nils-herrmann/clean_scirepeval huggingface dataset:
Profiling relational data: a survey

Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases.
Joint Embedding of Words and Labels for Text Classification

•  To force the label embedding as the anchor points for each classes, we regularize the learned label embeddings to be on its corresponding manifold Model Yahoo DBPedia AGNews Yelp P. Yelp F. Bag-ofwords 68.9 96.6 88.8 92.2 58 CNN 70.94 98.28 91.45 95.11 59.48 LSTM 70.84 98.55 86.06 94.74 58.17 Deep CNN 73.43 98.71 91.27 95.72 64.26 SWEM 73.53 98.42 92.24 93.76 61.11 fastText 72.3 98.6 92.5 95.7 63.9 HAN 75.8 Bi-BloSAN 76.28 98.77 93.32 94.56 62.13 LEAM 77.42 99.02 92.45 95.31 64.09 Test Accuracy on document classification tasks, in percentage
A Discriminative Feature Learning Approach for Deep Face Recognition

NTechLAB facenx_large Google FaceNet v8 Beijing Faceall Co. FaceAll_Norm_1600 Beijing Faceall Co. FaceAll_1600 large 73.300% 70.496% 64.803% 63.977% 85.081% 86.473% 67.118% 63.960% Barebones_FR cnn NTechLAB facenx_small 3DiVi Company – tdvm6 small 59.363% 58.218% 33.705% 59.036% 66.366% 36.927% model AModel BModel C(Proposed) small 41.863% 57.175% 65.234% 41.297% 69.897% 76.516% Method Protocol Identification Acc. (Set 1) Verification Acc. (Set 1) For generic object, scene or action recognition. The deeply learned features need to be separable. Because the classes of the possible testing samples are within the training set, the predicted labels dominate the performance.
The development of arithmetical abilities.

Arithmetical skills are essential to the effective exercise of citizenship in a numerate society. How these skills are acquired, or fail to be acquired, is of great importance not only to individual children but to the organisation of formal education and its role in society.



The evidence on the normal and abnormal developmental progression of arithmetical abilities is reviewed; in particular, evidence for arithmetical ability arising from innate specific cognitive skills (innate numerosity) vs. general cognitive abilities (the Piagetian view) is compared.



These include evidence from infancy research, neuropsychological studies of developmental dyscalculia, neuroimaging and genetics. The development of arithmetical abilities can be described in terms of the idea of numerosity -- the number of objects in a set. Early arithmetic is usually thought of as the effects on numerosity of operations on sets such as set union. The child's concept of numerosity appears to be innate, as infants, even in the first week of life, seem to discriminate visual arrays on the basis of numerosity. Development can be seen in terms of an increasingly sophisticated understanding of numerosity and its implications, and in increasing skill in manipulating numerosities. The impairment in the capacity to learn arithmetic -- dyscalculia -- can be interpreted in many cases as a deficit in the concept in the child's concept of numerosity. The neuroanatomical bases of arithmetical development and other outstanding issues are discussed.



The evidence broadly supports the idea of an innate specific capacity for acquiring arithmetical skills, but the effects of the content of learning, and the timing of learning in the course of development, requires further investigation.
Different emotional reactions to different groups: a sociofunctional threat-based approach to "prejudice".

The authors suggest that the traditional conception of prejudice--as a general attitude or evaluation--can problematically obscure the rich texturing of emotions that people feel toward different groups. Derived from a sociofunctional approach, the authors predicted that groups believed to pose qualitatively distinct threats to in-group resources or processes would evoke qualitatively distinct and functionally relevant emotional reactions. Participants' reactions to a range of social groups provided a data set unique in the scope of emotional reactions and threat beliefs explored. As predicted, different groups elicited different profiles of emotion and threat reactions, and this diversity was often masked by general measures of prejudice and threat. Moreover, threat and emotion profiles were associated with one another in the manner predicted: Specific classes of threat were linked to specific, functionally relevant emotions, and groups similar in the threat profiles they elicited were also similar in the emotion profiles they elicited.
WIDEBAND MICROSTRIP-FED PRINTED BOW-TIE ANTENNA FOR PHASED-ARRAY SYSTEMS

