nils-herrmann/clean_scirepeval huggingface dataset:
Oil content, tocopherol composition and fatty acid patterns of the seeds of 51 Cannabis sativa L. genotypes

The oil content, the tocopherol composition, the plastochromanol-8 (P-8) content and the fatty acid composition (19 fatty acids) of the seed of 51 hemp (Cannabis sativa L.) genotypes were studied in the 2000 and 2001 seasons. The oil content of the hemp seed ranged from 26.25% (w/w) to 37.50%. Analysis of variance revealed significant effects of genotype, year and of the interaction (genotype × year) on the oil content. The oil contents of the 51 genotypes in 2000 and 2001 were correlated (r = 0.37**) and averaged 33.19 ± 1.45% in 2000 and 31.21 ± 0.96% in 2001. The γ-tocopherol, α-tocopherol, δ-tocopherol, P-8- and β-tocopherol contents of the 51 genotypes averaged 21.68 ± 3.19, 1.82 ± 0.49, 1.20 ± 0.40, 0.18 ± 0.07 and 0.16 ± 0.04 mg 100g−1 of seeds, respectively (2000 and 2001 data pooled). Hierarchical clustering of the fatty acid data did not group the hemp genotypes according to their geographic origin. The γ-linolenic acid yield of hemp (3–30 kg ha−1) was similar to the γ-linolenic acid yield of plant species that are currently used as sources of γ-linolenic acid (borage (19–30 kg ha−1), evening primrose (7–30 kg ha−1)). The linoleic acid yield of hemp (129–326 kg ha−1) was similar to flax (102–250 kg ha−1), but less than in sunflower (868–1320 kg ha−1). Significant positive correlations were detected between some fatty acids and some tocopherols. Even though the average content of P-8 in hemp seeds was only 1/120th of the average γ-tocopherol content, P-8 content was more closely correlated with the unsaturated fatty acid content than γ-tocopherol or any other tocopherol fraction. The average broad-sense heritabilities of the oil content, the antioxidants (tocopherols and P-8) and the fatty acids were 0.53, 0.14 and 0.23, respectively. The genotypes Fibrimon 56, P57, Juso 31, GB29, Beniko, P60, FxT, Félina 34, Ramo and GB18 were capable of producing the largest amounts of high quality hemp oil.
A Wirelessly-Powered Platform for Sensing and Computation

We present WISP, a wireless, battery-free platform for sensing and computation that is powered and read by a standards compliant Ultra-High Frequency (UHF) RFID reader. To the reader, the WISP appears to be an ordinary RFID tag. The WISP platform includes a general-purpose programmable flash microcontroller and implements the bi-directional communication primitives required by the Electronic Product Code (EPC) RFID standard, which allows it to communicate arbitrary sensor data via an EPC RFID reader by dynamically changing the ID it presents to the reader. For each 64 bit “packet,” the WISP’s microcontroller dynamically computes the 16-bit CRC that the EPC standard requires of valid packets. Because the WISP device can control all bits of the presented ID, 64 bits of sensor data can be communicated with a single RFID read event. As an example of the system in operation, we present 13 hours of continuous-valued light-level data measured by the device. All the measurements were made using power harvested from the RFID reader. No battery, and no wired connections (for either power or data) were used. As far as we are aware, this paper reports the first fully programmable computing platform that can operate using power transmitted from a long-range (UHF) RFID reader and communicate arbitrary, multi-bit data in response to a single RFID reader poll event.
High throughput ultralong (20 cm) nanowire fabrication using a wafer-scale nanograting template.

Nanowires are being actively explored as promising nanostructured materials for high performance flexible electronics, biochemical sensors, photonic applications, solar cells, and secondary batteries. In particular, ultralong (centimeter-long) nanowires are highly attractive from the perspective of electronic performance, device throughput (or productivity), and the possibility of novel applications. However, most previous works on ultralong nanowires have issues related to limited length, productivity, difficult alignment, and deploying onto the planar substrate complying with well-matured device fabrication technologies. Here, we demonstrate a highly ordered ultralong (up to 20 cm) nanowire array, with a diameter of 50 nm (aspect ratio of up to 4,000,000:1), in an unprecedented large (8 in.) scale (2,000,000 strands on a wafer). We first devised a perfectly connected ultralong nanograting master template on the whole area of an 8 in. substrate using a top-down approach, with a density equivalent to that achieved with e-beam lithography (100 nm). Using this large-area, ultralong, high-density nanograting template, we developed a fast and effective method for fabricating up to 20 cm long nanowire arrays on a plastic substrate, composed of metal, dielectric, oxide, and ferroelectric materials. As a suggestion of practical application, a prototype of a large-area aluminum wire grid polarizer was demonstrated.
A complementary filter for attitude estimation of a fixed-wing UAV

