nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic bayesian networks: representation, inference and learning

Dynamic Bayesian Networks: Representation, Inference and Learning by Kevin Patrick Murphy Doctor of Philosophy in Computer Science University of California, Berkeley Professor Stuart Russell, Chair Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete random variable. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. In this thesis, I will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in DBNs, and how to learn DBN models from sequential data. In particular, the main novel technical contributions of this thesis are as follows: a way of representing Hierarchical HMMs as DBNs, which enables inference to be done in O(T ) time instead of O(T ), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T ) space instead of O(T ); a simple way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithm called factored frontier; an analysis of the relationship between the BK algorithm and loopy belief propagation; a way of applying Rao-Blackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main value of the thesis is its catholic presentation of the field of sequential data modelling.
How youth get engaged: grounded-theory research on motivational development in organized youth programs.

For youth to benefit from many of the developmental opportunities provided by organized programs, they need to not only attend but become psychologically engaged in program activities. This research was aimed at formulating empirically based grounded theory on the processes through which this engagement develops. Longitudinal interviews were conducted with 100 ethnically diverse youth (ages 14–21) in 10 urban and rural arts and leadership programs. Qualitative analysis focused on narrative accounts from the 44 youth who reported experiencing a positive turning point in their motivation or engagement. For 38 of these youth, this change process involved forming a personal connection. Similar to processes suggested by self-determination theory (Ryan & Deci, 2000), forming a personal connection involved youth's progressive integration of personal goals with the goals of program activities. Youth reported developing a connection to 3 personal goals that linked the self with the activity: learning for the future, developing competence, and pursuing a purpose. The role of purpose for many youth suggests that motivational change can be driven by goals that transcend self-needs. These findings suggest that youth need not enter programs intrinsically engaged--motivation can be fostered--and that programs should be creative in helping youth explore ways to form authentic connections to program activities.
Learning concepts through conversations in spoken dialogue systems

Spoken dialogue systems must be able to recover gracefully from unexpected user inputs. In many cases, these unexpected utterances may be within the scope of the system, but include previously unseen phrases that the system cannot interpret. In this work, we augment a spoken dialogue system with the ability to learn about new concepts by conversing with the user in natural language. We present a novel model that detects phrases corresponding to such concepts, using information from a neural slotfiller as well as syntactic cues. The system then prompts the user for a definition of the detected phrases, and uses these definitions to re-parse the original utterance. We demonstrate significant gains by learning from the user, compared to a baseline system.
The effects of music on time perception and performance of a driving game.

There is an established and growing body of evidence highlighting that music can influence behavior across a range of diverse domains (Miell, MacDonald, & Hargreaves 2005). One area of interest is the monitoring of "internal timing mechanisms", with features such as tempo, liking, perceived affective nature and everyday listening contexts implicated as important (North & Hargreaves, 2008). The current study addresses these issues by comparing the effects of self-selected and experimenter-selected music (fast and slow) on actual and perceived performance of a driving game activity. Seventy participants completed three laps of a driving game in seven sound conditions: (1) silence; (2) car sounds; (3) car sounds with self-selected music, and car sounds with experimenter-selected music; (4) high-arousal (70 bpm); (5) high-arousal (130 bpm); (6) low-arousal (70 bpm); and (7) low-arousal (130 bpm) music. Six performance measures (time, accuracy, speed, and retrospective perception of these), and four experience measures (perceived distraction, liking, appropriateness and enjoyment) were taken. Exposure to self-selected music resulted in overestimation of elapsed time and inaccuracy, while benefiting accuracy and experience. In contrast, exposure to experimenter-selected music resulted in poorest performance and experience. Increasing the tempo of experimenter-selected music resulted in faster performance and increased inaccuracy for high-arousal music, but did not impact experience. It is suggested that personal meaning and subjective associations connected to self-selected music promoted increased engagement with the activity, overriding detrimental effects attributed to unfamiliar, less liked and less appropriate experimenter-selected music.
A survey of multi-source domain adaptation

