Reviewerly/exHarmony huggingface dataset:
Scholars’ Perceptions of Relevance in Bibliography-Based People Recommender System

Collaboration and social networking are increasingly important for academics, yet identifying relevant collaborators requires remarkable effort. While there are various networking services optimized for seeking similarities between the users, the scholarly motive of producing new knowledge calls for assistance in identifying people with complementary qualities. However, there is little empirical understanding of how academics perceive relevance, complementarity, and diversity of individuals in their profession and how these concepts can be optimally embedded in social matching systems. This paper aims to support the development of diversity-enhancing people recommender systems by exploring senior researchers’ perceptions of recommended other scholars at different levels on a similar–different continuum. To conduct the study, we built a recommender system based on topic modeling of scholars’ publications in the DBLP computer science bibliography. A study of 18 senior researchers comprised a controlled experiment and semi-structured interviewing, focusing on their subjective perceptions regarding relevance, similarity, and familiarity of the given recommendations, as well as participants’ readiness to interact with the recommended people. The study implies that the homophily bias (behavioral tendency to select similar others) is strong despite the recognized need for complementarity. While the experiment indicated consistent and significant differences between the perceived relevance of most similar vs. other levels, the interview results imply that the evaluation of the relevance of people recommendations is complex and multifaceted. Despite the inherent bias in selection, the participants could identify highly interesting collaboration opportunities on all levels of similarity.
REDUCTION OF THE FRONTO-PARALLEL BIAS FOR WIDE-BASELINE SEMI-GLOBAL MATCHING

Abstract. Semi-Global Matching (SGM) is a widely-used technique for dense image matching that is popular because of its accuracy and speed. While it works well for textured scenes, it can fail on slanted surfaces particularly in wide-baseline configurations due to the so-called fronto-parallel bias. In this paper, we propose an extension of SGM that utilizes image warping to reduce the fronto-parallel bias in the data term, based on estimating dominant slanted planes. The latter are also used as surface priors improving the smoothness term. Our proposed method calculates disparity maps for each dominant slanted plane and fuses them to obtain the final disparity map. We have quantitatively evaluated our approach outperforming SGM and SGM-P on synthetic data and demonstrate its potential on real data by qualitative results. In this way, we underscore the need to tackle the fronto-parallel bias in particular for wide-baseline configurations in both the data term and the smoothness term of SGM.
Big data in Russian linguistics?

Summary With the advent of large web-based corpora, Russian linguistics steps into the era of “big data”. But how useful are large datasets in our field? What are the advantages? Which problems arise? The present study seeks to shed light on these questions based on an investigation of the Russian paucal construction in the RuTenTen corpus, a web-based corpus with more than ten billion words. The focus is on the choice between adjectives in the nominative ( dve / tri / četyre starye knigi ) and genitive ( dve / tri / četyre staryx knigi ) in paucal constructions with the numerals dve , tri or četyre and a feminine noun. Three generalizations emerge. First, the large RuTenTen dataset enables us to identify predictors that could not be explored in smaller corpora. In particular, it is shown that predicates, modifiers, prepositions and word-order affect the case of the adjective. Second, we identify situations where the RuTenTen data cannot be straightforwardly reconciled with findings from earlier studies or there appear to be discrepancies between different statistical models. In such cases, further research is called for. The effect of the numeral ( dve , tri vs. četyre ) and verbal government are relevant examples. Third, it is shown that adjectives in the nominative have more easily learnable predictors that cover larger classes of examples and show clearer preferences for the relevant case. It is therefore suggested that nominative adjectives have the potential to outcompete adjectives in the genitive over time. Although these three generalizations are valuable additions to our knowledge of Russian paucal constructions, three problems arise. Large internet-based corpora like the RuTenTen corpus (a) are not balanced, (b) involve a certain amount of “noise”, and (c) do not provide metadata. As a consequence of this, it is argued, it may be wise to exercise some caution with regard to conclusions based on “big data”.
A Description Method of Cloud Simulation Model Resources Based on Knowledge Graph