This paper considers the question of using a nonlinear complementary filter for attitude estimation of fixed-wing unmanned aerial vehicle (UAV) given only measurements from a low-cost inertial measurement unit. A nonlinear complementary filter is proposed that combines accelerometer output for low frequency attitude estimation with integrated gyrometer output for high frequency estimation. The raw accelerometer output includes a component corresponding to airframe acceleration, occurring primarily when the aircraft turns, as well as the gravitational acceleration that is required for the filter. The airframe acceleration is estimated using a simple centripetal force model (based on additional airspeed measurements), augmented by a first order dynamic model for angle-of-attack, and used to obtain estimates of the gravitational direction independent of the airplane manoeuvres. Experimental results are provided on a real-world data set and the performance of the filter is evaluated against the output from a full GPS/INS that was available for the data set.
Coresets and Sketches

Geometric data summarization has become an essential tool in both geometric approximation algorithms and where geometry intersects with big data problems. In linear or near-linear time large data sets can be compressed into a summary, and then more intricate algorithms can be run on the summaries whose results approximate those of the full data set. Coresets and sketches are the two most important classes of these summaries. A coreset is a reduced data set which can be used as proxy for the full data set; the same algorithm can be run on the coreset as the full data set, and the result on the coreset approximates that on the full data set. It is often required or desired that the coreset is a subset of the original data set, but in some cases this is relaxed. A weighted coreset is one where each point is assigned a weight, perhaps different than it had in the original set. A weak coreset associated with a set of queries is one where the error guarantee holds for a query which (nearly) optimizes some criteria, but not necessarily all queries; a strong coreset provides error guarantees for all queries. A sketch is a compressed mapping of the full data set onto a data structure which is easy to update with new or changed data, and allows certain queries whose results approximate queries on the full data set. A linear sketch is one where the mapping is a linear function of each data point, thus making it easy for data to be added, subtracted, or modified. These definitions can blend together, and some summaries can be classified as either or both. The overarching connection is that the summary size will ideally depend only on the approximation guarantee but not the size of the original data set, although in some cases logarithmic dependence is acceptable. We focus on five types of coresets and sketches: shape-fitting (Section 49.1), density estimation (Section 49.2), high-dimensional vectors (Section 49.3), highdimensional point sets / matrices (Section 49.4), and clustering (Section 49.5). There are many other types of coresets and sketches (e.g., for graphs  or Fourier transforms [IKP14]) which we do not cover for space or because they are less geometric.
Power to the people? The evolving recognition of human aspects of security

It is perhaps unsurprising to find much of the focus in IT and computer security being drawn towards the technical aspects of the discipline. However, it is increasingly recognised that technology alone cannot deliver a complete solution, and there is also a tangible need to address human aspects. At the core, people must understand the threats they face and be able to use the protection available to them, and although this has not been entirely ignored, it has not received the level of attention that it merits either. Indeed, security surveys commonly reveal that the more directly user-facing aspects such as policy, training and education are prone to receiving significantly less attention than technical controls such as firewalls, antivirus and intrusion detection. The underlying reason for such disparity is that the human aspects are in many ways a more challenging problem to approach, not least because they cannot be easily targeted with a product-based solution. There is also a direct overlap into the technical area, with issues such as the usability and acceptability of technology solutions having a direct impact upon the actual protection that they are able to deliver. This paper explores these themes, highlighting the need for human aspects to form part of a holistic security strategy alongside the necessary technologies. Taking the specific examples of security awareness and two user-facing technical controls (user authentication and antivirus), the discussion examines how things have evolved to the present day and considers how they need to be positioned for the future. a 2012 Elsevier Ltd. All rights reserved.
The Ever-Changing Social Perception of Autism Spectrum Disorders in the United States

This paper aims to examine the comprehensive social perception of autism spectrum disorders (ASDs) within the United States today. In order to study the broad public view of those with ASDs, this study investigates the evolution of the syndrome in both sociological and scientific realms. By drawing on the scientific progression of the syndrome and the mixture of this research with concurrent social issues and media representations, this study infers why such a significant amount of stigmatization has become attached to those with ASDs and how these stigmatizations have varied throughout history. After studying this evolving social perception of ASDs in the United States, the writer details suggestions for the betterment of this awareness, including boosted and specified research efforts, increased collaboration within those experts in autism, and positive visibility of those with ASDs and their families. Overall, the writer suggests that public awareness has increased and thus negative stigmatization has decreased in recent years; however, there remains much to be done to increase general social understanding of ASDs. “Autism is about having a pure heart and being very sensitive... It is about finding a way to survive in an overwhelming, confusing world... It is about developing differently, in a different pace and with different leaps.”  The identification of autism, in both sociological and scientific terms, has experienced a drastic evolution since its original definition in the early 20th century. From its original designation by Leo Kanner (1943), public understanding of autism spectrum disorders (ASDs) has been shrouded in mystery and misperception. The basic core features of all ASDs include problems with basic socialization and communication, strange intonation and facial expressions, and intense preoccupations or repetitive behaviors; however, one important aspect of what makes autism so complex is the wide variation in expression of the disorder (Lord, 2011). When comparing individuals with the same autism diagnosis, one will undoubtedly encounter many different personalities, strengths and weaknesses. This wide variability between individuals diagnosed with autism, along with the lack of basic understanding of the general public, accounts for a significant amount of social stigma in our society today. Social stigma stemming from this lack of knowledge has been reported in varying degrees since the original formation of the diagnosis. Studies conducted over the past two centuries have shown perceived negative stigma from the view of both the autistic individual and the family or caretakers
Anomaly Detection in Vessel Tracking Using Support Vector Machines (SVMs)

