nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Model the Tail

We describe an approach to learning from long-tailed, imbalanced datasets that are prevalent in real-world settings. Here, the challenge is to learn accurate “fewshot” models for classes in the tail of the class distribution, for which little data is available. We cast this problem as transfer learning, where knowledge from the data-rich classes in the head of the distribution is transferred to the data-poor classes in the tail. Our key insights are as follows. First, we propose to transfer meta-knowledge about learning-to-learn from the head classes. This knowledge is encoded with a meta-network that operates on the space of model parameters, that is trained to predict many-shot model parameters from few-shot model parameters. Second, we transfer this meta-knowledge in a progressive manner, from classes in the head to the “body”, and from the “body” to the tail. That is, we transfer knowledge in a gradual fashion, regularizing meta-networks for few-shot regression with those trained with more training data. This allows our final network to capture a notion of model dynamics, that predicts how model parameters are likely to change as more training data is gradually added. We demonstrate results on image classification datasets (SUN, Places, and ImageNet) tuned for the long-tailed setting, that significantly outperform common heuristics, such as data resampling or reweighting.
Speech enhancement based on wavelet thresholding the multitaper spectrum

It is well known that the "musical noise" encountered in most frequency domain speech enhancement algorithms is partially due to the large variance estimates of the spectra. To address this issue, we propose in this paper the use of low-variance spectral estimators based on wavelet thresholding the multitaper spectra for speech enhancement. A short-time spectral amplitude estimator is derived which incorporates the wavelet-thresholded multitaper spectra. Listening tests showed that the use of multitaper spectrum estimation combined with wavelet thresholding suppressed the musical noise and yielded better quality than the subspace and MMSE algorithms.
Autonomic schemes for threat mitigation in Internet of Things

Internet of Things (IoT) refers to the expansion of Internet technologies to include wireless sensor networks (WSNs) and smart objects by extensive interfacing of exclusively identifiable, distributed communication devices. Due to the close connection with the physical world, it is an important requirement for IoT technology to be self-secure in terms of a standard information security model components. Autonomic security should be considered as a critical priority and careful provisions must be taken in the design of dynamic techniques, architectures and self-sufficient frameworks for future IoT. Over the years, many researchers have proposed threat mitigation approaches for IoT and WSNs. This survey considers specific approaches requiring minimal human intervention and discusses them in relation to self-security. This survey addresses and brings together a broad range of ideas linked together by IoT, autonomy and security. More particularly, this paper looks at threat mitigation approaches in IoT using an autonomic taxonomy and finally sets down future directions. & 2014
Building a Robust Software-Based Router Using Network Processors

Recent efforts to add new services to the Internet have increased interest in software-based routers that are easy to extend and evolve. This paper describes our experiences using emerging network processors---in particular, the Intel IXP1200---to implement a router. We show it is possible to combine an IXP1200 development board and a PC to build an inexpensive router that forwards minimum-sized packets at a rate of 3.47Mpps. This is nearly an order of magnitude faster than existing pure PC-based routers, and sufficient to support 1.77Gbps of aggregate link bandwidth. At lesser aggregate line speeds, our design also allows the excess resources available on the IXP1200 to be used robustly for extra packet processing. For example, with 8 × 100Mbps links, 240 register operations and 96 bytes of state storage are available for each 64-byte packet. Using a hierarchical architecture we can guarantee line-speed forwarding rates for simple packets with the IXP1200, and still have extra capacity to process exceptional packets with the Pentium. Up to 310Kpps of the traffic can be routed through the Pentium to receive 1510 cycles of extra per-packet processing.
Unstable burst fractures of the thoraco-lumbar junction: treatment by posterior bisegmental correction/fixation and staged anterior corpectomy and titanium cage implantation

