nils-herrmann/clean_scirepeval huggingface dataset:
Scaling Distributed Machine Learning with System and Algorithm Co-design

Due to the rapid growth of data and the ever increasing model complexity, which often manifests itself in the large number of model parameters, today, many important machine learning problems cannot be efficiently solved by a single machine. Distributed optimization and inference is becoming more and more inevitable for solving large scale machine learning problems in both academia and industry. However, obtaining an efficient distributed implementation of an algorithm, is far from trivial. Both intensive computational workloads and the volume of data communication demand careful design of distributed computation systems and distributed machine learning algorithms. In this thesis, we focus on the co-design of distributed computing systems and distributed optimization algorithms that are specialized for large machine learning problems. In the first part, we propose two distributed computing frameworks: Parameter Server, a distributed machine learning framework that features efficient data communication between the machines; MXNet, a multi-language library that aims to simplify the development of deep neural network algorithms. We have witnessed the wide adoption of the two proposed systems in the past two years. They have enabled and will continue to enable more people to harness the power of distributed computing to design efficient large-scale machine learning applications. In the second part, we examine a number of distributed optimization problems in machine learning, leveraging the two computing platforms. We present new methods to accelerate the training process, such as data partitioning with better locality properties, communication friendly optimization methods, and more compact statistical models. We implement the new algorithms on the two systems and test on large scale real data sets. We successfully demonstrate that careful co-design of computing systems and learning algorithms can greatly accelerate large scale distributed machine learning.
Randoop: feedback-directed random testing for Java

R<scp>ANDOOP</scp> for Java generates unit tests for Java code using feedback-directed random test generation. Below we describe R<scp>ANDOOP</scp>'s input, output, and test generation algorithm. We also give an overview of RANDOOP's annotation-based interface for specifying configuration parameters that affect R<scp>ANDOOP</scp>'s behavior and output.
TIARA: a visual exploratory text analytic system

In this paper, we present a novel exploratory visual analytic system called TIARA (Text Insight via Automated Responsive Analytics), which combines text analytics and interactive visualization to help users explore and analyze large collections of text. Given a collection of documents, TIARA first uses topic analysis techniques to summarize the documents into a set of topics, each of which is represented by a set of keywords. In addition to extracting topics, TIARA derives time-sensitive keywords to depict the content evolution of each topic over time. To help users understand the topic-based summarization results, TIARA employs several interactive text visualization techniques to explain the summarization results and seamlessly link such results to the original text. We have applied TIARA to several real-world applications, including email summarization and patient record analysis. To measure the effectiveness of TIARA, we have conducted several experiments. Our experimental results and initial user feedback suggest that TIARA is effective in aiding users in their exploratory text analytic tasks.
Privacy Aspects of Recommender Systems

The popularity of online recommender systems has soared; they are deployed in numerous websites and gather tremendous amounts of user data that are necessary for the recommendation purposes. This data, however, may pose a severe threat to user privacy, if accessed by untrusted parties or used inappropriately. Hence, it is of paramount importance for recommender system designers and service providers to find a sweet spot, which allows them to generate accurate recommendations and guarantee the privacy of their users. In this chapter we overview the state of the art in privacy enhanced recommendations. We analyze the risks to user privacy imposed by recommender systems, survey the existing solutions, and discuss the privacy implications for the users of the recommenders. We conclude that a considerable effort is still required to develop practical recommendation solutions that provide adequate privacy guarantees, while at the same time facilitating the delivery of high-quality recommendations to their users.  arik.friedman@nicta.com.au Bart Knijnenburg UC Irvine, USA, e-mail: bart.k@uci.edu Kris Vanhecke iMinds Ghent University, Belgium,  Luc Martens iMinds Ghent University, Belgium,  shlomo.berkovsky@csiro.au 1 Accepted to be included in the 2nd edition of the Recommender Systems handbook. Please do not share publicly, and consult the authors before citing this chapter.
Genetic studies of human diversity in East Asia.