The paper is devoted to supervise method approach to identify the vessel anomaly behaviour in waterways using the Automated Identification System (AIS) vessel reporting data. In this work, we describe the use of SVMs to detect the vessel anomaly behaviour. The SVMs is a supervised method that needs some pre knowledge to extract the maritime movement patterns of AIS raw data into information. This is the basis to remodel information into a meaningful and valuable form. The result of this work shows that the SVMs technique is applicable to be used for the identification of vessel anomaly behaviour. It is proved that the best accuracy result is obtained from dividing raw data into 70% for training and 30% for testing stages.
Design of synchronous reluctance motor for hybrid electric vehicles

The paper deals with the design of a synchronous reluctance motor suited for hybrid electric vehicles. At first, the focus is on rotor design. In particular an useful approach is proposed to choose the proper angles of the flux-barrier ends with the aim of reducing the torque ripple due to the slot harmonics. In the second part a comparison among different rotor arrangements (one, two and three flux-barriers per pole) is presented. Furthermore an improvement in terms of low ripple and high average torque is given thanks to the ”Machaon” configuration. The third part of the paper deals with the analysis of the improved layout. The current vector control method used to maximize the torque according to the voltage and current constraints is shown. Saturation effects are taken into account. The impact of electrical design on the mechanical characteristics of the rotor (and natural frequencies) is discussed as well. At last the predicted mechanical characteristics of the reluctance motor are presented: torque versus speed behavior, power losses and power factor. The efficiency map of the machine based on Finite Elements analysis is also reported.
Efficient Indexing of Regional Maximum Activations of Convolutions using Full-Text Search Engines

In this paper, we adapt a surrogate text representation technique to develop efficient instance-level image retrieval using Regional Maximum Activations of Convolutions (R-MAC). R-MAC features have recently showed outstanding performance in visual instance retrieval. However, contrary to the activations of hidden layers adopting ReLU (Rectified Linear Unit), these features are dense. This constitutes an obstacle to the direct use of inverted indexes, which rely on sparsity of data. We propose the use of deep permutations, a recent approach for efficient evaluation of permutations, to generate surrogate text representation of R-MAC features, enabling indexing of visual features as text into a standard search-engine. The experiments, conducted on Lucene, show the effectiveness and efficiency of the proposed approach.
Statistical geometrical features for texture classification

This paper proposes a novel set of 16 features based on the statistics of geometrical attributes of connected regions in a sequence of binary images obtained from a texture image. Systematic comparison using all the Brodatz textures shows that the new set achieves a higher correct classification rate than the well-known Statistical Gray Level Dependence Matrix method, the recently proposed Statistical Feature Matrix, and Liu’s features. The deterioration in performance with the increase in the number of textures in the set is less with the new SGF features than with the other methods, indicating that SGF is capable of handling a larger texture population. The new method’s performance under additive noise is also shown to be the best of the four. Texture analvsis Feature extraction Statistical features Geometrical features Additive noise
Benefits of Augmented Reality in Educational Environments - A Systematic Literature Review

By augmenting the real world with virtual information, Augmented Reality (AR) provides new possibilities for education. Although AR is frequently applied in educational environments, the value of AR applications in these environments is not yet investigated in its entirety. Additionally, educators face different directions of AR applications, which may differ regarding their potential benefits. To help overcome these challenges, we conduct a systematic literature review to synthesize a set of 25 publications. We identify 14 different benefits and cluster these into six different groups. We use the Five Directions of AR in educational environments by Yuen et al.  to further detail possible benefits for different directions of AR applications. Our findings indicate that specific directions of AR applications are more likely to lead to certain benefits such as increased motivation. Future research is needed to investigate the causality between benefits and directions of AR in more detail.
Active learning with extremely sparse labeled examples