In many machine learning algorithms, a major assumption is that the training and the test samples are in the same feature space and have the same distribution. However, for many real applications this assumption does not hold. In this paper, we survey the problem where the training samples and the test samples are from different distributions. This problem can be referred as domain adaptation. The training samples, always with labels, are obtained from what is called source domains, while the test samples, which usually have no labels or only a few labels, are obtained from what is called target domains. The source domains and the target domains are different but related to some extent; the learners can learn some information from the source domains for the learning of the target domains. We focus on the multisource domain adaptation problem where there is more than one source domain available together with only one target domain. A key issue is how to select good sources and samples for the adaptation. In this survey, we review some theoretical results and well developed algorithms for the multi-source domain adaptation problem. We also discuss some open problems which can be explored in future work.
Action observation and acquired motor skills: an FMRI study with expert dancers.

When we observe someone performing an action, do our brains simulate making that action? Acquired motor skills offer a unique way to test this question, since people differ widely in the actions they have learned to perform. We used functional magnetic resonance imaging to study differences in brain activity between watching an action that one has learned to do and an action that one has not, in order to assess whether the brain processes of action observation are modulated by the expertise and motor repertoire of the observer. Experts in classical ballet, experts in capoeira and inexpert control subjects viewed videos of ballet or capoeira actions. Comparing the brain activity when dancers watched their own dance style versus the other style therefore reveals the influence of motor expertise on action observation. We found greater bilateral activations in premotor cortex and intraparietal sulcus, right superior parietal lobe and left posterior superior temporal sulcus when expert dancers viewed movements that they had been trained to perform compared to movements they had not. Our results show that this 'mirror system' integrates observed actions of others with an individual's personal motor repertoire, and suggest that the human brain understands actions by motor simulation.
Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network

We propose a new deep learning based approach for camera relocalization. Our approach localizes a given query image by using a convolutional neural network (CNN) for first retrieving similar database images and then predicting the relative pose between the query and the database images, whose poses are known. The camera location for the query image is obtained via triangulation from two relative translation estimates using a RANSAC based approach. Each relative pose estimate provides a hypothesis for the camera orientation and they are fused in a second RANSAC scheme. The neural network is trained for relative pose estimation in an end-to-end manner using training image pairs. In contrast to previous work, our approach does not require scene-specific training of the network, which improves scalability, and it can also be applied to scenes which are not available during the training of the network. As another main contribution, we release a challenging indoor localisation dataset covering 5 different scenes registered to a common coordinate frame. We evaluate our approach using both our own dataset and the standard 7 Scenes benchmark. The results show that the proposed approach generalizes well to previously unseen scenes and compares favourably to other recent CNN-based methods.
Building a Classification Model for Enrollment In Higher Educational Courses using Data Mining Techniques

Classification model for the Student's Enrollment process in higher educational courses using data mining techniques Data Mining is the process of extracting useful patterns from the huge amount of database and many data mining techniques are used for mining these patterns. Recently, one of the remarkable facts in higher educational institute is the rapid growth data and this educational data is expanding quickly without any advantage to the educational management. The main aim of the management is to refine the education standard; therefore by applying the various data mining techniques on this data one can get valuable information. This research study proposed the " classification model for the student's enrollment process in higher educational courses using data mining techniques ". Additionally, this study contributes to finding some patterns that are meaningful to management.
Ranking Relevance in Yahoo Search

Search engines play a crucial role in our daily lives. Relevance is the core problem of a commercial search engine. It has attracted thousands of researchers from both academia and industry and has been studied for decades. Relevance in a modern search engine has gone far beyond text matching, and now involves tremendous challenges. The semantic gap between queries and URLs is the main barrier for improving base relevance. Clicks help provide hints to improve relevance, but unfortunately for most tail queries, the click information is too sparse, noisy, or missing entirely. For comprehensive relevance, the recency and location sensitivity of results is also critical. In this paper, we give an overview of the solutions for relevance in the Yahoo search engine. We introduce three key techniques for base relevance -- ranking functions, semantic matching features and query rewriting. We also describe solutions for recency sensitive relevance and location sensitive relevance. This work builds upon 20 years of existing efforts on Yahoo search, summarizes the most recent advances and provides a series of practical relevance solutions. The performance reported is based on Yahoo's commercial search engine, where tens of billions of urls are indexed and served by the ranking system.
Challenges and success factors for large-scale agile transformations: A systematic literature review

