nils-herrmann/clean_scirepeval huggingface dataset:
Comparing deep neural networks against humans: object recognition when the signal gets weaker

Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual systems were the only ones capable of this remarkable computational feat. This has changed with the rise of a class of computer vision algorithms called deep neural networks (DNNs) that achieve human-level classification performance on object recognition tasks. Furthermore, a growing number of studies report similarities in the way DNNs and the human visual system process objects, suggesting that current DNNs may be good models of human visual object recognition. Yet there clearly exist important architectural and processing differences between stateof-the-art DNNs and the primate visual system. The potential behavioural consequences of these differences are not well understood. We aim to address this issue by comparing human and DNN generalisation abilities towards image degradations. We find the human visual system to be more robust to image manipulations like contrast reduction, additive noise or novel eidolon-distortions. In addition, we find progressively diverging classification error-patterns between man and DNNs when the signal gets weaker, indicating that there may still be marked differences in the way humans and current DNNs perform visual object recognition. We envision that our findings as well as our carefully measured and freely available behavioural datasets1 provide a new useful benchmark for the computer vision community to improve the robustness of DNNs and a motivation for neuroscientists to search for mechanisms in the brain that could facilitate this robustness.  1 ar X iv :1 70 6. 06 96 9v 1 [ cs .C V ] 2 1 Ju n 20 17
SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases

We present a shared task on automatically determining sentiment intensity of a word or a phrase. The words and phrases are taken from three domains: general English, English Twitter, and Arabic Twitter. The phrases include those composed of negators, modals, and degree adverbs as well as phrases formed by words with opposing polarities. For each of the three domains, we assembled the datasets that include multi-word phrases and their constituent words, both manually annotated for real-valued sentiment intensity scores. The three datasets were presented as the test sets for three separate tasks (each focusing on a specific domain). Five teams submitted nine system outputs for the three tasks. All datasets created for this shared task are freely available to the research community.
TDParse: Multi-target-specific sentiment recognition on Twitter

Existing target-specific sentiment recognition methods consider only a single target per tweet, and have been shown to miss nearly half of the actual targets mentioned. We present a corpus of UK election tweets, with an average of 3.09 entities per tweet and more than one type of sentiment in half of the tweets. This requires a method for multi-target specific sentiment recognition, which we develop by using the context around a target as well as syntactic dependencies involving the target. We present results of our method on both a benchmark corpus of single targets and the multi-target election corpus, showing state-of-the art performance in both corpora and outperforming previous approaches to multi-target sentiment task as well as deep learning models for singletarget sentiment.
Analysis of HMW glutenin subunits encoded by chromosome 1A of bread wheat (Triticum aestivum L.) indicates quantitative effects on grain quality

A gene encoding the high-molecular-weight (HMW) subunit of glutenin 1Ax1 was isolated from bread wheat cv Hope. Comparison of the deduced amino acid sequence with that previously reported for an allelic subunit, 1Ax2*, showed only minor differences, which were consistent with both subunits being associated with good bread-making quality. Quantitative analyses of total protein extracts from 22 cultivars of bread wheat showed that the presence of either subunit 1Ax1 or 1Ax2*, when compared with a null allele, resulted in an increase in the proportion of HMW subunit protein from ca. 8 to 10% of the total. It is suggested that this quantitative increase in HMW subunit protein may account for the association of 1Ax subunits with good quality.
Three stages of emotional word processing: an ERP study with rapid serial visual presentation.

Rapid responses to emotional words play a crucial role in social communication. This study employed event-related potentials to examine the time course of neural dynamics involved in emotional word processing. Participants performed a dual-target task in which positive, negative and neutral adjectives were rapidly presented. The early occipital P1 was found larger when elicited by negative words, indicating that the first stage of emotional word processing mainly differentiates between non-threatening and potentially threatening information. The N170 and the early posterior negativity were larger for positive and negative words, reflecting the emotional/non-emotional discrimination stage of word processing. The late positive component not only distinguished emotional words from neutral words, but also differentiated between positive and negative words. This represents the third stage of emotional word processing, the emotion separation. Present results indicated that, similar with the three-stage model of facial expression processing; the neural processing of emotional words can also be divided into three stages. These findings prompt us to believe that the nature of emotion can be analyzed by the brain independent of stimulus type, and that the three-stage scheme may be a common model for emotional information processing in the context of limited attentional resources.
Improving Dermoscopic Image Segmentation With Enhanced Convolutional-Deconvolutional Networks

Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is further exacerbated when dealing with a large amount of image data. In this paper, we extended our previous work by developing a deeper network architecture with smaller kernels to enhance its discriminant capacity. In addition, we explicitly included color information from multiple color spaces to facilitate network training and thus to further improve the segmentation performance. We participated and extensively evaluated our method on the ISBI 2017 skin lesion segmentation challenge. By training with the 2000 challenge training images, our method achieved an average Jaccard Index of 0.765 on the 600 challenge testing images, which ranked itself in the first place among 21 final submissions in the challenge.
An Emotion Cause Corpus for Chinese Microblogs with Multiple-User Structures

A notably challenging problem in emotion analysis is recognizing the cause of an emotion. Although there have been a few studies on emotion cause detection, most of them work on news reports or a few of them focus on microblogs using a single-user structure (i.e., all texts in a microblog are written by the same user). In this article, we focus on emotion cause detection for Chinese microblogs using a multiple-user structure (i.e., texts in a microblog are successively written by several users). First, based on the fact that the causes of an emotion of a focused user may be provided by other users in a microblog with the multiple-user structure, we design an emotion cause annotation scheme which can deal with such a complicated case, and then provide an emotion cause corpus using the annotation scheme. Second, based on the analysis of the emotion cause corpus, we formalize two emotion cause detection tasks for microblogs (current-subtweet-based emotion cause detection and original-subtweet-based emotion cause detection). Furthermore, in order to examine the difficulty of the two emotion cause detection tasks and the contributions of texts written by different users in a microblog with the multiple-user structure, we choose two popular classification methods (SVM and LSTM) to do emotion cause detection. Our experiments show that the current-subtweet-based emotion cause detection is much more difficult than the original-subtweet-based emotion cause detection, and texts written by different users are very helpful for both emotion cause detection tasks. This study presents a pilot study of emotion cause detection which deals with Chinese microblogs using a complicated structure.
Near-Field Microwave Imaging Based on Aperture Raster Scanning With TEM Horn Antennas

The design, fabrication, and characterization of an ultrawideband (UWB) antenna for near-field microwave imaging of dielectric objects are presented together with the imaging setup. The focus is on an application in microwave breast tumor detection. The new antenna operates as a sensor with the following properties: 1) direct contact with the imaged body; 2) more than 90% of the microwave power is coupled directly into the tissue; 3) UWB performance; 4) excellent de-coupling from the outside environment; 5) small size; and 6) simple fabrication. The antenna characterization includes return loss, total efficiency, near-field directivity, fidelity, and group velocity. The near-field imaging setup employs planar aperture raster scanning. It consists of two antennas aligned along each other's boresight and moving together to scan two parallel apertures. The imaged object lies between the two apertures. With a blind de-convolution algorithm, the images are de-blurred. Simulation and experimental results confirm the satisfactory performance of the antenna as an UWB sensor for near-field imaging.
Immersion and Emotion: Their Impact on the Sense of Presence

The present study is designed to test the role of immersion and media content in the sense of presence. Specifically, we are interested in the affective valence of the virtual environments. This paper describes an experiment that compares three immersive systems (a PC monitor, a rear projected video wall, and a head-mounted display) and two virtual environments, one involving emotional content and the other not. The purpose of the experiment was to test the interactive role of these two media characteristics (form and content). Scores on two self-report presence measurements were compared among six groups of 10 people each. The results suggest that both immersion and affective content have an impact on presence. However, immersion was more relevant for non-emotional environments than for emotional ones.
Multiple-event direct to histogram TDC in 65nm FPGA technology

A novel multiple-event Time to Digital Converter (TDC) with direct to histogram output is implemented in a 65nm Xilinx Virtex 5 FPGA. The delay-line based architecture achieves 16.3 ps temporal accuracy over a 2.86ns dynamic range. The measured maximum conversion rate of 6.17 Gsamples/s and the sampling rate of 61.7 Gsamples/s are the highest published in the literature. The system achieves a linearity of -0.9/+3 LSB DNL and -1.5/+5 LSB INL. The TDC is demonstrated in a direct time of flight optical ranging application with 12mm error over a 350mm range.
A second-order method for convex l1-regularized optimization with active-set prediction

We describe an active-set method for the minimization of an objective function φ that is the sum of a smooth convex function and an l1-regularization term. A distinctive feature of the method is the way in which active-set identification and second-order subspace minimization steps are integrated to combine the predictive power of the two approaches. At every iteration, the algorithm selects a candidate set of free and fixed variables, performs an (inexact) subspace phase, and then assesses the quality of the new active set. If it is not judged to be acceptable, then the set of free variables is restricted and a new active-set prediction is made. We establish global convergence for our approach, and compare the new method against the state-of-the-art code LIBLINEAR.
Progress and prospects in plant genome editing