A microstrip-fed printed bow-tie antenna is presented in order to achieve wide bandwidth, high gain, and size reduction. A comparison between the bow-tie and the quasi-Yagi (dipole and director) antennas shows that the bow-tie antenna has a wider bandwidth, higher gain, lower front-to-back ratio, lower cross-polarization level, and smaller size. Two-element arrays are designed and their characteristics are compared. The bow-tie antenna yields lower coupling for the same distance between elements.
1-MW solar power inverter with boost converter using all SiC power module

Recently, the market of the PV power plant is growing up in the Asian market. In the PV power plant, typically, an inverter which has the rated power of few hundreds kVA is applied to feed the power to the grid. A 1-MW solar power inverter which employs all SiC Power Modules has been developed. The developed solar power inverter consists of two conversion stages, first stage is a boost converter and second stage is a T-type NPC inverter. A chopper module in the boost converter is configured with SiC-based MOSFETs and Schottky Barrier Diodes, and 48 chopper modules are used in parallel. Each chopper module is controlled individually. The T-type NPC inverter stage is configured with Si-based IGBTs and RB-IGBTs. In this paper, the circuit configurations of the developed solar power inverter, employed SiC-based power devices, and the control scheme are described in detail. In the end, the total efficiencies for the minimum, nominal, and maximum DC voltages are experimentally measured. The measured efficiency at the rated output power varies from 98 % to 98.6 % depending on the values of DC input voltage. The maximum efficiency of 98.8 % is achieved in the case of maximum DC input voltage.
Motivation reconsidered: the concept of competence.

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading motivation reconsidered the concept of competence is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.
EMANCIPATED WOMEN OF THE GREAT GATSBY

In all Fitzgerald’s fiction women characters are decorative figures of seemingly fragile beauty, though in fact they are often vain, egoistical, even destructive and ruthless and thus frequently the survivors. As prime consumers, they are never capable of idealism or intellectual or artistic interests, nor do they experience passion. His last novel, The Last Tycoon, shows some development; for the first time the narrator is a young woman bent on trying to find the truth about the ruthless social and economic complexity of 1920s Hollywood, but she has no adult role to play in its sexual, artistic or political activities. Women characters are marginalized into purely personal areas of experience.
How Knowledge Transfer Impacts Performance: A Multilevel Model of Benefits and Liabilities

When does knowledge transfer benefit performance? Combining field data from a global consulting firm with an agent-based model, we examine how efforts to supplement one’s knowledge from coworkers interact with individual, organizational, and environmental characteristics to impact organizational performance. We find that once cost and interpersonal exchange are included in the analysis, the impact of knowledge transfer is highly contingent. Depending on specific characteristics and circumstances, knowledge transfer can better, matter little to, or even harm performance. Three illustrative studies clarify puzzling past results and offer specific boundary conditions: (1) At the individual level, better organizational support for employee learning diminishes the benefit of knowledge transfer for organizational performance. (2) At the organization level, broader access to organizational memory makes global knowledge transfer less beneficial to performance. (3) When the organizational environment becomes more turbulent, the organizational performance benefits of knowledge transfer decrease. The findings imply that organizations may forgo investments in both organizational memory and knowledge exchange, that wide-ranging knowledge exchange may be unimportant or even harmful for performance, and that organizations operating in turbulent environments may find that investment in knowledge exchange undermines performance rather than enhances it. At a time when practitioners are urged to make investments in facilitating knowledge transfer and collaboration, appreciation of the complex relationship between knowledge transfer and performance will help in reaping benefits while avoiding liabilities.
Modeling value propositions in e-Business

In this paper we provide a conceptual approach to modelling value propositions. We argue that rigorous modelling in an ontological style could improve several aspects of business. Modelling and mapping value propositions helps better understanding the value a company wants to offer its customers and makes it communicable between various stakeholders. Using a common language (ontology) in defining a company's offering brings manager's mental models into a common form. Further, conceptually seized value propositions are comparable to the value propositions of a firm's competitors because the follow a rigid framework and make it possible to identify the competitive position of a firm's value proposition.
Medical imaging diagnosis of early Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases that influences the central nervous system, often leading to dire consequences for quality of life. The disease goes through some stages mainly divided into early, moderate, and severe. Among them, the early stage is the most important as medical intervention has the potential to alter the natural progression of the condition. In practice, the early diagnosis is a challenge since the neurodegenerative changes can precede the onset of clinical symptoms by 10-15 years. This factor along with other known and unknown ones, hinder the ability for the early diagnosis and treatment of AD. Numerous research efforts have been proposed to address the complex characteristics of AD exploiting various tests including brain imaging that is massively utilized due to its powerful features. This paper aims to highlight our present knowledge on the clinical and computer-based attempts at early diagnosis of AD. We concluded that the door is still open for further research especially with the rapid advances in scanning and computer-based technologies.
Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer

