nils-herrmann/clean_scirepeval huggingface dataset:
iFPNA: A Flexible and Efficient Deep Neural Network Accelerator with a Programmable Data Flow Engine in 28nm CMOS

The paper presents iFPNA, instruction-and-fabric programmable neuron array: a general-purpose deep learning accelerator that achieves both energy efficiency and flexibility. The iFPNA has a programmable data flow engine with a custom instruction set, and 16 configurable neuron slices for parallel neuron operations of different bit-widths. Convolutional neural networks of different kernel sizes are implemented by choosing data flows among input stationary, row stationary and tunnel stationary, etc. Recurrent neural networks with element-wise operations are implemented by a universal activation engine. Measurement results show that the iFPNA achieves a peak energy efficiency of 1.72 TOPS/W running at 30 MHz clock rate and 0.63 V voltage supply. The measured latency on AlexNet is 60.8 ms and on LSTM-512 is 40 ms at 125 MHz clock rate.
Deep learning with support vector data description

One of the most critical problems for machine learning methods is overfitting. The overfitting problem is a phenomenon in which the accuracy of the model on unseen data is poor whereas the training accuracy is nearly perfect. This problem is particularly severe in complex models that have a large set of parameters. In this paper, we propose a deep learning neural network model that adopts the support vector data description (SVDD). The SVDD is a variant of the support vector machine, which has high generalization performance by acquiring a maximal margin in one-class classification problems. The proposed model strives to obtain the representational power of deep learning. Generalization performance is maintained using the SVDD. The experimental results showed that the proposed model can learn multiclass data without severe overfitting problems.
Agent-Based Distributed Learning Applied to Fraud Detection

Inductive learning and classification techniques have been applied in many problems in diverse areas. In this paper we describe an AI-based approach that combines inductive learning algorithms and meta-learning methods as a means to compute accurate classification models for detecting electronic fraud. Inductive learning algorithms are used to compute detectors of anomalous or errant behavior over inherently distributed data sets and meta-learning methods integrate their collective knowledge into higher level classification models or meta-classifiers. By supporting the exchange of models or classifier agents among data sites, our approach facilitates the cooperation between financial organizations and provides unified and cross-institution protection mechanisms against fraudulent transactions. Through experiments performed on actual credit card transaction data supplied by two different financial institutions, we evaluate this approach and we demonstrate its utility.
Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care

Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users' forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users' forum posts. We then introduced a novel network-based approach for modeling users' forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.
Total Recall: System Support for Automated Availability Management

Highly available data storage in large-scale distributed systems in which * Hosts are transiently inaccessible * Individual host failures are common Current peer-to-peer systems are prime examples * Highly dynamic, challenging environment * hosts join and leave frequently in short-term * Hosts leave permanently over long-term * Workload varies in terms of popularity, access patterns, file size These systems require automated availability management.
BotGAD: detecting botnets by capturing group activities in network traffic

Recent malicious attempts are intended to obtain financial benefits using a botnet which has become one of the major Internet security problems. Botnets can cause severe Internet threats such as DDoS attacks, identity theft, spamming, click fraud. In this paper, we define a group activity as an inherent property of the botnet. Based on the group activity model and metric, we develop a botnet detection mechanism, called BotGAD (Botnet Group Activity Detector). BotGAD enables to detect unknown botnets from large scale networks in real-time. Botnets frequently use DNS to rally infected hosts, launch attacks and update their codes. We implemented BotGAD using DNS traffic and showed the effectiveness by experiments on real-life network traces. BotGAD captured 20 unknown and 10 known botnets from two day campus network traces.
On lightweight mobile phone application certification

Users have begun downloading an increasingly large number of mobile phone applications in response to advancements in handsets and wireless networks. The increased number of applications results in a greater chance of installing Trojans and similar malware. In this paper, we propose the Kirin security service for Android, which performs lightweight certification of applications to mitigate malware at install time. Kirin certification uses security rules, which are templates designed to conservatively match undesirable properties in security configuration bundled with applications. We use a variant of security requirements engineering techniques to perform an in-depth security analysis of Android to produce a set of rules that match malware characteristics. In a sample of 311 of the most popular applications downloaded from the official Android Market, Kirin and our rules found 5 applications that implement dangerous functionality and therefore should be installed with extreme caution. Upon close inspection, another five applications asserted dangerous rights, but were within the scope of reasonable functional needs. These results indicate that security configuration bundled with Android applications provides practical means of detecting malware.
Real-time visual tracking using compressive sensing