Controversy exists about the best treatment of unstable thoraco-lumbar (TL) burst fractures. Kyphosis correction and canal decompression in case of a neurological deficit are recognized treatment objectives, and various conservative and surgical strategies have been proposed. This prospective observational study evaluates the benefits and risks of a posterior bisegmental transpedicular correction/fixation and staged anterior corpectomy and titanium cage implantation in unstable TL junction burst fractures.  20 consecutive patients with a single-level traumatic unstable burst fracture at the TL junction were operated on by a bisegmental posterior correction/fixation, followed by anterior corpectomy and titanium cage implantation 7–10 days later. The radiological and clinical course is documented over a period of 24 months.  The mean posttraumatic loss of anterior vertebral body height was 58% (45–70%). The posttraumatic mean regional kyphosis was 16° and could be corrected by the posterior approach to a mean lordosis of 2°. Mean secondary loss of the kyphosis correction was 3° over 24 months. No hardware failure occurred, and construct stability was observed in all 20 patients. One surgical complication occurred during the posterior approach, and three transient surgical complications by the anterior approach. 12 of the 14 patients with an initial neurological deficit recovered an average of 1.5 grades on the ASIA scale. At 24 months postoperatively, the mean regional TL back pain on a VAS (0–10) was 1.6, and the mean pain at the anterior approach site was 1.2.  Posterior bisegmental transpedicular correction/fixation and staged anterior corpectomy and titanium cage implantation is a safe and reliable surgical treatment option in unstable TL junction burst fractures. The advantages of this technique are a complete kyphosis correction, immediate stability, maintenance of kyphosis correction, and complete spinal canal decompression in case of a neurological deficit. However, these advantages have to be carefully weighed against the double approach morbidity.
Training and testing of recommender systems on data missing not at random

Users typically rate only a small fraction of all available items. We show that the absence of ratings carries useful information for improving the top-k hit rate concerning all items, a natural accuracy measure for recommendations. As to test recommender systems, we present two performance measures that can be estimated, under mild assumptions, without bias from data even when ratings are missing not at random (MNAR). As to achieve optimal test results, we present appropriate surrogate objective functions for efficient training on MNAR data. Their main property is to account for all ratings - whether observed or missing in the data. Concerning the top-k hit rate on test data, our experiments indicate dramatic improvements over even sophisticated methods that are optimized on observed ratings only.
Inside Intel® Core microarchitecture

This article consists of a collection of slides from the author's conference presentation on Intel's Core product line's microarchitecture, a new foundation for Intel architecture-based mobile, desktop, and server processors that incorporates advanced innovations which optimize performance over a range of market segments. Some of the specific topics discussed include: the special features and system specifications of Intel Core; memory management and prefetching capabilities; system performance and flexibility; multithreading capabilities; and a summary of key features and processing facilities.
A Student Friendly Toolbox for Power System Analysis Using MATLAB

There are various premier software packages available in the market, either for free use or found at a high price, to analyse the century old electrical power system. Universities in the developed countries expend thousands of dollars per year to bring these commercial applications to the desktops of students, teachers and researchers. For teachers and researchers this is regarded as a good long-term investment. As well, for the postgraduate students these packages are very important to validate the model developed during course of study. For simulating different test cases and/or standard systems, which are readily available with these widely used commercial software packages, such enriched software plays an important role. But in case of underdeveloped and developing countries the high amount of money needed to be expended per year to purchase commercial software is a farfetched idea. In addition, undergraduate students who are learning power system for the very first time find these packages incongruous for them since they are not familiar with the detailed input required to run the program. Even if it is a simple load flow program to find the steady-state behaviour of the system, or an elementary symmetrical fault analysis test case these packages require numerous inputs since they mimic a practical power system rather than considering simple test cases. In effect, undergraduate students tend to stay away from these packages. So rather than aiding the study in power system, these create a bad impression on students‘ mind about the very much interesting course.
MRI-PET registration with automated algorithm.

We have previously reported an automated method for within-modality (e.g., PET-to-PET) image alignment. We now describe modifications to this method that allow for cross-modality registration of MRI and PET brain images obtained from a single subject.



This method does not require fiducial markers and the user is not required to identify common structures on the two image sets. To align the images, the algorithm seeks to minimize the standard deviation of the PET pixel values that correspond to each MRI pixel value. The MR images must be edited to exclude nonbrain regions prior to using the algorithm.



The method has been validated quantitatively using data from patients with stereotaxic fiducial markers rigidly fixed in the skull. Maximal three-dimensional errors of < 3 mm and mean three-dimensional errors of < 2 mm were measured. Computation time on a SPARCstation IPX varies from 3 to 9 min to align MR image sets with [18F]fluorodeoxyglucose PET images. The MR alignment with noisy H2(15)O PET images typically requires 20-30 min.
Characteristics of network traffic flow anomalies