The description of model is the important foundation of realizing cloud computing-based simulation. Current description methods of simulation model resources have difficulty supporting search and discovery of simulation model in cloud architecture environment. So this article proposes a description method of cloud simulation model resources based on knowledge graph, concluding the information that cloud simulation model resources description is needed, and transforms the information into four different RDF triples to construct knowledge graphs for describing cloud simulation model resources. Taking the descriptions of three aircraft models as case study, this article tests the function of search and discovery of simulation model for the specific needs of users on the knowledge graph constructed from the case to prove the availability and effectiveness of this method.
Redundancy-Free Computation Graphs for Graph Neural Networks

Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes' neighbors in a graph. However, because common neighbors are shared between different nodes, this leads to repeated and inefficient computations. We propose Hierarchically Aggregated computation Graphs (HAGs), a new GNN graph representation that explicitly avoids redundancy by managing intermediate aggregation results hierarchically, eliminating repeated computations and unnecessary data transfers in GNN training and inference. We introduce an accurate cost function to quantitatively evaluate the runtime performance of different HAGs and use a novel HAG search algorithm to find optimized HAGs. Experiments show that the HAG representation significantly outperforms the standard GNN graph representation by increasing the end-to-end training throughput by up to 2.8x and reducing the aggregations and data transfers in GNN training by up to 6.3x and 5.6x, while maintaining the original model accuracy.
Indefinite abductive explanations

This paper stems from previous works of the authors in which a new measure of the simplicity of an explanation based on its degree of arbitrariness is proposed: The more the explanation is arbitray, the less appealing it is, with explanations having no arbitrariness – called constrained – being the preferred ones. In previous works, as commonly done in the literature of abductive logic programming, a set of hypotheses is not an explanation, unless it is definite, i.e. it explains all the data belonging to the observation. In this paper, we follow a different perspective and define the concept of indefinite constrained explanations, i.e. constrained explanations that are not definite, but admit some indefiniteness. An indefinite constrained explanation captures the intuition of the existence of an explanation (indefinite explanation) that would best explain the given evidence, while not making arbitrary choices (constrained explanation). The main contribution of the paper is the study of abduction in indefinite deductive theories: specifically, the paper studies the framework of abductive logic programming extended with integrity constraints in the setting in which both the initial knowledge base and the abductive explanations are indefinite (may contain occurrences of null values) and the domain is possibly infinite. Furthermore, the paper discusses the complexity of problems concerning indefinite (constrained) explanations.
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes. In this paper, we introduce: 1) an agreement score to evaluate the performance of routing processes at instance level; 2) an adaptive optimizer to enhance the reliability of routing; 3) capsule compression and partial routing to improve the scalability of capsule networks. We validate our approach on two NLP tasks, namely: multi-label text classification and question answering. Experimental results show that our approach considerably improves over strong competitors on both tasks. In addition, we gain the best results in low-resource settings with few training instances.
2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019

We present a retrieval based system for landmark retrieval and recognition challenge.There are five parts in retrieval competition system, including feature extraction and matching to get candidates queue; database augmentation and query extension searching; reranking from recognition results and local feature matching. In recognition challenge including: landmark and non-landmark recognition, multiple recognition results voting and reranking using combination of recognition and retrieval results. All of models trained and predicted by PaddlePaddle framework. Using our method, we achieved 2nd place in the Google Landmark Recognition 2019 and 2nd place in the Google Landmark Retrieval 2019 on kaggle. The source code is available at here.
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization

Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings. However, orthogonal mapping only works on language pairs whose embeddings are naturally isomorphic. For non-isomorphic pairs, our method (Iterative Normalization) transforms monolingual embeddings to make orthogonal alignment easier by simultaneously enforcing that (1) individual word vectors are unit length, and (2) each language's average vector is zero. Iterative Normalization consistently improves word translation accuracy of three CLWE methods, with the largest improvement observed on English-Japanese (from 2% to 44% test accuracy).
Citation CountPrediction using Abstracts