The emergence of sequence-specific nucleases that enable genome editing is revolutionizing basic and applied biology. Since the introduction of CRISPR–Cas9, genome editing has become widely used in transformable plants for characterizing gene function and improving traits, mainly by inducing mutations through non-homologous end joining of double-stranded breaks generated by CRISPR–Cas9. However, it would be highly desirable to perform precision gene editing in plants, especially in transformation-recalcitrant species. Recently developed Cas9 variants, novel RNA-guided nucleases and base-editing systems, and DNA-free CRISPR–Cas9 delivery methods now provide great opportunities for plant genome engineering. In this Review Article, we describe the current status of plant genome editing, focusing on newly developed genome editing tools and methods and their potential applications in plants. We also discuss the specific challenges facing plant genome editing, and future prospects.
Mining Local Process Models

In this paper we describe a method to discover frequent behavioral patterns in event logs. We express these patterns as local process models. Local process model mining can be positioned in-between process discovery and episode / sequential pattern mining. The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining. However, we do not look at start-to-end models, which distinguishes our approach from process discovery and creates a link to episode / sequential pattern mining. We propose an incremental procedure for building local process models capturing frequent patterns based on so-called process trees. We propose five quality dimensions and corresponding metrics for local process models, given an event log. We show monotonicity properties for some quality dimensions, enabling a speedup of local process model discovery through pruning. We demonstrate through a real life case study that mining local patterns allows us to get insights in processes where regular start-to-end process discovery techniques are only able to learn unstructured, flower-like, models.
GLOBAL-SCALE OBJECT DETECTION USING SATELLITE IMAGERY

In recent years, there has been a substantial increase in the availability of high-resolution commercial satellite imagery, enabling a variety of new remote-sensing applications. One of the main challenges for these applications is the accurate and efficient extraction of semantic information from satellite imagery. In this work, we investigate an important instance of this class of challenges which involves automatic detection of multiple objects in satellite images. We present a system for large-scale object training and detection, leveraging recent advances in feature representation and aggregation within the bag-of-words paradigm. Given the scale of the problem, one of the key challenges in learning object detectors is the acquisition and curation of labeled training data. We present a crowd-sourcing based framework that allows efficient acquisition of labeled training data, along with an iterative mechanism to overcome the label noise introduced by the crowd during the labeling process. To show the competence of the presented scheme, we show detection results over several object-classes using training data captured from close to 200 cities and tested over multiple geographic locations.
Intelligent Smart Home Automation and Security System Using Arduino and Wi-fi

This paper provides a low cost-effective and flexible home control and monitoring system with the aid of an integrated micro-web server with internet protocol (IP) connectivity for access and to control of equipment and devices remotely using Android-based smartphone app. The proposed system does not require a dedicated server PC with respect to similar systems and offers a new communication protocol for monitoring and controlling the home environment with more than just switching functionality. Smart home interfaces and device definitions to ensure interoperability between Wi-fi devices from various manufacturers of electrical equipment, meters and smart energy enables products to allow manufactured. In this project gives the intelligent operation for lamps and fans. Here the system is connected with temperature control and lamp control. Light dependent resistor (LDR) and Temperature sensor (LM35) are the main components for this automatic control of lamps and fans. Here the LDR is responsible for lamp control and LM35 is responsible for controlling the operation of fan. The proposed home energy control systems design intelligent services for users and provides, The proposed system are implemented with smartphone.
Positive Psychology Research in the Middle East and North Africa

