nils-herrmann/clean_scirepeval huggingface dataset:
Computational analysis of the role of the hippocampus in memory.

The authors draw together the results of a series of detailed computational studies and show how they are contributing to the development of a theory of hippocampal function. A new part of the theory introduced here is a quantitative analysis of how backprojections from the hippocampus to the neocortex could lead to the recall of recent memories. The theory is then compared with other theories of hippocampal function. First, what is computed by the hippocampus is considered. The hypothesis the authors advocate, on the basis of the effects of damage to the hippocampus and neuronal activity recorded in it, is that it is involved in the formation of new memories by acting as an intermediate-term buffer store for information about episodes, particularly for spatial, but probably also for some nonspatial, information. The authors analyze how the hippocampus could perform this function, by producing a computational theory of how it operates, based on neuroanatomical and neurophysiological information about the different neuronal systems contained within the hippocampus. Key hypotheses are that the CA3 pyramidal cells operate as a single autoassociation network to store new episodic information as it arrives via a number of specialized preprocessing stages from many association areas of the cerebral cortex, and that the dentate granule cell/mossy fiber system is important, particularly during learning, to help to produce a new pattern of firing in the CA3 cells for each episode. The computational analysis shows how many memories could be stored in the hippocampus and how quickly the CA3 autoassociation system would operate during recall. The analysis is then extended to show how the CA3 system could be used to recall a whole episodic memory when only a fragment of it is presented. It is shown how this recall could operate using modified synapses in backprojection pathways from the hippocampus to the cerebral neocortex, resulting in reinstatement of neuronal activity in association areas of the cerebral neocortex similar to that present during the original episode. The recalled information in the cerebral neocortex could then be used by the neocortex in the formation of long-term memories.
Mining Complex Data

In recent years, the complexity of data objects in data mining applications has increased as well as their plain numbers. As a result, there exist various feature transformations and thus multiple object representations. For example, an image can be described by a text annotation, a color histogram and some texture features. To cluster thesemulti-represented objects, dedicated datamining algorithms have been shown to achieve improved results. In this paper, we will therefore introduce a method for hierarchical density-based clustering of multi-represented objects which is insensitive w.r.t. the choice of parameters. Furthermore, we will introduce a theoretical model that allows us to draw conclusions about the interaction of representations. Additionally, we will show how these conclusions can be used for defining a suitable combination method for multiple representations. To back up the usability of our proposed method, we present encouraging results for clustering a real world image data set that is described by 4 different representations.
A framework of sensor-cloud integration opportunities and challenges

In the past few years, wireless sensor networks (WSNs) have been gaining increasing attention because of their potential of enabling of novel and attractive solutions in areas such as industrial automation, environmental monitoring, transportation business, health-care etc. If we add this collection of sensor derived data to various Web-based social networks or virtual communities, blogs etc., we can have a remarkable transformation in our ability to "see" ourselves and our planet. Our primary goal is to facilitate connecting sensors, people and software objects to build community-centric sensing applications. However, the computational tools needed to launch this exploration may be more appropriately built from the data center "Cloud" computing model than the traditional HPC approaches. In this paper, we propose a framework to enable this exploration by integrating sensor networks to the emerging data center "cloud" model of computing. But there are many challenges to enable this framework. We propose a pub-sub based model which simplifies the integration of sensor networks with cloud based community-centric applications. Also there is a need for internetworking cloud providers in case of violation of service level agreement with users. We discussed these issues and proposed reasonable solutions.
Performance Engineering for Microservices: Research Challenges and Directions

Microservices complement approaches like DevOps and continuous delivery in terms of software architecture. Along with this architectural style, several important deployment technologies, such as container-based virtualization and container orchestration solutions, have emerged. These technologies allow to efficiently exploit cloud platforms, providing a high degree of scalability, availability, and portability for microservices.
 Despite the obvious importance of a sufficient level of performance, there is still a lack of performance engineering approaches explicitly taking into account the particularities of microservices. In this paper, we argue why new solutions to performance engineering for microservices are needed. Furthermore, we identify open issues and outline possible research directions with regard to performance-aware testing, monitoring, and modeling of microservices.
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation

Image segmentation is a fundamental problem in biomedical image analysis. Recent advances in deep learning have achieved promising results on many biomedical image segmentation benchmarks. However, due to large variations in biomedical images (different modalities, image settings, objects, noise, etc), to utilize deep learning on a new application, it usually needs a new set of training data. This can incur a great deal of annotation effort and cost, because only biomedical experts can annotate effectively, and often there are too many instances in images (e.g., cells) to annotate. In this paper, we aim to address the following question: With limited effort (e.g., time) for annotation, what instances should be annotated in order to attain the best performance? We present a deep active learning framework that combines fully convolutional network (FCN) and active learning to significantly reduce annotation effort by making judicious suggestions on the most effective annotation areas. We utilize uncertainty and similarity information provided by FCN and formulate a generalized version of the maximum set cover problem to determine the most representative and uncertain areas for annotation. Extensive experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node ultrasound image segmentation dataset show that, using annotation suggestions by our method, state-of-the-art segmentation performance can be achieved by using only 50% of training data.
Safe Control under Uncertainty

Controller synthesis for hybrid systems that satisfy temporal specifications expressing various system properties is a challenging problem that has drawn the attention of many researchers. However, making the assumption that such temporal properties are deterministic is far from the reality. For example, many of the properties the controller has to satisfy are learned through machine learning techniques based on sensor input data. In this paper, we propose a new logic, Probabilistic Signal Temporal Logic (PrSTL), as an expressive language to define the stochastic properties, and enforce probabilistic guarantees on them. We further show how to synthesize safe controllers using this logic for cyber-physical systems under the assumption that the stochastic properties are based on a set of Gaussian random variables. One of the key distinguishing features of PrSTL is that the encoded logic is adaptive and changes as the system encounters additional data and updates its beliefs about the latent random variables that define the safety properties. We demonstrate our approach by synthesizing safe controllers under the PrSTL specifications for multiple case studies including control of quadrotors and autonomous vehicles in dynamic environments.
Diagnostic Testing and Decision-Making: Beauty Is Not Just in the Eye of the Beholder

To use a diagnostic test effectively and consistently in their practice, clinicians need to know how well the test distinguishes between those patients who have the suspected acute or chronic disease and those patients who do not. Clinicians are equally interested and usually more concerned whether, based on the results of a screening test, a given patient actually: (1) does or does not have the suspected disease; or (2) will or will not subsequently experience the adverse event or outcome. Medical tests that are performed to screen for a risk factor, diagnose a disease, or to estimate a patient's prognosis are frequently a key component of a clinical research study. Like therapeutic interventions, medical tests require proper analysis and demonstrated efficacy before being incorporated into routine clinical practice. This basic statistical tutorial, thus, discusses the fundamental concepts and techniques related to diagnostic testing and medical decision-making, including sensitivity and specificity, positive predictive value and negative predictive value, positive and negative likelihood ratio, receiver operating characteristic curve, diagnostic accuracy, choosing a best cut-point for a continuous variable biomarker, comparing methods on diagnostic accuracy, and design of a diagnostic accuracy study.
A Framework for Information Systems Architecture

With increasing size and complexity of the implementations of information systems, it is necessary to use some logical construct (or architecture) for defining and controlling the interfaces and the integration of all of the components of the system. This paper defines information systems architecture by creating a d e scriptive framework from disciplines quite independent of information systems, then by analogy specifies information systems architecture based upon the neutral , objective framework. Also, some preliminary conclusions about the implications of the resultant descriptive framework are drawn. The discussion is limited to architecture and does not include a strategic planning methodology. T he subject of information systems architecture is beginning to receive considerable attention. The increased scope of design and levels of complexity of information systems implementations are forcing the use of some logical construct (or architecture) for defining and controlling the interfaces and the integration of all of the components of the system. Thirty years ago this issue was not at all significant because the technology itself did not provide for either breadth in scope or depth in complexity in information systems. The inherent limitations of the then-available 4K machines, for example, constrained design and necessitated suboptimal approaches for automating a business. Current technology is rapidly removing both conceptual and financial constraints. It is not hard to speculate about, if not realize, very large, very complex systems implementations, extending in scope and complexity to encompass an entire enterprise. One can readily delineate the merits of the large, complex, enterprise-oriented approaches. Such systems allow flexibility in managing business changes and coher-ency in the management of business resources. However , there also is merit in the more traditional, smaller, suboptimal systems design approach. Such systems are relatively economical, quickly implemented , and easier to design and manage. In either case, since the technology permits " distributing " large amounts of computing facilities in small packages to remote locations, some kind of structure (or architecture) is imperative because decentralization without structure is chaos. Therefore, to keep the business from disintegrating, the concept of information systems architecture is becoming less an option and more a necessity for establishing some order and control in the investment of information systems resources. The cost involved and the success of the business depending increasingly on its information systems require a disciplined approach to the management of those systems. On the assumption that an understanding of information systems architecture …
Application of the Artificial Neural Network for blood pressure evaluation with smartphones

