nils-herrmann/clean_scirepeval huggingface dataset:
CloudBLAST: Combining MapReduce and Virtualization on Distributed Resources for Bioinformatics Applications

This paper proposes and evaluates an approach to the parallelization, deployment and management of bioinformatics applications that integrates several emerging technologies for distributed computing. The proposed approach uses the MapReduce paradigm to parallelize tools and manage their execution, machine virtualization to encapsulate their execution environments and commonly used data sets into flexibly deployable virtual machines, and network virtualization to connect resources behind firewalls/NATs while preserving the necessary performance and the communication environment. An implementation of this approach is described and used to demonstrate and evaluate the proposed approach. The implementation integrates Hadoop, Virtual Workspaces, and ViNe as the MapReduce, virtual machine and virtual network technologies, respectively, to deploy the commonly used bioinformatics tool NCBI BLAST on a WAN-based test bed consisting of clusters at two distinct locations, the University of Florida and the University of Chicago. This WAN-based implementation, called CloudBLAST, was evaluated against both non-virtualized and LAN-based implementations in order to assess the overheads of machine and network virtualization, which were shown to be insignificant. To compare the proposed approach against an MPI-based solution, CloudBLAST performance was experimentally contrasted against the publicly available mpiBLAST on the same WAN-based test bed. Both versions demonstrated performance gains as the number of available processors increased, with CloudBLAST delivering speedups of 57 against 52.4 of MPI version, when 64 processors on 2 sites were used. The results encourage the use of the proposed approach for the execution of large-scale bioinformatics applications on emerging distributed environments that provide access to computing resources as a service.
Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images

This paper presents and evaluates a fully automatic method for detection of ductal carcinoma in situ (DCIS) in digitized hematoxylin and eosin (H&E) stained histopathological slides of breast tissue. The proposed method applies multi-scale superpixel classification to detect epithelial regions in whole-slide images (WSIs). Subsequently, spatial clustering is utilized to delineate regions representing meaningful structures within the tissue such as ducts and lobules. A region-based classifier employing a large set of features including statistical and structural texture features and architectural features is then trained to discriminate between DCIS and benign/normal structures. The system is evaluated on two datasets containing a total of 205 WSIs of breast tissue. Evaluation was conducted both on the slide and the lesion level using FROC analysis. The results show that to detect at least one true positive in every DCIS containing slide, the system finds 2.6 false positives per WSI. The results of the per-lesion evaluation show that it is possible to detect 80% and 83% of the DCIS lesions in an abnormal slide, at an average of 2.0 and 3.0 false positives per WSI, respectively. Collectively, the result of the experiments demonstrate the efficacy and accuracy of the proposed method as well as its potential for application in routine pathological diagnostics. To the best of our knowledge, this is the first DCIS detection algorithm working fully automatically on WSIs.
Learning utterance-level representations for speech emotion and age/gender recognition using deep neural networks

Accurately recognizing speaker emotion and age/gender from speech can provide better user experience for many spoken dialogue systems. In this study, we propose to use deep neural networks (DNNs) to encode each utterance into a fixed-length vector by pooling the activations of the last hidden layer over time. The feature encoding process is designed to be jointly trained with the utterance-level classifier for better classification. A kernel extreme learning machine (ELM) is further trained on the encoded vectors for better utterance-level classification. Experiments on a Mandarin dataset demonstrate the effectiveness of our proposed methods on speech emotion and age/gender recognition tasks.
Challenges of QoE management for cloud applications

Cloud computing is currently gaining enormous momentum due to a number of promised benefits: ease of use in terms of deployment, administration, and maintenance, along with high scalability and flexibility to create new services. However, as more personal and business applications migrate to the cloud, service quality will become an important differentiator between providers. In particular, quality of experience as perceived by users has the potential to become the guiding paradigm for managing quality in the cloud. In this article, we discuss technical challenges emerging from shifting services to the cloud, as well as how this shift impacts QoE and QoE management. Thereby, a particular focus is on multimedia cloud applications. Together with a novel QoE-based classification scheme of cloud applications, these challenges drive the research agenda on QoE management for cloud applications.
A type and effect system for deterministic parallel Java

Today's shared-memory parallel programming models are complex and error-prone.While many parallel programs are intended to be deterministic, unanticipated thread interleavings can lead to subtle bugs and nondeterministic semantics. In this paper, we demonstrate that a practical type and effect system can simplify parallel programming by guaranteeing deterministic semantics with modular, compile-time type checking even in a rich, concurrent object-oriented language such as Java. We describe an object-oriented type and effect system that provides several new capabilities over previous systems for expressing deterministic parallel algorithms.We also describe a language called Deterministic Parallel Java (DPJ) that incorporates the new type system features, and we show that a core subset of DPJ is sound. We describe an experimental validation showing thatDPJ can express a wide range of realistic parallel programs; that the new type system features are useful for such programs; and that the parallel programs exhibit good performance gains (coming close to or beating equivalent, nondeterministic multithreaded programs where those are available).
Googling for a concept map: towards automatic concept-map-based query information

