nils-herrmann/clean_scirepeval huggingface dataset:
Distilling Knowledge from Ensembles of Neural Networks for Speech Recognition

Speech recognition systems that combine multiple types of acoustic models have been shown to outperform single-model systems. However, such systems can be complex to implement and too resource-intensive to use in production. This paper describes how to use knowledge distillation to combine acoustic models in a way that has the best of many worlds: It improves recognition accuracy significantly, can be implemented with standard training tools, and requires no additional complexity during recognition. First, we identify a simple but particularly strong type of ensemble: a late combination of recurrent neural networks with different architectures and training objectives. To harness such an ensemble, we use a variant of standard cross-entropy training to distill it into a single model and then discriminatively fine-tune the result. An evaluation on 2,000-hour large vocabulary tasks in 5 languages shows that the distilled models provide up to 8.9% relative WER improvement over conventionally-trained baselines with an identical number of parameters.
Mometasone furoate in the treatment of vulvar lichen sclerosus: could its formulation influence efficacy, tolerability and adherence to treatment?

To assess the effectiveness, tolerability, and convenience of the cream formulation of mometasone furoate 0.1% (MMF) in the treatment of active vulvar lichen sclerosus (VLS) and to compare the cream with the ointment formulation.



The following efficacy parameters were assessed in 27 VLS patients treated with MMF cream for 12 weeks (group A): (i) response rate, (ii) percentage of patients achieving an improvement from baseline of ≥75% in subjective and objective scores, and (iii) mean reduction in subjective and objective scores. These efficacy assessments, as well as those regarding safety and adherence, were compared with the assessments recorded among 37 VLS patients treated with MMF ointment (group B).



59.3% (group A) and 78.4% (group B) of patients were considered responders; 44.4% and 40.7% of patients in group A and 54.1% and 45.9% in group B achieved an improvement of at least 75% in subjective and objective scores, respectively. MMF ointment obtained a significantly higher improvement in symptom scores in comparison with the cream formulation.



MMF in ointment formulation seems to be more effective in treating active VLS in comparison with MMF cream. Both formulations are well tolerated and there is no difference in patient adherence and satisfaction.
Digital Entrepreneurship of Born Digital and Grown Digital Firms: Comparing the Effectuation Process of Yihaodian and Suning

This paper examines digital entrepreneurship in born digital and grown digital firms. Of late, digital entrepreneurship has garnered renewed attention from national leaders, practitioners, and academics due to the phenomenal success of a number of digital startups and the radical disruptions brought by the innovative use of technologies. Despite the significance and the promises of digital technology, especially amid the gloomy economy, digital entrepreneurship has remained inadequately analyzed for its predominant focus on entrepreneurs (human agency) and newly founded firms (context). This study offers an in-depth analysis of two entrepreneurial firms, Yihaodian and Suning, which are among the largest e-commerce players in China. By adopting effectuation as our theoretical lens, this study aims to generate an understanding of the digital entrepreneurial process in both born digital and grown digital firms.
5.2 Distributed system of digitally controlled microregulators enabling per-core DVFS for the POWER8TM microprocessor

Integrated voltage regulator modules (iVRMs)  provide a cost-effective path to realizing per-core dynamic voltage and frequency scaling (DVFS), which can be used to optimize the performance of a power-constrained multi-core processor. This paper presents an iVRM system developed for the POWER8™ microprocessor, which functions as a very fast, accurate low-dropout regulator (LDO), with 90.5% peak power efficiency (only 3.1% worse than an ideal LDO). At low output voltages, efficiency is reduced but still sufficient to realize beneficial energy savings with DVFS. Each iVRM features a bypass mode so that some of the cores can be operated at maximum performance with no regulator loss. With the iVRM area including the input decoupling capacitance (DCAP) (but not the output DCAP inherent to the cores), the iVRMs achieve a power density of 34.5W/mm2, which exceeds that of inductor-based or SC converters by at least 3.4× .
A (Very) Brief History of Artificial Intelligence

telligence are traced to philosophy, fiction, and imagination. Early inventions in electronics, engineering, and many other disciplines have influenced AI. Some early milestones include work in problems solving which included basic work in learning, knowledge representation, and inference as well as demonstration programs in language understanding, translation, theorem proving, associative memory, and knowledge-based systems. The article ends with a brief examination of influential organizations and current issues facing the field.
Attention-Based Convolutional Neural Network for Semantic Relation Extraction

Nowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model makes full use of word embedding, part-of-speech tag embedding and position embedding information. Word level attention mechanism is able to better determine which parts of the sentence are most influential with respect to the two entities of interest. This architecture enables learning some important features from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model achieves better performances than several stateof-the-art neural network models and can achieve a competitive performance just with minimal feature engineering.
The YAP Prolog System

Yet Another Prolog (YAP) is a Prolog system originally developed in the mid-eighties and that has been under almost constant development since then. This paper presents the general structure and design of the YAP system, focusing on three important contributions to the Logic Programming community. First, it describes the main techniques used in YAP to achieve an efficient Prolog engine. Second, most Logic Programming systems have a rather limited indexing algorithm. YAP contributes to this area by providing a dynamic indexing mechanism, or just-in-time indexer (JITI). Third, a important contribution of the YAP system has been the integration of both or-parallelism and tabling in a single Logic Programming system.
Case Study On Social Engineering Techniques for Persuasion

T There are plenty of security software in market; each claiming the best, still we daily face problem of viruses and other malicious activities. If we know the basic working principal of such malware then we can very easily prevent most of them even without security software. Hackers and crackers are experts in psychology to manipulate people into giving them access or the information necessary to get access. This paper discusses the inner working of such attacks. Case study of Spyware is provided. In this case study, we got 100% success using social engineering techniques for deception on Linux operating system, which is considered as the most secure operating system. Few basic principal of defend, for the individual as well as for the organization, are discussed here, which will prevent most of such attack if followed.
A Data-driven Method for the Detection of Close Submitters in Online Learning Environments

Online learning has become very popular over the last decade. However, there are still many details that remain unknown about the strategies that students follow while studying online. In this study, we focus on the direction of detecting ‘invisible’ collaboration ties between students in online learning environments. Specifically, the paper presents a method developed to detect student ties based on temporal proximity of their assignment submissions. The paper reports on findings of a study that made use of the proposed method to investigate the presence of close submitters in two different massive open online courses. The results show that most of the students (i.e., student user accounts) were grouped as couples, though some bigger communities were also detected. The study also compared the population detected by the algorithm with the rest of user accounts and found that close submitters needed a statistically significant lower amount of activity with the platform to achieve a certificate of completion in a MOOC. These results confirm that the detected close submitters were performing some collaboration or even engaged in unethical behaviors, which facilitates their way into a certificate. However, more work is required in the future to specify various strategies adopted by close submitters and possible associations between the user accounts.
A Tidy Data Model for Natural Language Processing using cleanNLP

Recent advances in natural language processing have produced libraries that extract lowlevel features from a collection of raw texts. These features, known as annotations, are usually stored internally in hierarchical, tree-based data structures. This paper proposes a data model to represent annotations as a collection of normalized relational data tables optimized for exploratory data analysis and predictive modeling. The R package cleanNLP, which calls one of two state of the art NLP libraries (CoreNLP or spaCy), is presented as an implementation of this data model. It takes raw text as an input and returns a list of normalized tables. Specific annotations provided include tokenization, part of speech tagging, named entity recognition, sentiment analysis, dependency parsing, coreference resolution, and word embeddings. The package currently supports input text in English, German, French, and Spanish.
Guaranteed-Quality Mesh Generation for Curved Surfaces

For several commonly-used solution techniques for partial differential equations, the first step is to divide the problem region into simply-shaped elements, creating a mesh. We present a technique for creating high-quality triangular meshes for regions on curved surfaces. This technique is an extension of previous methods we developed for regions in the plane. For both flat and curved surfaces, the resulting meshes are guaranteed to exhibit the following properties: (1) internal and external boundaries are respected, (2) element shapes are guaranteed—all elements are triangles with angles between 30 and 120 degrees (with the exception of badly shaped elements that may be required by the specified boundary), and (3) element density can be controlled, producing small elements in “interesting” areas and large elements elsewhere. An additional contribution of this paper is the development of a practical generalization of Delaunay triangulation to curved surfaces.
An integrated framework on mining logs files for computing system management

