nils-herrmann/clean_scirepeval huggingface dataset:
Using degree-day accumulations and host phenology for predicting larval emergence patterns of the olive psyllid, Euphyllura phillyreae

The olive psyllid, Euphyllura phillyreae Foerster is one of the most destructive pests on buds and flowers of olive tree (Olea europaea L.) in May when the olive growers cannot apply any insecticides against the pest. Temperature-dependent development of the psyllid was studied at constant temperatures ranged 16–26°C. A degree-day (DD) model was developed to predict the larval emergence using the weekly cumulative larval counts and daily mean temperatures. Linear regression analysis estimated a lower developmental threshold of 4.1 and 4.3°C and a thermal constant of 164.17 and 466.13 DD for development of egg and larva, respectively. The cumulative larval counts of E. phillyreae approximated by probit transformation were plotted against time, expressed as the sum of DD above 4.3°C, the starting date when the olive tree phenology was the period of flower cluster initiation. A linear model was used to describe the relationship of DDs and probit values of larval emergence patterns of E. phillyreae and predicted that 10, 50 and 95% emergence of the larvae required 235.81, 360.22 and 519.93 DD, respectively, with errors of 1–3 days compared to observed values. Based on biofix depends the development of olive tree phenology; the DD model can be used as a forecasting method for proper timing of insecticide applications against E. phillyreae larvae in olive groves.
Path planning for autonomous vehicles using model predictive control

Path planning for autonomous vehicles in dynamic environments is an important but challenging problem, due to the constraints of vehicle dynamics and existence of surrounding vehicles. Typical trajectories of vehicles involve different modes of maneuvers, including lane keeping, lane change, ramp merging, and intersection crossing. There exist prior arts using the rule-based high-level decision making approaches to decide the mode switching. Instead of using explicit rules, we propose a unified path planning approach using Model Predictive Control (MPC), which automatically decides the mode of maneuvers. To ensure safety, we model surrounding vehicles as polygons and develop a type of constraints in MPC to enforce the collision avoidance between the ego vehicle and surrounding vehicles. To achieve comfortable and natural maneuvers, we include a lane-associated potential field in the objective function of the MPC. We have simulated the proposed method in different test scenarios and the results demonstrate the effectiveness of the proposed approach in automatically generating reasonable maneuvers while guaranteeing the safety of the autonomous vehicle.
DeepQ Arrhythmia Database: A Large-Scale Dataset for Arrhythmia Detector Evaluation

DeepQ Arrhythmia Database, the first generally available large-scale dataset for arrhythmia detector evaluation, contains 897 annotated single-lead ECG recordings from 299 unique patients. DeepQ includes beat-by-beat, rhythm episodes, and heartbeats fiducial points annotations. Each patient was engaged in a sequence of lying down, sitting, and walking activities during the ECG measurement and contributed three five-minute records to the database. Annotations were manually labeled by a group of certified cardiographic technicians and audited by a cardiologist at Taipei Veteran General Hospital, Taiwan. The aim of this database is in three folds. First, from the scale perspective, we build this database to be the largest representative reference set with greater number of unique patients and more variety of arrhythmic heartbeats. Second, from the diversity perspective, our database contains fully annotated ECG measures from three different activity modes and facilitates the arrhythmia classifier training for wearable ECG patches and AAMI assessment. Thirdly, from the quality point of view, it serves as a complement to the MIT-BIH Arrhythmia Database in the development and evaluation of the arrhythmia detector. The addition of this dataset can help facilitate the exhaustive studies using machine learning models and deep neural networks, and address the inter-patient variability. Further, we describe the development and annotation procedure of this database, as well as our on-going enhancement. We plan to make DeepQ database publicly available to advance medical research in developing outpatient, mobile arrhythmia detectors.
Spectral Ensemble Clustering

Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area. Nevertheless, the relatively high time and space complexity preclude it from real-life large-scale data clustering. We therefore propose SEC, an efficient Spectral Ensemble Clustering method based on co-association matrix. We show that SEC has theoretical equivalence to weighted K-means clustering and results in vastly reduced algorithmic complexity. We then derive the latent consensus function of SEC, which to our best knowledge is among the first to bridge co-association matrix based method to the methods with explicit object functions. The robustness and generalizability of SEC are then investigated to prove the superiority of SEC in theory. We finally extend SEC to meet the challenge rising from incomplete basic partitions, based on which a scheme for big data clustering can be formed. Experimental results on various real-world data sets demonstrate that SEC is an effective and efficient competitor to some state-of-the-art ensemble clustering methods and is also suitable for big data clustering.
Image Manipulation Detection using Deep Learning in Tensor Flow

Images and videos becomes one of the principle means of communication these days. Validating the authenticity of the image has been the active research area for last decade. When an image or video is obtained as the evidence it can be used as probative only if it is authentic. Convolution Neural Networks (CNN) have been widely used in automatic image classification, Image Recognition and Identifying image Manipulation. CNN is efficient deep neural network that can study concurrently with the help of large datasets. Recent studies have indicated that the architectures of CNN tailored for identifying manipulated image will provide least efficiency when the image is directly fed into the network. Deep Learning is the branch of machine learning that learns the features by hierarchical representation where higher-level features are defined from lower-level concepts. In this paper, we make use of deep learning known as CNN to classify the manipulated image which is capable of automatically learning traces left by editing of the image by applying the filter that retrieves altered relationship among the pixels of the image and experiments were done in TensorFlow framework. Results showed that manipulations like median filtering, Gaussian blurring, resizing and cut and paste forgery can be detected with an average accuracy of 97%.
Text mining and probabilistic language modeling for online review spam detection

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.
Computers and modern art: digital art museum

This paper focuses on the relationship between fine art movements in the 20th C and the pioneers of digital art from 1956 to 1986. The research is part of a project called Digital Art Museum, which is an electronic archive devoted to the history and practice of computer art, and is also active in curating exhibitions of the work. While computer art genres never became mainstream art movements, there are clear areas of common interest, even when these are separated by some decades.
Learning Graph Convolution Filters from Data Manifold

Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending convolution operations to the nonEuclidean geometry. Although various types of convolution operations have been proposed for graphs or manifolds, their connections with traditional convolution over grid-structured data are not well-understood. In this paper, we show that depthwise separable convolution can be successfully generalized for the unification of both graph-based and grid-based convolution methods. Based on this insight we propose a novel Depthwise Separable Graph Convolution (DSGC) approach which is compatible with the tradition convolution network and subsumes existing convolution methods as special cases. It is equipped with the combined strengths in model expressiveness, compatibility (relatively small number of parameters), modularity and computational efficiency in training. Extensive experiments show the outstanding performance of DSGC in comparison with strong baselines on multi-domain benchmark datasets.
Vulvar and Vaginal Atrophy: Physiology, Clinical Presentation, and Treatment Considerations.

Vulvovaginal atrophy is a common condition associated with decreased estrogenization of the vaginal tissue. Symptoms include vaginal dryness, irritation, itching, soreness, burning, dyspareunia, discharge, urinary frequency, and urgency. It can occur at any time in a woman's life cycle, although more commonly in the postmenopausal phase, during which the prevalence is approximately 50%. Despite the high prevalence and the substantial effect on quality of life, vulvovaginal atrophy often remains underreported and undertreated. This article aims to review the physiology, clinical presentation, assessment, and current recommendations for treatment, including aspects of effectiveness and safety of local vaginal estrogen therapies.
Observer-Based Measurement of Facial Expression With the Facial Action Coding System

to name a few. Because of its importance to the study of emotion, a number of observer-based systems of facial expression measurement have been developed Using FACS and viewing video-recorded facial behavior at frame rate and slow motion, coders can manually code nearly all possible facial expressions, which are decomposed into action units (AUs). Action units, with some qualifications , are the smallest visually discriminable facial movements. By comparison, other systems are less thorough (Malatesta et al., 1989), fail to differentiate between some anatomically distinct movements (Oster, Hegley, & Nagel, 1992), consider movements that are not anatomically distinct as separable (Oster et al., 1992), and often assume a one-to-one mapping between facial expression and emotion (for a review of these systems, see Cohn & Ekman, in press). Unlike systems that use emotion labels to describe expression , FACS explicitly distinguishes between facial actions and inferences about what they mean. FACS itself is descriptive and includes no emotion-specified descriptors. Hypotheses and inferences about the emotional meaning of facial actions are extrinsic to FACS. If one wishes to make emotion based inferences from FACS codes, a variety of related resources exist. These include the FACS Investigators' Guide These resources suggest combination rules for defining emotion-specified expressions from FACS action units, but this inferential step remains extrinsic to FACS. Because of its descriptive power, FACS is regarded by many as the standard measure for facial behavior and is used widely in diverse fields. Beyond emotion science, these include facial neuromuscular disorders
VGGFace2: A Dataset for Recognising Faces across Pose and Age

