nils-herrmann/clean_scirepeval huggingface dataset:
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations

Multi-agent reinforcement learning systems aim to provide interacting agents with the ability to collaboratively learn and adapt to the behaviour of other agents. In many real-world applications, the agents can only acquire a partial view of the world. Here we consider a setting whereby most agents’ observations are also extremely noisy, hence only weakly correlated to the true state of the environment. Under these circumstances, learning an optimal policy becomes particularly challenging, even in the unrealistic case that an agent’s policy can be made conditional upon all other agents’ observations. To overcome these difficulties, we propose a multi-agent deep deterministic policy gradient algorithm enhanced by a communication medium (MADDPG-M), which implements a two-level, concurrent learning mechanism. An agent’s policy depends on its own private observations as well as those explicitly shared by others through a communication medium. At any given point in time, an agent must decide whether its private observations are sufficiently informative to be shared with others. However, our environments provide no explicit feedback informing an agent whether a communication action is beneficial, rather the communication policies must also be learned through experience concurrently to the main policies. Our experimental results demonstrate that the algorithm performs well in six highly non-stationary environments of progressively higher complexity, and offers substantial performance gains compared to the baselines.
Whanau: A Sybil-proof Distributed Hash Table

Whānau is a novel routing protocol for distributed hash tables (DHTs) that is efficient and strongly resistant to the Sybil attack. Whānau uses the social connections between users to build routing tables that enable Sybil-resistant lookups. The number of Sybils in the social network does not affect the protocol’s performance, but links between honest users and Sybils do.When there are n well-connected honest nodes, Whānau can tolerate up to O(n/ log n) such “attack edges”. This means that an adversary must convince a large fraction of the honest users to make a social connection with the adversary’s Sybils before any lookups will fail. Whānau uses ideas from structured DHTs to build routing tables that contain O( √ n log n) entries per node. It introduces the idea of layered identifiers to counter clustering attacks, a class of Sybil attacks challenging for previous DHTs to handle. Using the constructed tables, lookups provably take constant time. Simulation results, using social network graphs from LiveJournal, Flickr, YouTube, and DBLP, confirm the analytic results. Experimental results on PlanetLab confirm that the protocol can handle modest churn.
An Intrusion Detection System Based on KDD-99 Data using Data Mining Techniques and Feature Selection

Internet and internet users are increasing day by day. Also due to rapid development of internet technology , security is becoming big issue. Intruders are monitoring comput er network continuously for attacks. A sophisticated firewall with efficient intrusion detection system (IDS) is required to pre vent computer network from attacks. A comprehensive study of litera tures proves that data mining techniques are more powerful techni que to develop IDS as a classifier. Performance of classif ier is a crucial issue in terms of its efficiency, also number of fe ature to be scanned by the IDS should also be optimized. In thi s paper two techniques C5.0 and artificial neural network (ANN) ar e utilized with feature selection. Feature selection techniques will discard some irrelevant features while C5.0 and ANN acts as a classifier to classify the data in either normal type or one of t he five types of attack.KDD99 data set is used to train and test the models ,C5.0 model with numbers of features is producing better r sults with all most 100% accuracy. Performances were also verified in terms of data partition size.
Big Data Investment, Skills, and Firm Value

Because this type of data analysis has, in many cases, outpaced existing technological capabilities, interest is growing in the potential economic impact of big data technology investments—as well as challenges that may prevent implementation. One particular challenge for enterprises is the difficulty of acquiring the technical skills required to support big-data tools. A 2012 report1 about Sears’ early adoption of Hadoop, for instance, noted that as demand for big data technologies grows, so do the problems of finding sufficient skills. Questions have been raised about whether talent shortages could limit the rate of productivity growth.
An Analysis of Dag-Consistent Distributed Shared-Memory Algorithms

