nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Image-to-Image Translation with Generative Adversarial Networks

It’s useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.), while keeping the original structure or semantics. We define this requirement as the ”image-to-image translation” problem, and propose a general approach to achieve it, based on deep convolutional and conditional generative adversarial networks (GANs), which has gained a phenomenal success to learn mapping images from noise input since 2014. In this work, we develop a two step (unsupervised) learning method to translate images between different domains by using unlabeled images without specifying any correspondence between them, so that to avoid the cost of acquiring labeled data. Compared with prior works, we demonstrated the capacity of generality in our model, by which variance of translations can be conduct by a single type of model. Such capability is desirable in applications like bidirectional translation
TOWARDS A GLOBAL ELEVATION PRODUCT : COMBINATION OF MULTI-SOURCE DIGITAL ELEVATION MODELS

Mapping and monitoring the Earth’s surface is subject to various application fields. This three-dimensional problem is usually split into a two-dimensional planar description of the Earth’s surface supplemented with the height information provided as separate digital elevation models. However, from the global point of view there is still a tremendous need for suitable height information at a resolution level of about 1 to 3 arc seconds. The Shuttle Radar Topography Mission (SRTM) will help to fill this gap by providing high quality digital elevation model (DEM). In order to provide terrain information for areas where either SRTM data will not be available or the corresponding resolution is not sufficient a combination with other sources will be required. The German Remote Sensing Data Center (DFD) of DLR intends to implement such a global DEM service. All SRTM/X-SAR data will be processed to elevation data and will serve as the backbone as it provides a global net of homogenous elevation information. This net can be used for the absolute orientation of other DEMs as geometric reference, but also for the improvement of the height quality by integrating elevation data from a variety of other sources by DEM fusion and mosaicking techniques. The paper describes the principles and corresponding accuracy of space borne missions for the derivation of DEMs. The main focus is on the DEM products of SRTM/X-SAR. Furthermore, the ERS-1/2 tandem configuration and the MOMS-2P mission are described. The technique to combine multi-source DEMs is outlined, which is based on the concept of height error maps. The method is illustrated by practical examples. Finally, an outlook is given on further investigations.
Review on simultaneous localization and mapping (SLAM)

Simultaneous localization and mapping (SLAM) is a technique applied in artificial intelligence mobile robot for a self-exploration in numerous geographical environment. SLAM becomes fundamental research area in recent days as it promising solution in solving most of problems which related to the self-exploratory oriented artificial intelligence mobile robot field. For example, the capability to explore without any prior knowledge on environment it explores and without any human interference. The unique feature in SLAM is that the process of mapping and localization is done concurrently and recursively. Since SLAM introduction, many SLAM algorithms have been proposed to apply SLAM technique in real practice. The aim of this paper is to provide an insightful review on information background, recent development, feature, implementation and recent issue in SLAM.
Paper Abstract Writing through Editing Mechanism

We present a paper abstract writing system based on an attentive neural sequenceto-sequence model that can take a title as input and automatically generate an abstract. We design a novel Writing-editing Network that can attend to both the title and the previously generated abstract drafts and then iteratively revise and polish the abstract. With two series of Turing tests, where the human judges are asked to distinguish the system-generated abstracts from human-written ones, our system passes Turing tests by junior domain experts at a rate up to 30% and by nonexpert at a rate up to 80%.1
Computational personality traits assessment: A review

Computational personality traits assessment is one of an interesting areas in affective computing. It becomes popular because personality identification can be used in many areas and get benefits. Such areas are business, politics, education, social media, medicine, and user interface design. The famous statement "Face is a mirror of the mind" proves that person's appearance depends on the inner aspects of a person. Conversely, Person's behavior and appearance describe the person's personality, so an analyze on appearance and behavior gives knowledge on personality traits. There are varieties of methods have been discovered by researchers to assess personality computationally with various machine learning algorithms. In this paper reviews methods and theories involved in psychological traits assessment and evolution of computational psychological traits assessment with different machine learning algorithms and different feature sets.
Structural mechanism of muscle contraction.