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians). The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimise the label noise. We describe how the dataset was collected, in particular the automated and manual filtering stages to ensure a high accuracy for the images of each identity. To assess face recognition performance using the new dataset, we train ResNet-50 (with and without Squeeze-and-Excitation blocks) Convolutional Neural Networks on VGGFace2, on MS-Celeb-1M, and on their union, and show that training on VGGFace2 leads to improved recognition performance over pose and age. Finally, using the models trained on these datasets, we demonstrate state-of-the-art performance on the IJB-A and IJB-B face recognition benchmarks, exceeding the previous state-of-the-art by a large margin. The dataset and models are publicly available.
Merging supply chain and blockchain technologies

Technology has been playing a major role in our lives. One definition for technology is “all the knowledge, products, processes, tools, methods and systems employed in the creation of goods or in providing services”. This makes technological innovations raise the competitiveness between organizations that depend on supply chain and logistics in the global market. With increasing competitiveness, new challenges arise due to lack of information and assets tractability. This paper introduces three scenarios for solving these challenges using the Blockchain technology. In this work, Blockchain technology targets two main issues within the supply chain, namely, data transparency and resource sharing. These issues are reflected into the organization's strategies and
Deep Hashing Network for Efficient Similarity Retrieval

Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest neighbor search for large-scale multimedia retrieval. Supervised hashing, which improves the quality of hash coding by exploiting the semantic similarity on data pairs, has received increasing attention recently. For most existing supervised hashing methods for image retrieval, an image is first represented as a vector of hand-crafted or machine-learned features, followed by another separate quantization step that generates binary codes. However, suboptimal hash coding may be produced, because the quantization error is not statistically minimized and the feature representation is not optimally compatible with the binary coding. In this paper, we propose a novel Deep Hashing Network (DHN) architecture for supervised hashing, in which we jointly learn good image representation tailored to hash coding and formally control the quantization error. The DHN model constitutes four key components: (1) a subnetwork with multiple convolution-pooling layers to capture image representations; (2) a fully-connected hashing layer to generate compact binary hash codes; (3) a pairwise crossentropy loss layer for similarity-preserving learning; and (4) a pairwise quantization loss for controlling hashing quality. Extensive experiments on standard image retrieval datasets show the proposed DHN model yields substantial boosts over latest state-of-the-art hashing methods.
The biology of human psychosexual differentiation

Most attempts to identify biological underpinnings of gender identity and sexual orientation in humans have investigated effects of sex steroids, so pivotal in the differentiation of the genitalia, showing strong parallels between animals and the human. The information on humans is derived from the so-called 'experiments of nature', clinical entities with a lesser-than-normal androgen exposure in XY subjects and a higher than normal androgen exposure in XX subjects. Prenatal androgenization appears to predispose to a male gender identity development, but apparently not decisively since 40-50% of 46,XY intersexed children with a history of prenatal androgen exposure do not develop a male gender identity. Obviously, male-to-female transsexuals, with a normal androgen exposure prenatally (there is no serious evidence to the contrary) develop a female gender identity, through unknown biological mechanisms apparently overriding the effects of prenatal androgens. The latest studies in 46, XX subjects exposed to prenatal androgens show that prenatal androgenization of 46,XX fetuses leads to marked masculinization of later gender-related behavior but does not lead to gender confusion/dysphoria. The example of female-to-male transsexuals, without evidence of prenatal androgen exposure, indicates that a male gender identity can develop without a significant androgen stimulus. So we are far away from any comprehensive understanding of hormonal imprinting on gender identity formation. Brain studies in homosexuals have not held up in replication studies or are in need of replication in transsexuals. Genetic studies and the fraternal birth order hypothesis provide indications of familial clustering of homosexuality but in many homosexuals these genetic patterns cannot be identified. The biological explanations advanced for the birth order hypothesis lack any experimental support.
Temporal Attention as a Scaffold for Language Development