The smartphone is proposed to evaluate the Blood Pressure (BP) anywhere and anytime. The tasks performed by smartphone are (i) extraction of the PhotoPlethysmoGram (PPG) signal from a frame sequence acquired by the integrated camera, and (ii) processing it by Artificial Neural Network for the evaluation of the BP. The PPG signal is evaluated by analyzing the volumetric blood variation of the fingertip on the frame sequence. Successively, parameters characterizing the pulses of the PPG signal are sent to the Fit Forward Neural Network for the simultaneously evaluation of the systolic and the diastolic BP. The validation of the results is performed by comparing them with the ones obtained by the Ambulatory Blood Pressure monitor ABP Spacelabs 90207. Preliminary experimental results show useful information to address the future research devoted to reduce the maximum error.
Image Segmentation-Based Multi-Focus Image Fusion Through Multi-Scale Convolutional Neural Network

A decision map contains complete and clear information about the image to be fused, and detecting the decision map is crucial to various image fusion issues, especially multi-focus image fusion. Nevertheless, in an attempt to obtain an approving image fusion effect, it is necessary and always difficult to obtain a decision map. In this paper, we address this problem with a novel image segmentation-based multi-focus image fusion algorithm, in which the task of detecting the decision map is treated as image segmentation between the focused and defocused regions in the source images. The proposed method achieves segmentation through a multi-scale convolutional neural network, which performs a multi-scale analysis on each input image to derive the respective feature maps on the region boundaries between the focused and defocused regions. The feature maps are then inter-fused to produce a fused feature map. Afterward, the fused map is post-processed using initial segmentation, morphological operation, and watershed to obtain the segmentation map/decision map. We illustrate that the decision map gained from the multi-scale convolutional neural network is trustworthy and that it can lead to high-quality fusion results. Experimental results evidently validate that the proposed algorithm can achieve an optimum fusion performance in light of both qualitative and quantitative evaluations.
The CAPIO 2017 Conversational Speech Recognition System

In this paper we show how we have achieved the state-of-the-art performance on the industry-standard NIST 2000 Hub5 English evaluation set. We propose densely connected LSTMs, (namely, dense LSTMs), inspired by the densely connected convolutional networks recently introduced for image classification tasks. It is shown that the proposed dense LSTMs would provide more reliable performances as compared to the conventional, residual LSTMs as more LSTM layers are stacked in neural networks. We also propose an acoustic model adaptation scheme that simply averages the parameters of a seed neural network acoustic model and its adapted version. This method was applied with the CallHome training corpus and improved individual system performances by on average 6.1% (relative) against the CallHome portion of the evaluation set with no performance loss on the Switchboard portion. With RNN-LM rescoring and lattice combination on the 5 systems trained across three different phone sets, our 2017 speech recognition system has obtained 5.0% and 9.1% on Switchboard and CallHome, respectively, both of which are the best word error rates reported thus far. According to IBM in their latest work to compare human and machine transcriptions, our reported Switchboard word error rate can be considered to surpass the human parity (5.1%) of transcribing conversational telephone speech. We also share the performance numbers of our system on non-telephony environments for readers’ benefits.
Variable gain differential current feedback amplifier

A new variable gain differential current feedback amplifier (CFA) is presented. In comparison to common CFA structures, the presented CFA provides several novelties, like symmetrical high impedance inputs, accurate and temperature stable gain, simple gain control without need of resistor feedback network and wide input dynamic range. Input offset is comparable to common voltage feedback amplifier (VFA) structures. All common CFA fruitful features, like gain independent band width (BW) are provided. A schematic implementation in 0.35 /spl mu/m CMOS and measured results are shown.
Delineation of line patterns in images using B-COSFIRE filters