Traditional approaches to system management have been largely based on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. This has been well known and experienced as a cumbersome, labor intensive, and error prone process. In addition, this process is difficult to keep up with the rapidly changing environments. In this paper, we will describe our research efforts on establishing an integrated framework for mining system log files for automatic management. In particular, we apply text mining techniques to categorize messages in log files into common situations, improve categorization accuracy by considering the temporal characteristics of log messages, develop temporal mining techniques to discover the relationships between different events, and utilize visualization tools to evaluate and validate the interesting temporal patterns for system management.
Sequence-to-Dependency Neural Machine Translation

Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned. Inspired by the success of using syntactic knowledge of target language for improving statistical machine translation, in this paper we propose a novel Sequence-to-Dependency Neural Machine Translation (SD-NMT) method, in which the target word sequence and its corresponding dependency structure are jointly constructed and modeled, and this structure is used as context to facilitate word generations. Experimental results show that the proposed method significantly outperforms state-of-the-art baselines on Chinese-English and JapaneseEnglish translation tasks.
A Framework based on SDN and Containers for Dynamic Service Chains on IoT Gateways

In this paper, we describe a new approach for managing service function chains in scenarios where data from Internet of Things (IoT) devices is partially processed at the network edge. Our framework is enabled by two emerging technologies, Software-Defined Networking (SDN) and container based virtualization, which ensure several benefits in terms of flexibility, easy programmability, and versatility. These features are well suitable with the increasingly stringent requirements of IoT applications, and allow a dynamic and automated network service chaining. An extensive performance evaluation, which has been carried out by means of a testbed, seeks to understand how our proposed framework performs in terms of computational overhead, network bandwidth, and energy consumption. By accounting for the constraints of typical IoT gateways, our evaluation tries to shed light on the actual deployability of the framework on low-power nodes.
Evoked-potential correlates of stimulus uncertainty.

The average evoked-potential waveforms to sound and light stimuli recorded from scalp in awake human subjects show differences as a function of the subject's degree of uncertainty with respect to the sensory modality of the stimulus to be presented. Differences are also found in the evoked potential as a function of whether or not the sensorymodality of the stimulus was anticipated correctly. The major waveform alteration is in the amplitude of a positive-going component which reaches peak amplitude at about 300 milliseconds.
An empirical study of the important factors for knowledge-management adoption in the SME sector

To investigate the critical success factors (CSFs) for adopting knowledge management (KM) in small and medium-sized enterprises (SMEs) – an area that has, to date, received very little attention in the literature.  A survey instrument comprising 11 factors and 66 elements was developed. Through a postal survey, data were sought from SMEs in the UK. A parallel one was also administered to a group of academics, consultants and practitioners in the KM field in order to provide a more holistic view of the CSFs.  The survey instrument was shown to be both reliable and valid. Pertinent statistical analyses were then performed. By integrating the results from both groups of respondents, a prioritised list of CSFs, in order of importance for implementing KM, was generated.  The number of responses received was rather small since KM is a new and emerging discipline, and not many SMEs have formally implemented it.  The results of this study would help SMEs to better understand the KM discipline, to facilitate its adoption and to prioritise its practices. Academics can use the results to build models that would further expand the KM domain.  This study is probably the first to systematically determine the CSFs for KM implementation in the SME sector. It offers a beneficial source of information to SMEs, which are still lagging far behind when it comes to KM practices.
Automatic Question Generation system

The process of automating the question generation consists of many tasks. Selecting the target content (what to ask), question type (who, why, how) and actual question generation are the major issue of Automatic Question Generation. Certain definitions retrieved is available in Wikipedia either directly or is the outcome of executing set of sub queries for each key phrase categories The problem in the existing system is that some of the definition sentences which are taken out from Wikipedia were implicit. The proposed system overcomes the problems by using Supervised Learning Approach, Naïve Bayes method. It also extends its work to use Summarization, Noun Filtering and Question Generation in the aim of generating semantically correct questions.
Automated Assembly Using 3D and 2D Cameras