Human body part parsing, or human semantic part segmentation, is fundamental to many computer vision tasks. In conventional semantic segmentation methods, the ground truth segmentations are provided, and fully convolutional networks (FCN) are trained in an end-to-end scheme. Although these methods have demonstrated impressive results, their performance highly depends on the quantity and quality of training data. In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations. Our key idea is to exploit the anatomical similarity among human to transfer the parsing results of a person to another person with similar pose. Using these estimated results as additional training data, our semi-supervised model outperforms its strong-supervised counterpart by 6 mIOU on the PASCAL-Person-Part dataset [6], and we achieve state-of-the-art human parsing results. Our approach is general and can be readily extended to other object/animal parsing task assuming that their anatomical similarity can be annotated by keypoints. The proposed model and accompanying source code will be made publicly available.
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction

We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep apnea (0.8298). We compare two semi-supervised training methods, semi-supervised sequence learning and heuristic pretraining, and show they outperform hand-engineered biomarkers from the medical literature. We believe our work suggests a new approach to patient risk stratification based on cardiovascular risk scores derived from popular wearables such as Fitbit, Apple Watch, or Android Wear.
Sentiment Analysis: Mining Opinions, Sentiments, and Emotions

With the increasing development of Web 2.0, such as social media and online businesses, the need for perception of opinions, attitudes, and emotions grows rapidly. Sentiment analysis, the topic studying such subjective feelings expressed in text, has attracted significant attention from both the research community and industry. Although we have known sentiment analysis as a task of mining opinions expressed in text and analyzing the entailed sentiments and emotions, so far the task is still vaguely defined in the research literature because it involves many overlapping concepts and sub-tasks. Because this is an important area of scientific research, the field needs to clear this vagueness and define various directions and aspects in detail, especially for students, scholars, and developers new to the field. In fact, the field includes numerous natural language processing tasks with different aims (such as sentiment classification, opinion information extraction, opinion summarization, sentiment retrieval, etc.) and these have multiple solution paths. Bing Liu has done a great job in this book in providing a thorough exploration and an anatomy of the sentiment analysis problem and conveyed a wealth of knowledge about different aspects of the field.
An Entropy-Based Network Anomaly Detection Method

Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i) preparation of a concept of original entropy-based network anomaly detection method, (ii) implementation of the method, (iii) preparation of original dataset, (iv) evaluation of the method.
Recurrent neural networks for language understanding

Recurrent Neural Network Language Models (RNN-LMs) have recently shown exceptional performance across a variety of applications. In this paper, we modify the architecture to perform Language Understanding, and advance the state-of-the-art for the widely used ATIS dataset. The core of our approach is to take words as input as in a standard RNN-LM, and then to predict slot labels rather than words on the output side. We present several variations that differ in the amount of word context that is used on the input side, and in the use of non-lexical features. Remarkably, our simplest model produces state-of-the-art results, and we advance state-of-the-art through the use of bagof-words, word embedding, named-entity, syntactic, and wordclass features. Analysis indicates that the superior performance is attributable to the task-specific word representations learned by the RNN.
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks

We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, accounting for prior knowledge such as the users active vocabularies in previous documents. As applications of personalized image captioning, we tackle two post automation tasks: hashtag prediction and post generation, on our newly collected Instagram dataset, consisting of 1.1M posts from 6.3K users. We propose a novel captioning model named Context Sequence Memory Network (CSMN). Its unique updates over previous memory network models include (i) exploiting memory as a repository for multiple types of context information, (ii) appending previously generated words into memory to capture long-term information without suffering from the vanishing gradient problem, and (iii) adopting CNN memory structure to jointly represent nearby ordered memory slots for better context understanding. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the effectiveness of the three novel features of CSMN and its performance enhancement for personalized image captioning over state-of-the-art captioning models.
Neural Headline Generation on Abstract Meaning Representation