East Asia is one of the most important regions for studying evolution and genetic diversity of human populations. Recognizing the relevance of characterizing the genetic diversity and structure of East Asian populations for understanding their genetic history and designing and interpreting genetic studies of human diseases, in recent years researchers in China have made substantial efforts to collect samples and generate data especially for markers on Y chromosomes and mtDNA. The hallmark of these efforts is the discovery and confirmation of consistent distinction between northern and southern East Asian populations at genetic markers across the genome. With the confirmation of an African origin for East Asian populations and the observation of a dominating impact of the gene flow entering East Asia from the south in early human settlement, interpretation of the north-south division in this context poses the challenge to the field. Other areas of interest that have been studied include the gene flow between East Asia and its neighbouring regions (i.e. Central Asia, the Sub-continent, America and the Pacific Islands), the origin of Sino-Tibetan populations and expansion of the Chinese.
Random Forests

Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
Multi-view learning with supervision for transformed bottleneck features

Previous work has shown that acoustic features can be improved by unsupervised learning of transformations based on canonical correlation analysis (CCA) using articulatory measurements that are available at training time. In this paper, we investigate whether this second view (articulatory data) still helps even when labels are also available at training time. We begin with strong baseline bottleneck features, which can be learned when the training set is phonetically labeled. We then compare several options for learning transformations of the bottleneck features in the presence of both articulatory measurements and phonetic labels for the training data. The methods compared include combinations of LDA and CCA, as well as a three-view extension of CCA that simultaneously uses the labels and articulatory measurements as additional views. Phonetic recognition experiments on data from the University of Wisconsin X-ray microbeam database show that the learned features improve performance over using either just the labels or just the articulatory measurements for learning acoustic transformations.
GIST: A Model for Design and Management of Content and Interactivity of Customer-Centric Web Sites

Customer-centric Web-based systems, such as ecommerce Web sites, or sites that support customer relationship management (CRM) activities, are themselves information systems, but their design and maintenance need to follow vastly different approaches from the traditional systems lifecycle approach. Based on marketing frameworks that are applicable to the online world, and following design science principles, we develop a model to guide the design and the continuous management of such sites. The model makes extensive use of current technologies for tracking the customers and their behaviors, and combines elements of data mining and statistical analyses. A case study based on a financial services Web site is used to provide a preliminary validation and design evaluation of our approach. The case study showed considerable measured improvement in the effectiveness of the companys Web site. In addition, it also highlighted an important benefit of the our approach: the identification of previously unknown or unexpected segments of visitors. This finding can lead to promising new business opportunities.
Role of Open Source Software and ICT in Disaster Management

A disaster is a situation in which the community is incapable of coping. It is a natural or human-caused event which causes intense negative impacts on people, goods, services and/or the environment, exceeding the affected community’s capability to respond; therefore the community seeks the assistance of government and international agencies. In this chaotic environment, new alliances, groupings, collaboration, and trust need to be quickly formed between government organizations, civil society, foreign aid groups, and volunteers to effectively muster their energies into a holistic response effort. Information technology can play a valuable coordination role for situation awareness and enable responders to act effectively. In particular, open source software has been found to be a good fit for the dynamic “bazaar”like environment of a disaster and a good match to the humanitarian codes of conduct. "Free and open source software is increasingly being used in many spheres of development including disaster management. The paper illustrates Disaster management and empowerment of communities in developing nations through appropriate open source applications and also describes the functioning of the “Sahana” disaster management system deployed during the 2004, Indian Ocean Tsunami. Sahana used free and open source software to create number of functionalities.
Principles of Robot Motion: Theory, Algorithms, and Implementations [Book Review]

introduction to autonomous mobile robots intelligent robotics and autonomous agents series PDF disaster robotics intelligent robotics and autonomous agents series PDF probabilistic robotics intelligent robotics and autonomous agents series PDF self-reconfigurable robots an introduction intelligent robotics and autonomous agents series PDF mechanics of robotic manipulation intelligent robotics and autonomous agents PDF strategic negotiation in multiagent environments intelligent robotics and autonomous agents PDF random finite sets for robot mapping & slam new concepts in autonomous robotic map representations springer tracts in advanced robotics PDF
Improved Detection of LSB Steganography in Grayscale Images

