nils-herrmann/clean_scirepeval huggingface dataset:
Sequential Pattern Mining : Survey and Current Research Challenges

The concept of sequence Data Mining was first introduced by Rakesh Agrawal and Ramakrishnan Srikant in the year 1995. The problem was first introduced in the context of market analysis. It aimed to retrieve frequent patterns in the sequences of products purchased by customers through time ordered transactions. Later on its application was extended to complex applications like telecommunication, network detection, DNA research, etc. Several algorithms were proposed. The very first was Apriori algorithm, which was put forward by the founders themselves. Later more scalable algorithms for complex applications were developed. E.g. GSP, Spade, PrefixSpan etc. The area underwent considerable advancements since its introduction in a short span. In this paper, a systematic survey of the sequential pattern mining algorithms is performed. This paper investigates these algorithms by classifying study of sequential pattern-mining algorithms into two broad categories. First, on the basis of algorithms which are designed to increase efficiency of mining and second, on the basis of various extensions of sequential pattern mining designed for certain application. At the end, comparative analysis is done on the basis of important key features supported by various algorithms and current research challenges are discussed in this field of data mining.
Social Informatics Evolving

The study of people, information, and communication technologies and the contexts in which these technologies are designed, implemented, and used has long interested scholars in a wide range of disciplines, including the social study of computing, science and technology studies, the sociology of technology, and management information systems. As ICT use has spread from organizations into the larger world, these devices have become routine information appliances in our social lives, researchers have begun to ask deeper and more profound questions about how our lives have become bound up with technologies. A common theme running through this research is that the relationships among people, technology, and context are dynamic, complex, and critically important to understand. This book explores social informatics (SI), one important and dynamic approach that researchers have used to study these complex relationships. SI is “the interdisciplinary study of the design, uses and consequences of information technology that takes into account their interaction with institutional and cultural contexts” (Kling 1998, p. 52; 1999). SI provides flexible frameworks to explore complex and dynamic socio-technical interactions. As a domain of study related largely by common vocabulary and conclusions, SI critically examines common conceptions of and expectations for technology, by providing contextual evidence. This book describes the evolution of SI research and identifies challenges and opportunities for future research. In what might be seen as an example of socio-technical “natural selection,” SI emerged in six different locations during the 1980s and 1990s: Norway, Slovenia, Japan, the former Soviet Union, the UK and, last, the U.S. As SI evolved, the version popularized in the US became globally dominant. The evolution of SI is presented in five stages: emergence, foundational, expansion, coherence, and transformation. Thus, we divide SI research into five major periods: an emergence stage, when various forms of SI emerged around the globe, an early period of foundational work which grounds SI (Pre-1990s), a period of expansion (1990s), a robust period of coherence and influence by Rob Kling (2000–2005),1 and a period of transformation (2006–present). Following the description of the five periods we discuss the evolution throughout the periods under five sections: principles, concepts, approaches, topics, and findings. Principles refer to the overarching motivations and labels employed to describe scholarly work. Approaches describe the theories, frameworks, and models employed in analysis, emphasizing the multi-disciplinary and interdisciplinary nature of SI. Concepts include specific processes, entities, themes, and elements 1 This time period extends two years after Rob Kling’s death because some of his publications appeared later, and many publications that appeared immediately after his death were not only strongly inspired by him but also a tribute to him and his SI research.
Vision based 3-D shape sensing of flexible manipulators

Rigid robotic manipulators employ traditional sensors such as encoders or potentiometers to measure joint angles and determine end-effector position. Manipulators that are flexible, however, introduce motions that are much more difficult to measure. This is especially true for continuum manipulators that articulate by means of material compliance. In this paper, we present a vision based system for quantifying the 3-D shape of a flexible manipulator in real-time. The sensor system is validated for accuracy with known point measurements and for precision by estimating a known 3-D shape. We present two applications of the validated system relating to the open-loop control of a tendon driven continuum manipulator. In the first application, we present a new continuum manipulator model and use the sensor to quantify 3-D performance. In the second application, we use the shape sensor system for model parameter estimation in the absence of tendon tension information.
Exploring Spin Transfer Torque Devices for Unconventional Computing