In this paper, we analyze the performance of parallel mttltithreaded algorithms that use dag-consistent distributed shared memory. Specifically, we analyze execution time, page faults, and space requirements for multithreaded algorithms executed by a workstealing thread scheduler and the BACKER coherence algorithm for maintaining dag consistency. We prove that if the accesses to the backing store are random and independent (the BACKER algorithm actually uses hashing), then the expected execution time of a “fully strict” multithreaded computation on P processors, each with an LRU cache of C pages, is O(T1 (C)/P+ ntC7”), where T1(C) is the total work of the computation including page faults, L is its criticalpath length excluding page faults, and m is the minimum page transfer time. As a corollary to this theorem, we show that the expected number of page faults incurred by a computation executed on P processors, each with an LRU cache of C pages, is F] (C) + O(CPT~), where Fl (C) is the number of serial page faults. Finally, we give simple bounds on the number of page faults and the space requirements for “regular” divide-and-conquer algorithms. We use these bounds to analyze parallel multithreaded algorithms for matrix multiplication and LU-decomposition.
The Arcade Learning Environment: An Evaluation Platform for General Agents

In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.
Towards Frequent Subgraph Mining on Single Large Uncertain Graphs

Uncertainty is intrinsic to a wide spectrum of real-life applications, which inevitably applies to graph data. Representative uncertain graphs are seen in bio-informatics, social networks, etc. This paper motivates the problem of frequent subgraph mining on single uncertain graphs. We present an enumeration-evaluation algorithm to solve the problem. By showing support computation on an uncertain graph is #P-hard, we develop an approximation algorithm with accuracy guarantee for this purpose. To enhance the solution, we devise optimization techniques to achieve better mining performance. Experiment results on real-life data confirm the usability of the algorithm.
Online human gesture recognition from motion data streams

Online human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. Recent introduction of cost-effective depth cameras brings on a new trend of research on body-movement gesture recognition. However, there are two major challenges: i) how to continuously recognize gestures from unsegmented streams, and ii) how to differentiate different styles of a same gesture from other types of gestures. In this paper, we solve these two problems with a new effective and efficient feature extraction method that uses a dynamic matching approach to construct a feature vector for each frame and improves sensitivity to the features of different gestures and decreases sensitivity to the features of gestures within the same class. Our comprehensive experiments on MSRC-12 Kinect Gesture and MSR-Action3D datasets have demonstrated a superior performance than the stat-of-the-art approaches.
Ontology-enhanced automatic chief complaint classification for syndromic surveillance

Emergency department free-text chief complaints (CCs) are a major data source for syndromic surveillance. CCs need to be classified into syndromic categories for subsequent automatic analysis. However, the lack of a standard vocabulary and high-quality encodings of CCs hinder effective classification. This paper presents a new ontology-enhanced automatic CC classification approach. Exploiting semantic relations in a medical ontology, this approach is motivated to address the CC vocabulary variation problem in general and to meet the specific need for a classification approach capable of handling multiple sets of syndromic categories. We report an experimental study comparing our approach with two popular CC classification methods using a real-world dataset. This study indicates that our ontology-enhanced approach performs significantly better than the benchmark methods in terms of sensitivity, F measure, and F2 measure.
Max-Margin Early Event Detectors

The need for early detection of temporal events from sequential data arises in a wide spectrum of applications ranging from human-robot interaction to video security. While temporal event detection has been extensively studied, early detection is a relatively unexplored problem. This paper proposes a maximum-margin framework for training temporal event detectors to recognize partial events, enabling early detection. Our method is based on Structured Output SVM, but extends it to accommodate sequential data. Experiments on datasets of varying complexity, for detecting facial expressions, hand gestures, and human activities, demonstrate the benefits of our approach.
Selected techniques for data mining in medicine

Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis to be coupled with methods for efficient computer-assisted analysis. This paper presents selected data mining techniques that can be applied in medicine, and in particular some machine learning techniques including the mechanisms that make them better suited for the analysis of medical databases (derivation of symbolic rules, use of background knowledge, sensitivity and specificity of induced descriptions). The importance of the interpretability of results of data analysis is discussed and illustrated on selected medical applications.
Multiple view semantic segmentation for street view images

We propose a simple but powerful multi-view semantic segmentation framework for images captured by a camera mounted on a car driving along streets. In our approach, a pair-wise Markov Random Field (MRF) is laid out across multiple views. Both 2D and 3D features are extracted at a super-pixel level to train classifiers for the unary data terms of MRF. For smoothness terms, our approach makes use of color differences in the same image to identify accurate segmentation boundaries, and dense pixel-to-pixel correspondences to enforce consistency across different views. To speed up training and to improve the recognition quality, our approach adaptively selects the most similar training data for each scene from the label pool. Furthermore, we also propose a powerful approach within the same framework to enable large-scale labeling in both the 3D space and 2D images. We demonstrate our approach on more than 10,000 images from Google Maps Street View.
Deep and Broad Learning on Content-Aware POI Recommendation