We consider methods for answering reliably the question of whether an image contains hidden data; the focus is on grayscale bitmap images and simple LSB steganography. Using a distributed computation network and a library of over 30,000 images we have been carefully evaluating the reliability of various steganalysis methods. The results suggest a number of improvements to the standard techiques, with particular benefits gained by not attempting to estimate the hidden message length. Extensive experimentation shows that the improved methods allow reliable detection of LSB steganography with between 2 and 6 times smaller embedded messages.
Efficient Graph-Based Image Segmentation

This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.
Integration of it governance and security risk management: A systematic literature review

GRC is an umbrella acronym covering the three disciplines of governance, risk management and compliance. In this context, IT GRC is the subset of GRC dealing with IT aspects of GRC. The main challenge of GRC is to have an approach as integrated as possible of the three domains. The objective of our paper is to study one facet of IT GRC: the links and integration between IT governance and risk management that we consider today as the least integrated. To do so, the method followed in this paper is a systematic literature review, in order to identify the existing research works in this field. The resulting contribution of the paper is a set of recommendations established for practitioners and for researchers on how better deal with the integration between IT governance and risk management.
Ontology-based deep learning for human behavior prediction with explanations in health social networks

Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems actually will be adopted, thus driving engagement and loyalty. However, most prediction models do not provide explanations for the behaviors they predict. In this paper, we study the research problem, human behavior prediction with explanations, for healthcare intervention systems in health social networks. We propose an ontology-based deep learning model (ORBM+) for human behavior prediction over undirected and nodes-attributed graphs. We first propose a bottom-up algorithm to learn the user representation from health ontologies. Then the user representation is utilized to incorporate self-motivation, social influences, and environmental events together in a human behavior prediction model, which extends a well-known deep learning method, the Restricted Boltzmann Machine. ORBM+ not only predicts human behaviors accurately, but also, it generates explanations for each predicted behavior. Experiments conducted on both real and synthetic health social networks have shown the tremendous effectiveness of our approach compared with conventional methods.
Tight bounds for rumor spreading in graphs of a given conductance

We study the connection between the rate at which a rumor spreads throughout a graph and the conductance of the graph—a standard measure of a graph’s expansion properties. We show that for any n-node graph with conductance φ, the classical PUSH-PULL algorithm distributes a rumor to all nodes of the graph in O(φ log n) rounds with high probability (w.h.p.). This bound improves a recent result of Chierichetti, Lattanzi, and Panconesi [6], and it is tight in the sense that there exist graphs where Ω(φ log n) rounds of the PUSH-PULL algorithm are required to distribute a rumor w.h.p. We also explore the PUSH and the PULL algorithms, and derive conditions that are both necessary and sufficient for the above upper bound to hold for those algorithms as well. An interesting finding is that every graph contains a node such that the PULL algorithm takes O(φ log n) rounds w.h.p. to distribute a rumor started at that node. In contrast, there are graphs where the PUSH algorithm requires significantly more rounds for any start node.
Click Trajectories: End-to-End Analysis of the Spam Value Chain

Spam-based advertising is a business. While it has engendered both widespread antipathy and a multi-billion dollar anti-spam industry, it continues to exist because it fuels a profitable enterprise. We lack, however, a solid understanding of this enterprise's full structure, and thus most anti-Spam interventions focus on only one facet of the overall spam value chain (e.g., spam filtering, URL blacklisting, site takedown).In this paper we present a holistic analysis that quantifies the full set of resources employed to monetize spam email -- including naming, hosting, payment and fulfillment -- usingextensive measurements of three months of diverse spam data, broad crawling of naming and hosting infrastructures, and over 100 purchases from spam-advertised sites. We relate these resources to the organizations who administer them and then use this data to characterize the relative prospects for defensive interventions at each link in the spam value chain. In particular, we provide the first strong evidence of payment bottlenecks in the spam value chain, 95% of spam-advertised pharmaceutical, replica and software products are monetized using merchant services from just a handful of banks.
Autonomous Helicopter Flight via Reinforcement Learning