X-ray crystallography shows the myosin cross-bridge to exist in two conformations, the beginning and end of the "power stroke." A long lever-arm undergoes a 60 degrees to 70 degrees rotation between the two states. This rotation is coupled with changes in the active site (OPEN to CLOSED) and phosphate release. Actin binding mediates the transition from CLOSED to OPEN. Kinetics shows that the binding of myosin to actin is a two-step process which affects ATP and ADP affinity. The structural basis of these effects is not explained by the presently known conformers of myosin. Therefore, other states of the myosin cross-bridge must exist. Moreover, cryoelectronmicroscopy has revealed other angles of the cross-bridge lever arm induced by ADP binding. These structural states are presently being characterized by site-directed mutagenesis coupled with kinetic analysis.
Linking ImageNet WordNet Synsets with Wikidata

The linkage of ImageNet WordNet synsets to Wikidata items will leverage deep learning algorithm with access to a rich multilingual knowledge graph. Here I will describe our ongoing efforts in linking the two resources and issues faced in matching the Wikidata and WordNet knowledge graphs. I show an example on how the linkage can be used in a deep learning setting with real-time image classification and labeling in a non-English language and discuss what opportunities lies ahead.
Explainable Reasoning over Knowledge Graphs for Recommendation

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. In this paper, we contribute a new model named Knowledgeaware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.  Prior efforts have shown the importance of incorporating auxiliary data into recommender systems, such as user profiles (Wang et al. 2018c) and item attributes (Bayer et al. 2017). Recently, knowledge graphs (KGs) have attracted increasing attention (Zhang et al. 2016; Shu et al. 2018; Wang et al. 2018a), due to its comprehensive auxiliary data: background knowledge of items and their relations amongst them. It usually organizes the facts of items in the form of triplets like (Ed Sheeran, IsSingerOf, Shape of You), which can be seamlessly integrated with user-item interactions (Chaudhari, Azaria, and Mitchell 2016; Cao et al. 2017). More important, by exploring the interlinks within ∗The first three authors have equal contribution. †Dingxian Wang is the corresponding author.  Knowledge Graph Shape of You Ed Sheeran SungBy WrittenBy ÷
Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms

Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information. However, events in one sentence are usually interdependent and sentence-level information is often insufficient to resolve ambiguities for some types of events. This paper proposes a novel framework dubbed as Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (HBTNGMA) to solve the two problems simultaneously. Firstly, we propose a hierarchical and bias tagging networks to detect multiple events in one sentence collectively. Then, we devise a gated multi-level attention to automatically extract and dynamically fuse the sentence-level and document-level information. The experimental results on the widely used ACE 2005 dataset show that our approach significantly outperforms other state-of-the-art methods.
Learning from one's own errors and those of others.

Three experiments investigated the effects of making errors oneself, as compared to just hearing the correct answer without error generation, hearing another person make an error, or being "on-the-hook," that is, possibly but not necessarily being the person who would be "called-on" to give a response. In all three experiments, generating either an error of commission or generating the correct response, oneself, out loud, compared to being a person who heard another's commission errors (or correct responses), was beneficial for later recall of the correct answer. Experiment 1 suggested that the decrement in recall from self- to other-generation could be partially offset by being "on-the-hook." However, this benefit was fragile and did not hold up either at a delay or when the presence of the other participants was downplayed. The beneficial effect of self-generation, both of correct responses and of errors of commission is consistent with reconsolidation theory. That theory holds that retrieval has a special status as a memory process that renders the retrieved traces labile. If the person was correct, reconsolidating the correct trace results in strengthening. If wrong, the malleability of the retrieved trace implied by reconsolidation theory makes it open to enhanced modification and correction. If the person was not the agent who retrieved, though, such as when someone else retrieves information, or when nothing is retrieved as is the case with omission errors (which we argue is truly how the term "unsuccessful retrieval" should be used), the benefit conferred by the special malleability entailed by the postulated reconsolidation process does not obtain.
Core competencies in social constructionist supervision?