One of the primary tasks of network administrators is monitoring routers and switches for anomalous traffic behavior such as outages, configuration changes, flash crowds and abuse. Recognizing and identifying anomalous behavior is often based on ad hoc methods developed from years of experience in managing networks. A variety of commercial and open source tools have been developed to assist in this process, however these require policies and/or or thresholds to be defined by the user in order to trigger alerts. The better the description of the anomalous behavior, the more effective these tools become. In this extended abstract we describe a project focused on precise characterization of anomalous network traffic behavior. The first step in our project is to gather passive measurements of network traffic at the IP flow level. IP flow level data as defined in  is a unidirectional series of IP packets of a given protocol traveling between a source and a destination IP/port pair within a certain period of time. While flow level data is certainly not as precise as passive measurements of packet level data, we demonstrate that it is sufficient for exposing many different types of aberrant network traffic behavior in close to real time. It also has the benefit of generating much smaller data sets than packet level measurements which can become a significant issue in large, heavily used networks. We use the FlowScan  open source software to gather and analyze network flow data. FlowScan takes Netflow [3] feeds from Cisco or other Lightweight Flow Accounting Protocol (LFAP) enabled routers, processes the data and then it in an efficient data structure. FlowScan also has a graphical interface which is currently the principal mean s for anomaly identification by network managers. FlowScan is currently deployed at the border router at the University of Wisconsin Madison as well as over 100 other sites nation wide.
Wide-Area Traffic Monitoring With the SAR/GMTI System PAMIR

This paper presents a wide area traffic monitoring experiment under real conditions, using the scan-MTI mode of the airborne radar sensor PAMIR. This flexible GMTI (Ground Moving Target Indication) mode was designed in order to rapidly monitor wide areas for moving targets. The scan operation enables the detection of targets from different aspect angles with a high revisit rate. The parameters (e.g., radial velocity, signal-to-noise ratio, and positioning accuracy) of the detected vehicles are investigated and compared to the expected theoretical GMTI performance. It will be shown that the scan-MTI mode is particularly adapted to perform an efficient wide-area traffic monitoring.
Powering E-Learning In the New Millennium: An Overview of E-Learning and Enabling Technology

In today’s new economy characterized by industrial change, globalization, increased intensive competition, knowledge sharing and transfer, and information technology revolution, traditional classroom education or training does not always satisfy all the needs of the new world of lifelong learning. Learning is shifting from instructor-centered to learner-centered, and is undertaken anywhere, from classrooms to homes and offices. E-Learning, referring to learning via the Internet, provides people with a flexible and personalized way to learn. It offers learning-on-demand opportunities and reduces learning cost. This paper describes the demands for e-Learning and related research, and presents a variety of enabling technologies that can facilitate the design and implementation of e-Learning systems. Armed with the advanced information and communication technologies, e-Learning is having a far-reaching impact on learning in the new millennium.
Low-resource OCR error detection and correction in French Clinical Texts

In this paper we present a simple yet effective approach to automatic OCR error detection and correction on a corpus of French clinical reports of variable OCR quality within the domain of foetopathology. While traditional OCR error detection and correction systems rely heavily on external information such as domain-specific lexicons, OCR process information or manually corrected training material, these are not always available given the constraints placed on using medical corpora. We therefore propose a novel method that only needs a representative corpus of acceptable OCR quality in order to train models. Our method uses recurrent neural networks (RNNs) to model sequential information on character level for a given medical text corpus. By inserting noise during the training process we can simultaneously learn the underlying (character-level) language model and as well as learning to detect and eliminate random noise from the textual input. The resulting models are robust to the variability of OCR quality but do not require additional, external information such as lexicons. We compare two different ways of injecting noise into the training process and evaluate our models on a manually corrected data set. We find that the best performing system achieves a 73% accuracy.
A Threat Model Driven Approach for Security Testing

In this paper, we propose a novel threat model-driven security testing approach for detecting undesirable threat behavior at runtime. Threats to security policies are modelled with UML (Unified Modeling Language) sequence diagrams. From a design-level threat model we extract a set of threat traces, each of which is an event sequence that should not occur during the system execution. The same threat model is also used to decide what kind of information should be collected at runtime and to guide the code instrumentation. The instrumented code is recompiled and executed using test cases randomly generated. The execution traces are collected and analyzed to verify whether the aforementioned undesirable threat traces are matched. If an execution trace is an instance of a threat trace, security violations are reported and actions should be taken to mitigate the threat in the system. Thus the linkage between models, code implementations, and security testing are extended to form a systematic methodology that can test certain security policies.
Modified Partial Product Generator for Redundant Binary Multiplier with High Modularity and Carry-Free Addition

