nils-herrmann/clean_scirepeval huggingface dataset:
Manifold Learning for Medical Image Registration , Segmentation , and Classification

The term manifold learning encompasses a class of machine learning techniques that convert data from a high to lower dimensional representation while respecting the intrinsic geometry of the data. The intuition underlying the use of manifold learning in the context of image analysis is that, while each image may be viewed as a single point in a very high-dimensional space, a set of such points for a population of images may be well represented by a sub-manifold of the space that is likely to be non-linear and of a significantly lower dimension. Recently, manifold learning techniques have begun to be applied to the field of medical image analysis. This chapter will review the most popular manifold learning techniques such as Multi-Dimensional Scaling (MDS), Isomap, Local linear embedding, and Laplacian eigenmaps. It will also demonstrate how these techniques can be used for image registration, segmentation, and biomarker discovery from medical images.
To catch a chorus: using chroma-based representations for audio thumbnailing

An important application for use with multimedia databases is a browsing aid, which allows a user to quickly and efficiently preview selections from either a database or from the results of a database query. Methods for facilitating browsing, though, are necessarily media dependent. We present one such method that produces short, representative samples (or “audio thumbnails”) of selections of popular music. This method attempts to identify the chorus or refrain of a song by identifying repeated sections of the audio waveform. A reduced spectral representation of the selection based on a chroma transformation of the spectrum is used to find repeating patterns. This representation encodes harmonic relationships in a signal and thus is ideal for popular music, which is often characterized by prominent harmonic progressions. The method is evaluated over a sizable database of popular music and found to perform well, with most of the errors resulting from songs that do not meet our structural assumptions.
Design and fabrication of posfet devices for tactile sensing

This work presents the design and fabrication of novel piezoelectric polymer-FET touch sensing devices. With thin piezoelectric polymer (PVDF-TrFE) film directly spun and processed in situ on the gate area of MOS transistor, the device can also be called as POSFET (Piezoelectric Oxide Semiconductor Field Effect Transistor) touch sensing device. The integral POSFET “sensotronics” unit, comprising of transducer and the transistor, senses and conditions the signal at ‘same site’. Challenging issues like in situ poling of piezoelectric polymer film, without damaging or altering the characteristics of underlying MOS devices, have been successfully dealt with.
Inter-media hashing for large-scale retrieval from heterogeneous data sources

In this paper, we present a new multimedia retrieval paradigm to innovate large-scale search of heterogenous multimedia data. It is able to return results of different media types from heterogeneous data sources, e.g., using a query image to retrieve relevant text documents or images from different data sources. This utilizes the widely available data from different sources and caters for the current users' demand of receiving a result list simultaneously containing multiple types of data to obtain a comprehensive understanding of the query's results. To enable large-scale inter-media retrieval, we propose a novel inter-media hashing (IMH) model to explore the correlations among multiple media types from different data sources and tackle the scalability issue. To this end, multimedia data from heterogeneous data sources are transformed into a common Hamming space, in which fast search can be easily implemented by XOR and bit-count operations. Furthermore, we integrate a linear regression model to learn hashing functions so that the hash codes for new data points can be efficiently generated. Experiments conducted on real-world large-scale multimedia datasets demonstrate the superiority of our proposed method compared with state-of-the-art techniques.
Exploratory and confirmatory factor analysis of the

The objective of this study is to investigate the evidence of construct validity of a phonological awareness instrument. Exploratory factor analysis was carried out on data collected from 510 elementary and middle school students in 2nd and 6th grades attending two different public schools in the city of São Paulo, Brazil; most were males with mean age of 8.4 years. Confirmatory factor analysis was carried out on data collected from 427 students from other four Brazilian states in the same grades; most were females with mean age of 9.3 years. The instrument used was the Roteiro de Avaliação da Consciência Fonológica, a phonological awareness test. The exploratory factor analysis showed a three-factor solution. As for the confirmatory factor analysis, of the two models tested, the one that indicated better model fit indices was composed of three factors. The model found is adequate for the task carried out in this study. However, more studies should be carried out to further refine the instrument.
Social LSTM: Human Trajectory Prediction in Crowded Spaces

Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting the future trajectory of people based on their past positions. Following the recent success of Recurrent Neural Network (RNN) models for sequence prediction tasks, we propose an LSTM model which can learn general human movement and predict their future trajectories. This is in contrast to traditional approaches which use hand-crafted functions such as Social forces. We demonstrate the performance of our method on several public datasets. Our model outperforms state-of-the-art methods on some of these datasets. We also analyze the trajectories predicted by our model to demonstrate the motion behaviour learned by our model.
Information Technology Capability and Firm Performance: Contradictory Findings and Their Possible Causes

IT Leader Sample SIC Code Control Sample SIC Code Consol Energy Inc 1220 Walter Energy Inc 1220 Halliburton Co 1389 Schlumberger Ltd 1389 Standard Pacific Corp 1531 M/I Homes Inc 1531 Beazer Homes USA Inc 1531 Hovnanian Entrprs Inc -Cl A 1531 Toll Brothers Inc 1531 MDC  1531 D R Horton Inc 1531 Ryland Group Inc 1531 Lennar Corp 1531 KB Home 1531 Granite Construction Inc 1600 Empresas Ica Soc Ctl ADR 1600 Fluor Corp 1600 Alstom ADR 1600 Gold Kist Inc 2015 Sadia Sa ADR 2015 Kraft  2000 ConAgra Foods Inc 2000 Smithfield  2011 Hormel Foods Corp 2011 Campbell Soup Co 2030 Heinz (H J) Co 2030 General Mills Inc 2040 Kellogg Co 2040 Imperial Sugar Co 2060 Wrigley (Wm) Jr Co 2060 Hershey Co 2060 Tate & Lyle Plc ADR 2060 Molson Coors Brewing Co 2082 Comp Bebidas Americas ADR 2082 Constellation Brands Cl A 2084 Gruma S.A.B. de C.V. ADR B 2040 Brown-Forman Cl B 2085 Coca Cola Hellenic Bttlg ADR 2086
Querying and mining data streams: you only get one look a tutorial

1.  Database Management Systems (DBMS) software is built on the concept of persistent data sets, that are stored reliably in stable storage and queried/updated several times throughout their lifetime. For several emerging application domains, however, data arrives and needs to be processed on a continuous ( ) basis, without the benefit of several passes over a static, persistent data image. Such continuous data streams arise naturally, for example, in the network installations of large Telecom and Internet service providers where detailed usage information (Call-Detail-Records (CDRs), SNMP/RMON packet-flow data, etc.) from different parts of the underlying network needs to be continuously collected and analyzed for interesting trends. Other applications that generate rapid, continuous and large volumes of stream data include transactions in retail chains, ATM and credit card operations in banks, financial tickers, Web server log records, etc. In most such applications, the data stream is actually accumulated and archived in the DBMS of a (perhaps, off-site) data warehouse, often making access to the archived data prohibitively expensive. Further, the ability to make decisions and infer interesting patterns on-line (i.e., as the data stream arrives) is crucial for several mission-critical tasks that can have significant dollar value for a large corporation (e.g., telecom fraud detection). As a result, recent years have witnessed an increasing interest in designing data-processing algorithms that work over continuous data streams, i.e., algorithms that provide results to user queries while looking at the relevant data items only once and in a fixed order (determined by the stream-arrival pattern). Two key parameters for query processing over continuous datastreams are (1) the amount of memory made available to the online algorithm, and (2) the per-item processing time required by the query processor. The former constitutes an important constraint on the design of stream processing algorithms, since in a typical streaming environment, only limited memory resources are available to the query-processing algorithms. In these situations, we need algorithms that can summarize the data stream(s) involved in a concise, but reasonably accurate, synopsis that can be stored in the allotted (small) amount of memory and can be used to provide approximate answers to user queries along with some reasonable guarantees on the quality of the approximation. Such approx-
Aligning plot synopses to videos for story-based retrieval

We propose a method to facilitate search through the storyline of TV series episodes. To this end, we use human written, crowdsourced descriptions—plot synopses—of the story conveyed in the video. We obtain such synopses from websites such as Wikipedia and propose various methods to align each sentence of the plot to shots in the video. Thus, the semantic story-based video retrieval problem is transformed into a much simpler text-based search. Finally, we return the set of shots aligned to the sentences as the video snippet corresponding to the query. The alignment is performed by first computing a similarity score between every shot and sentence through cues such as character identities and keyword matches between plot synopses and subtitles. We then formulate the alignment as an optimization problem and solve it efficiently using dynamic programming. We evaluate our methods on the fifth season of a TV series Buffy the Vampire Slayer and show encouraging results for both the alignment and the retrieval of story events.
Robust Entity Linking via Random Walks

