nils-herrmann/clean_scirepeval huggingface dataset:
SWEET: Serving the Web by Exploiting Email Tunnels

Open communications over the Internet pose serious threats to countries with repressive regimes, leading them to develop and deploy censorship mechanisms within their networks. Unfortunately, existing censorship circumvention systems do not provide high availability guarantees to their users, as censors can easily identify, hence disrupt, the traffic belonging to these systems using today’s advanced censorship technologies. In this paper, we propose Serving the Web by Exploiting Email Tunnels SWEET, a highly available censorship-resistant infrastructure. SWEET works by encapsulating a censored user’s traffic inside email messages that are carried over public email services like Gmail and Yahoo Mail. As the operation of SWEET is not bound to any specific email provider, we argue that a censor will need to block email communications all together in order to disrupt SWEET, which is unlikely as email constitutes an important part of today’s Internet. Through experiments with a prototype of our system, we find that SWEET’s performance is sufficient for Web browsing. In particular, regular Websites are downloaded within couple of seconds.
Analyzing gene expression time-courses

Measuring gene expression over time can provide important insights into basic cellular processes. Identifying groups of genes with similar expression time-courses is a crucial first step in the analysis. As biologically relevant groups frequently overlap, due to genes having several distinct roles in those cellular processes, this is a difficult problem for classical clustering methods. We use a mixture model to circumvent this principal problem, with hidden Markov models (HMMs) as effective and flexible components. We show that the ensuing estimation problem can be addressed with additional labeled data¿partially supervised learning of mixtures¿through a modification of the Expectation-Maximization (EM) algorithm. Good starting points for the mixture estimation are obtained through a modification to Bayesian model merging, which allows us to learn a collection of initial HMMs. We infer groups from mixtures with a simple information-theoretic decoding heuristic, which quantifies the level of ambiguity in group assignment. The effectiveness is shown with high-quality annotation data. As the HMMs we propose capture asynchronous behavior by design, the groups we find are also asynchronous. Synchronous subgroups are obtained from a novel algorithm based on Viterbi paths. We show the suitability of our HMM mixture approach on biological and simulated data and through the favorable comparison with previous approaches. A software implementing the method is freely available under the GPL from http://ghmm.org/gql.
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence’s representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) The ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNNs; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNNs achieve state-of-the-art performance on AS, PI and TE tasks. We release code at: https://github.com/yinwenpeng/Answer_Selection.
User friendly SLAM initialization

The development of new Simultaneous Localization and Mapping (SLAM) techniques is quickly advancing in research communities and rapidly transitioning into commercial products. Creating accurate and high-quality SLAM maps relies on a robust initialization process. However, the robustness and usability of SLAM initialization for end-users has often been disregarded. This paper presents and evaluates a novel tracking system for 6DOF pose tracking between a single keyframe and the current camera frame, without any prior scene knowledge. Our system is particularly suitable for SLAM initialization, since it allows 6DOF pose tracking in the intermediate frames before a wide-enough baseline between two keyframes has formed. We investigate how our tracking system can be used to interactively guide users in performing an optimal motion for SLAM initialization. However, our findings from a pilot study indicate that the need for such motion can be completely hidden from the user and outsourced to our tracking system. Results from a second user study show that letting our tracking system create a SLAM map as soon as possible is a viable and usable solution. Our work provides important insight for SLAM systems, showing how our novel tracking system can be integrated with a user interface to support fast, robust and user-friendly SLAM initialization.
Deep Learning with Limited Numerical Precision

Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network’s behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding.
Accuracy in optical tracking with fiducial markers: an accuracy function for ARToolKit

Optical tracking with fiducial markers is commonly used in augmented reality (AR) systems - AR systems that rely on the ARToolKit (Kato and Billinghurst, 1999) are prominent examples. The information obtained by the tracking subsystem are widely used in AR, e.g. in order to calculate how virtual objects should be located and oriented. The results of extensive accuracy experiments with single markers are reported and made operational by the definition of an accuracy function. The results show a specific distribution of tracking accuracy dependent on distance as well as angle between camera and marker. This insight is applicable for designing the set-up of AR applications in general that rely on optical tracking.
Piranha: a scalable architecture based on single-chip multiprocessing

