nils-herrmann/clean_scirepeval huggingface dataset:
Multi-accent deep neural network acoustic model with accent-specific top layer using the KLD-regularized model adaptation

We propose a multi-accent deep neural network acoustic model with an accent-specific top layer and shared bottom hidden layers. The accent-specific top layer is used to model the distinct accent specific patterns. The shared bottom hidden layers allow maximum knowledge sharing between the native and the accent models. This design is particularly attractive when considering deploying such a system to a live speech service due to its computational efficiency. We applied the KL-divergence (KLD) regularized model adaptation to train the accent-specific top layer. On the mobile short message dictation task (SMD), with 1K, 10K, and 100K British or Indian accent adaptation utterances, the proposed approach achieves 18.1%, 26.0%, and 28.5% or 16.1%, 25.4%, and 30.6% word error rate reduction (WERR) for the British and the Indian accent respectively against a baseline cross entropy (CE) model trained from 400 hour data. On the 100K utterance accent adaptation setup, comparable performance gain can be obtained against a baseline CE model trained with 2000 hour data. We observe smaller yet significant WER reduction on a baseline model trained using the MMI sequence-level criterion.
An analytical model for multi-tier internet services and its applications

Since many Internet applications employ a multi-tier architecture, in this paper, we focus on the problem of analytically modeling the behavior of such applications. We present a model based on a network of queues, where the queues represent different tiers of the application. Our model is sufficiently general to capture (i) the behavior of tiers with significantly different performance characteristics and (ii) application idiosyncrasies such as session-based workloads, concurrency limits, and caching at intermediate tiers. We validate our model using real multi-tier applications running on a Linux server cluster. Our experiments indicate that our model faithfully captures the performance of these applications for a number of workloads and configurations. For a variety of scenarios, including those with caching at one of the application tiers, the average response times predicted by our model were within the 95% confidence intervals of the observed average response times. Our experiments also demonstrate the utility of the model for dynamic capacity provisioning, performance prediction, bottleneck identification, and session policing. In one scenario, where the request arrival rate increased from less than 1500 to nearly 4200 requests/min, a dynamic provisioning technique employing our model was able to maintain response time targets by increasing the capacity of two of the application tiers by factors of 2 and 3.5, respectively.
Panel 1 : Terms used in qualitative

A broad base of medical and scientific knowledge is needed if medicine is to maintain its identity as a discipline founded on scientific knowledge. However, interpretive action must also be included in medical knowledge. In my first article, 1 I investigated the nature of clinical knowledge in medicine, exposed some of the shortcomings of quantitative research methods, and briefly introduced qualitative methods as an approach for improved understanding. Here, I shall discuss how scientific quality can be maintained when qualitative research methods are applied. I present some overall standards, describe specific challenges met when the medical researcher uses qualitative research methods, and subsequently propose guidelines for qualitative inquiry in medical research. I do not intend to provide comprehensive guidance for the inexperienced qualitative researcher, who must be prepared to acquire basic skills of qualitative research from the relevant literature. Some of the specific terms that I use are presented in panel 1. Standards Qualitative research methods involve the systematic collection, organisation, and interpretation of textual material derived from talk or observation. It is used in the exploration of meanings of social phenomena as experienced by individuals themselves, in their natural context. Qualitative research is still regarded with scepticism by the medical community, accused of its subjective nature and the absence of facts. Although the adequacy of guidelines has been vigorously debated within this cross-disciplinary field, 6,7 scientific standards, criteria, and checklists do exist. 3,8–11 However, as Chapple and Rogers 12 point out, medical researchers often encounter difficulties when they try to apply guidelines designed by social scientists, which deal with issues important in their own discipline, but which are not necessarily generically valid as scientific standards. Hamberg and colleagues, 13 for example, claim that the established criteria for scientific rigour in quantitative research cannot be applied to qualitative studies. Referring to Lincoln and Guba, 2 they suggest alternative criteria: credibility, dependability, confirmability, and trans-ferability. They admit that these criteria correspond with traditional ones in some ways, comparing credibility with internal validity, confirmability with objectivity, and transferability with generalisability. Mays and Pope, 7 however, maintain that qualitative research can be assessed with reference to the same broad criteria as quantitative research, albeit used in a different way. Referring to Hammersley, 14 they suggest that validity and relevance are essential. Neither of these criteria are straightforward to assess though, and each requires judgments to be made. To improve validity, Mays and Pope …
Accelerating fish detection and recognition by sharing CNNs with objectness learning