2D and 3D computer vision systems are frequently being used in automated production to detect and determine the position of objects. Accuracy is important in the production industry, and computer vision systems require structured environments to function optimally. For 2D vision systems, a change in surfaces, lighting and viewpoint angles can reduce the accuracy of a method, maybe even to a degree that it will be erroneous, while for 3D vision systems, the accuracy mainly depends on the 3D laser sensors. Commercially available 3D cameras lack the precision found in high-grade 3D laser scanners, and are therefore not suited for accurate measurements in industrial use. In this paper, we show that it is possible to identify and locate objects using a combination of 2D and 3D cameras. A rough estimate of the object pose is first found using a commercially available 3D camera. Then, a robotic arm with an eye-in-hand 2D camera is used to determine the pose accurately. We show that this increases the accuracy to < 1 mm and < 1◦. This was demonstrated in a real industrial assembly task where high accuracy is required.
Network traffic anomaly detection using clustering techniques and performance comparison

Real-time network traffic anomaly detection is crucial for the confidentiality, integrity, and security of network information. Machine learning approaches are widely used to distinguish traffic flow outliers based on different anomalies with unique statistical characteristics. K-means clustering and Gaussian Mixture Model (GMM) are effective clustering techniques with many variations and easy to implement. Fuzzy clustering is more flexible than hard clustering and is practical for intrusion detection because of the natural treatment of data using fuzzy clustering. Fuzzy c-means clustering (FCM) is an iteratively optimal algorithm normally based on the least square method to partition data sets, which has high computational overhead. This paper proposes modifications to the objective function and the distance function that reduce the computational complexity of FCM while keeping clustering accurate. A combination of FCM clustering GMM, and feature transformation methods are proposed and a comparison of the related testing results and clustering methods is presented.
Facial recognition using histogram of gradients and support vector machines

Face recognition is widely used in computer vision and in many other biometric applications where security is a major concern. The most common problem in recognizing a face arises due to pose variations, different illumination conditions and so on. The main focus of this paper is to recognize whether a given face input corresponds to a registered person in the database. Face recognition is done using Histogram of Oriented Gradients (HOG) technique in AT & T database with an inclusion of a real time subject to evaluate the performance of the algorithm. The feature vectors generated by HOG descriptor are used to train Support Vector Machines (SVM) and results are verified against a given test input. The proposed method checks whether a test image in different pose and lighting conditions is matched correctly with trained images of the facial database. The results of the proposed approach show minimal false positives and improved detection accuracy.
H-TCP : TCP for high-speed and long-distance networks

In this paper we present a congestion control protocol that is suitable for deployment in highspeed and long-distance networks. The new protocol, H-TCP, is shown to fair when deployed in homogeneous networks, to be friendly when competing with conventional TCP sources, to rapidly respond to bandwidth as it becomes available, and to utilise link bandwidth in an efficient manner. Further, when deployed in conventional networks, H-TCP behaves as a conventional TCP-variant.
Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation

In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and di erentiability of the cost surface add yet another con icting element to the decision process. While \better" solutions should be rated higher than \worse" ones, the resulting cost landscape must also comply with such requirements. Evolutionary algorithms (EAs), which have found application in many areas not amenable to optimization by other methods, possess many characteristics desirable in a multiobjective optimizer, most notably the concerted handling of multiple candidate solutions. However, EAs are essentially unconstrained search techniques which require the assignment of a scalar measure of quality, or tness, to such candidate solutions. After reviewing current evolutionary approaches to multiobjective and constrained optimization, the paper proposes that tness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies. Finally, the ranking of an arbitrary number of candidates is considered. The e ect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem. The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision strategy. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape.
Evidence for a mitotic clock in human hematopoietic stem cells: loss of telomeric DNA with age.

The proliferative life-span of the stem cells that sustain hematopoiesis throughout life is not known. It has been proposed that the sequential loss of telomeric DNA from the ends of human chromosomes with each somatic cell division eventually reaches a critical point that triggers cellular senescence. We now show that candidate human stem cells with a CD34+CD38lo phenotype that were purified from adult bone marrow have shorter telomeres than cells from fetal liver or umbilical cord blood. We also found that cells produced in cytokine-supplemented cultures of purified precursor cells show a proliferation-associated loss of telomeric DNA. These findings strongly suggest that the proliferative potential of most, if not all, hematopoietic stem cells is limited and decreases with age, a concept that has widespread implications for models of normal and abnormal hematopoiesis as well as gene therapy.