Delineation of line patterns in images is a basic step required in various applications such as blood vessel detection in medical images, segmentation of rivers or roads in aerial images, detection of cracks in walls or pavements, etc. In this paper we present trainable B-COSFIRE filters, which are a model of some neurons in area V1 of the primary visual cortex, and apply it to the delineation of line patterns in different kinds of images. B-COSFIRE filters are trainable as their selectivity is determined in an automatic configuration process given a prototype pattern of interest. They are configurable to detect any preferred line structure (e.g. segments, corners, cross-overs, etc.), so usable for automatic data representation learning. We carried out experiments on two data sets, namely a line-network data set from INRIA and a data set of retinal fundus images named IOSTAR. The results that we achieved confirm the robustness of the proposed approach and its effectiveness in the delineation of line structures in different kinds of images
Exploiting 2D Floorplan for Building-Scale Panorama RGBD Alignment

This paper presents a novel algorithm that utilizes a 2D floorplan to align panorama RGBD scans. While effective panorama RGBD alignment techniques exist, such a system requires extremely dense RGBD image sampling. Our approach can significantly reduce the number of necessary scans with the aid of a floorplan image. We formulate a novel Markov Random Field inference problem as a scan placement over the floorplan, as opposed to the conventional scan-to-scan alignment. The technical contributions lie in multi-modal image correspondence cues (between scans and schematic floorplan) as well as a novel coverage potential avoiding an inherent stacking bias. The proposed approach has been evaluated on five challenging large indoor spaces. To the best of our knowledge, we present the first effective system that utilizes a 2D floorplan image for building-scale 3D pointcloud alignment. The source code and the data are shared with the community to further enhance indoor mapping research.
Music training improves verbal but not visual memory: cross-sectional and longitudinal explorations in children.

The hypothesis that music training can improve verbal memory was tested in children. The results showed that children with music training demonstrated better verbal but not visual memory than did their counterparts without such training. When these children were followed up after a year, those who had begun or continued music training demonstrated significant verbal memory improvement. Students who discontinued the training did not show any improvement. Contrary to the differences in verbal memory between the groups, their changes in visual memory were not significantly different. Consistent with previous findings for adults (A. S. Chan, Y. Ho, & M. Cheung, 1998), the results suggest that music training systematically affects memory processing in accordance with possible neuroanatomical modifications in the left temporal lobe.
Partition-Based Trapdoor Ciphers

This paper deals with block ciphers embedding a trapdoor which consists in mapping a partition of the plaintext space to a partition of the ciphertext space. In a first part, this issue is reduced to the study of the S-boxes of the cipher satisfying a few criteria. Then, differential and linear properties of such S-boxes are assessed and an algorithm to build optimal S-boxes is provided. Finally, these primitives are used to design a small trapdoor cipher resistant to both linear and differential cryptanalysis. This trapdoor makes it possible to recover the κ-bit master key with only one plaintext/ciphertext pair and an effort of 2κ2 encryptions.
An ontological analysis of the economic primitives of the extended-REA enterprise information architecture

The Resource-Event-Agent (REA) model for enterprise economic phenomena was first published in The Accounting Review in 1982. Since that time its concepts and use have been extended far beyond its original accountability infrastructure to a framework for enterprise information architectures. The granularity of the model has been extended both up (to enterprise value chains) and down (to workflow or task specification) in the aggregation plane, and additional conceptual type-images and commitment-images have been proposed as well. The REA model actually fits the notion of domain ontology well, a notion that is becoming increasingly important in the era of e-commerce and virtual companies. However, its present and future components have never been analyzed formally from an ontological perspective. This paper intends to do just that, relying primarily on the conceptual terminology of John Sowa. The economic primitives of the original REA model (economic resources, economic events, economic agents, duality relationships, stock-flow relationships, control relationships and responsibility relationships) plus some newer components (commitments, types, custody, reciprocal, etc.) will be analyzed individually and collectively as a specific domain ontology. Such a review can be used to guide further conceptual development of REA extensions.
Can Your Smartphone Infer Your Mood ?

Our driving vision is a smartphone service, called MoodSense, that can infer its owner’s mood based on information already available in today’s smartphones. The service will fundamentally enhance context-awareness by providing clues about mobile users’ mental states. We report early results from studying 25 iPhone users in the field and the correlation between their mood and phone usage. We show that user mood can be inferred into four major types with an average accuracy of 91%. This is achieved using only three weeks of training data and simple smartphone usage statistics. The results, though preliminary, strongly suggest the feasibility of mood inference without using the powerhungry and socially invasive microphone and camera.