The ℓ1 tracker obtains robustness by seeking a sparse representation of the tracking object via ℓ1 norm minimization. However, the high computational complexity involved in the ℓ1 tracker may hamper its applications in real-time processing scenarios. Here we propose Real-time Com-pressive Sensing Tracking (RTCST) by exploiting the signal recovery power of Compressive Sensing (CS). Dimensionality reduction and a customized Orthogonal Matching Pursuit (OMP) algorithm are adopted to accelerate the CS tracking. As a result, our algorithm achieves a realtime speed that is up to 5,000 times faster than that of the ℓ1 tracker. Meanwhile, RTCST still produces competitive (sometimes even superior) tracking accuracy compared to the ℓ1 tracker. Furthermore, for a stationary camera, a refined tracker is designed by integrating a CS-based background model (CSBM) into tracking. This CSBM-equipped tracker, termed RTCST-B, outperforms most state-of-the-art trackers in terms of both accuracy and robustness. Finally, our experimental results on various video sequences, which are verified by a new metric — Tracking Success Probability (TSP), demonstrate the excellence of the proposed algorithms.
Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems

Automatically solving algebra word problems has raised considerable interest recently. Existing state-of-the-art approaches mainly rely on learning from human annotated equations. In this paper, we demonstrate that it is possible to efficiently mine algebra problems and their numerical solutions with little to no manual effort. To leverage the mined dataset, we propose a novel structured-output learning algorithm that aims to learn from both explicit (e.g., equations) and implicit (e.g., solutions) supervision signals jointly. Enabled by this new algorithm, our model gains 4.6% absolute improvement in accuracy on the ALG514 benchmark compared to the one without using implicit supervision. The final model also outperforms the current state-of-the-art approach by 3%.
Efficient Support Vector Classifiers for Named Entity Recognition

NamedEntity (NE) recognitionis a task in which proper nouns and numerical information are extractedfrom documentsandareclassifiedinto categoriessuchas person,organization,and date. It is a key technologyof InformationExtractionand Open-DomainQuestionAnswering.First,weshow thatanNE recognizerbasedonSupportVectorMachines(SVMs)givesbetterscoresthanconventional systems.However, off-the-shelfSVM classifiersare too inefficient for this task.Therefore,we presenta methodthat makes the systemsubstantiallyfaster . This approachcan also be applied to other similar taskssuchaschunkingandpart-of-speechtagging. We alsopresentanSVM-basedfeatureselection methodandanefficient trainingmethod.
Ensemble GradientBoost for increasing classification accuracy of credit scoring

The method for Credit Scoring has been developed to select a better model in predicting credit risk. Data mining methods are superior to the statistical methods of dealing with Credit Scoring issues, especially for nonlinear relationships between variables. By flashing the ensemble method with statistical methods, proven to achieve a higher level of accuracy than the method of data mining. This paper proposes a credit scoring algorithm using Ensemble Logistic Regression by boosting the method using the GradientBoost algorithm. Two datasets for implementing the algorithm, i.e., German and Australian Dataset. The results showed that GradientBoost Ensemble managed to improve the performance of a single classification Logistic Regression and achieve the highest level of accuracy in both datasets. The proposed method produces accuracy of 81% for German datasets and 88.4% for Australian datasets.
BeAMS: A Beacon-Based Angle Measurement Sensor for Mobile Robot Positioning

Positioning is a fundamental issue in mobile robot applications, and it can be achieved in multiple ways. Among these methods, triangulation based on angle measurements is widely used, robust, accurate, and flexible. This paper presents BeAMS, which is a new active beacon-based angle measurement system used for mobile robot positioning. BeAMS introduces several major innovations. One innovation is the use of a unique unsynchronized channel with on-off keying modulated infrared signals to measure angles and to identify the beacons. We also introduce a new mechanism to measure angles: Our system detects a beacon when it enters and leaves an angular window. We show that the estimator resulting from the center of this angular window provides an unbiased estimate of the beacon angle. A theoretical framework for a thorough performance analysis of BeAMS is provided. We establish the upper bound of the variance and validate this bound through experiments and simulations; the overall error measure of BeAMS is lower than 0.24° for an acquisition rate of 10 Hz. In conclusion, BeAMS is a low-power, flexible, and robust solution for angle measurement and a reliable component for robot positioning.
On a Methodology for Robust Segmentation of Nonideal Iris Images