Agile methods have become an appealing alternative for companies striving to improve their performance, but the methods were originally designed for small and individual teams. This creates unique challenges when introducing agile at scale, when development teams must synchronize their activities, and there might be a need to interface with other organizational units. In this paper we present a systematic literature review on how agile methods and lean software development has been adopted at scale, focusing on reported challenges and success factors in the transformation. We conducted a systematic literature review of industrial large-scale agile transformations. Our keyword search found 1875 papers. We included 52 publications describing 42 industrial cases presenting the process of taking large-scale agile development into use. Almost 90% of the included papers were experience reports, indicating a lack of sound academic research on the topic. We identified 35 reported challenges grouped into nine categories, and 29 success factors, grouped into eleven categories. The most salient success factor categories were management support, choosing and customizing the agile model, training and coaching, and mindset and
Cotton Pests and Diseases Detection based on Image Processing

Extract the damaged image form the cotton image in order to measure the damage ratio of the cotton leaf which caused by the diseases or pests. Several algorithms like image enhancement, image filtering which suit for cotton leaf processing were explored in this paper. Three different color models for extracting the damaged image from cotton leaf images were implemented, namely RGB color model, HSI color model, and YCbCr color model. The ratio of damage (γ) was chosen as feature to measure the degree of damage which caused by diseases or pests. This paper also shows the comparison of the results obtained by the implementing in different color models, the comparison of results shows good accuracy in both color models and YCbCr color space is considered as the best color model for extracting the damaged image.
Design, Materials, and Mechanobiology of Biodegradable Scaffolds for Bone Tissue Engineering

A review about design, manufacture, and mechanobiology of biodegradable scaffolds for bone tissue engineering is given. First, fundamental aspects about bone tissue engineering and considerations related to scaffold design are established. Second, issues related to scaffold biomaterials and manufacturing processes are discussed. Finally, mechanobiology of bone tissue and computational models developed for simulating how bone healing occurs inside a scaffold are described.
Tighter Bounds for Graph Steiner Tree Approximation

The classical Steiner tree problem in weighted graphs seeks a minimum weight connected subgraph containing a given subset of the vertices (terminals). We present a new polynomialtime heuristic that achieves a best-known approximation ratio of 1 + ln 3 2 ≈ 1.55 for general graphs, and best-known approximation ratios of ≈ 1.28 for quasi-bipartite graphs (i.e., where no two non-terminals are adjacent) and for complete graphs with edge weights 1 and 2. Our method is considerably simpler and easier to implement than previous approaches. We also prove the first known non-trivial performance bound (1.5 · OPT) for the Iterated 1-Steiner heuristic of Kahng and Robins in quasi-bipartite graphs.
metapath 2 vec Scalable Representation Learning for Heterogeneous Networks

We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. e metapath2vec model formalizes meta-pathbased random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. e metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classication, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects. CCS CONCEPTS •Information systems→Social networks; •Computingmethodologies →Unsupervised learning; Learning latent representations; Knowledge representation and reasoning;
Ultra-low power digital subthreshold logic circuits

Numerous efforts in balancing the trade-off between power, area and performance have been done in the medium performance, medium power region of the design spectrum. However, not much study has been done at the two extreme ends of the design spectrum, namely the ultra-low power with acceptable performance at one end (the focus of this paper), and high performance with power within limit at the other. One solution to achieve the ultra-low power requirement is to operate the digital logic gates in subthreshold region. We analyze both CMOS and Pseudo-NMOS logic families operating in subthreshold region. We compare the results with CMOS in normal strong inversion region and with other known low-power logic, namely, energy recovery logic. Our results show an energy per switching reduction of two orders of magnitude for an 8x8 carry save array multiplier when it is operated in subthreshold region.
A phase space model of Fourier ptychographic microscopy.