Daily increasing underwater visual data makes automatic object detection and recognition a great demand and challenging task. In this paper, we adopt a region proposal network to accelerate underwater object detection and recognition from Faster R-CNN. This process implement detection acceleration by using convolutional networks to generate high-quality object candidates, and by sharing these networks with original detection networks. Applied to a real-world fish dataset, which includes 24,277 ImageCLEF fish images belonging to 12 classes, this automatic detection and recognition system has a nearly real-time frame rate of 9.8 ftps, while yielding 15.1% higher Mean Average Precision (mAP) than the Deformable Parts Model (DPM) baseline.
Practical recommendations for gradient-based training of deep architectures

Learning algorithms related to artificial neural networks and in particular for Deep Learning may seem to involve many bells and whistles, called hyperparameters. This chapter is meant as a practical guide with recommendations for some of the most commonly used hyper-parameters, in particular in the context of learning algorithms based on backpropagated gradient and gradient-based optimization. It also discusses how to deal with the fact that more interesting results can be obtained when allowing one to adjust many hyper-parameters. Overall, it describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks. It closes with open questions about the training difficulties observed with deeper architectures.
An Empirical Study of Rich Subgroup Fairness for Machine Learning

Kearns, Neel, Roth, and Wu [ICML 2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across protected groups), but then asks that this constraint hold over an exponentially or infinitely large collection of subgroups defined by a class of functions with bounded VC dimension. They give an algorithm guaranteed to learn subject to this constraint, under the condition that it has access to oracles for perfectly learning absent a fairness constraint. In this paper, we undertake an extensive empirical evaluation of the algorithm of Kearns et al. On four real datasets for which fairness is a concern, we investigate the basic convergence of the algorithm when instantiated with fast heuristics in place of learning oracles, measure the tradeoffs between fairness and accuracy, and compare this approach with the recent algorithm of Agarwal, Beygelzeimer, Dudik, Langford, and Wallach [ICML 2018], which implements weaker and more traditional marginal fairness constraints defined by individual protected attributes. We find that in general, the Kearns et al. algorithm converges quickly, large gains in fairness can be obtained with mild costs to accuracy, and that optimizing accuracy subject only to marginal fairness leads to classifiers with substantial subgroup unfairness. We also provide a number of analyses and visualizations of the dynamics and behavior of the Kearns et al. algorithm. Overall we find this algorithm to be effective on real data, and rich subgroup fairness to be a viable notion in practice.
A Machine Learning Approach for Automatic Student Model Discovery

Student modeling is one of the key factors that affects automated tutoring systems in making instructional decisions. A student model is a model to predict the probability of a student making errors on given problems. A good student model that matches with student behavior patterns often provides useful information on learning task difficulty and transfer of learning between related problems, and thus often yields better instruction. Manual construction of such models usually requires substantial human effort, and may still miss distinctions in content and learning that have important instructional implications. In this paper, we propose an approach that automatically discovers student models using a state-of-art machine learning agent, SimStudent. We show that the discovered model is of higher quality than human-generated models, and demonstrate how the discovered model can be used to improve a tutoring system’s instruction strategy.
ARMageddon: Cache Attacks on Mobile Devices

In the last 10 years, cache attacks on Intel x86 CPUs have gained increasing attention among the scientific community and powerful techniques to exploit cache side channels have been developed. However, modern smartphones use one or more multi-core ARM CPUs that have a different cache organization and instruction set than Intel x86 CPUs. So far, no cross-core cache attacks have been demonstrated on non-rooted Android smartphones. In this work, we demonstrate how to solve key challenges to perform the most powerful cross-core cache attacks Prime+Probe, Flush+Reload, Evict+Reload, and Flush+Flush on non-rooted ARM-based devices without any privileges. Based on our techniques, we demonstrate covert channels that outperform state-of-the-art covert channels on Android by several orders of magnitude. Moreover, we present attacks to monitor tap and swipe events as well as keystrokes, and even derive the lengths of words entered on the touchscreen. Eventually, we are the first to attack cryptographic primitives implemented in Java. Our attacks work across CPUs and can even monitor cache activity in the ARM TrustZone from the normal world. The techniques we present can be used to attack hundreds of millions of Android devices.
Sentiment Analysis: From Opinion Mining to Human-Agent Interaction