Iris biometric is one of the most reliable biometrics with respect to performance. However, this reliability is a function of the ideality of the data. One of the most important steps in processing nonideal data is reliable and precise segmentation of the iris pattern from remaining background. In this paper, a segmentation methodology that aims at compensating various nonidealities contained in iris images during segmentation is proposed. The virtue of this methodology lies in its capability to reliably segment nonideal imagery that is simultaneously affected with such factors as specular reflection, blur, lighting variation, occlusion, and off-angle images. We demonstrate the robustness of our segmentation methodology by evaluating ideal and nonideal data sets, namely, the Chinese Academy of Sciences iris data version 3 interval subdirectory, the iris challenge evaluation data, the West Virginia University (WVU) data, and the WVU off-angle data. Furthermore, we compare our performance to that of our implementation of Camus and Wildes's algorithm and Masek's algorithm. We demonstrate considerable improvement in segmentation performance over the formerly mentioned algorithms.
Classification and Algorithmic Management of Constricted Ears: A 22-Year Experience.

The classification and corrective methods for the constricted ears continue to be controversial. To clarify them, the authors have reviewed and analyzed cases operated on at their Center from January of 1992 to January of 2014.



A total of 164 ear cases (involving 139 patients), showing features of lidded helix, compression of scapha and fossa triangularis, and/or cup-like protrusions, were included in their study (microtias with constricted ear features were excluded) and reviewed through medical records, photographs, analysis of surgical methods, and postoperative outcomes.



The deformed ears were classified into four graded types by means of the antihelical tubing test and the scapha-helix push test as follows: type I (n = 21), type II (n = 63), type III (n = 38), and type IV (n = 42). Throughout this series, the authors have continued the improvement and performance of consistent operative techniques. Antihelical tubing, concha cartilage grafting, tumbling concha-cartilage flap, antihelical wrapping, and helical expansion were the preferred techniques. A total of 144 ear cases (88 percent) were followed for 1 month to 14 years (average, 13.2 months). The average score of aesthetic outcomes, rated on a four-point Likert scale (i.e., 1 = poor, 2 = fair, 3 = good, and 4 = excellent), was 3.3. Corrective methods and aesthetic outcomes for patients with each graded type of deformity were described.



Constricted ears were effectively corrected by a graded surgical approach. All corrections were performed in one stage, without removal of deformed auricular cartilage.



Therapeutic, IV.
Centroid-based summarization of multiple documents: sentence extraction utility-based evaluation, and user studies

We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also describe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization.
Active compensation of current unbalance in paralleled silicon carbide MOSFETs

Current unbalance in paralleled power electronic devices can affect the performance and reliability of them. In this paper, the factors causing current unbalance in parallel connected silicon carbide (SiC) MOSFETs are analyzed, and the threshold mismatch is identified as the major factor. Then the distribution and temperature dependence of SiC MOSFETs' threshold voltage are studied experimentally. Based on the analysis and study, an active current balancing (ACB) scheme is presented. The scheme directly measures the unbalance current, and eliminates it in closed loop by varying the gate delay to each device. The turn-on and turn-off current unbalance are sensed and independently compensated to yield an optimal performance at both switching transitions. The proposed scheme provides robust compensation of current unbalance in fast-switching wide-band-gap devices while keeping circuit complexity and cost low. The performance of the proposed ACB scheme is verified by both simulation and experimental results.
EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks

In recent years, there has been a growing interest in wireless sensor networks. One of the major issues in wireless sensor network is developing an energy-efficient clustering protocol. Hierarchical clustering algorithms are very important in increasing the network’s life time. Each clustering algorithm is composed of two phases, the setup phase and steady state phase. The hot point in these algorithms is the cluster head selection. In this paper, we study the impact of heterogeneity of nodes in terms of their energy in wireless sensor networks that are hierarchically clustered. We assume that a percentage of the population of sensor nodes is equipped with the additional energy resources. We also assume that the sensor nodes are randomly distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. Homogeneous clustering protocols assume that all the sensor nodes are equipped with the same amount of energy and as a result, they cannot take the advantage of the presence of node heterogeneity. Adapting this approach, we introduce an energy efficient heterogeneous clustered scheme for wireless sensor networks based on weighted election probabilities of each node to become a cluster head according to the residual energy in each node. Finally, the simulation results demonstrate that our proposed heterogeneous clustering approach is more effective in prolonging the network lifetime compared with LEACH.
A SURE Approach for Digital Signal/Image Deconvolution Problems