POI recommendation has attracted lots of research attentions recently. There are several key factors that need to be modeled towards effective POI recommendation - POI properties, user preference and sequential momentum of check- ins. The challenge lies in how to synergistically learn multi-source heterogeneous data. Previous work tries to model multi-source information in a flat manner, using either embedding based methods or sequential prediction models in a cross-related space, which cannot generate mutually reinforce results. In this paper, a deep and broad learning approach based on a Deep Context- aware POI Recommendation (DCPR) model was proposed to structurally learn POI and user characteristics. The proposed DCPR model includes three collaborative layers, a CNN layer for POI feature mining, an RNN layer for sequential dependency and user preference modeling, and an interactive layer based on matrix factorization to jointly optimize the overall model. Experiments over three data sets demonstrate that DCPR model achieves significant improvement over state-of-the-art POI recommendation algorithms and other deep recommendation models.
A two-dimensional model for the study of interpersonal attraction.

We describe a model for understanding interpersonal attraction in which attraction can be understood as a product of the initial evaluations we make about others. The model posits that targets are evaluated on two basic dimensions, capacity and willingness, such that affective and behavioral attraction result from evaluations of (a) a target's capacity to facilitate the perceiver's goals/needs and (b) a target's potential willingness to facilitate those goals/needs. The plausibility of the two-dimensional model of attraction is evaluated vis-à-vis the extant literature on various attraction phenomena including the reciprocity of liking effect, pratfall effect, matching hypothesis, arousal effects, and similarity effect. We conclude that considerable evidence across a wide range of phenomena supports the idea that interpersonal attraction is principally determined by inferences about the target's capacity and willingness.
Efficient Disk-Based Directed Graph Processing: A Strongly Connected Component Approach

Recently, there have been many disk-based systems proposed for iterative graph processing. In the popular vertex/edge-centric systems, an iterative directed graph algorithm needs to reprocess many partitions so as to update their vertices’ states according to other non-convergent vertices for the unawareness of their dependencies. As a result, it induces high data access cost and a long time to converge. To tackle this problem, we propose a novel system for iterative directed graph processing with taking advantage of the strongly connected component (SCC) structure. It stores a directed graph into a directed acyclic graph (DAG) sketch, with each node representing a SCC in the original data graph. During execution, the SCCs are loaded into memory for processing in a parallel way according to the topological order of the DAG sketch, and the vertices in each SCC are tried to be handled along the directed paths. In this way, each SCC is able to reach convergence in order and needs to be loaded into the main memory for exactly once, getting much lower data access cost and faster convergence. Besides, the vertices of each SCC need fewer updates for convergence. We further develop a lightweight approach to maintain the DAG sketch and handle SCCs in an incremental way for evolving graphs. Compared with the state-of-the-art methods, experimental results show that our approach achieves a performance improvements of 1.46-8.37 times for static graphs, and can reduce the execution time by 61.4-72.7 percent for evolving graphs.
Analyze this! 145 questions for data scientists in software engineering

In this paper, we present the results from two surveys related to data science applied to software engineering. The first survey solicited questions that software engineers would like data scientists to investigate about software, about software processes and practices, and about software engineers. Our analyses resulted in a list of 145 questions grouped into 12 categories. The second survey asked a different pool of software engineers to rate these 145 questions and identify the most important ones to work on first. Respondents favored questions that focus on how customers typically use their applications. We also saw opposition to questions that assess the performance of individual employees or compare them with one another. Our categorization and catalog of 145 questions can help researchers, practitioners, and educators to more easily focus their efforts on topics that are important to the software industry.
W Enabling Technologies Yohan Baillot Nrl Virtual Reality Lab/itt Advanced Engineering Recent Advances in Augmented Reality