Electronic concept mapping tools provide a flexible vehicle for constructing concept maps, linking concept maps to other concept maps and related resources, and distributing concept maps to others. As electronic concept maps are constructed, it is often helpful for users to consult additional resources, in order to jog their memories or to locate resources to link to the map under construction. The World Wide Web provides a rich range of resources for these tasks—if the right resources can be found. This paper presents ongoing research on how to automatically generate Web queries from concept maps under construction, in order to proactively suggest related information to aid concept mapping. First, it examines how concept map structure and content can be exploited to automatically select terms to include in initial queries, based on studies of (1) how concept map structure influences human judgments of concept importance, and (2) the relative value of including information from concept labels and linking phrases. Second, it examines how a concept map can be used to refine future queries by reinforcing the weights of terms that have proven to be good discriminators for the topic of the concept map. The described methods are being applied to developing “intelligent suggesters” to support the concept mapping process.
Understanding the Energy Consumption of Dynamic Random Access Memories

Energy consumption has become a major constraint on the capabilities of computer systems. In large systems the energy consumed by Dynamic Random Access Memories (DRAM) is a significant part of the total energy consumption. It is possible to calculate the energy consumption of currently available DRAMs from their datasheets, but datasheets don’t allow extrapolation to future DRAM technologies and don’t show how other changes like increasing bandwidth requirements change DRAM energy consumption. This paper first presents a flexible DRAM power model which uses a description of DRAM architecture, technology and operation to calculate power usage and verifies it against datasheet values. Then the model is used together with assumptions about the DRAM roadmap to extrapolate DRAM energy consumption to future DRAM generations. Using this model we evaluate some of the proposed DRAM power reduction schemes.
DIET , LIFESTYLE , AND THE RISK OF TYPE 2 DIABETES MELLITUS IN WOMEN

A BSTRACT  Previous studies have examined individual dietary and lifestyle factors in relation to type 2 diabetes, but the combined effects of these factors are largely unknown.  We followed 84,941 female nurses from 1980 to 1996; these women were free of diagnosed cardiovascular disease, diabetes, and cancer at base line. Information about their diet and lifestyle was updated periodically. A low-risk group was defined according to a combination of five variables: a bodymass index (the weight in kilograms divided by the square of the height in meters) of less than 25; a diet high in cereal fiber and polyunsaturated fat and low in trans fat and glycemic load (which reflects the effect of diet on the blood glucose level); engagement in moderate-to-vigorous physical activity for at least half an hour per day; no current smoking; and the consumption of an average of at least half a drink of an alcoholic beverage per day.  During 16 years of follow-up, we documented 3300 new cases of type 2 diabetes. Overweight or obesity was the single most important predictor of diabetes. Lack of exercise, a poor diet, current smoking, and abstinence from alcohol use were all associated with a significantly increased risk of diabetes, even after adjustment for the body-mass index. As compared with the rest of the cohort, women in the low-risk group (3.4 percent of the women) had a relative risk of diabetes of 0.09 (95 percent confidence interval, 0.05 to 0.17). A total of 91 percent of the cases of diabetes in this cohort (95 percent confidence interval, 83 to 95 percent) could be attributed to habits and forms of behavior that did not conform to the low-risk pattern.  Our findings support the hypothesis that the majority of cases of type 2 diabetes could be prevented by the adoption of a healthier lifestyle. ( 2001;345:790-7.
Data Augmentation for Visual Question Answering

Data augmentation is widely used to train deep neural networks for image classification tasks. Simply flipping images can help learning by increasing the number of training images by a factor of two. However, data augmentation in natural language processing is much less studied. Here, we describe two methods for data augmentation for Visual Question Answering (VQA). The first uses existing semantic annotations to generate new questions. The second method is a generative approach using recurrent neural networks. Experiments show the proposed schemes improve performance of baseline and state-of-the-art VQA algorithms.
Strategic Management of Technology and Innovation