The microprocessor industry is currently struggling with higher development costs and longer design times that arise from exceedingly complex processors that are pushing the limits of instruction-level parallelism. Meanwhile, such designs are especially ill suited for important commercial applications, such as on-line transaction processing (OLTP), which suffer from large memory stall times and exhibit little instruction-level parallelism. Given that commercial applications constitute by far the most important market for high-performance servers, the above trends emphasize the need to consider alternative processor designs that specifically target such workloads. The abundance of explicit thread-level parallelism in commercial workloads, along with advances in semiconductor integration density, identify chip multiprocessing (CMP) as potentially the most promising approach for designing processors targeted at commercial servers.
 This paper describes the Piranha system, a research prototype being developed at Compaq that aggressively exploits chip multi-processing by integrating eight simple Alpha processor cores along with a two-level cache hierarchy onto a single chip. Piranha also integrates further on-chip functionality to allow for scalable multiprocessor configurations to be built in a glueless and modular fashion. The use of simple processor cores combined with an industry-standard ASIC design methodology allow us to complete our prototype within a short time-frame, with a team size and investment that are an order of magnitude smaller than that of a commercial microprocessor. Our detailed simulation results show that while each Piranha processor core is substantially slower than an aggressive next-generation processor, the integration of eight cores onto a single chip allows Piranha to outperform next-generation processors by up to 2.9 times (on a per chip basis) on important workloads such as OLTP. This performance advantage can approach a factor of five by using full-custom instead of ASIC logic. In addition to exploiting chip multiprocessing, the Piranha prototype incorporates several other unique design choices including a shared second-level cache with no inclusion, a highly optimized cache coherence protocol, and a novel I/O architecture.
Integration of acoustic and visual speech signals using neural networks

Results from a series of experiments that use neural networks to process the visual speech signals of a male talker are presented. In these preliminary experiments, the results are limited to static images of vowels. It is demonstrated that these networks are able to extract speech information from the visual images and that this information can be used to improve automatic vowel recognition. The structure of speech and its corresponding acoustic and visual signals are reviewed. The specific data that was used in the experiments along with the network architectures and algorithms are described. The results of integrating the visual and auditory signals for vowel recognition in the presence of acoustic noise are presented.<<ETX>>
Semi-synthetic artemisinin: a model for the use of synthetic biology in pharmaceutical development

Recent developments in synthetic biology, combined with continued progress in systems biology and metabolic engineering, have enabled the engineering of microorganisms to produce heterologous molecules in a manner that was previously unfeasible. The successful synthesis and recent entry of semi-synthetic artemisinin into commercial production is the first demonstration of the potential of synthetic biology for the development and production of pharmaceutical agents. In this Review, we describe the metabolic engineering and synthetic biology approaches that were used to develop this important antimalarial drug precursor. This not only demonstrates the incredible potential of the available technologies but also illuminates how lessons learned from this work could be applied to the production of other pharmaceutical agents.
of Domain Adaptation with Unlabeled Data