This paper reviews the potential of spin-transfer torque devices as an alternative to complementary metal-oxide-semiconductor for non-von Neumann and non-Boolean computing. Recent experiments on spin-transfer torque devices have demonstrated high-speed magnetization switching of nanoscale magnets with small current densities. Coupled with other properties, such as nonvolatility, zero leakage current, high integration density, we discuss that the spin-transfer torque devices can be inherently suitable for some unconventional computing models for information processing. We review several spintronic devices in which magnetization can be manipulated by current induced spin transfer torque and explore their applications in neuromorphic computing and reconfigurable memory-based computing.
Effects of Mindfulness-Based Stress Reduction on Medical and Premedical Students

The inability to cope successfully with the enormous stress of medical education may lead to a cascade of consequences at both a personal and professional level. The present study examined the short-term effects of an 8-week meditation-based stress reduction intervention on premedical and medical students using a well-controlled statistical design. Findings indicate that participation in the intervention can effectively (1) reduce self-reported state and trait anxiety, (2) reduce reports of overall psychological distress including depression, (3) increase scores on overall empathy levels, and (4) increase scores on a measure of spiritual experiences assessed at termination of intervention. These results (5) replicated in the wait-list control group, (6) held across different experiments, and (7) were observed during the exam period. Future research should address potential long-term effects of mindfulness training for medical and premedical students.
Binarising Camera Images for OCR

In this paper we describe a new binarisation method designed specifically for OCR of low quality camera images:  Surface Thresholding or BST. This method is robust to lighting variations and produces images with very little noise and consistent stroke width. BST computes a ”surface” of background intensities at every point in the image and performs adaptive thresholding based on this result. The surface is estimated by identifying regions of lowresolution text and interpolating neighbouring background intensities into these regions. The final threshold is a combination of this surface and a global offset. According to our evaluation BST produces considerably fewer OCR errors than Niblack’s local average method while also being more runtime efficient.
Zero-voltage-switching PWM hybrid full-bridge three-level converter

This paper proposes a zero-voltage-switching (ZVS) pulse-width modulation (PWM) hybrid full-bridge three-level converter, which has a three-level leg and a two-level leg. The switches of the three-level leg sustain only the half of the input voltage, and they can realize ZVS in a wide load range. The switches of the two-level leg sustain the input voltage, and they can realize ZVS with the use of the resonant inductance. The secondary rectified voltage is a three-level waveform having lower high-frequency content compared with that of the traditional full-bridge converters, which can reduce the output filter, and as a result, the dynamic response of the converter is improved. The voltage stress of the rectifier diode is reduced also. The input current of the converter has quite little ripple, so the input filter can also be significantly reduced. The operation principle of the proposed converter is analyzed and verified by the experimental results.
Twitter as a Corpus for Sentiment Analysis and Opinion Mining

Microblogging today has become a very popular communication tool among Internet users. Millions of users share opinions on different aspects of life everyday. Therefore microblogging web-sites are rich sources of data for opinion mining and sentiment analysis. Because microblogging has appeared relatively recently, there are a few research works that were devoted to this topic. In our paper, we focus on using Twitter, the most popular microblogging platform, for the task of sentiment analysis. We show how to automatically collect a corpus for sentiment analysis and opinion mining purposes. We perform linguistic analysis of the collected corpus and explain discovered phenomena. Using the corpus, we build a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document. Experimental evaluations show that our proposed techniques are efficient and performs better than previously proposed methods. In our research, we worked with English, however, the proposed technique can be used with any other language.
3D Object Dense Reconstruction from a Single Depth View

In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of 2563 by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.
Speech parameter generation algorithms for HMM-based speech synthesis

This paper derives a speech parameter generation algorithm for HMM-based speech synthesis, in which speech parameter sequence is generated from HMMs whose observation vector consists of spectral parameter vector and its dynamic feature vectors. In the algorithm, we assume that the state sequence (state and mixture sequence for the multi-mixture case) or a part of the state sequence is unobservable (i.e., hidden or latent). As a result, the algorithm iterates the forward-backward algorithm and the parameter generation algorithm for the case where state sequence is given. Experimental results show that by using the algorithm, we can reproduce clear formant structure from multi-mixture HMMs as compared with that produced from single-mixture HMMs.
Unsupervised learning of hierarchical representations with convolutional deep belief networks