Entity Linking is the task of assigning entities from a Knowledge Base to textual mentions of such entities in a document. State-of-the-art approaches rely on lexical and statistical features which are abundant for popular entities but sparse for unpopular ones, resulting in a clear bias towards popular entities and poor accuracy for less popular ones. In this work, we present a novel approach that is guided by a natural notion of semantic similarity which is less amenable to such bias. We adopt a unified semantic representation for entities and documents - the probability distribution obtained from a random walk on a subgraph of the knowledge base - which can overcome the feature sparsity issue that affects previous work. Our algorithm continuously updates the semantic signature of the document as mentions are disambiguated, thus focusing the search based on context. Our experimental evaluation uses well-known benchmarks and different samples of a Wikipedia-based benchmark with varying entity popularity; the results illustrate well the bias of previous methods and the superiority of our approach, especially for the less popular entities.
Information governance in the banking industry

As a consequence of explosive growth in the amount of information generated, stored, accessed, and used by organizations, the concept of Information Governance (IG) is becoming important in the information systems management profession. The paper focuses on information governance in the banking industry. It involves a study of thirteen banks in three countries, all with highly developed financial systems: Brazil, Hong Kong (SAR of China) and the United States. In addition to reporting on the perceived need for IG in today's banks, the study proposes and verifies the possibilities of an information governance framework (IGF) with banking industry executives.
Difficult venous access in children: taking control.

Difficulty in placing a peripheral intravenous line is a very common and frustrating experience for nurses, especially those who care for children. This article presents the consensus of experts in pediatric emergency medicine, nursing, hospital medicine, anesthesia, and critical care on the clinical impact of peripheral difficult venous access (DVA) in children, and the role of the nurse and physician in caring for patients in these challenging situations. This article focuses primarily on the nurse’s role; the Panel’s recommendations for physicians will be published separately. The Consensus Panel was co-chaired by Daniel Rauch, MD, FAAP, and Laura L. Kuensting, MSN(R), RN, CPNP, and was made possible by an educational grant from Baxter Healthcare. The main objectives of the meeting were to develop terminology to accurately describe the condition; explore the frequency and impact of peripheral DVA in pediatric patients; list the risk factors that may help identify children with DVA; describe its clinical and personal impact on the patient, family, and clinician; discuss strategies for the prevention and management of DVA; and develop considerations and recommendations for nursing practice.
Building Machines That Learn and Think Like People

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.
On the localness of software

The n-gram language model, which has its roots in statistical natural language processing, has been shown to successfully capture the repetitive and predictable regularities (“naturalness") of source code, and help with tasks such as code suggestion, porting, and designing assistive coding devices. However, we show in this paper that this natural-language-based model fails to exploit a special property of source code: localness. We find that human-written programs are localized: they have useful local regularities that can be captured and exploited. We introduce a novel cache language model that consists of both an n-gram and an added “cache" component to exploit localness. We show empirically that the additional cache component greatly improves the n-gram approach by capturing the localness of software, as measured by both cross-entropy and suggestion accuracy. Our model’s suggestion accuracy is actually comparable to a state-of-the-art, semantically augmented language model; but it is simpler and easier to implement. Our cache language model requires nothing beyond lexicalization, and thus is applicable to all programming languages.
The Debate on Automated Essay Grading

The ability to communicate in natural language has long been considered a defining characteristic of human intelligence. Furthermore, we hold our ability to express ideas in writing as a pinnacle of this uniquely human language facility—it defies formulaic or algorithmic specification. So it comes as no surprise that attempts to devise computer programs that evaluate writing are often met with resounding skepticism. Nevertheless, automated writing-evaluation systems might provide precisely the platforms we need to elucidate many of the features that characterize good and bad writing, and many of the linguistic, cognitive, and other skills that underlie the human capacity for both reading and writing. Using computers to increase our understanding of the textual features and cognitive skills involved in creating and comprehending written text will have clear benefits. It will help us develop more effective instructional materials for improving reading, writing, and other human communication abilities. It will also help us develop more effective technologies, such as search engines and questionanswering systems, for providing universal access to electronic information. A sketch of the brief history of automated writing-evaluation research and its future directions might lend some credence to this argument.
Classification and type synthesis of 1-DOF remote center of motion mechanisms