Family therapy is moving increasingly toward evidence-based practice and competency-based training. This article explores what might seem to be an unlikely link between social constructionist supervision, which is based on dialogic and fluid processes of meaning-making, and the increasing reliance on discrete core competencies in the education and training of family therapists. We propose an alternate approach to competencies for supervision with therapists in training that, among other things, invites accountability and provides evaluative props. The approach we propose is based on a set of orientations that we hope reflect the dialogic and contextual nature of social constructionist practice and supervision. These orientations consist of reflexivity and attention to power, fostering polyphony and generativity, collaborative stance, and focus on client resourcefulness. Ideas and questions for supervisors and therapists in training to address the orientations are articulated.
Content-Based Citation Recommendation

We present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as candidates, and rerank the candidates using a discriminative model trained to distinguish between observed and unobserved citations. Unlike previous work, our method does not require metadata such as author names which can be missing, e.g., during the peer review process. Without using metadata, our method outperforms the best reported results on PubMed and DBLP datasets with relative improvements of over 18% in F1@20 and over 22% in MRR. We show empirically that, although adding metadata improves the performance on standard metrics, it favors selfcitations which are less useful in a citation recommendation setup. We release an online portal for citation recommendation based on our method,1 and a new dataset OpenCorpus of 7 million research articles to facilitate future research on this task.
A Capsule Network-based Embedding Model for Search Personalization

Search personalization aims to tailor search results to each specific user based on the user’s personal interests and preferences (i.e., the user profile). Recent research approaches to search personalization by modelling the potential 3-way relationship between the submitted query, the user and the search results (i.e., documents). That relationship is then used to personalize the search results to that user. In this paper, we introduce a novel embedding model based on capsule network, which recently is a breakthrough in deep learning, to model the 3-way relationships for search personalization. In the model, each user (submitted query or returned document) is embedded by a vector in the same vector space. The 3-way relationship is described as a triple of (query, user, document) which is then modeled as a 3-column matrix containing the three embedding vectors. After that, the 3-column matrix is fed into a deep learning architecture to re-rank the search results returned by a basis ranker. Experimental results on query logs from a commercial web search engine show that our model achieves better performances than the basis ranker as well as strong search personalization baselines.
Fusion of Infrared and Visible Images for Robust Person Detection

In the current context of increased surveillance and security, more sophisticated and robust surveillance systems are needed. One idea relies on the use of pairs of video (visible spectrum) and thermal infrared (IR) cameras located around premises of interest. To automate the system, a robust person detection algorithm and the development of an efficient technique enabling the fusion of the information provided by the two sensors becomes necessary and these are described in this chapter. Recently, multi-sensor based image fusion system is a challenging task and fundamental to several modern day image processing applications, such as security systems, defence applications, and intelligent machines. Image fusion techniques have been actively investigated and have wide application in various fields. It is often a vital pre-processing procedure to many computer vision and image processing tasks which are dependent on the acquisition of imaging data via sensors, such as IR and visible. One such task is that of human detection. To detect humans with an artificial system is difficult for a number of reasons as shown in Figure 1 (Gavrila, 2001). The main challenge for a vision-based pedestrian detector is the high degree of variability with the human appearance due to articulated motion, body size, partial occlusion, inconsistent cloth texture, highly cluttered backgrounds and changing lighting conditions.
Performance of recommender algorithms on top-n recommendation tasks

In many commercial systems, the 'best bet' recommendations are shown, but the predicted rating values are not. This is usually referred to as a top-N recommendation task, where the goal of the recommender system is to find a few specific items which are supposed to be most appealing to the user. Common methodologies based on error metrics (such as RMSE) are not a natural fit for evaluating the top-N recommendation task. Rather, top-N performance can be directly measured by alternative methodologies based on accuracy metrics (such as precision/recall).
 An extensive evaluation of several state-of-the art recommender algorithms suggests that algorithms optimized for minimizing RMSE do not necessarily perform as expected in terms of top-N recommendation task. Results show that improvements in RMSE often do not translate into accuracy improvements. In particular, a naive non-personalized algorithm can outperform some common recommendation approaches and almost match the accuracy of sophisticated algorithms. Another finding is that the very few top popular items can skew the top-N performance. The analysis points out that when evaluating a recommender algorithm on the top-N recommendation task, the test set should be chosen carefully in order to not bias accuracy metrics towards non-personalized solutions. Finally, we offer practitioners new variants of two collaborative filtering algorithms that, regardless of their RMSE, significantly outperform other recommender algorithms in pursuing the top-N recommendation task, with offering additional practical advantages. This comes at surprise given the simplicity of these two methods.
Demographic Inference on Twitter using Recursive Neural Networks