Since the original call by Seligman and Csikszentmihalyi (2000) for a new science of happiness, excellence, and optimal human functioning, there has been considerable momentum in the research in positive psychology. A systematic review of the literature explicitly linked to the positive psychology movement assessed the extent of authorship, empirical and theoretical publications, and engagement of local samples in the indigenous research emerging from the Middle Eastern and North African region. An in-depth review of these articles (n = 53) was conducted to examine the trends in publication, author locations, sample locations, topics, research approaches and findings from exemplary articles that attend to key issues. Highlights from cross-national comparisons, cross-cultural validations and replications, research on positive constructs, and research on issues particular to the Middle East and North Africa are reviewed. Finally, unique trends of the research emerging from the Middle Eastern and North African region and five pathways for future research directions are discussed. ثحبلا صخلم : ( يلاهيمتنيزكيسو نامغيليس قلطأ نأ ذنم ٢٠٠٠ يناسنلإا ءادلأاو زيمتلاو ةداعسلل ديدج ملع سيسأتل امهءادن ) عب ةقلعتملا ثاحبلأا تدهش ،لثملأا ةماه ةرفط يباجيلإا سفنلا مل . تمت يتلا ةيلمعلاو ةيرظنلا ثاحبلأا مييقتب ةساردلا هذه موقتو ف ،راطلإا اذه يف .يباجيلإا سفنلا ملع ةكرحب ةقلعتملا تايبدلأل ةيجهنم ةعجارم للاخ نم ايقيرفإ لامشو طسولأا قرشلا ي عومجم( ةروشنملا تلااقملا تعضخأ ٥٣ نيفلؤملا نطومو ثحبلا يف ةبلاغلا تاهجوتلا ةفرعم لجأ نم ةقمعم ةساردل ) وملاو ثاحبلأا اهيطغت يتلا قطانملاو نيثحابلاو ثحبلا يف اهدامتعا مت يتلا تابراقملاو اهيلإ قرطتلا مت يتلا ةيساسلأا عيضا ميهافملا يف ثحب نمو ةيفاقثلاو ةينطولا تاقايسلا نيب تانراقم نم ردص ام ةعجارم تمت امك .ثاحبلأا هذه هيلإ تلصوت امو ا هذه ماتخ يفو .طسولأا قرشلل بسنلاب ةيمهأ تاذ عيضاوملا لك يفو ةيباجيلإا ةبلاغلا تاهجوتلا ليلحتب ثحابلا موقي ،ةساردل .ةقطنملا هذه يف ثحبلا اهذخأيس يتلا ةيلبقتسملا تاهاجتلاا فارشتساو ايقيرفإ لامشو طسولأا قرشلا يف
A catalog of information systems outsourcing risks

Information systems outsourcing risks are a vital component in the decision and management process associated to the provision of information systems and technology services by a provider to a customer. Although there is a rich literature on information systems outsourcing risks, the accumulated knowledge on this area is fragmented. In view of this situation, an argument is put forward on the usefulness of having a theory that integrates the various constructs related to information systems outsourcing risks. This study aims to contribute towards the synthesis of that theory, by proposing a conceptual framework for interpreting the literature and presenting a catalog of information systems outsourcing risks. The conceptual framework articulates together six key risk elements, namely dangers, negative outcomes, undesirable consequences, factors and mitigation actions. The catalog condenses and categorizes the information systems outsourcing risk elements found on the literature reviewed, both from the perspective of the outsourcing customer and from the perspective of the outsourcing provider. Proposals for subsequent work towards the generation of the theory of information systems outsourcing risk are suggested.
Spoken Language Recognition: From Fundamentals to Practice

Spoken language recognition refers to the automatic process through which we determine or verify the identity of the language spoken in a speech sample. We study a computational framework that allows such a decision to be made in a quantitative manner. In recent decades, we have made tremendous progress in spoken language recognition, which benefited from technological breakthroughs in related areas, such as signal processing, pattern recognition, cognitive science, and machine learning. In this paper, we attempt to provide an introductory tutorial on the fundamentals of the theory and the state-of-the-art solutions, from both phonological and computational aspects. We also give a comprehensive review of current trends and future research directions using the language recognition evaluation (LRE) formulated by the National Institute of Standards and Technology (NIST) as the case studies.
Spatiotemporal dynamics of functional clusters of neurons in the mouse motor cortex during a voluntary movement.

Functional clustering of neurons is frequently observed in the motor cortex. However, it is unknown if, when, and how fine-scale (<100 μm) functional clusters form relative to voluntary forelimb movements. In addition, the implications of clustering remain unclear. To address these issues, we conducted two-photon calcium imaging of mouse layer 2/3 motor cortex during a self-initiated lever-pull task. In the imaging session after 8-9 days of training, head-restrained mice had to pull a lever for ∼600 ms to receive a water drop, and then had to wait for >3 s to pull it again. We found two types of task-related cells in the mice: cells whose peak activities occurred during lever pulls (pull cells) and cells whose peak activities occurred after the end of lever pulls. The activity of pull cells was strongly associated with lever-pull duration. In ∼40% of imaged fields, functional clusterings were temporally detected during the lever pulls. Spatially, there were ∼70-μm-scale clusters that consisted of more than four pull cells in ∼50% of the fields. Ensemble and individual activities of pull cells within the cluster more accurately predicted lever movement trajectories than activities of pull cells outside the cluster. This was likely because clustered pull cells were more often active in the individual trials than pull cells outside the cluster. This higher fidelity of activity was related to higher trial-to-trial correlations of activities of pairs within the cluster. We propose that strong recurrent network clusters may represent the execution of voluntary movements.