Autonomous helicopter flight represents a challenging cont rol problem, with complex, noisy, dynamics. In this paper, we describe a s uccessful application of reinforcement learning to autonomous helic opter flight. We first fit a stochastic, nonlinear model of the helicopter dy namics. We then use the model to learn to hover in place, and to fly a number of maneuvers taken from an RC helicopter competition.
Acoustic Modeling for Google Home

This paper describes the technical and system building advances made to the Google Home multichannel speech recognition system, which was launched in November 2016. Technical advances include an adaptive dereverberation frontend, the use of neural network models that do multichannel processing jointly with acoustic modeling, and Grid-LSTMs to model frequency variations. On the system level, improvements include adapting the model using Google Home specific data. We present results on a variety of multichannel sets. The combination of technical and system advances result in a reduction of WER of 8-28% relative compared to the current production system.
Modeling topic specific credibility on twitter

This paper presents and evaluates three computational models for recommending credible topic-specific information in Twitter. The first model focuses on credibility at the user level, harnessing various dynamics of information flow in the underlying social graph to compute a rating. The second model applies a content-based strategy to compute a finer-grained credibility score for individual tweets. Lastly, we discuss a third model which combines facets from both models in a hybrid method, using both averaging and filtering hybrid strategies. To evaluate our novel credibility models, we perform an evaluation on 7 topic specific data sets mined from the Twitter streaming API, with specific focus on a data set of 37K users who tweeted about the topic "Libya". Results show that the social model outperfoms hybrid and content-based prediction models in terms of predictive accuracy over a set of manually collected credibility ratings on the "Libya" dataset.
Hierarchical models of object recognition in cortex

Visual processing in cortex is classically modeled as a hierarchy of increasingly sophisticated representations, naturally extending the model of simple to complex cells of Hubel and Wiesel. Surprisingly, little quantitative modeling has been done to explore the biological feasibility of this class of models to explain aspects of higher-level visual processing such as object recognition. We describe a new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions. The model is based on a MAX-like operation applied to inputs to certain cortical neurons that may have a general role in cortical function.
Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer

Gastric cancer is a heterogeneous disease with diverse molecular and histological subtypes. We performed whole-genome sequencing in 100 tumor-normal pairs, along with DNA copy number, gene expression and methylation profiling, for integrative genomic analysis. We found subtype-specific genetic and epigenetic perturbations and unique mutational signatures. We identified previously known (TP53, ARID1A and CDH1) and new (MUC6, CTNNA2, GLI3, RNF43 and others) significantly mutated driver genes. Specifically, we found RHOA mutations in 14.3% of diffuse-type tumors but not in intestinal-type tumors (P < 0.001). The mutations clustered in recurrent hotspots affecting functional domains and caused defective RHOA signaling, promoting escape from anoikis in organoid cultures. The top perturbed pathways in gastric cancer included adherens junction and focal adhesion, in which RHOA and other mutated genes we identified participate as key players. These findings illustrate a multidimensional and comprehensive genomic landscape that highlights the molecular complexity of gastric cancer and provides a road map to facilitate genome-guided personalized therapy.
High stiffness capacitive type torque sensor with flexure structure for cooperative industrial robots

In this paper, a capacitive type torque sensor for cooperative robot manipulators is proposed. Flexure structures are used to increase its sensitivity while maintaining the high stiffness between input and output. The sensor can measure external torque of the joint by sensing capacitance of the parallel electrode plates which are attached to the flexure structures. Symmetrically placed 4 sets of electrodes can compensate the torque ripples caused by harmonic drive gears and absorb undesired off-axis moments. The basic concept and detailed design are presented and the experimental results of the implemented sensor are shown.