nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning for Medical Image Segmentation

This report provides an overview of the current state of the art deep learning architectures and optimisation techniques, and uses the ADNI hippocampus MRI dataset as an example to compare the effectiveness and efficiency of different convolutional architectures on the task of patch-based 3dimensional hippocampal segmentation, which is important in the diagnosis of Alzheimer’s Disease. We found that a slightly unconventional ”stacked 2D” approach provides much better classification performance than simple 2D patches without requiring significantly more computational power. We also examined the popular ”tri-planar” approach used in some recently published studies, and found that it provides much better results than the 2D approaches, but also with a moderate increase in computational power requirement. Finally, we evaluated a full 3D convolutional architecture, and found that it provides marginally better results than the tri-planar approach, but at the cost of a very significant increase in computational power requirement. ar X iv :1 50 5. 02 00 0v 1 [ cs .L G ] 8 M ay 2 01 5
On the use of the IC stripline to evaluate the susceptibility to EMI of small integrated circuits

The IC stripline method is one of those suggested in IEC-62132 to evaluate the susceptibility of ICs to radiated electromagnetic interference. In practice, it allows the multiple injection of the interference through the capacitive and inductive coupling of the IC package with the guiding structure (the stripline) in which the device under test is inserted. The pros and cons of this method are discussed and a variant of it is proposed with the aim to address the main problems that arise when evaluating the susceptibility of ICs encapsulated in small packages.
E-COMMERCE IN DEVELOPING COUNTRIES : ISSUES AND INFLUENCES

Differing characteristics of local environments, both infrastructural and socio-economic, have created a significant level of variation in the acceptance and growth of e-commerce in different regions of the world. Our findings show that, in development and diffusion of ecommerce in China, cultural issues such as “socializing effect of commerce”, “transactional and institutional trust”, and “attitudes toward debt” play a very major role. In this paper, we present and discuss these findings, and identify changes that will be required for broader acceptance and diffusion of e-commerce in China and propose approaches that businesses can use to enhance this development.
Evaluation of dynamic sensitivity control algorithm for IEEE 802.11ax

The explosive growth in the usage of IEEE 802.11 network has resulted in dense deployments in diverse environments. Most recently, the IEEE working group has triggered the IEEE 802.11ax project, which aims to amend the current IEEE 802.11 standard to improve efficiency of dense WLANs. In this paper, we evaluate the Dynamic Sensitivity Control (DSC) Algorithm proposed for IEEE 802.11ax. This algorithm dynamically adjusts the Carrier Sense Threshold (CST) based on the average received signal strength. We show that the aggregate throughput of a dense network utilizing DSC is considerably improved (i.e. up to 20%) when compared with the IEEE 802.11 legacy network.
Anomalous lattice vibrations of single- and few-layer MoS2.

Molybdenum disulfide (MoS(2)) of single- and few-layer thickness was exfoliated on SiO(2)/Si substrate and characterized by Raman spectroscopy. The number of S-Mo-S layers of the samples was independently determined by contact-mode atomic force microscopy. Two Raman modes, E(1)(2g) and A(1g), exhibited sensitive thickness dependence, with the frequency of the former decreasing and that of the latter increasing with thickness. The results provide a convenient and reliable means for determining layer thickness with atomic-level precision. The opposite direction of the frequency shifts, which cannot be explained solely by van der Waals interlayer coupling, is attributed to Coulombic interactions and possible stacking-induced changes of the intralayer bonding. This work exemplifies the evolution of structural parameters in layered materials in changing from the three-dimensional to the two-dimensional regime.
Machine Recognition of Music Emotion: A Review