The opinion mining and human-agent interaction communities are currently addressing sentiment analysis from different perspectives that comprise, on the one hand, disparate sentiment-related phenomena and computational representations, and on the other hand, different detection and dialog management methods. In this paper we identify and discuss the growing opportunities for cross-disciplinary work that may increase individual advances. Sentiment/opinion detection methods used in human-agent interaction are indeed rare and, when they are employed, they are not different from the ones used in opinion mining and consequently not designed for socio-affective interactions (timing constraint of the interaction, sentiment analysis as an input and an output of interaction strategies). To support our claims, we present a comparative state of the art which analyzes the sentiment-related phenomena and the sentiment detection methods used in both communities and makes an overview of the goals of socio-affective human-agent strategies. We propose then different possibilities for mutual benefit, specifying several research tracks and discussing the open questions and prospects. To show the feasibility of the general guidelines proposed we also approach them from a specific perspective by applying them to the case of the Greta embodied conversational agents platform and discuss the way they can be used to make a more significative sentiment analysis for human-agent interactions in two different use cases: job interviews and dialogs with museum visitors.
Mobile Agents: Are They a Good Idea?

Mobile agents are programs, typically written in a script language, which may be dispatched from a client computer and transported to a remote server computer for execution. Several authors have suggested that mobile agents offer an important new method of performing transactions and information retrieval in networks. Other writers have pointed out, however, that mobile agent introduce severe concerns for security. We consider the advantages offered by mobile agents and assess them against alternate methods of achieving the same function. We conclude that, while the individual advantages of agents do not represent an overwhelming motivation for their adoption, the creation of a pervasive agent framework facilitates a very large number of network services and applications.
MicroTE: fine grained traffic engineering for data centers

The effects of data center traffic characteristics on data center traffic engineering is not well understood. In particular, it is unclear how existing traffic engineering techniques perform under various traffic patterns, namely how do the computed routes differ from the optimal routes. Our study reveals that existing traffic engineering techniques perform 15% to 20% worse than the optimal solution. We find that these techniques suffer mainly due to their inability to utilize global knowledge about flow characteristics and make coordinated decision for scheduling flows.
 To this end, we have developed MicroTE, a system that adapts to traffic variations by leveraging the short term and partial predictability of the traffic matrix. We implement MicroTE within the OpenFlow framework and with minor modification to the end hosts. In our evaluations, we show that our system performs close to the optimal solution and imposes minimal overhead on the network making it appropriate for current and future data centers.
Overview of Privacy in Social Networking Sites (SNS)

Social Networking Sites (SNS) have become an integral part of communication and life style of people in today&apos;s world. Because of the wide range of services offered by SNSs mostly for free of cost, these sites are attracting the attention of all possible Internet users. Most importantly, users from all age groups have become members of SNSs. Since many of the users are not aware of the data thefts associated with information sharing, they freely share their personal information with SNSs. Therefore, SNSs may be used for investigating users&apos; character and social habits by familiar or even unknown persons and agencies. Such commercial and social scenario, has led to number of privacy and security threats. Though, all major issues in SNSs need to be addressed, by SNS providers, privacy of SNS users is the most crucial. And therefore, focus of this paper is on &apos;privacy in SNSs&apos;. Different ways of Personally Identifiable Information (PII) leakages from SNSs, information revelation to third-party domains without user consent and privacy related threats associated with such information sharing are discussed in this paper. This comprehensive overview on privacy in SNSs will definitely help in raising user awareness about sharing data and managing their privacy with SNSs. It will also help SNS providers to rethink about their privacy policies.
A real-time method for depth enhanced visual odometry