In supervised learning, it is typically assumed that the labeled training data comes from the same distribution as the test data to which the system will be applied. In recent years, machine-learning researchers have investigated methods to handle mismatch between the training and test domains, with the goal of building a classifier using the labeled data in the old domain that will perform well on the test data in the new domain. This problem is called domain adaptation or transfer learning, and it is a common scenario in speech processing applications. Labeled training data are often produced by an expensive hand-annotation process, and may consist of only one or two annotated corpora which are used to train virtually all systems regardless of the target domain. Often little or no labeled data is available for the new domain. In this work, we review the statistical machine learning literature dealing with the problem of “domain adaptation” or “transfer learning”. We focus on unsupervised domain adaptation methods, as opposed to model adaptation or supervised adaptation in which some labeled data is available from the test distribution. We consider four main classes of approaches in the literature: instance weighting for covariate shift; selflabeling methods; changes in feature representation; and cluster-based learning. Covariate shift methods re-weight training samples in the old domain to try to match the new domain, putting more weight on samples in populous regions in the new domain. Self-labeling methods incorporate unlabeled target domain examples into the training algorithm by making an initial guess about their labels and then iteratively refining the guesses or labeling more examples. Feature representation approaches try to find a new feature representation of the data, either to make the new and old distributions look similar, or to find an abstracted representation for domain-specific features. Cluster-based methods rely on the assumption that samples connected by high-density paths are likely to have the same label. Domain adaptation is a large area of research, with related work under several frameworks (and several names). A limited review from March 2008 can be found in , and one from Oct 2010 can be found in . A recent book  investigates train/test distribution mismatch in machine learning (particularly focused on covariate shift.) Some of the organization here roughly follows that in .
The Mobile Sensing Platform: An Embedded Activity Recognition System

Activity-aware systems have inspired novel user interfaces and new applications in smart environments, surveillance, emergency response, and military missions. Systems that recognize human activities from body-worn sensors can further open the door to a world of healthcare applications, such as fitness monitoring, eldercare support, long-term preventive and chronic care, and cognitive assistance. Wearable systems have the advantage of being with the user continuously. So, for example, a fitness application could use real-time activity information to encourage users to perform opportunistic activities. Furthermore, the general public is more likely to accept such activity recognition systems because they are usually easy to turn off or remove.
Evaluating Computer Intrusion Detection Systems: A Survey of Common Practices

The evaluation of computer intrusion detection systems (which we refer to as intrusion detection systems) is an active research area. In this article, we survey and systematize common practices in the area of evaluation of such systems. For this purpose, we define a design space structured into three parts: workload, metrics, and measurement methodology. We then provide an overview of the common practices in evaluation of intrusion detection systems by surveying evaluation approaches and methods related to each part of the design space. Finally, we discuss open issues and challenges focusing on evaluation methodologies for novel intrusion detection systems.
The impact of cyberloafing towards Malaysia employees' productivity: A conceptual framework

The increasing use of the internet has changed the way we live and work. Moreover, in countries where there is a use of internet on a large scale, changes in the content and context of work have been observed. The use of Internet has become a tool to achieve competitive advantage by organizations. Also, it has been associated with many negative consequences such as reduced employees' productivity. Despite its importance, there is a lack of cyberloafing studies in Malaysia. The paper proposes a modified version of Triandis's theory of interpersonal behaviour by incorporating a new independent variable, ability to hide cyberloafing that directly predicts employees cyberloafing. This paper also recommends examining the relationship between cyberloafing and employees' productivity. Some studies believe that cyberloafing is beneficial for employees' productivity but other studies suggest that it is harmful to employees' productivity. By understanding what causes cyberloafing and its impacts, organizations can be more effective in managing the issue.
NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction

This paper describes our system used in the Aspect Based Sentiment Analysis Task 12 of SemEval-2015. Our system is based on two supervised machine learning algorithms: sigmoidal feedforward network to train binary classifiers for aspect category classification (Slot 1), and Conditional Random Fields to train classifiers for opinion target extraction (Slot 2). We extract a variety of lexicon and syntactic features, as well as cluster features induced from unlabeled data. Our system achieves state-of-the-art performances, ranking 1st for three of the evaluations (Slot 1 for both restaurant and laptop domains, and Slot 1 & 2) and 2nd for Slot 2 evaluation.
Visual Classification With Multitask Joint Sparse Representation

We address the problem of visual classification with multiple features and/or multiple instances. Motivated by the recent success of multitask joint covariate selection, we formulate this problem as a multitask joint sparse representation model to combine the strength of multiple features and/or instances for recognition. A joint sparsity-inducing norm is utilized to enforce class-level joint sparsity patterns among the multiple representation vectors. The proposed model can be efficiently optimized by a proximal gradient method. Furthermore, we extend our method to the setup where features are described in kernel matrices. We then investigate into two applications of our method to visual classification: 1) fusing multiple kernel features for object categorization and 2) robust face recognition in video with an ensemble of query images. Extensive experiments on challenging real-world data sets demonstrate that the proposed method is competitive to the state-of-the-art methods in respective applications.
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

