nils-herrmann/clean_scirepeval huggingface dataset:
Polyphenol composition and in vitro antioxidant activity of amaranth , quinoa buckwheat and wheat as affected by sprouting and baking

This study examined the polyphenol composition and antioxidant properties of methanolic extracts from amaranth, quinoa, buckwheat and wheat, and evaluated how these properties were affected following two types of processing: sprouting and baking. The total phenol content amongst the seed extracts were significantly higher in buckwheat (323.4 mgGAE/100 g) and decreased in the following order: buckwheat > quinoa > wheat > amaranth. Antioxidant capacity, measured by the radical 2,2-diphenyl-1-picylhydrazyl scavenging capacity and the ferric ion reducing antioxidant power assays was also highest for buckwheat seed extract (p < 0.01). Total phenol content and antioxidant activity was generally found to increase with sprouting, and a decrease in levels was observed following breadmaking. Analysis by liquid chromatography coupled with diode array detector revealed the presence of phenolic acids, catechins, flavanol, flavone and flavonol glycosides. Overall, quinoa and buckwheat seeds and sprouts represent potential rich sources of polyphenol compounds for enhancing the nutritive properties of foods such as gluten-free breads.
Asymmetry Mitigation through Line Swapping in IEEE 802 . 3 Ethernet

One of the basic services in a distributed network is clock synchronization as it enables a palette of services, such as synchronized measurements, coordinated actions, or time-based access to a shared communication medium. The IEEE 1588 standard defines the Precision Time Protocol (PTP) and provides a framework to synchronize multiple slave clocks to a master by means of synchronization event messages. While PTP is capable for synchronization accuracies below 1 ns, practical synchronization approaches are hitting a new barrier due to asymmetric line delays. Although compensation fields for the asymmetry are present in PTP version 2008, no specific measures to estimate the asymmetry are defined in the standard. In this paper we present a solution to estimate the line asymmetry in 100Base-TX networks based on line swapping. This approach seems appealing for existing installations as most Ethernet PHYs have the line swapping feature built in, and it only delays the network startup, but does not alter the operation of the network. We show by an FPGA-based prototype system that our approach is able to improve the synchronization offset from more than 10 ns down to below 200 ps.
Replication issues in syntax-based aspect extraction for opinion mining

Reproducing experiments is an important instrument to validate previous work and build upon existing approaches. It has been tackled numerous times in different areas of science. In this paper, we introduce an empirical replicability study of three well-known algorithms for syntactic centric aspect-based opinion mining. We show that reproducing results continues to be a difficult endeavor, mainly due to the lack of details regarding preprocessing and parameter setting, as well as due to the absence of available implementations that clarify these details. We consider these are important threats to validity of the research on the field, specifically when compared to other problems in NLP where public datasets and code availability are critical validity components. We conclude by encouraging code-based research, which we think has a key role in helping researchers to understand the meaning of the state-of-the-art better and to generate continuous advances.
Rule-based System for Automatic Grammar Correction Using Syntactic N-grams for English Language Learning (L2)

We describe the system developed for the CoNLL-2013 shared task—automatic English L2 grammar error correction. The system is based on the rule-based approach. It uses very few additional resources: a morphological analyzer and a list of 250 common uncountable nouns, along with the training data provided by the organizers. The system uses the syntactic information available in the training data: this information is represented as syntactic n-grams, i.e. n-grams extracted by following the paths in dependency trees. The system is simple and was developed in a short period of time (1 month). Since it does not employ any additional resources or any sophisticated machine learning methods, it does not achieve high scores (specifically, it has low recall) but could be considered as a baseline system for the task. On the other hand, it shows what can be obtained using a simple rule-based approach and presents a few situations where the rule-based approach can perform better than ML ap-
A work-efficient parallel breadth-first search algorithm (or how to cope with the nondeterminism of reducers)