Visual odometry can be augmented by depth information such as provided by RGB-D cameras, or from lidars associated with cameras. However, such depth information can be limited by the sensors, leaving large areas in the visual images where depth is unavailable. Here, we propose a method to utilize the depth, even if sparsely available, in recovery of camera motion. In addition, the method utilizes depth by structure from motion using the previously estimated motion, and salient visual features for which depth is unavailable. Therefore, the method is able to extend RGBD visual odometry to large scale, open environments where depth often cannot be sufficiently acquired. The core of our method is a bundle adjustment step that refines the motion estimates in parallel by processing a sequence of images, in a batch optimization. We have evaluated our method in three sensor setups, one using an RGB-D camera, and two using combinations of a camera and a 3D lidar. Our method is rated #4 on the KITTI odometry benchmark irrespective of sensing modality—compared to stereo visual odometry methods which retrieve depth by triangulation. The resulting average position error is 1.14% of the distance traveled.
Soft Robotics : A Perspective — Current Trends and Prospects for the Future

Soft robots are primarily composed of easily deformable matter such as fluids, gels, and elastomers that match the elastic and rheological properties of biological tissue and organs. Like an octopus squeezing through a narrow opening or a caterpillar rolling through uneven terrain, a soft robot must adapt its shape and locomotion strategy for a broad range of tasks, obstacles, and environmental conditions. This emerging class of elastically soft, versatile, and biologically inspired machines represents an exciting and highly interdisciplinary paradigm in engineering that could revolutionize the role of robotics in healthcare, field exploration, and cooperative human assistance.
A 1.2V buck converter with a novel on-chip low-voltage current-sensing scheme

A low-voltage on-chip current-sensing circuit for buck power converter is proposed. The minimum supply voltage of the proposed current-sensing circuit is 1.2V (where V/sub th/ = 0.85V at room temperature), and the sensing accuracy is higher than 94%. The proposed buck converter is able to operate at a 1.2-V supply with higher than 98% power-conversion efficiency and with a maximum output current of 120mA.
Linked Data - The Story So Far

The term Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions the Web of Data. In this article we present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. We describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.
Sample-Based Tree Search with Fixed and Adaptive State Abstractions

Sample-based tree search (SBTS) is an approach to solving Markov decision problems based on constructing a lookahead search tree using random samples from a generative model of the MDP. It encompasses Monte Carlo tree search (MCTS) algorithms like UCT as well as algorithms such as sparse sampling. SBTS is well-suited to solving MDPs with large state spaces due to the relative insensitivity of SBTS algorithms to the size of the state space. The limiting factor in the performance of SBTS tends to be the exponential dependence of sample complexity on the depth of the search tree. The number of samples required to build a search tree is O((|A|B)), where |A| is the number of available actions, B is the number of possible random outcomes of taking an action, and d is the depth of the tree. State abstraction can be used to reduce B by aggregating random outcomes together into abstract states. Recent work has shown that abstract tree search often performs substantially better than tree search conducted in the ground state space. This paper presents a theoretical and empirical evaluation of tree search with both fixed and adaptive state abstractions. We derive a bound on regret due to state abstraction in tree search that decomposes abstraction error into three components arising from properties of the abstraction and the search algorithm. We describe versions of popular SBTS algorithms that use fixed state abstractions, and we introduce the Progressive Abstraction Refinement in Sparse Sampling (PARSS) algorithm, which adapts its abstraction during search. We evaluate PARSS as well as sparse sampling with fixed abstractions on 12 experimental problems, and find that PARSS outperforms search with a fixed abstraction and that search with even highly inaccurate fixed abstractions outperforms search without abstraction. These results establish progressive abstraction refinement as a promising basis for new tree search algorithms, and we propose directions for future work within the progressive refinement framework.
A Corpus of Proverbs Annotated with Metaphors

Proverbs are commonly metaphoric in nature and the mapping across domains is commonly established in proverbs. The abundance of proverbs in terms of metaphors makes them an extremely valuable linguistic resource since they can be utilized as a gold standard for various metaphor related linguistic tasks such as metaphor identification or interpretation. Besides, a collection of proverbs from various languages annotated with metaphors would also be essential for social scientists to explore the cultural differences between those languages. In this paper, we introduce PROMETHEUS, a dataset consisting of English proverbs and their equivalents in Italian. In addition to the word-level metaphor annotations for each proverb, PROMETHEUS contains other types of information such as the metaphoricity degree of the overall proverb, its meaning, the century that it was first recorded in and a pair of subjective questions responded by the annotators. To the best of our knowledge, this is the first multi-lingual and open-domain corpus of proverbs annotated with word-level metaphors.