Type synthesis of 1-DOF remote center of motion (RCM) mechanisms is the preliminary for research on many multiDOF RCM mechanisms. Since types of existing RCM mechanisms are few, it is necessary to find an efficient way to create more new RCM mechanisms. In this paper, existing 1-DOF RCM mechanisms are first classified, then base on a proposed concept of the planar virtual center (VC) mechanism, which is a more generalized concept than a RCM mechanism, two approaches of type synthesis for 1-DOF RCM mechanisms are addressed. One case is that a 1-DOF parallel or serial–parallel RCM mechanism can be constructed by assembling two planar VC mechanisms; the other case, a VC mechanism can be expanded to a serial–parallel RCM mechanism. Concrete samples are provided accordingly, some of which are new types.
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Recent years have witnessed amazing progress in AI related fields such as computer vision, machine learning and autonomous vehicles. As with any rapidly growing field, however, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several topic specific survey papers have been written, to date no general survey on problems, datasets and methods in computer vision for autonomous vehicles exists. This paper attempts to narrow this gap by providing a state-of-the-art survey on this topic. Our survey includes both the historically most relevant literature as well as the current state-of-the-art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding and end-to-end learning. Towards this goal, we first provide a taxonomy to classify each approach and then analyze the performance of the state-of-the-art on several challenging benchmarking datasets including KITTI, ISPRS, MOT and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we will also provide an interactive platform which allows to navigate topics and methods, and provides additional information and project links for each paper.
Unrolled Optimization with Deep Priors

A broad class of problems at the core of computational imaging, sensing, and low-level computer vision reduces to the inverse problem of extracting latent images that follow a prior distribution, from measurements taken under a known physical image formation model. Traditionally, hand-crafted priors along with iterative optimization methods have been used to solve such problems. In this paper we present unrolled optimization with deep priors, a principled framework for infusing knowledge of the image formation into deep networks that solve inverse problems in imaging, inspired by classical iterative methods. We show that instances of the framework outperform the state-of-the-art by a substantial margin for a wide variety of imaging problems, such as denoising, deblurring, and compressed sensing magnetic resonance imaging (MRI). Moreover, we conduct experiments that explain how the framework is best used and why it outperforms previous methods.
Handrix: animating the human hand

The human hand is a complex organ capable of both gross grasp and fine motor skills. Despite many successful high-level skeletal control techniques, animating realistic hand motion remains tedious and challenging. This paper presents research motivated by the complex finger positioning required to play musical instruments, such as the guitar. We first describe a data driven algorithm to add sympathetic finger motion to arbitrarily animated hands. We then present a procedural algorithm to generate the motion of the fretting hand playing a given musical passage on a guitar. The work here is aimed as a tool for music education and analysis. The contributions of this paper are a general architecture for the skeletal control of interdependent articulations performing multiple concurrent reaching tasks, and a procedural tool for musicians and animators that captures the motion complexity of guitar fingering.
Texture-Based Vanishing Point Voting for Road Shape Estimation

Many rural roads lack sharp, smoothly curving edges and a homogeneous surface appearance, hampering traditional vision-based road-following methods. However, they often have strong texture cues parallel to the road direction in the form of ruts and tracks left by other vehicles. This paper describes an unsupervised algorithm for following ill-structured roads in which dominant texture orientations computed with Gabor wavelet filters vote for a consensus road vanishing point location. The technique is first described for estimating the direction of straight-road segments, then extended to curved and undulating roads by tracking the vanishing point indicated by a differential “strip” of voters moving up toward the nominal vanishing line. Finally, the vanishing point is used to constrain a search for the road boundaries by maximizing textureand color-based region discriminant functions. Results are shown for a variety of road scenes including gravel roads, dirt trails, and highways.
Learning to Adapt: Meta-Learning for Model-Based Control

Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations can cause proficient but specialized policies to fail at test time. In this work, we propose to learn how to quickly and effectively adapt online to perturbations and new situations. To enable sample-efficient learning, we consider learning online adaptation in the context of model-based reinforcement learning. Our approach uses meta-learning to train a global model such that, when combined with recent data, the model can be rapidly adapted to the local context. Our experiments demonstrate that our approach can enable simulated agents to adapt their behavior online to novel terrains, crippled body parts, and highly-dynamic environments.3