Because of its high modularity and carry-free addition, a redundant binary (RB) representation can be used when designing high performance multipliers. The conventional RB multiplier needs for an additional RB partial product (RBPP) row, because an error-correcting word (ECW) is created by both the radix-8 and radix-4 Modified Booth encodings (MBE). This becomes subject in an additional RBPP accumulation stage for the MBE multiplier. A new RB modified partial product generator (RBMPPG) was proposed in this paper; it takes off the extra ECW and hence, it rescues one RBPP accumulation stage. Therefore, than a conventional RB MBE multiplier, the proposed RBMPPG produces fewer partial product rows. Simulation results show that the proposed RBMPPG based designs sufficiently make better the area and power consumption when the word length of each operand in the multiplier is at least 32 bits; these decreases over previous NB multiplier designs need in a small delay increase (approximately 5%). The power-delay product can be making smaller by up to 59% using the proposed RB multipliers when estimated with existing RB multipliers.
Control of a switched reluctance generator for variable-speed wind energy applications

This paper presents a novel control system for the operation of a switched reluctance generator (SRG) driven by a variable speed wind turbine. The SRG is controlled to drive a wind energy conversion system (WECS) to the point of maximum aerodynamic efficiency using closed loop control of the power output. In the medium and low speed range, the SRG phase current is regulated using pulsewidth-modulation (PWM) control of the magnetizing voltage. For high speeds the generator is controlled using a single pulse mode. In order to interface the SRG to the grid (or ac load) a voltage-source PWM inverter is used. A 2.5-kW experimental prototype has been constructed. Wind turbine characteristics are emulated using a cage induction machine drive. The performance of the system has been tested over the whole speed range using wind profiles and power impacts. Experimental results are presented confirming the system performance.
Recommender systems inspired by the structure of quantum theory

Physicists use quantum models to describe the behavior of physical systems. Quantum models owe their success to their interpretability, to their relation to probabilistic models (quantization of classical models) and to their high predictive power. Beyond physics, these properties are valuable in general data science. This motivates the use of quantum models to analyze general nonphysical datasets. Here we provide both empirical and theoretical insights into the application of quantum models in data science. In the theoretical part of this paper, we firstly show that quantum models can be exponentially more efficient than probabilistic models because there exist datasets that admit low-dimensional quantum models and only exponentially high-dimensional probabilistic models. Secondly, we explain in what sense quantum models realize a useful relaxation of compressed probabilistic models. Thirdly, we show that sparse datasets admit low-dimensional quantum models and finally, we introduce a method to compute hierarchical orderings of properties of users (e.g., personality traits) and items (e.g., genres of movies). In the empirical part of the paper, we evaluate quantum models in item recommendation and observe that the predictive power of quantum-inspired recommender systems can compete with state-of-the-art recommender systems like SVD++ and PureSVD. Furthermore, we make use of the interpretability of quantum models by computing hierarchical orderings of properties of users and items. This work establishes a connection between data science (item recommendation), information theory (communication complexity), mathematical programming (positive semidefinite factorizations) and physics (quantum models).
Pruning Convolutional Neural Networks for Resource Efficient Inference

We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with finetuning by backpropagation—a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters. We focus on transfer learning, where large pretrained networks are adapted to specialized tasks. The proposed criterion demonstrates superior performance compared to other criteria, e.g. the norm of kernel weights or feature map activation, for pruning large CNNs after adaptation to fine-grained classification tasks (Birds-200 and Flowers-102) relaying only on the first order gradient information. We also show that pruning can lead to more than 10× theoretical reduction in adapted 3D-convolutional filters with a small drop in accuracy in a recurrent gesture classifier. Finally, we show results for the largescale ImageNet dataset to emphasize the flexibility of our approach.
A Survey on Security Issues in Cloud Computing

Cloud Computing holds the potential to eliminate the requirements for setting up of highcost computing infrastructure for the IT-based solutions and services that the industry uses. It promises to provide a flexible IT architecture, accessible through internet for lightweight portable devices. This would allow many-fold increase in the capacity or capabilities of the existing and new software. In a cloud computing environment, the entire data reside over a set of networked resources, enabling the data to be accessed through virtual machines. Since these data centres may lie in any corner of the world beyond the reach and control of users, there are multifarious security and privacy challenges that need to be understood and taken care of. Also, one can never deny the possibility of a server breakdown that has been witnessed, rather quite often in the recent times. There are various issues that need to be dealt with respect to security and privacy in a cloud computing scenario. This extensive survey paper aims to elaborate and analyze the numerous unresolved issues threatening the Cloud computing adoption and diffusion affecting the various stake-holders linked to it.