Language is one of the most fascinating abilities that humans possess. Infants demonstrate an amazing repertoire of linguistic abilities from very early on and reach an adult-like form incredibly fast. However, language is not acquired all at once but in an incremental fashion. In this article we propose that the attentional system may be one of the sources for this developmental trajectory in language acquisition. At birth, infants are endowed with an attentional system fully driven by salient stimuli in their environment, such as prosodic information (e.g., rhythm or pitch). Early stages of language acquisition could benefit from this readily available, stimulus-driven attention to simplify the complex speech input and allow word segmentation. At later stages of development, infants are progressively able to selectively attend to specific elements while disregarding others. This attentional ability could allow them to learn distant non-adjacent rules needed for morphosyntactic acquisition. Because non-adjacent dependencies occur at distant moments in time, learning these dependencies may require correctly orienting attention in the temporal domain. Here, we gather evidence uncovering the intimate relationship between the development of attention and language. We aim to provide a novel approach to human development, bridging together temporal attention and language acquisition.
A Simulated Autonomous Car

This dissertation describes a simulated autonomous car capable of driving on urbanstyle roads. The system is built around TORCS, an open source racing car simulator. Two real-time solutions are implemented; a reactive prototype using a neural network and a more complex deliberative approach using a sense, plan, act architecture. The deliberative system uses vision data fused with simulated laser range data to reliably detect road markings. The detected road markings are then used to plan a parabolic path and compute a safe speed for the vehicle. The vehicle uses a simulated global positioning/inertial measurement sensor to guide it along the desired path with the throttle, brakes, and steering being controlled using proportional controllers. The vehicle is able to reliably navigate the test track maintaining a safe road position at speeds of up to 40km/h.
Threat analysis of software systems: A systematic literature review

Architectural threat analysis has become an important cornerstone for organizations concerned with developing secure software. Due to the large number of existing techniques it is becoming more challenging for practitioners to select an appropriate threat analysis technique. Therefore, we conducted a systematic literature review (SLR) of the existing techniques for threat analysis. In our study we compare 26 methodologies for what concerns their applicability, characteristics of the required input for analysis, characteristics of analysis procedure, characteristics of analysis outcomes and ease of adoption. We also provide insight into the obstacles for adopting the existing approaches and discuss the current state of their adoption in software engineering trends (e.g. Agile, DevOps, etc.). As a summary of our findings we have observed that: the analysis procedure is not precisely defined, there is a lack of quality assurance of analysis outcomes and tool support and validation are limited.
An Automatic Knowledge Graph Creation Framework from Natural Language Text

Knowledge graphs (KG) play a crucial role in many modern applications. However, constructing a KG from natural language text is challenging due to the complex structure of the text. Recently, many approaches have been proposed to transform natural language text to triples to obtain KGs. Such approaches have not yet provided efficient results for mapping extracted elements of triples, especially the predicate, to their equivalent elements in a KG. Predicate mapping is essential because it can reduce the heterogeneity of the data and increase the searchability over a KG. In this article, we propose T2KG, an automatic KG creation framework for natural language text, to more effectively map natural language text to predicates. In our framework, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its corresponding predicate in a KG. Based on experimental results, the hybrid approach can identify more similar predicate pairs than a baseline method in the predicate mapping task. An experiment on KG creation is also conducted to investigate the performance of the T2KG. The experimental results show that the T2KG also outperforms the baseline in KG creation. Although KG creation is conducted in open domains, in which prior knowledge is not provided, the T2KG still achieves an F1 score of approximately 50% when generating triples in the KG creation task. In addition, an empirical study on knowledge population using various text sources is conducted, and the results indicate the T2KG could be used to obtain knowledge that is not currently available from DBpedia. key words: knowledge graph, knowledge discovery, knowledge extraction, linked data