This paper discusses the importance and need of incorporating technology and innovation strategy into business strategy to achieve overall competitive advantage for the company. The use of technology and innovation in value creation system is highlighted in a way that it plays a pivotal role in productivity, economic growth, increasing wealth in socioeconomic environment, and evolution of entire industries. Strategy formation and execution in the context of technology is discussed that technology strategy should be aligned to corporate strategy in order to reap out benefits like performance and competitiveness for the company. Furthermore, concepts like technology strategy, forces affecting technology strategy formation and execution, technology management, innovation management, and what benefits companies can get from these are highlighted and discussed in relation to corporate business strategy.
A search engine for RDF metadata

This paper presents a search engine for RDF metadata. Existing search facilities in this environment only support exact queries. We argue that a fuzzy approach as known from classic (full-text) Information Retrieval, providing fuzzy result sets ranked by relevance, is also desirable for metadata-based searches. Consequently, we develop an information retrieval model based on the similarity of RDF descriptions. The role of an ontology and implicit information that can be inferred from it will be discussed and the implementation within a knowledge portal prototype will be presented
Lockless multi-core high-throughput buffering scheme for kernel tracing

Studying execution of concurrent real-time online systems, to identify far-reaching and hard to reproduce latency and performance problems, requires a mechanism able to cope with voluminous information extracted from execution traces. Furthermore, the workload must not be disturbed by tracing, thereby causing the problematic behavior to become unreproducible.
 In order to satisfy this low-disturbance constraint, we created the LTTng kernel tracer. It is designed to enable safe and race-free attachment of probes virtually anywhere in the operating system, including sites executed in non-maskable interrupt context.
 In addition to being reentrant with respect to all kernel execution contexts, LTTng offers good performance and scalability, mainly due to its use of per-CPU data structures, local atomic operations as main buffer synchronization primitive, and RCU (Read-Copy Update) mechanism to control tracing.
 Given that kernel infrastructure used by the tracer could lead to infinite recursion if traced, and typically requires non-atomic synchronization, this paper proposes an asynchronous mechanism to inform the kernel that a buffer is ready to read. This ensures that tracing sites do not require any kernel primitive, and therefore protects from infinite recursion.
 This paper presents the core of LTTng's buffering algorithms and measures its performance.
Checksum-Aware Fuzzing Combined with Dynamic Taint Analysis and Symbolic Execution

Fuzz testing has proven successful in finding security vulnerabilities in large programs. However, traditional fuzz testing tools have a well-known common drawback: they are ineffective if most generated inputs are rejected at the early stage of program running, especially when target programs employ checksum mechanisms to verify the integrity of inputs. This article presents TaintScope, an automatic fuzzing system using dynamic taint analysis and symbolic execution techniques, to tackle the above problem. TaintScope has several novel features: (1) TaintScope is a checksum-aware fuzzing tool. It can identify checksum fields in inputs, accurately locate checksum-based integrity checks by using branch profiling techniques, and bypass such checks via control flow alteration. Furthermore, it can fix checksum values in generated inputs using combined concrete and symbolic execution techniques. (2) TaintScope is a taint-based fuzzing tool working at the x86 binary level. Based on fine-grained dynamic taint tracing, TaintScope identifies the “hot bytes” in a well-formed input that are used in security-sensitive operations (e.g., invoking system/library calls), and then focuses on modifying such bytes with random or boundary values. (3) TaintScope is also a symbolic-execution-based fuzzing tool. It can symbolically evaluate a trace, reason about all possible values that can execute the trace, and then detect potential vulnerabilities on the trace.
 We evaluate TaintScope on a number of large real-world applications. Experimental results show that TaintScope can accurately locate the checksum checks in programs and dramatically improve the effectiveness of fuzz testing. TaintScope has already found 30 previously unknown vulnerabilities in several widely used applications, including Adobe Acrobat, Flash Player, Google Picasa, and Microsoft Paint. Most of these severe vulnerabilities have been confirmed by Secunia and oCERT, and assigned CVE identifiers (such as CVE-2009-1882, CVE-2009-2688). Vendor patches have been released or are in preparation based on our reports.
Scale Drift-Aware Large Scale Monocular SLAM