In this paper, we are interested in the classical problem of restoring data degraded by a convolution and the addition of a white Gaussian noise. The originality of the proposed approach is twofold. First, we formulate the restoration problem as a nonlinear estimation problem leading to the minimization of a criterion derived from Stein's unbiased quadratic risk estimate. Secondly, the deconvolution procedure is performed using any analysis and synthesis frames that can be overcomplete or not. New theoretical results concerning the calculation of the variance of the Stein's risk estimate are also provided in this work. Simulations carried out on natural images show the good performance of our method with respect to conventional wavelet-based restoration methods.
A Provably Secure True Random Number Generator with Built-In Tolerance to Active Attacks

This paper is a contribution to the theory of true random number generators based on sampling phase jitter in oscillator rings. After discussing several misconceptions and apparently insurmountable obstacles, we propose a general model which, under mild assumptions, will generate provably random bits with some tolerance to adversarial manipulation and running in the megabit-per-second range. A key idea throughout the paper is the fill rate, which measures the fraction of the time domain in which the analog output signal is arguably random. Our study shows that an exponential increase in the number of oscillators is required to obtain a constant factor improvement in the fill rate. Yet, we overcome this problem by introducing a postprocessing step which consists of an application of an appropriate resilient function. These allow the designer to extract random samples only from a signal with only moderate fill rate and, therefore, many fewer oscillators than in other designs. Last, we develop fault-attack models and we employ the properties of resilient functions to withstand such attacks. All of our analysis is based on rigorous methods, enabling us to develop a framework in which we accurately quantify the performance and the degree of resilience of the design
Transfer Learning for Text Mining

Over the years, transfer learning has received much attention in machine learning research and practice. Researchers have found that a major bottleneck associated with machine learning and text mining is the lack of high-quality annotated examples to help train a model. In response, transfer learning offers an attractive solution for this problem. Various transfer learning methods are designed to extract the useful knowledge from different but related auxiliary domains. In its connection to text mining, transfer learning has found novel and useful applications. In this chapter, we will review some most recent developments in transfer learning for text mining, explain related algorithms in detail, and project future developments of this field. We focus on two important topics: cross-domain text document classification and heterogeneous transfer learning that uses labeled text documents to help classify images.
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping

We introduce a model for bidirectional retrieval of images and sentences through a deep, multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree relations) into a common space. We then introduce a structured max-margin objective that allows our model to explicitly associate these fragments across modalities. Extensive experimental evaluation shows that reasoning on both the global level of images and sentences and the finer level of their respective fragments improves performance on image-sentence retrieval tasks. Additionally, our model provides interpretable predictions for the image-sentence retrieval task since the inferred inter-modal alignment of fragments is explicit.
W-band scalable phased arrays for imaging and communications

This article discusses the benefits and challenges associated with the design of multi-function scalable phased arrays at millimeter wave frequencies. First, applications for phased arrays with tens to hundreds of elements are discussed. Existing solutions for scaling silicon-based phased arrays from microwave to terahertz frequencies are reviewed. The challenges and tradeoffs associated with multiple integration options for W-band phased arrays are analyzed, with special consideration given to packaging and antenna performance. Finally, a solution based on SiGe ICs and organic packages for a 64-element dual-polarized 94 GHz phased array is described, along with associated measurement results.
Overview of Full-Dimension MIMO in LTE-Advanced Pro

Multiple-input multiple-output (MIMO) systems with a large number of base station antennas, often called massive MIMO, have received much attention in academia and industry as a means to improve the spectral efficiency, energy efficiency, and processing complexity of next generation cellular systems. The mobile communication industry has initiated a feasibility study of massive MIMO systems to meet the increasing demand of future wireless systems. Field trials of the proof-of-concept systems have demonstrated the potential gain of the Full-Dimension MIMO (FD-MIMO), an official name for the MIMO enhancement in the 3rd generation partnership project (3GPP). 3GPP initiated standardization activity for the seamless integration of this technology into current 4G LTE systems. In this article, we provide an overview of FD-MIMO systems, with emphasis on the discussion and debate conducted on the standardization process of Release 13. We present key features for FD-MIMO systems, a summary of the major issues for the standardization and practical system design, and performance evaluations for typical FD-MIMO scenarios.