We have developed a multithreaded implementation of breadth-first search (BFS) of a sparse graph using the Cilk++ extensions to C++. Our PBFS program on a single processor runs as quickly as a standar. C++ breadth-first search implementation. PBFS achieves high work-efficiency by using a novel implementation of a multiset data structure, called a "bag," in place of the FIFO queue usually employed in serial breadth-first search algorithms. For a variety of benchmark input graphs whose diameters are significantly smaller than the number of vertices -- a condition met by many real-world graphs -- PBFS demonstrates good speedup with the number of processing cores.
 Since PBFS employs a nonconstant-time "reducer" -- "hyperobject" feature of Cilk++ -- the work inherent in a PBFS execution depends nondeterministically on how the underlying work-stealing scheduler load-balances the computation. We provide a general method for analyzing nondeterministic programs that use reducers. PBFS also is nondeterministic in that it contains benign races which affect its performance but not its correctness. Fixing these races with mutual-exclusion locks slows down PBFS empirically, but it makes the algorithm amenable to analysis. In particular, we show that for a graph G=(V,E) with diameter D and bounded out-degree, this data-race-free version of PBFS algorithm runs it time O((V+E)/P + Dlg3(V/D)) on P processors, which means that it attains near-perfect linear speedup if P << (V+E)/Dlg3(V/D).
An automated fine-grain pipelining using domino style asynchronous library

Register transfer level (RTL) synthesis model which simplified the design of clocked circuits allowed design automation boost and VLSI progress for more than a decade. Shrinking technology and progressive increase in clock frequency are bringing clock to its physical limits. Asynchronous circuits, which are believed to replace globally clocked designs in the future, remain out of the competition due to the design complexity of some automated approaches and poor results of other techniques. Successful asynchronous designs are known but they are primarily custom. This work sketches an automated approach for automatically re-implementing conventional RTL designs as fine-grain pipelined asynchronous quasi-delay-insensitive (QDI) circuits and presents a framework for automated synthesis of such implementations from high-level behavior specifications. Experimental results are presented using our new dynamic asynchronous library.
Successful entrepreneurs minimize risk

Many scholars see entrepreneurs as action-oriented individuals who use rules of thumb and other mental heuristics to make decisions, but who do little systematic planning and analysis. We argue that what distinguishes successful from unsuccessful entrepreneurs is precisely that the former vary their decisionmaking styles, sometimes relying on heuristics and sometimes relying on systematic analysis. In our proposed framework, successful entrepreneurs assess their level of expertise and the level of ambiguity in a particular decision context and then tailor their decision-making process to reduce risk.
Netdispatcher: a Tcp Connection Router Netdispatcher: a Tcp Connection Router

LIMITED DISTRIBUTION NOTICE This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for e a rly dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and speciic requests. After outside publication, requests should be lled only by reprints or legally obtained copies of the article e.g., payment of royalties. Abstract NetDispatcher is a software router of TCP connections that supports load sharing across multiple TCP servers. It consists of the Executor, an operating system kernel extension that supports fast IP packet forwarding, and a user level Manager process that controls it. The Manager implements a novel dynamic load-sharing algorithm for allocation of TCP connections among servers according to their real-time load and responsiveness. This algorithm produces weights that are used by the Executor to quickly select a server for each new connection request. This allocation method was shown to be highly eecient in real tests, for large Internet sites serving millions of TCP connections per day. The Executor forwards client TCP packets to the servers without performing any TCPPIP header translations. Outgoing server-to-client packets are not handled by NetDispatcher and can follow a separate network route to the clients. Depending on the workload traac, the performance beneet of this half-connection method can be signiicant. Prototypes of NetDispatcher were used to scale up several large and high-load Internet sites.
Detection of Damage and Failure Events of Critical Public Infrastructure using Social Sensor Big Data

Public infrastructure systems provide many of the services that are critical to the health, functioning, and security of society. Many of these infrastructures, however, lack continuous physical sensor monitoring to be able to detect failure events or damage that has occurred to these systems. We propose the use of social sensor big data to detect these events. We focus on two main infrastructure systems, transportation and energy, and use data from Twitter streams to detect damage to bridges, highways, gas lines, and power infrastructure. Through a three-step filtering approach and assignment to geographical cells, we are able to filter out noise in this data to produce relevant geolocated tweets identifying failure events. Applying the strategy to real-world data, we demonstrate the ability of our approach to utilize social sensor big data to detect damage and failure events in these critical public infrastructures.
Physiological time-series analysis using approximate entropy and sample entropy.

Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, however, well suited to analysis of the short and noisy data sets encountered in cardiovascular and other biological studies. Pincus introduced approximate entropy (ApEn), a set of measures of system complexity closely related to entropy, which is easily applied to clinical cardiovascular and other time series. ApEn statistics, however, lead to inconsistent results. We have developed a new and related complexity measure, sample entropy (SampEn), and have compared ApEn and SampEn by using them to analyze sets of random numbers with known probabilistic character. We have also evaluated cross-ApEn and cross-SampEn, which use cardiovascular data sets to measure the similarity of two distinct time series. SampEn agreed with theory much more closely than ApEn over a broad range of conditions. The improved accuracy of SampEn statistics should make them useful in the study of experimental clinical cardiovascular and other biological time series.
Integration of SNPs-FMRI-methylation data with sparse multi-CCA for schizophrenia study

Schizophrenia (SZ) is a complex mental disorder associated with genetic variations, brain development and activities, and environmental factors. There is an increasing interest in combining genetic, epigenetic and neuroimaging datasets to explore different level of biomarkers for the correlation and interaction between these diverse factors. Sparse Multi-Canonical Correlation Analysis (sMCCA) is a powerful tool that can analyze the correlation of three or more datasets. In this paper, we propose the sMCCA model for imaging genomics study. We show the advantage of sMCCA over sparse CCA (sCCA) through the simulation testing, and further apply it to the analysis of real data (SNPs, fMRI and methylation) from schizophrenia study. Some new genes and brain regions related to SZ disease are discovered by sMCCA and the relationships among these biomarkers are further discussed.
1 Capacity-approaching DNA storage

Humanity produces data at exponential rates, creating a growing demand for better storage devices. DNA molecules are an attractive medium to store digital information due to their durability and high information density. Recent studies have made large strides in developing DNA storage schemes by exploiting the advent of massive parallel synthesis of DNA oligos and the high throughput of sequencing platforms. However, most of these experiments reported small gaps and errors in the retrieved information. Here, we report a strategy to store and retrieve DNA information that is robust and approaches the theoretical maximum of information that can be stored per nucleotide. The success of our strategy lies in careful adaption of recent developments in coding theory to the domain specific constrains of DNA storage. To test our strategy, we stored an entire computer operating system, a movie, a gift card, and other computer files with a total of 2.14×10 bytes in DNA oligos. We were able to fully retrieve the information without a single error even with a sequencing throughput on the scale of a single tile of an Illumina sequencing flow cell. To further stress our strategy, we created a deep copy of the data by PCR amplifying the oligo pool in a total of nine successive reactions, reflecting one complete path of an exponential process to copy the file 218×10 times. We perfectly retrieved the original data with only five million reads. Taken together, our approach opens the possibility of highly reliable DNA-based storage that approaches the information capacity of DNA molecules and enables virtually unlimited data retrieval. . CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this  (which was not . http://dx.doi.org/10.1101/074237 doi: bioRxiv  first posted online Sep. 9, 2016;
Energy-efficient inexact speculative adder with high performance and accuracy control

Inexact and approximate circuit design is a promising approach to improve performance and energy efficiency in technology-scaled and low-power digital systems. Such strategy is suitable for error-tolerant applications involving perceptive or statistical outputs. This paper presents a novel architecture of an Inexact Speculative Adder with optimized hardware efficiency and advanced compensation technique with either error correction or error reduction. This general topology of speculative adders improves performance and enables precise accuracy control. A brief design methodology and comparative study of this speculative adder are also presented herein, demonstrating power savings up to 26 % and energy-delay-area reductions up to 60% at equivalent accuracy compared to the state-of-the-art.
Security analysis of Internet-of-Things: A case study of august smart lock

To realize the vision of Internet-of-Things (IoT), numerous IoT devices have been developed for improving daily lives, in which smart home devices are among the most popular ones. Smart locks rely on smartphones to ease the burden of physical key management and keep tracking the door opening/close status, the security of which have aroused great interests from the security community. As security is of utmost importance for the IoT environment, we try to investigate the security of IoT by examining smart lock security. Specifically, we focus on analyzing the security of August smart lock. The threat models are illustrated for attacking August smart lock. We then demonstrate several practical attacks based on the threat models toward August smart lock including handshake key leakage, owner account leakage, personal information leakage, and denial-of-service (DoS) attacks. We also propose the corresponding defense methods to counteract these attacks.
Brain-Computer Interfacing [In the Spotlight]