Neural network-based encoder-decoder models are among recent attractive methodologies for tackling natural language generation tasks. This paper investigates the usefulness of structural syntactic and semantic information additionally incorporated in a baseline neural attention-based model. We encode results obtained from an abstract meaning representation (AMR) parser using a modified version of Tree-LSTM. Our proposed attention-based AMR encoder-decoder model improves headline generation benchmarks compared with the baseline neural attention-based model.
What factors influence knowledge sharing in organizations? A social dilemma perspective of social media communication

Enterprise  social  media  platforms  provide  new  ways  of  sharing  knowledge  and communicating within organizations to benefit  from the social capital and valuable knowledge that employees have. Drawing on social dilemma and self‐determination theory, the aim of the study is to understand what factors drive employees’ participation and what factors hamper their participation in enterprise social media.  The model  is  tested using  statistical methods on a  sample of 114 respondents  in  Denmark.  Qualitative  data  is  used  to  elaborate  and  explain  quantitative  results‘ findings.  The proposed knowledge sharing framework helps to understand what factors impact  engagement  on  social  media.  Furthermore  the  article  suggests  different  types  of interventions to overcome the social dilemma of knowledge sharing.   Our findings pinpoint towards the general drivers and barriers to knowledge sharing within organizations.  The  significant  drivers  are:  enjoy  helping  others, monetary  rewards, management support, change of knowledge sharing behavior and recognition. The significant identified barriers to knowledge sharing are: change of behavior, lack of trust and lack of time.  The study contributes to an understanding of factors leading to the success or failure of enterprise social media drawing on self‐determination and social dilemma theory.
Hierarchical and Spatio-Temporal Sparse Representation for Human Action Recognition

In this paper, we present a novel two-layer video representation for human action recognition employing hierarchical group sparse encoding technique and spatio-temporal structure. In the first layer, a new sparse encoding method named locally consistent group sparse coding (LCGSC) is proposed to make full use of motion and appearance information of local features. LCGSC method not only encodes global layouts of features within the same video-level groups, but also captures local correlations between them, which obtains expressive sparse representations of video sequences. Meanwhile, two kinds of efficient location estimation models, namely an absolute location model and a relative location model, are developed to incorporate spatio-temporal structure into LCGSC representations. In the second layer, action-level group is established, where a hierarchical LCGSC encoding scheme is applied to describe videos at different levels of abstractions. On the one hand, the new layer captures higher order dependency between video sequences; on the other hand, it takes label information into consideration to improve discrimination of videos’ representations. The superiorities of our hierarchical framework are demonstrated on several challenging datasets.
Penile Enhancement Procedures with Simultaneous Penile Prosthesis Placement

Here we present an overview of various techniques performed concomitantly during penile prosthesis surgery to enhance penile length and girth. We report on the technique of ventral phalloplasty and its outcomes along with augmentation corporoplasty, suprapubic lipectomy, suspensory ligament release, and girth enhancement procedures. For the serious implanter, outcomes can be improved by combining the use of techniques for each scar incision. These adjuvant procedures are a key addition in the armamentarium for the serious implant surgeon.
Session title: ASTRONOMICAL ASPECTS OF ARCHAEOLOGICAL CULTURES

Astronomy is an inseparable part of ancient cultures. Archaeoastronomical surveys in different countries vary according to the period researchers are most interested in or have the most information of. The aim of the session is to get closer to the notion of what was perceived as important in the sky or on the horizon but in connection with the celestial bodies. One of the major interests is also the significance that was ascribed to it in ancient cultures and in societies, explored by archaeology. The complex relationship between astronomy and other aspects of a culture's life, such as politics and economy, makes astronomical part extremely interesting and revealing. The interdisciplinarity of the subject logically employs modification of anthropological and ethnological approaches in the least. A culture's astronomical concepts interact mostly with religious ideas; people strived to adjust their activities with natural cycles or with especial events in the natural world (on the sky or in connection of the sky with the horizons). Archaeoastronomical research supported by a theoretical erudition can assist in elucidation of such interactions.