hat is augmented reality? An AR system supplements the real world with virtual (computer-generated) objects that appear to coexist in the same space as the real world. While many researchers broaden the definition of AR beyond this vision, we define an AR system to have the following properties: I combines real and virtual objects in a real environment; I runs interactively, and in real time; and I registers (aligns) real and virtual objects with each other. Note that we don't restrict this definition of AR to particular display technologies, such as a head-mounted display (HMD). Nor do we limit it to our sense of sight. AR can potentially apply to all senses, including hearing, touch, and smell.  AR applications also require removing real objects from the perceived environment, in addition to adding virtual objects. For example, an AR visualization of a building that stood at a certain location might remove the building that exists there today. Some researchers call the task of removing real objects mediated or diminished reality, but we consider it a subset of AR. Milgram 1 defined a continuum of real-to-virtual environments , in which AR is one part of the general area of mixed reality . In both augmented virtuality, in which real objects are added to virtual ones, and virtual environments (or virtual reality), the surrounding environment is virtual, while in AR the surrounding environment is real. We focus on AR and don't cover augmented virtuality or virtual environments. The beginnings of AR, as we define it, date back to Sutherland's work in the 1960s, which used a see-through HMD to present 3D graphics. 2 However, only over the past decade has there been enough work to refer to AR as a research field. In 1997, Azuma published a survey 3 that defined the field, described many problems, and summarized the developments up to that point. Since then, AR's growth and progress have been remarkable. In the late 1990s, several conferences on AR began, including the International Workshop and Symposium on Augmented Reality, the International Symposium on Mixed Reality, and the Designing Augmented Reality Environments workshop. Some well-funded organizations formed that focused on AR, notably the Mixed Reality Systems Lab in Japan and the Arvika consortium in Germany. A software toolkit (the ARToolkit) for rapidly building AR applications is now freely available at http://cause of the wealth of new developments, this field needs …
Basis function construction for hierarchical reinforcement learning

Much past work on solving Markov decision processes (MDPs) using reinforcement learning (RL) has relied on combining parameter estimation methods with hand-designed function approximation architectures for representing value functions. Recently, there has been growing interest in a broader framework that combines representation discovery and control learning, where value functions are approximated using a linear combination of task-dependent basis functions learned during the course of solving a particular MDP. This paper introduces an approach to automatic basis function construction for hierarchical reinforcement learning (HRL). Our approach generalizes past work on basis construction to multi-level action hierarchies by forming a compressed representation of a semi-Markov decision process (SMDP) at multiple levels of temporal abstraction. The specific approach is based on hierarchical spectral analysis of graphs induced on an SMDP’s state space from sample trajectories. We present experimental results on benchmark SMDPs, showing significant speedups when compared to handdesigned approximation architectures.
AirShare: Distributed coherent transmission made seamless

Distributed coherent transmission is necessary for a variety of high-gain communication protocols such as distributed MIMO and creating codes over the air. Unfortunately, however, distributed coherent transmission is intrinsically difficult because different nodes are driven by independent clocks, which do not have the exact same frequency. This causes the nodes to have frequency offsets relative to each other, and hence their transmissions fail to combine coherently over the air. This paper presents AirShare, a primitive that makes distributed coherent transmission seamless. AirShare transmits a shared clock on the air and feeds it to the wireless nodes as a reference clock, hence eliminating the root cause for incoherent transmissions. The paper addresses the challenges in designing and delivering such a shared clock. It also implements AirShare in a network of USRP software radios, and demonstrates that it achieves tight phase coherence. Further, to illustrate AirShare's versatility, the paper uses it to deliver a coherent-radio abstraction on top of which it demonstrates two cooperative protocols: distributed MIMO, and distributed rate adaptation.
The Pascal Visual Object Classes (VOC) Challenge

The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience

Social media technologies collapse multiple audiences into single contexts, making it difficult for people to use the same techniques online that they do to handle multiplicity in face-to-face conversation. This article investigates how content producers navigate ‘imagined audiences’ on Twitter. We talked with participants who have different types of followings to understand their techniques, including targeting different audiences, concealing subjects, and maintaining authenticity. Some techniques of audience management resemble the practices of ‘micro-celebrity’ and personal branding, both strategic self-commodification. Our model of the networked audience assumes a manyto-many communication through which individuals conceptualize an imagined audience evoked through their tweets.