Recently, CNN reported on the future of brain-computer interfaces (BCIs). BCIs are devices that process a user's brain signals to allow direct communication and interaction with the environment. BCIs bypass the normal neuromuscular output pathways and rely on digital signal processing and machine learning to translate brain signals to action . Historically, BCIs were developed with biomedical applications in mind, such as restoring communication in completely paralyzed individuals and replacing lost motor function. More recent applications have targeted nondisabled individuals by exploring the use of BCIs as a novel input device for entertainment and gaming. The task of the BCI is to identify and predict behaviorally induced changes or "cognitive states" in a user's brain signals. Brain signals are recorded either noninvasively from electrodes placed on the scalp [electroencephalogram (EEG)] or invasively from electrodes placed on the surface of or inside the brain. BCIs based on these recording techniques have allowed healthy and disabled individuals to control a variety of devices. In this article, we will describe different challenges and proposed solutions for noninvasive brain-computer interfacing.
An intelligent tutoring system for deaf learners of written English

This paper describes progress toward a prototype implementation of a tool which aims to improve literacy in deaf high school and college students who are native (or near native) signers of American Sign Language (ASL). We envision a system that will take a piece of text written by a deaf student, analyze that text for grammatical errors, and engage that student in a tutorial dialogue, enabling the student to generate appropriate corrections to the text. A strong focus of this work is to develop a system which adapts this process to the knowledge level and learning strengths of the user and which has the flexibility to engage in multi-modal, multilingual tutorial instruction utilizing both English and the native language of the user.
Using Graphical Features To Improve Demographic Prediction From Smart Phone Data

Demographic information such as gender, age, ethnicity, level of education, disabilities, employment, and socio-economic status are important in the area of social science, survey and marketing. But it is difficult to obtain the demographic information from users due to reluctance of users to participate and low response rate. Through automated demographics prediction from smart phone sensor data, researchers can obtain this valuable information in a nonintrusive and cost-effective manner. We approach the problem of demographic prediction, namely, classification of gender, age group and job type, through the use of a graphical feature based framework. The framework represents information collected from sensor networks as graphs, extracts useful and relevant graphical features, and predicts demographic information. We evaluated our approach on the Nokia Mobile Phone dataset for the three classification tasks: gender, age-group and job-type. Our approach produced comparable results with most of the state of the art methods while having the additional advantage of general applicability to sensor networks without using sophisticated and application-specific feature generation techniques, background knowledge and special techniques to address class imbalance.
A proposal for semantic map representation and evaluation

Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset.
A Model of Corticostriatal Plasticity for Learning Oculomotor Associations and Sequences

We present models that learn context-dependent oculomotor behavior in (1) conditional visual discrimination and (2) sequence reproduction tasks, based on the following three principles: (1) Visual input and efferent copies of motor output produce patterns of activity in cortex. (2) Cortex influences the saccade system in part via corticostriatal projections. (3) A reinforcement learning mechanism modifies corticostriatal synapses to link patterns of cortical activity to the correct saccade responses during trial-and-error learning. Our conditional visual discrimination model learns to associate visual cues with the corresponding saccades to one of two left-right targets. A visual cue produces patterns of neuronal activity in inferotemporal cortex (IT) that projects to the oculomotor region of the striatum. Initially random saccadic "guesses," when directed to the correct target for the current cue, result in increased synaptic strength between the cue-related IT cells and the striatal cells that participate in the correct saccade, increasing the probability that this cue will later elicit the correct saccade. We show that the model generates "inhibitory gradients" on the striatum as the substrate for spatial generalization. Our sequence reproduction model learns, when presented with temporal sequences of spatial targets, to reproduce the corresponding sequence of saccades. At any point in the execution of a saccade sequence, the current pattern of activity in pre-frontal cortex (PFC), combined with visual input and the motor efferent copy of the previous saccade, produces a new pattern of activity in PFC to be associated with the next saccade. Like IT, PFC also projects to the oculomotor region of the striatum. Correct guesses for the subsequent saccade in the sequence results in strengthening of corticostriatal synapses between active PFC cells and striatal cells involved in the correct saccade. The sequence is thus reproduced as a concatenation of associations. We compare the results of this model with data previously obtained in the monkey and discuss the nature of cortical representations of spatiotemporal information.