nils-herrmann/clean_scirepeval huggingface dataset:
Voltage buffer compensation using Flipped Voltage Follower in a two-stage CMOS op-amp

In Miller and current buffer compensation techniques, the compensation capacitor often loads the output node. If a voltage buffer is used in feedback, the compensation capacitor obviates the loading on the output node. In this paper, we introduce an implementation of a voltage buffer compensation using a Flipped Voltage Follower (FVF) for stabilizing a two-stage CMOS op-amp. The op-amps are implemented in a 180-nm CMOS process with a power supply of 1.8V while operating with a quiescent current of 110μA. Results indicate that the proposed voltage buffer compensation using FVF improves the Unity Gain Frequency from 5.5MHz to 12.2MHz compared to Miller compensation. Also, the proposed technique enhances the transient response while lowering the compensation capacitance by 47% and 17.7% compared to Miller and common-drain compensation topologies. Utilization of FVF or its variants as a voltage buffer in a feedback compensation network has wide potential applications in the analog design space.
Vectorization for SIMD architectures with alignment constraints

When vectorizing for SIMD architectures that are commonly employed by today's multimedia extensions, one of the new challenges that arise is the handling of memory alignment. Prior research has focused primarily on vectorizing loops where all memory references are properly aligned. An important aspect of this problem, namely, how to vectorize misaligned memory references, still remains unaddressed.This paper presents a compilation scheme that systematically vectorizes loops in the presence of misaligned memory references. The core of our technique is to automatically reorganize data in registers to satisfy the alignment requirement imposed by the hardware. To reduce the data reorganization overhead, we propose several techniques to minimize the number of data reorganization operations generated. During the code generation, our algorithm also exploits temporal reuse when aligning references that access contiguous memory across loop iterations. Our code generation scheme guarantees to never load the same data associated with a single static access twice. Experimental results indicate near peak speedup factors, e.g., 3.71 for 4 data per vector and 6.06 for 8 data per vector, respectively, for a set of loops where 75% or more of the static memory references are misaligned.
Efficient Publish/Subscribe Through a Self-Organizing Broker Overlay and its Application to SIENA

Recently many scalable and efficient solutions for event dissemination in publish/subscribe (pub/sub) systems have appeared in the literature. This dissemination is usually done over an overlay network of brokers and its cost can be measured as the number of messages sent over the overlay to allow the event to reach all intended subscribers. Efficient solutions to this problem are often obtained through smart dissemination algorithms that avoid flooding events on the overlay. In this paper we propose a complementary approach, that is obtaining efficient event dissemination by reorganizing the overlay network topology. More specifically, this reorganization is done through a self-organizing algorithm executed by brokers whose aim is to directly connect, through overlay links, pairs of brokers matching same events. In this way, on average, the number of brokers involved in an event dissemination decreases, thus, reducing its cost. Even though the paradigm of the self-organizing algorithm is general and then applicable to any overlay-based pub/sub system, its concrete implementation depends on the specific system. As a consequence we studied the effect of the introduction of the self-organizing algorithm in the context of a specific system implementing a tree-based routing strategy, namely SIENA, showing the actual performance benefits through an extensive simulation study. In particular performance results point out the capacity of the algorithm to converge to an overlay topology accommodating efficient event dissemination with respect to a specific scenario. Moreover, the algorithm shows a significant capacity to adapt the overlay network topology to continuously changing scenarios while keeping an efficient behavior with respect to event dissemination.
Cell biology of protein misfolding: The examples of Alzheimer's and Parkinson's diseases

The salutary intersection of fundamental cell biology with the study of disease is well illustrated by the emerging elucidation of neurodegenerative disorders. Novel mechanisms in cell biology have been uncovered through disease-orientated research; for example, the discovery of presenilin as an intramembrane aspartyl protease that processes many diverse proteins within the lipid bilayer. A common theme has arisen in this field: normally-soluble proteins accumulate, misfold and oligomerize, inducing cytotoxic effects that are particularly devastating in the post-mitotic milieu of the neuron.
Efficient Scheduling of Arbitrary TAsk Graphs to Multiprocessors Using a Parallel Genetic Algorithm

Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. This multiprocessor scheduling problem is NP-complete even with simplifying assumptions and becomes more complex under relaxed assumptions such as arbitrary precedence constraints, and arbitrary task execution and communication times. The present literature on this topic is a large repertoire of heuristics that produce good solutions in a reasonable amount of time. These heuristics, however, have restricted applicability in a practical environment because they have a number of fundamental problems including high time complexity, lack of scalability, and no performance guarantee with respect to optimal solutions. Recently, genetic algorithms (GAs) have been widely reckoned as a useful vehicle for obtaining high quality or even optimal solutions for a broad range of combinatorial optimization problems. While a few GAs for scheduling have already been suggested, in this paper we propose a novel GA-based algorithm with an objective to simultaneously meet the goals of high performance, scalability, and fast running time. The proposed parallel genetic scheduling (PGS) algorithm itself is a parallel algorithm which generates high quality solutions in a short time. By encoding the scheduling list as a chromosome, the PGS algorithm can potentially generate an optimal scheduling list which in turn leads to an optimal schedule. The major strength of the PGS algorithm lies in its two efficient genetic operators: the order crossover and mutation. These operators effectively combine the building-blocks of good scheduling lists to construct better lists. The proposed algorithm is evaluated through a robust comparison with two heuristics best known in terms of performance and time complexity. It outperforms both heuristics while taking considerably less running time. When evaluated with random task graphs for which optimal solutions are known, the PGS algorithm generates optimal solutions for more than half of the test cases and close-to-optimal for the other half.
Exploring cell tower data dumps for supervised learning-based point-of-interest prediction

Exploring massive mobile data for location-based services (LBS) becomes one of the key challenges in mobile data mining. In this paper, we propose a framework that uses large-scale cell tower data dumps and extracts points-of-interest (POIs) from a social network web site called Weibo, and provides new LBS based on these two data sets, i.e., predicting the existence of POIs and the number of POIs in a certain area. We use Voronoi diagram to divide a city area into non-overlapping regions, and a k-means clustering algorithm to aggregate neighboring cell towers into region groups. A supervised learning algorithm is adopted to build up a model between the number of connections of cell towers and the POIs in different region groups, where a classification or regression model is used to predict the POI existence or the number of POIs, respectively. We studied 12 state-of-the-art classification and regression algorithms, and the experimental results demonstrate the feasibility and effectiveness of the proposed framework.
Wireless based Smart Parking System using Zigbee

One of main issues of developing big parking space for shopping complexes, office complexes and other types of building that requires large parking space is to notify the visitors of occupied and nonoccupied parking space. Most of the visitors might spending up to 30 to 45 minutes just to find an empty parking space. In most recent technology, some parking lot system offered a system that could automatically count when the car entering the empty car space and blocking an infrared signal thus notify the system to count for it. However, this type of sensors actually has an increase of budgeting in order to install and to be maintained. In this project, we have developed a unique solution by providing cost effective solution by using Zigbee technology in parking lot system technology. Instead of using and maintain cable that need to be installed at the ceiling of the parking lot, we developed a system that use wireless technology of Zigbee and it could notify the visitors of empty and non-empty parking lot. Keywords-parking, zigbee, wireless system;
On the adoption of abductive reasoning for time series interpretation

Time series interpretation aims to provide an explanation of what is observed in terms of its underlying processes. The present work is based on the assumption that common classification-based approaches to time series interpretation suffer from a set of inherent weaknesses whose ultimate cause lies in the monotonic nature of the deductive reasoning paradigm. In this document we propose a new approach to this problem based on the initial hypothesis that abductive reasoning properly accounts for the human ability to identify and characterize patterns appearing in a time series. The result of the interpretation is a set of conjectures in the form of observations, organized into an abstraction hierarchy, and explaining what has been observed. A knowledge-based framework and a set of algorithms for the interpretation task are provided, implementing a hypothesize-and-test cycle guided by an attentional mechanism. As a promising application domain, the interpretation of the electrocardiogram allows us to highlight the strengths of the present approach in comparison with traditional classification-based approaches.
Intrusion Detection Model Based On Particle Swarm Optimization and Support Vector Machine

Advance in information and communication technologies, force us to keep most of the information electronically, consequently, the security of information has become a fundamental issue. The traditional intrusion detection systems look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. However, normal operation often produces traffic that matches likely "attack signature", resulting in false alarms. One main drawback is the inability of detecting new attacks which do not have known signatures. In this paper particle swarm optimization (PSO) is used to implement a feature selection, and support vector machine (SVMs) with the one-versus-rest method serve as a fitness function of PSO for classification problems from the literature. Experimental result shows that our method allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. Our method simplifies features effectively and obtains a higher classification accuracy compared to other methods
Hop-count filtering: an effective defense against spoofed DDoS traffic