The proliferation of MP3 players and the exploding amount of digital music content call for novel ways of music organization and retrieval to meet the ever-increasing demand for easy and effective information access. As almost every music piece is created to convey emotion, music organization and retrieval by emotion is a reasonable way of accessing music information. A good deal of effort has been made in the music information retrieval community to train a machine to automatically recognize the emotion of a music signal. A central issue of machine recognition of music emotion is the conceptualization of emotion and the associated emotion taxonomy. Different viewpoints on this issue have led to the proposal of different ways of emotion annotation, model training, and result visualization. This article provides a comprehensive review of the methods that have been proposed for music emotion recognition. Moreover, as music emotion recognition is still in its infancy, there are many open issues. We review the solutions that have been proposed to address these issues and conclude with suggestions for further research.
EDITH PENROSE ’ S CONTRIBUTION TO THE RESOURCE-BASED VIEW OF STRATEGIC MANAGEMENT

Edith Penrose’s (1959) book, The Theory of the Growth of the Firm, is considered by many scholars in the strategy field to be the seminal work that provided the intellectual foundations for the modern, resource-based theory of the firm. However, the present paper suggests that Penrose’s direct or intended contribution to resource-based thinking has been misinterpreted. Penrose never aimed to provide useful strategy prescriptions for managers to create a sustainable stream of rents; rather, she tried to rigorously describe the processes through which firms grow. In her theory, rents were generally assumed not to occur. If they arose this reflected an inefficient macro-level outcome of an otherwise efficient micro-level growth process. Nevertheless, her ideas have undoubtedly stimulated ‘good conversation’ within the strategy field in the spirit of Mahoney and Pandian (1992); their emerging use by some scholars as building blocks in models that show how sustainable competitive advantage and rents can be achieved is undeniable, although such use was never intended by Edith Penrose herself.
Towards Trojan-Free Trusted ICs: Problem Analysis and Detection Scheme

There have been serious concerns recently about the security of microchips from hardware trojan horse insertion during manufacturing. This issue has been raised recently due to outsourcing of the chip manufacturing processes to reduce cost. This is an important consideration especially in critical applications such as avionics, communications, military, industrial and so on. A trojan is inserted into a main circuit at manufacturing and is mostly inactive unless it is triggered by a rare value or time event; then it produces a payload error in the circuit, potentially catastrophic. Because of its nature, a trojan may not be easily detected by functional or ATPG testing. The problem of trojan detection has been addressed only recently in very few works. Our work analyzes and formulates the trojan detection problem based on a frequency analysis under rare trigger values and provides procedures to generate input trigger vectors and trojan test vectors to detect trojan effects. We also provide experimental results.
The Impact of Operating System Scheduling Policies and Synchronization Methods of the Performance of Parallel Application

Shared-memory multiprocessors are frequently used as compute servers with multiple parallel applications executing at the same time. In such environments, the efficiency of a parallel application can be significantly affected by the operating system scheduling policy. In this paper, we use detailed simulation studies to evaluate the performance of several different scheduling strategies, These include regular priority scheduling, coscheduling or gang scheduling, process control with processor partitioning, handoff scheduling, and affinity-based scheduling. We also explore tradeoffs between the use of busy-waiting and blocking synchronization primitives and their interactions with the scheduling strategies. Since effective use of caches is essential to achieving high performance, a key focus is on the impact of the scheduling strategies on the caching behavior of the applications.Our results show that in situations where the number of processes exceeds the number of processors, regular priority-based scheduling in conjunction with busy-waiting synchronization primitives results in extremely poor processor utilization. In such situations, use of blocking synchronization primitives can significantly improve performance. Process control and gang scheduling strategies are shown to offer the highest performance, and their performance is relatively independent of the synchronization method used. However, for applications that have sizable working sets that fit into the cache, process control performs better than gang scheduling. For the applications considered, the performance gains due to handoff scheduling and processor affinity are shown to be small.
Alternative Narratives of Crisis Events: Communities and Social Botnets Engaged on Social Media

This paper presents an early stage exploratory analysis of communities engaging in alternative narratives about crisis events on social media. Over 11,000 user accounts on Twitter engaged in conversations questioning the mainstream narrative of the Paris Attacks and Umpqua Community College Shootings in Autumn 2015. We analyze the social network of communication, examining the composition of clusters of accounts with shared audiences. We describe some of the common traits within communities and note the prevalence of automated accounts. Our results shed light on the nature of the communities espousing alternative narratives and factors in the spread of the content.
NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.
Collective Semi-Supervised Learning for User Profiling in Social Media