A new computational imaging technique, termed Fourier ptychographic microscopy (FPM), uses a sequence of low-resolution images captured under varied illumination to iteratively converge upon a high-resolution complex sample estimate. Here, we propose a mathematical model of FPM that explicitly connects its operation to conventional ptychography, a common procedure applied to electron and X-ray diffractive imaging. Our mathematical framework demonstrates that under ideal illumination conditions, conventional ptychography and FPM both produce datasets that are mathematically linked by a linear transformation. We hope this finding encourages the future cross-pollination of ideas between two otherwise unconnected experimental imaging procedures. In addition, the coherence state of the illumination source used by each imaging platform is critical to successful operation, yet currently not well understood. We apply our mathematical framework to demonstrate that partial coherence uniquely alters both conventional ptychography's and FPM's captured data, but up to a certain threshold can still lead to accurate resolution-enhanced imaging through appropriate computational post-processing. We verify this theoretical finding through simulation and experiment.
Stock Market Prediction Model by Combining Numeric and News Textual Mining

This paper proposes a novel method for the prediction of stock market closing price. Many researchers have contributed in this area of chaotic forecast in their ways. Data mining techniques can be used more in financial markets to make qualitative decisions for investors. Fundamental and technical analyses are the traditional approaches so far. ANN is a popular way to identify unknown and hidden patterns in data is used for share market prediction. A multilayered feed-forward neural network is built by using combination of data and textual mining. The Neural Network is trained on the stock quotes and extracted key phrases using the Backpropagation Algorithm which is used to predict share market closing price. This paper is an attempt to determine whether the BSE market news in combination with the historical quotes can efficiently help in the calculation of the BSE closing index for a given trading day.
CU-Net: Coupled U-Nets

We design a new connectivity pattern for the U-Net architecture. Given several stacked U-Nets, we couple each U-Net pair through the connections of their semantic blocks, resulting in the coupled U-Nets (CU-Net). The coupling connections could make the information flow more efficiently across U-Nets. The feature reuse across U-Nets makes each U-Net very parameter efficient. We evaluate the coupled U-Nets on two benchmark datasets of human pose estimation. Both the accuracy and model parameter number are compared. The CU-Net obtains comparable accuracy as state-of-the-art methods. However, it only has at least 60% fewer parameters than other approaches.
The Role of Social Learning Networks in Mobile Assisted Language Learning: Edmodo as a Case Study

In this study the effect of the Edmodo social learning environment on mobile assisted language learning (MALL) was examined by seeking the opinions of students. Using a quantitative experimental approach, this study was conducted by conducting a questionnaire before and after using the social learning network Edmodo. Students attended lessons with their mobile devices. The course materials were shared in the network via Edmodo group sharing tools. The students exchanged idea and developed projects, and felt as though they were in a real classroom setting. The students were also able to access various multimedia content. The results of the study indicate that Edmodo improves students’ foreign language learning, increases their success, strengthens communication among students, and serves as an entertaining learning environment for them. The educationally suitable sharing structure and the positive user opinions described in this study indicate that Edmodo is also usable in other lessons. Edmodo can be used on various mobile devices, including smartphones, in addition to the web. This advantageous feature contributes to the usefulness of Edmodo as a scaffold for education.
Instabranding : Shaping the Personalities of the Top Food Brands on Instagram

Instagram is a growing social media platform that provides a means of self-expression and communication through creative visuals. Businesses are responding to this trend by using it as a cost-effective marketing tool. This paper examined the accounts of the leading food brands on Instagram: McDonald’s, Taco Bell, Shredz, Ben & Jerry’s, and Oreo. Photos were classified according to 11 elements: product, person and product, people and product, humor and product, world events, recipes, campaign with no products, user-generated, regram from a celebrity, and video. They were further analyzed along five dimensions of personality: sincerity, excitement, competence, sophistication, and ruggedness. Results presented common themes revealing that brands are using Instagram to promote their products and, more significantly, to colorfully express their personalities.