In the setting of active learning there exists a general assumption that labeled examples are available for training a classifier, which in turn is used to examine unlabeled data to select the most ‘informative’ examples for manual labeling. However, in some domain applications there are a limited number of labeled examples available, such as in the most extreme cases of having a single labeled example per without initially making an assumption on label assignment. In this paper we present a method for finding high-informative examples for manual labeling based on extremely limited labeled data available during training. We propose using canonical correlation analysis to investigate the correlation between different views of the available data and demonstrate that this measure can be used as a selection criterion for the novel application of active learning using only a single labeled example from each class. We demonstrate our method with promising experimental results on text classification, advertisement removal and multi-class image classification tasks. & 2010 Elsevier B.V. All rights reserved.
Adversarial Images for Variational Autoencoders

We investigate adversarial attacks for autoencoders. We propose a procedure that distorts the input image to mislead the autoencoder in reconstructing a completely different target image. We attack the internal latent representations, attempting to make the adversarial input produce an internal representation as similar as possible as the target’s. We find that autoencoders are much more robust to the attack than classifiers: while some examples have tolerably small input distortion, and reasonable similarity to the target image, there is a quasi-linear trade-off between those aims. We report results on MNIST and SVHN datasets, and also test regular deterministic autoencoders, reaching similar conclusions in all cases. Finally, we show that the usual adversarial attack for classifiers, while being much easier, also presents a direct proportion between distortion on the input, and misdirection on the output. That proportionality however is hidden by the normalization of the output, which maps a linear layer into non-linear probabilities.
Some Alternative Formulations of the Event Calculus

The Event Calculus is a narrative based formalism for reasoning about actions and change originally proposed in logic programming form by Kowalski and Sergot. In this paper we summarise how variants of the Event Calculus may be expressed as classical logic axiomatisations, and how under certain circumstances these theories may be reformulated as “action description language” domain descriptions using the Language E . This enables the classical logic Event Calculus to inherit various provably correct automated reasoning procedures recently developed for E .
Intrusion detection system: A comprehensive review

With the increasing amount of network throughput and security threat, the study of intrusion detection systems (IDSs) has received a lot of attention throughout the computer science field. Current IDSs pose challenges on not only capricious intrusion categories, but also huge computational power. Though there is a number of existing literatures to IDS issues, we attempt to give a more elaborate image for a comprehensive review. Through the extensive survey and sophisticated organization, we propose the taxonomy to outline modern IDSs. In addition, tables and figures we summarized in the content contribute to easily grasp the overall picture of IDSs. & 2012 Elsevier Ltd. All rights reserved.
GPU centric extensions for parallel strongly connected components computation

Finding Strongly Connected Components (SCC) of a directed graph is a fundamental graph problem. Many of the state-of-the-art sequential algorithms use depth-first search (DFS) to find SCCs. Since, in general DFS is hard to parallelize, researchers rely on other approaches such as Forward-Backward-Trim and Coloring. In this work, we have extended the two state-of-the-art multicore parallel algorithms to take advantage of the highly powerful GPU devices. Unfortunately, state-of-the-art parallel implementations have three performance limitations: (i) selection of ineffective pivots for running multiple forward-backward, (ii) load imbalance across threads while computing reachability closures, and (iii) serializability bottleneck while computing forward-backward sets. We address the first limitation using an improved pivot selection scheme. This improves the chances of finding the largest SCC in real-world graphs. We handle the other two limitations by adding parallelism during closure computation. This improves load balance across warp-threads and reduces thread-serialization. In effect, the resultant codes perform much better compared to the state-of-the-art. The evaluation results show that our methods achieve up to 8× speedup over Tarjan's sequential algorithm and up to 2× speedup over a previous CUDA implementation.
Automatic Discrimination between Printed and Handwritten Text in Documents

Recognition techniques for printed and handwritten text in scanned documents are significantly different. In this paper we address the problem of identifying each type. We can list at least four steps: digitalization, preprocessing, feature extraction and decision or classification. A new aspect of our approach is the use of data mining techniques on the decision step. A new set of features extracted of each word is proposed as well. Classification rules are mining and used to discern printed text from handwritten. The proposed system was tested in two public image databases. All possible measures of efficiency were computed achieving on every occasion quantities above 80%.
D-band grid-array antenna integrated in the lid of a surface-mountable chip-package

A packaging solution for single-chip millimeter-wave transceivers is presented. It integrates an antenna into the lid of the chip package. Multi-layer organic materials are used as antenna substrate. A prototype demonstrates the feasibility of the packaging process and the antenna performance. It consists of three parts: A package base; a MMIC dummy that allows measuring the antenna including the flip chip interconnect; and a 122-GHz grid array antenna. The complete antenna-in-package prototype is measured using a probe based measurement system.