IP spoofing has been exploited by Distributed Denial of Service (DDoS) attacks to (1) conceal flooding sources and localities in flooding traffic, and (2) coax legitimate hosts into becoming reflectors, redirecting and amplifying flooding traffic. Thus, the ability to filter spoofed IP packets near victims is essential to their own protection as well as to their avoidance of becoming involuntary DoS reflectors. Although an attacker can forge any field in the IP header, he or she cannot falsify the number of hops an IP packet takes to reach its destination. This hop-count information can be inferred from the Time-to-Live (TTL) value in the IP header. Using a mapping between IP addresses and their hop-counts to an Internet server, the server can distinguish spoofed IP packets from legitimate ones. Base on this observation, we present a novel filtering technique that is immediately deployable to weed out spoofed IP packets. Through analysis using network measurement data, we show that Hop-Count Filtering (HCF) can identify close to 90% of spoofed IP packets, and then discard them with little collateral damage. We implement and evaluate HCF in the Linux kernel, demonstrating its benefits using experimental measurements.
Putting Egocentric and Allocentric into Perspective

In the last decade many studies examined egocentric and allocentric spatial relations. For various tasks, navigators profit from both kinds of relations. However, their interrelation seems to be underspecified. We present four elementary representations of allocentric and egocentric relations (sensorimotor contingencies, egocentric coordinate systems, allocentric coordinate systems, and perspective-free representations) and discuss them with respect to their encoding and retrieval. Elementary representations are problematic for capturing large spaces and situations which encompass both allocentric and egocentric relations at the same time. Complex spatial representations provide a solution to this problem. They combine elementary coordinate representations either by pair-wise connections or by hierarchical embedding. We discuss complex spatial representations with respect to computational requirements and their plausibility regarding behavioral and neural findings. This work is meant to clarify concepts of egocentric and allocentric, to show their limitations, benefits and empirical plausibility and to point out new directions for future research.
Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions

Current-voltage and power-voltage characteristics of large photovoltaic (PV) arrays under partially shaded conditions are characterized by multiple steps and peaks. This makes the tracking of the actual maximum power point (MPP) [global peak (GP)] a difficult task. In addition, most of the existing schemes are unable to extract maximum power from the PV array under these conditions. This paper proposes a novel algorithm to track the global power peak under partially shaded conditions. The formulation of the algorithm is based on several critical observations made out of an extensive study of the PV characteristics and the behavior of the global and local peaks under partially shaded conditions. The proposed algorithm works in conjunction with a DC-DC converter to track the GP. In order to accelerate the tracking speed, a feedforward control scheme for operating the DC-DC converter is also proposed, which uses the reference voltage information from the tracking algorithm to shift the operation toward the MPP. The tracking time with this controller is about one-tenth as compared to a conventional controller. All the observations and conclusions, including simulation and experimental results, are presented.
Adding dynamically-typed language support to a statically-typed language compiler: performance evaluation, analysis, and tradeoffs

Applications written in dynamically typed scripting languages are increasingly popular for Web software development. Even on the server side, programmers are using dynamically typed scripting languages such as Ruby and Python to build complex applications quickly. As the number and complexity of dynamically typed scripting language applications grows, optimizing their performance is becoming important. Some of the best performing compilers and optimizers for dynamically typed scripting languages are developed entirely from scratch and target a specific language. This approach is not scalable, given the variety of dynamically typed scripting languages, and the effort involved in developing and maintaining separate infrastructures for each. In this paper, we evaluate the feasibility of adapting and extending an existing production-quality method-based Just-In-Time (JIT) compiler for a language with dynamic types. Our goal is to identify the challenges and shortcomings with the current infrastructure, and to propose and evaluate runtime techniques and optimizations that can be incorporated into a common optimization infrastructure for static and dynamic languages. We discuss three extensions to the compiler to support dynamically typed languages: (1) simplification of control flow graphs, (2) mapping of memory locations to stack-allocated variables, and (3) reduction of runtime overhead using language semantics. We also propose four new optimizations for Python in (2) and (3). These extensions are effective in reduction of compiler working memory and improvement of runtime performance. We present a detailed performance evaluation of our approach for Python, finding an overall improvement of 1.69x on average (up to 2.74x) over our JIT compiler without any optimization for dynamically typed languages and Python.
Metal-enhanced PicoGreen fluorescence: application for double-stranded DNA quantification.