State of the art visual SLAM systems have recently been presented which are capable of accurate, large-scale and real-time performance, but most of these require stereo vision. Important application areas in robotics and beyond open up if similar performance can be demonstrated using monocular vision, since a single camera will always be cheaper, more compact and easier to calibrate than a multi-camera rig. With high quality estimation, a single camera moving through a static scene of course effectively provides its own stereo geometry via frames distributed over time. However, a classic issue with monocular visual SLAM is that due to the purely projective nature of a single camera, motion estimates and map structure can only be recovered up to scale. Without the known inter-camera distance of a stereo rig to serve as an anchor, the scale of locally constructed map portions and the corresponding motion estimates is therefore liable to drift over time. In this paper we describe a new near real-time visual SLAM system which adopts the continuous keyframe optimisation approach of the best current stereo systems, but accounts for the additional challenges presented by monocular input. In particular, we present a new pose-graph optimisation technique which allows for the efficient correction of rotation, translation and scale drift at loop closures. Especially, we describe the Lie group of similarity transformations and its relation to the corresponding Lie algebra. We also present in detail the system’s new image processing front-end which is able accurately to track hundreds of features per frame, and a filter-based approach for feature initialisation within keyframe-based SLAM. Our approach is proven via large-scale simulation and real-world experiments where a camera completes large looped trajectories.
Taxonomy, technology and applications of smart objects

Deployment of embedded technologies is increasingly being examined in industrial supply chains as a means for improving efficiency through greater control over purchase orders, inventory and product related information. Central to this development has been the advent of technologies such as bar codes, Radio Frequency Identification (RFID) systems, and wireless sensors which when attached to a product, form part of the product’s embedded systems infrastructure. The increasing integration of these technologies dramatically contributes to the evolving notion of a “smart product”, a product which is capable of incorporating itself into both physical and information environments. The future of this revolution in objects equipped with smart embedded technologies is one in which objects can not only identify themselves, but can also sense and store their condition, communicate This work was partly funded as part of the BRIDGE project by the European Commission within the Sixth Framework Programme (2002-2006) IP Nr. IST-FP6-033546. T. Sánchez López (B) · B. Patkai · D. McFarlane Engineering Department, Institute for Manufacturing, University of Cambridge, 16 Mill Lane, Cambridge CB2 1RX, UK e-mail: tsl26@cam.ac.uk B. Patkai e-mail: bp282@cam.ac.uk D. McFarlane e-mail: dcm@cam.ac.uk D. C. Ranasinghe The School of Computer Science, The University of Adelaide, Adelaide, South Australia, 5005,  damith@cs.adelaide.edu.au with other objects and distributed infrastructures, and take decisions related to managing their life cycle. The object can essentially “plug” itself into a compatible systems infrastructure owned by different partners in a supply chain. However, as in any development process that will involve more than one end user, the establishment of a common foundation and understanding is essential for interoperability, efficient communication among involved parties and for developing novel applications. In this paper, we contribute to creating that common ground by providing a characterization to aid the specification and construction of “smart objects” and their underlying technologies. Furthermore, our work provides an extensive set of examples and potential applications of different categories of smart objects.
SilentWhispers: Enforcing Security and Privacy in Decentralized Credit Networks

Credit networks model transitive trust (or credit) between users in a distributed environment and have recently seen a rapid increase of popularity due to their flexible design and robustness against intrusion. They serve today as a backbone of real-world IOweYou transaction settlement networks such as Ripple and Stellar, which are deployed by various banks worldwide, as well as several other systems, such as spamresistant communication protocols and Sybil-tolerant social networks. Current solutions, however, raise serious privacy concerns, as the network topology as well as the credit value of the links are made public for apparent transparency purposes and any changes are logged. In payment scenarios, for instance, this means that all transactions have to be public and everybody knows who paid what to whom. In this work, we question the necessity of a privacy-invasive transaction ledger. In particular, we present SilentWhispers, the first distributed, privacy-preserving credit network that does not require any ledger to protect the integrity of transactions. Yet, SilentWhispers guarantees integrity and privacy of link values and transactions even in the presence of distrustful users and malicious neighbors, whose misbehavior in changing link values is detected and such users can be held accountable. We formalize these properties as ideal functionalities in the universal composability framework and present a secure realization based on a novel combination of secret-sharing-based multiparty computation and digital signature chains. SilentWhispers can handle network churn, and it is efficient as demonstrated with a prototype implementation evaluated using payments data extracted from the currently deployed Ripple payment system.
Timeline Generation through Evolutionary Trans-Temporal Summarization

We investigate an important and challenging problem in summary generation, i.e.,  ETTS greatly facilitates fast news browsing and knowledge comprehension, and hence is a necessity. Given the collection of time-stamped web documents related to the evolving news, ETTS aims to return news evolution along the timeline, consisting of individual but correlated summaries on each date. Existing summarization algorithms fail to utilize trans-temporal characteristics among these component summaries. We propose to model trans-temporal correlations among component summaries for timelines, using inter-date and intra-date sentence dependencies, and present a novel combination. We develop experimental systems to compare 5 rival algorithms on 6 instinctively different datasets which amount to 10251 documents. Evaluation results in ROUGE metrics indicate the effectiveness of the proposed approach based on trans-temporal information.