The abundance of user-generated data in social media has incentivized the development of methods to infer the latent attributes of users, which are crucially useful for personalization, advertising and recommendation. However, the current user profiling approaches have limited success, due to the lack of a principled way to integrate different types of social relationships of a user, and the reliance on scarcely-available labeled data in building a prediction model. In this paper, we present a novel solution termed Collective Semi-Supervised Learning (CSL), which provides a principled means to integrate different types of social relationship and unlabeled data under a unified computational framework. The joint learning from multiple relationships and unlabeled data yields a computationally sound and accurate approach to model user attributes in social media. Extensive experiments using Twitter data have demonstrated the efficacy of our CSL approach in inferring user attributes such as account type and marital status. We also show how CSL can be used to determine important user features, and to make inference on a larger user population.
Dynamoth: A Scalable Pub/Sub Middleware for Latency-Constrained Applications in the Cloud

This paper presents Dynamoth, a dynamic, scalable, channel-based pub/sub middleware targeted at large scale, distributed and latency constrained systems. Our approach provides a software layer that balances the load generated by a high number of publishers, subscribers and messages across multiple, standard pub/sub servers that can be deployed in the Cloud. In order to optimize Cloud infrastructure usage, pub/sub servers can be added or removed as needed. Balancing takes into account the live characteristics of each channel and is done in an hierarchical manner across channels (macro) as well as within individual channels (micro) to maintain acceptable performance and low latencies despite highly varying conditions. Load monitoring is performed in an unintrusive way, and rebalancing employs a lazy approach in order to minimize its temporal impact on performance while ensuring successful and timely delivery of all messages. Extensive real-world experiments that illustrate the practicality of the approach within a massively multiplayer game setting are presented. Results indicate that with a given number of servers, Dynamoth was able to handle 60% more simultaneous clients than the consistent hashing approach, and that it was properly able to deal with highly varying conditions in the context of large workloads.
A polyhedral branch-and-cut approach to global optimization

A variety of nonlinear, including semidefinite, relaxations have been developed in recent years for nonconvex optimization problems. Their potential can be realized only if they can be solved with sufficient speed and reliability. Unfortunately, state-of-the-art nonlinear programming codes are significantly slower and numerically unstable compared to linear programming software. In this paper, we facilitate the reliable use of nonlinear convex relaxations in global optimization via a polyhedral branch-and-cut approach. Our algorithm exploits convexity, either identified automatically or supplied through a suitable modeling language construct, in order to generate polyhedral cutting planes and relaxations for multivariate nonconvex problems. We prove that, if the convexity of a univariate or multivariate function is apparent by decomposing it into convex subexpressions, our relaxation constructor automatically exploits this convexity in a manner that is much superior to developing polyhedral outer approximators for the original function. The convexity of functional expressions that are composed to form nonconvex expressions is also automatically exploited. Root-node relaxations are computed for 87 problems from globallib and minlplib, and detailed computational results are presented for globally solving 26 of these problems with BARON 7.2, which implements the proposed techniques. The use of cutting planes for these problems reduces root-node relaxation gaps by up to 100% and expedites the solution process, often by several orders of magnitude.
A STUDY OF TRANSLATION ERROR RATE WITH TARGETED HUMAN ANNOTATION

We define a new, intuitive measure for evaluating machine translation output that avoids the knowledge intensiveness of more meaning-based approaches, and the labor-intensiveness of human judgments. Translation Error Rate (TER) measures the amount of editing that a human would have to perform to change a system output so it exactly matches a reference translation. We also compute a human-targeted TER (or HTER), where the minimum TER of the translation is computed against a human ‘targeted reference’ that preserves the meaning (provided by the reference translations) and is fluent, but is chosen to minimize the TER score for a particular system output. We show that: (1) The single-reference variant of TER correlates as well with human judgments of MT quality as the four-reference variant of BLEU; (2) The human-targeted HTER yields a 33% error-rate reduction and is shown to be very well correlated with human judgments; (3) The four-reference variant of TER and the single-reference variant of HTER yield higher correlations with human judgments than BLEU; (4) HTER yields higher correlations with human judgments than METEOR or its human-targeted variant (HMETEOR); and (5) The four-reference variant of TER correlates as well with a single human judgment as a second human judgment does, while HTER, HBLEU, and HMETEOR correlate significantly better with a human judgment than a second human judgment does. This work has been supported, in part, by BBNT contract number 9500006806.
Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks

A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to the existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers the enhanced capability of multiple source localization. A multiresolution search algorithm and an expectation-maximization (EM) like iterative algorithm are proposed to expedite the computation of source locations. The Crame/spl acute/r-Rao Bound (CRB) of the ML source location estimate has been derived. The CRB is used to analyze the impacts of sensor placement to the accuracy of location estimates for single target scenario. Extensive simulations have been conducted. It is observed that the proposed ML method consistently outperforms existing acoustic energy based source localization methods. An example applying this method to track military vehicles using real world experiment data also demonstrates the performance advantage of this proposed method over a previously proposed acoustic energy source localization method.
Multi-View Multi-Exposure Stereo

Multi-view stereo algorithms typically rely on same-exposure images as inputs due to the brightness constancy assumption. While state-of-the-art depth results are excellent, they do not produce high-dynamic range textures required for high-quality view reconstruction. In this paper, we propose a technique that adapts multi-view stereo for different exposure inputs to simultaneously recover reliable dense depth and high dynamic range textures. In our technique, we use an exposure-invariant similarity statistic to establish correspondences, through which we robustly extract the camera radiometric response function and the image exposures. This enables us to then convert all images to radiance space and selectively use the radiance data for dense depth and high dynamic range texture recovery. We show results for synthetic and real scenes.
Smart Lighting Solutions for Smart Cities

Smart cities play an increasingly important role for the sustainable economic development of a determined area. Smart cities are considered a key element for generating wealth, knowledge and diversity, both economically and socially. A Smart City is the engine to reach the sustainability of its infrastructure and facilitate the sustainable development of its industry, buildings and citizens. The first goal to reach that sustainability is reduce the energy consumption and the levels of greenhouse gases (GHG). For that purpose, it is required scalability, extensibility and integration of new resources in order to reach a higher awareness about the energy consumption, distribution and generation, which allows a suitable modeling which can enable new countermeasure and action plans to mitigate the current excessive power consumption effects. Smart Cities should offer efficient support for global communications and access to the services and information. It is required to enable a homogenous and seamless machine to machine (M2M) communication in the different solutions and use cases. This work presents how to reach an interoperable Smart Lighting solution over the emerging M2M protocols such as CoAP built over REST architecture. This follows up the guidelines defined by the IP for Smart Objects Alliance (IPSO Alliance) in order to implement and interoperable semantic level for the street lighting, and describes the integration of the communications and logic over the existing street lighting infrastructure.
Optimizing energy consumption of GUIs in Android apps: a multi-objective approach

The wide diffusion of mobile devices has motivated research towards optimizing energy consumption of software systems— including apps—targeting such devices. Besides efforts aimed at dealing with various kinds of energy bugs, the adoption of Organic Light-Emitting Diode (OLED) screens has motivated research towards reducing energy consumption by choosing an appropriate color palette. Whilst past research in this area aimed at optimizing energy while keeping an acceptable level of contrast, this paper proposes an approach, named GEMMA (Gui Energy Multi-objective optiMization for Android apps), for generating color palettes using a multi- objective optimization technique, which produces color solutions optimizing energy consumption and contrast while using consistent colors with respect to the original color palette. An empirical evaluation that we performed on 25 Android apps demonstrates not only significant improvements in terms of the three different objectives, but also confirmed that in most cases users still perceived the choices of colors as attractive. Finally, for several apps we interviewed the original developers, who in some cases expressed the intent to adopt the proposed choice of color palette, whereas in other cases pointed out directions for future improvements