
Atrial septal puncture technique in percutaneous transvenous mitral commissurotomy: mitral valvuloplasty using the Inoue balloon catheter technique.

Transseptal catheterization is a vital component of percutaneous transvenous mitral commissurotomy. Therefore, a well-executed transseptal catheterization is the key to a safe and successful percutaneous transvenous mitral commissurotomy. Two major problems inherent in atrial septal puncture for percutaneous transvenous mitral commissurotomy are cardiac perforation and puncture of an inappropriate atrial septal site. The former may lead to serious complication of cardiac tamponade and the latter to possible difficulty in maneuvering the Inoue balloon catheter across the mitral orifice. This article details atrial septal puncture technique, including landmark selection for optimal septal puncture sites, avoidance of inappropriate puncture sites, and step-by-step description of atrial septal puncture.
A Flexible System for Scheduling Drivers

A substantial part of the operating costs of public transport is attributable to drivers, whose efficient use therefore is important. The compilation of optimal work packages is difficult, being NP-hard. In practice, algorithmic advances and enhanced computing power have led to significant progress in achieving better schedules. However, differences in labor practices among modes of transport and operating companies make production of a truly general system with acceptable performance a difficult proposition. TRACS II has overcome these difficulties, being used with success by a substantial number of bus and train operators. Many theoretical aspects of the system have been published previously. This paper shows for the first time how theory and practice have been brought together, explaining the many features which have been added to the algorithmic kernel to provide a user-friendly and adaptable system designed to provide maximum flexibility in practice. We discuss the extent to which users have been involved in system development, leading to many practical successes, and we summarize some recent achievements.
A comparative study on Thai word segmentation approaches

In this paper, we analyze and compare various approaches for Thai word segmentation. The word segmentation approaches could be classified into two distinct types, dictionary based (DCB) and machine learning based (MLB). The DCB approach relies on a set of terms for parsing and segmenting input texts. Whereas the MLB approach relies on a model trained from a corpus by using machine learning techniques. We compare between two algorithms from the DCB approach: longest-matching and maximal matching, and four algorithms from the MLB approach: Naive Bayes (NB), decision tree, support vector machine (SVM), and conditional random field (CRF). From the experimental results, the DCB approach yielded better performance than the NB, decision tree and SVM algorithms from the MLB approach. However, the best performance was obtained from the CRF algorithm with the precision and recall of 95.79% and 94.98%, respectively.