In social media, demographic inference is a critical task in order to gain a better understanding of a cohort and to facilitate interacting with one’s audience. Most previous work has made independence assumptions over topological, textual and label information on social networks. In this work, we employ recursive neural networks to break down these independence assumptions to obtain inference about demographic characteristics on Twitter. We show that our model performs better than existing models including the state-of-theart.
Abnormal event detection in tourism video based on salient spatio-temporal features and sparse combination learning

With the booming development of tourism, travel security problems are becoming more and more prominent. Congestion, stampedes, fights and other tourism emergency events occurred frequently, which should be a wake-up call for tourism security. Therefore, it is of great research value and application prospect to real-time monitor tourists and detect abnormal events in tourism surveillance video by using computer vision and video intelligent processing technology, which can realize the timely forecast and early warning of tourism emergencies. At present, although most of the video-based abnormal event detection methods work well in simple scenes, there are often problems such as low detection rate and high false positive rate in complex motion scenarios, and the detection of abnormal events can’t be processed in real time. To tackle these issues, we propose an abnormal event detection model in tourism video based on salient spatio-temporal features and sparse combination learning, which has good robustness and timeliness in complex motion scenarios and can be adapted to real-time anomaly detection in practical applications. Specifically, spatio-temporal gradient model is combined with foreground detection to extract 3D gradient features on the foreground target of video sequence as the salient spatio-temporal features, which can eliminate the interference of the background. Sparse combination learning algorithm is used to establish the abnormal event detection model, which can realize the real-time detection of abnormal events. In addition, we construct a new ScenicSpot dataset with 18 video clips (5964 frames) containing both normal and abnormal events. The experimental results on ScenicSpot dataset and two standard benchmark datasets show that our method can realize the automatic detection and recognition of tourists’ abnormal behavior, and has better performance compared with the classical methods.
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study

Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, which captures key structural elements in the resulting embedding space. However, to date, there has been little work exploring exactly which topological structures are being learned in the embeddings process. In this paper, we investigate if graph embeddings are approximating something analogous with traditional vertex level graph features. If such a relationship can be found, it could be used to provide a theoretical insight into how graph embedding approaches function. We perform this investigation by predicting known topological features, using supervised and unsupervised methods, directly from the embedding space. If a mapping between the embeddings and topological features can be found, then we argue that the structural information encapsulated by the features is represented in the embedding space. To explore this, we present extensive experimental evaluation from five stateof-the-art unsupervised graph embedding techniques, across a range of empirical graph datasets, measuring a selection of topological features. We demonstrate that several topological features are indeed being approximated by the embedding space, allowing key insight into how graph embeddings create good representations.
Towards Efficient Model for Automatic Text Summarization

Automatic text summarization aims at producing summary from a document or a set of documents. It has become a widely explored area of research as the need for immediate access to relevant and precise information that can effectively represent huge amount of information. Because relevant information is scattered across a given document, every user is faced with the problem of going through a large amount of information to get to the main gist of a text. This calls for the need to be able to view a smaller portion of large documents without necessarily losing the important aspect of the information contained therein. This paper provides an overview of current technologies, techniques and challenges in automatic text summarization. Consequently, we discuss our efforts at providing an efficient model for compact and concise documents summarization using sentence scoring algorithm and a sentence reduction algorithm. Based on comparison with the wellknown Copernic summarizer and the FreeSummarizer, our system showed that the summarized sentences contain more relevant information such that selected sentences are relevant to the query posed by the user CS Concepts • Computing methodologies ➝Artificial intelligence ➝Natural language processing ➝Information extraction • Information Systems ➝Information Retrieval ➝Retrieval tasks and goals ➝