There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks (DBNs); however, scaling such models to full-sized, high-dimensional images remains a difficult problem. To address this problem, we present the convolutional deep belief network, a hierarchical generative model that scales to realistic image sizes. This model is translation-invariant and supports efficient bottom-up and top-down probabilistic inference. Key to our approach is probabilistic max-pooling, a novel technique that shrinks the representations of higher layers in a probabilistically sound way. Our experiments show that the algorithm learns useful high-level visual features, such as object parts, from unlabeled images of objects and natural scenes. We demonstrate excellent performance on several visual recognition tasks and show that our model can perform hierarchical (bottom-up and top-down) inference over full-sized images.
Sentiment Classification in Chinese Microblogs: Lexicon-based and Learning-based Approaches

Sentiment classification in Chinese microblogs is more challenging than that of Twitter for numerous reasons. In this paper, two kinds of approaches are proposed to classify opinionated Chinesemicroblog posts: 1) lexicon-based approaches combining Simple Sentiment Word-Count Method with 3 Chinese sentiment lexicons, 2) machine learning models with multiple features. According to our experiment, lexicon-based approaches can yield relatively fine results and machine learning classifiers outperform both the majority baseline and lexicon-based approaches. Among all the machine learning-based approaches, Random Forests works best and the results are satisfactory.
Estimating a Dirichlet distribution

The Dirichlet distribution and its compound variant, the Dirichlet-multinomial, are two of the most basic models for proportional data, such as the mix of vocabulary words in a text document. Yet the maximum-likelihood estimate of these distributions is not available in closed-form. This paper describes simple and efficient iterative schemes for obtaining parameter estimates in these models. In each case, a fixed-point iteration and a Newton-Raphson (or generalized Newton-Raphson) iteration is provided. 1 The Dirichlet distribution The Dirichlet distribution is a model of how proportions vary. Let p denote a random vector whose elements sum to 1, so that pk represents the proportion of item k. Under the Dirichlet model with parameter vector α, the probability density at p is p(p) ∼ D(α1, ..., αK) = Γ( ∑ k αk) ∏ k Γ(αk) ∏ k pk k (1) where pk > 0 (2)
Ontology-Mediated Queries for NOSQL Databases

This paradigm enables a uniform access to heterogeneous data sources, also coping with incomplete information. Whether OBDA is suitable also for non-relational structures, like those shared by increasingly popular NOSQL languages, is still an open question. In this paper, we study the problem of answering ontology-mediated queries on top of key-value stores. We formalize the data model and core queries of these systems, and introduce a rule language to express lightweight ontologies on top of data. We study the decidability and data complexity of query answering in this setting.
A virtual machine placement algorithm for balanced resource utilization in cloud data centers

Virtual machine (VM) placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Several approaches have been proposed to find a solution to this problem. However, most of the existing solutions only consider a limited number of resource types, thus resulting in unbalanced load or in the unnecessary activation of physical servers. In this article, we propose an algorithm, called Max-BRU, that maximizes the resource utilization and balances the usage of resources across multiple dimensions. Our algorithm is based on multiple resource-constraint metrics that help to find the most suitable server for deploying VMs in large cloud data centers. The proposed Max-BRU algorithm is evaluated by simulations based on synthetic datasets. Experimental results show two major improvements over the existing approaches for VM placement. First, Max-BRU increases the resource utilization by minimizing the amount of physical servers used. Second, Max-BRU effectively balances the utilization of multiple types of resources.
A methodology for constructing of philosophy ontology based on philosophical texts

An ontology is a crucial factor for the success of the Semantic Web and other knowledge-based systems in terms of share and reuse of domain knowledge. However, there are a few concrete ontologies within actual knowledge domains including learning domains. In this paper, we develop an ontology which is an explicit formal specification of concepts and semantic relations among them in philosophy. We call it a philosophy ontology. Our philosophy is a formal specification of philosophical knowledge including knowledge of contents of classical texts of philosophy. We propose a methodology, which consists of detailed guidelines and templates, for constructing text-based ontology. Our methodology consists of 3 major steps and 14 minor steps. To implement the philosophy ontology, we develop an ontology management system based on Topic Maps. Our system includes a semi-automatic translator for creating Topic Map documents from the output of conceptualization steps and other tools to construct, store, retrieve ontologies based on Topic Maps. Our methodology and tools can be applied to other learning domain ontologies, such as history, literature, arts, and music.
Design and Implementation of a Homomorphic-Encryption Library