PicoGreen (PG) is a fluorescent probe for both double-stranded DNA (dsDNA) detection and quantification based on its ability to form a luminescent complex with dsDNA as compared with the free dye in solution. To expand the sensitivity of PG detection, we have studied the spectral properties of PG, both free and in complex with DNA in solution, when the fluorophore is in proximity to silver nanoparticles. We show that for a broad range of PG concentrations (20 pM-3.5 microM), it does not form dimers/oligomers and it exists in a monomeric state. On binding to DNA in the absence of silver, PG fluorescence increases approximately 1100-fold. Deposition of PG/DNA complex onto silver island films (SiFs) increases fluorescence approximately 7-fold due to the metal-enhanced fluorescence (MEF) effect, yielding fluorescence enhancement of 7700-fold as compared with the free dye on glass. In contrast to PG in complex with DNA, the free dye on SiFs demonstrates a decrease in brightness approximately 5-fold. Therefore, the total enhancement of PG on binding to DNA on silver reaches a value of approximately 38,000 as compared with free PG on SiFs. Consequently, the metal-enhanced detection of PG fluorescence is likely to find important utility for amplified dsDNA quantification.
Scaling Neural Machine Translation

Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine. This paper shows that reduced precision and large batch training can speedup training by nearly 5x on a single 8GPU machine with careful tuning and implementation.1 On WMT’14 English-German translation, we match the accuracy of Vaswani et al. (2017) in under 5 hours when training on 8 GPUs and we obtain a new state of the art of 29.3 BLEU after training for 85 minutes on 128 GPUs. We further improve these results to 29.8 BLEU by training on the much larger Paracrawl dataset. On the WMT’14 EnglishFrench task, we obtain a state-of-the-art BLEU of 43.2 in 8.5 hours on 128 GPUs.
Generating Synthetic Missing Data: A Review by Missing Mechanism

The performance evaluation of imputation algorithms often involves the generation of missing values. Missing values can be inserted in only one feature (univariate configuration) or in several features (multivariate configuration) at different percentages (missing rates) and according to distinct missing mechanisms, namely, missing completely at random, missing at random, and missing not at random. Since the missing data generation process defines the basis for the imputation experiments (configuration, missing rate, and missing mechanism), it is essential that it is appropriately applied; otherwise, conclusions derived from ill-defined setups may be invalid. The goal of this paper is to review the different approaches to synthetic missing data generation found in the literature and discuss their practical details, elaborating on their strengths and weaknesses. Our analysis revealed that creating missing at random and missing not at random scenarios in datasets comprising qualitative features is the most challenging issue in the related work and, therefore, should be the focus of future work in the field.
Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks

Liver tumors segmentation from computed tomography (CT) images is an essential task for diagnosis and treatments of liver cancer. However, it is difficult owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions. In this paper, an automatic method based on convolutional neural networks (CNNs) is presented to segment lesions from CT images. The CNNs is one of deep learning models with some convolutional filters which can learn hierarchical features from data. We compared the CNNs model to popular machine learning algorithms: AdaBoost, Random Forests (RF), and support vector machine (SVM). These classifiers were trained by handcrafted features containing mean, variance, and contextual features. Experimental evaluation was performed on 30 portal phase enhanced CT images using leave-one-out cross validation. The average Dice Similarity Coefficient (DSC), precision, and recall achieved of 80.06% ± 1.63%, 82.67% ± 1.43%, and 84.34% ± 1.61%, respectively. The results show that the CNNs method has better performance than other methods and is promising in liver tumor segmentation.
Face recognition in unconstrained videos with matched background similarity

Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions. (a) We present a comprehensive database of labeled videos of faces in challenging, uncontrolled conditions (i.e., ‘in the wild’), the ‘YouTube Faces’ database, along with benchmark, pair-matching tests1. (b) We employ our benchmark to survey and compare the performance of a large variety of existing video face recognition techniques. Finally, (c) we describe a novel set-to-set similarity measure, the Matched Background Similarity (MBGS). This similarity is shown to considerably improve performance on the benchmark tests.
Control and kinematic performance analysis of an Actuated Finger Exoskeleton for hand rehabilitation following stroke

Finger impairment following stroke results in significant deficit in hand manipulation and the performance of everyday tasks. Recent advances in rehabilitation robotics have shown improvement in efficacy of rehabilitation. Current devices, however, lack the capacity to accurately interface with the human finger at levels of velocity and torque comparable to the performance of everyday hand manipulation tasks. To fill this need, we have developed the Actuated Finger Exoskeleton (AFX), a three degree-of-freedom robotic exoskeleton for the index finger. This paper outlines the implementation and initial kinematic analysis of the AFX.