This paper presents a new approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of the value functions of the smaller MDPs. The decomposition, known as the MAXQ decomposition, has both a procedural semantics|as a subroutine hierarchy|and a declarative semantics|as a representation of the value function of a hierarchical policy. MAXQ uni es and extends previous work on hierarchical reinforcement learning by Singh, Kaelbling, and Dayan and Hinton. It is based on the assumption that the programmer can identify useful subgoals and de ne subtasks that achieve these subgoals. By de ning such subgoals, the programmer constrains the set of policies that need to be considered during reinforcement learning. The MAXQ value function decomposition can represent the value function of any policy that is consistent with the given hierarchy. The decomposition also creates opportunities to exploit state abstractions, so that individual MDPs within the hierarchy can ignore large parts of the state space. This is important for the practical application of the method. This paper de nes the MAXQ hierarchy, proves formal results on its representational power, and establishes ve conditions for the safe use of state abstractions. The paper presents an online model-free learning algorithm, MAXQ-Q, and proves that it converges with probability 1 to a kind of locally-optimal policy known as a recursively optimal policy, even in the presence of the ve kinds of state abstraction. The paper evaluates the MAXQ representation and MAXQ-Q through a series of experiments in three domains and shows experimentally that MAXQ-Q (with state abstractions) converges to a recursively optimal policy much faster than at Q learning. The fact that MAXQ learns a representation of the value function has an important bene t: it makes it possible to compute and execute an improved, non-hierarchical policy via a procedure similar to the policy improvement step of policy iteration. The paper demonstrates the e ectiveness of this non-hierarchical execution experimentally. Finally, the paper concludes with a comparison to related work and a discussion of the design tradeo s in hierarchical reinforcement learning.
KVM/ARM: the design and implementation of the linux ARM hypervisor

As ARM CPUs become increasingly common in mobile devices and servers, there is a growing demand for providing the benefits of virtualization for ARM-based devices. We present our experiences building the Linux ARM hypervisor, KVM/ARM, the first full system ARM virtualization solution that can run unmodified guest operating systems on ARM multicore hardware. KVM/ARM introduces split-mode virtualization, allowing a hypervisor to split its execution across CPU modes and be integrated into the Linux kernel. This allows KVM/ARM to leverage existing Linux hardware support and functionality to simplify hypervisor development and maintainability while utilizing recent ARM hardware virtualization extensions to run virtual machines with comparable performance to native execution. KVM/ARM has been successfully merged into the mainline Linux kernel, ensuring that it will gain wide adoption as the virtualization platform of choice for ARM. We provide the first measurements on real hardware of a complete hypervisor using ARM hardware virtualization support. Our results demonstrate that KVM/ARM has modest virtualization performance and power costs, and can achieve lower performance and power costs compared to x86-based Linux virtualization on multicore hardware.
Survey on Collaborative AR for Multi-user in Urban Simulation

This paper describes an augmented reality (AR) environment that allows multiple participants or multi-user to interact with 2D and 3D data. AR simply can provide a collaborative interactive AR environment for urban simulation, where users can interact naturally and intuitively. In addition, the collaborative AR makes multi-user in urban simulation to share simultaneously a real world and virtual world. The fusion between real and virtual world, existed in AR environment by see-through HMDs, achieves higher interactivity as a key features of collaborative AR. In real-time, precise registration between both worlds and multiuser are crucial for the collaborations. Collaborative AR allow multi-user to simultaneously share a real world surrounding them and a virtual world. Common problems in AR environment will be discussed and major issues in collaborative AR will be explained details in this paper. The features of collaboration in AR environment are will be identified and the requirements of collaborative AR will be defined. This paper will give an overview on collaborative AR environment for multi-user in urban studies and planning. The work will also cover numerous systems of collaborative AR environments for multi-user.