We describe the design and implementation of a software library that implements the Brakerski-Gentry-Vaikuntanathan (BGV) homomorphic encryption scheme, along with many optimizations to make homomorphic evaluation runs faster, focusing mostly on effective use of the Smart-Vercauteren ciphertext packing techniques. Our library is written in C++ and uses the NTL mathematical library. It is distributed under the terms of the GNU General Public License (GPL). Partially supported by DARPA under agreement number FA8750-11-C-0096. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the U.S. Government. Distribution Statement “A” (Approved for Public Release, Distribution Unlimited). Also partially supported by the Intelligence Advanced Research Projects Activity (EARP) via Department of Interior National Business Center (DoI/NBC) contract number D11PC20202. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.  The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the U.S. Government.
Evaluation of the Intel RealSense SR300 camera for image-guided interventions and application in vertebral level localization

Optical pose tracking of medical instruments is often used in image-guided interventions. Unfortunately, compared to commonly used computing devices, optical trackers tend to be large, heavy, and expensive devices. Compact 3D vision systems, such as Intel RealSense cameras can capture 3D pose information at several magnitudes lower cost, size, and weight. We propose to use Intel SR300 device for applications where it is not practical or feasible to use conventional trackers and limited range and tracking accuracy is acceptable. We also put forward a vertebral level localization application utilizing the SR300 to reduce risk of wrong-level surgery.  The SR300 was utilized as an object tracker by extending the PLUS toolkit to support data collection from RealSense cameras. Accuracy of the camera was tested by comparing to a high-accuracy optical tracker. CT images of a lumbar spine phantom were obtained and used to create a 3D model in 3D Slicer. The SR300 was used to obtain a surface model of the phantom. Markers were attached to the phantom and a pointer and tracked using Intel RealSense SDK’s built-in object tracking feature. 3D Slicer was used to align CT image with phantom using landmark registration and display the CT image overlaid on the optical image.  Accuracy of the camera yielded a median position error of 3.3mm (95 percentile 6.7mm) and orientation error of 1.6 (95 percentile 4.3) in a 20x16x10cm workspace, constantly maintaining proper marker orientation. The model and surface correctly aligned demonstrating the vertebral level localization application.  The SR300 may be usable for pose tracking in medical procedures where limited accuracy is acceptable. Initial results suggest the SR300 is suitable for vertebral level localization.
FFRob: An Efficient Heuristic for Task and Motion Planning

Manipulation problems involving many objects present substantial challenges for motion planning algorithms due to the high dimensionality and multi-modality of the search space. Symbolic task planners can efficiently construct plans involving many entities but cannot incorporate the constraints from geometry and kinematics. In this paper, we show how to extend the heuristic ideas from one of the most successful symbolic planners in recent years, the FastForward (FF) planner, to motion planning, and to compute it efficiently. We use a multi-query roadmap structure that can be conditionalized to model different placements of movable objects. The resulting tightly integrated planner is simple and performs efficiently in a collection of tasks involving manipulation of many objects.
Genome engineering using the CRISPR-Cas9 system

Targeted nucleases are powerful tools for mediating genome alteration with high precision. The RNA-guided Cas9 nuclease from the microbial clustered regularly interspaced short palindromic repeats (CRISPR) adaptive immune system can be used to facilitate efficient genome engineering in eukaryotic cells by simply specifying a 20-nt targeting sequence within its guide RNA. Here we describe a set of tools for Cas9-mediated genome editing via nonhomologous end joining (NHEJ) or homology-directed repair (HDR) in mammalian cells, as well as generation of modified cell lines for downstream functional studies. To minimize off-target cleavage, we further describe a double-nicking strategy using the Cas9 nickase mutant with paired guide RNAs. This protocol provides experimentally derived guidelines for the selection of target sites, evaluation of cleavage efficiency and analysis of off-target activity. Beginning with target design, gene modifications can be achieved within as little as 1–2 weeks, and modified clonal cell lines can be derived within 2–3 weeks.
The Rocket Chip Generator

Rocket Chip is an open-source Sysem-on-Chip design generator that emits synthesizable RTL. It leverages the Chisel hardware construction language to compose a library of sophisticated generators for cores, caches, and interconnects into an integrated SoC. Rocket Chip generates general-purpose processor cores that use the open RISC-V ISA, and provides both an in-order core generator (Rocket) and an out-of-order core generator (BOOM). For SoC designers interested in utilizing heterogeneous specialization for added efficiency gains, Rocket Chip supports the integration of custom accelerators in the form of instruction set extensions, coprocessors, or fully independent novel cores. Rocket Chip has been taped out (manufactured) eleven times, and yielded functional silicon prototypes capable of booting Linux.