Researchers are expected to find previous literature that is related to their research and potentially has a scientific impact from among a large number of publications. This paper addresses the problem of predicting the citation count of each research paper, that is, the number of citations from other papers to that paper. Previous literature related to the problem claims that the textual data of papers do not deeply affect the prediction compared with data about the authors and venues of publication. In contrast, the authors of this paper detected the citation counts of papers using only the paper abstracts. Additionally, they investigated the effect of technical terms used in the abstracts on the detection. They classified abstracts of papers with high and low citation counts and applied the classification to the abstracts modified by hiding the technical terms used in them. The results of their experiments indicate that the high and low of citation counts of research papers can be detected using their abstracts, and the effective features used in the prediction are related to the trend of research topics.
METHODS TOWARD ENHANCING RSA ALGORITHM : A SURVEY

Cryptography defines different methods and technologies used in ensuring communication between two parties over any communication medium is secure, especially in presence of a third part.This is achieved through the use of several methods, such as encryption, decryption, signing, generating of pseudo-random numbers, among many others.Cryptography uses a key, or some sort of a password to either encrypt or decrypt a message that needs to be kept secret.This is made possible using two classes of key-based encryption and decryption algorithms, namely symmetric and asymmetric algorithms.The best known and the most widely used public key system is RSA.This algorithm comprises of three phases, which are the key generation phase, encryption phase, and the decryption phase.Owing to the advancement in computing technology, RSA is prone to some security risks, which makes it less secure.The following paper preview different proposals on different methods used to enhance the RSA algorithm and increase its security.Some of these enhancements include combining the RSA algorithm with Diffie-Hellman or ElGamal algorithm, modification of RSA to include three or four prime numbers, offline storage of generated keys, a secured algorithm for RSA where the message can be encrypted using dual encryption keys, etc.
AUTOMATED VISIBILITY FIELD EVALUATION OF TRAFFIC SIGN BASED ON 3D LIDAR POINT CLOUDS

Abstract. Maintaining high visibility of traffic signs is very important for traffic safety. Manual inspection and removal of occlusion in front of traffic signs is one of the daily tasks of the traffic management department. This paper presents a method that can automatically detect the occlusion and continuously quantitative estimate the visibility of traffic sign cover all the road surface based on Mobile Laser Scanning (MLS) systems. The concept of traffic sign’s visibility field is proposed in this paper. One of important innovation of this paper is that we use retinal imaging area to evaluate the visibility of a traffic sign. And this makes our method is in line with human vision. To validate the reasonable and accuracy of our method, we use the 2D and 3D registration technology to observe the consistence of the occlusion ratio in point clouds with it in photo. Experiment of implementation on large scale traffic environments show that our method is feasible and efficient.
Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection

We introduce a detection framework for dense crowd counting and eliminate the need for the prevalent density regression paradigm. Typical counting models predict crowd density for an image as opposed to detecting every person. These regression methods, in general, fail to localize persons accurate enough for most applications other than counting. Hence, we adopt an architecture that locates every person in the crowd, sizes the spotted heads with bounding box and then counts them. Compared to normal object or face detectors, there exist certain unique challenges in designing such a detection system. Some of them are direct consequences of the huge diversity in dense crowds along with the need to predict boxes contiguously. We solve these issues and develop our LSC-CNN model, which can reliably detect heads of people across sparse to dense crowds. LSC-CNN employs a multi-column architecture with top-down feedback processing to better resolve persons and produce refined predictions at multiple resolutions. Interestingly, the proposed training regime requires only point head annotation, but can estimate approximate size information of heads. We show that LSC-CNN not only has superior localization than existing density regressors, but outperforms in counting as well. The code for our approach is available at https://github.com/val-iisc/lsc-cnn.
The Implementation of SQ3R Method to Develop Students’ Reading Skill on Islamic Texts in EFL Class in Indonesia

The study aims at analyzing the development of students’ reading skill on Islamic texts in EFL class in Indonesia through SQ3R method. The research was designed as a collaborative classroom action research which was carried out at TBI-4 FTIK IAIN Palu. The subject of the research was the third semester students of TBI-4 FTIK IAIN Palu consisting of 24 students. The research was conducted in one cycle through stages of planning, acting, observing and reflecting. The cycle consists of three meetings. The researcher with his collaborator collected the data by using observation checklists, field notes, camera recording, and test. The result showed that the SQ3R method could develop students’ reading skill. This fact was supported by the result of achievement test. There were 19 students (75%) who got score greater than 80 of 24 students, there were 6 students who could not reach the score. The research was stopped after the third meeting of cycle 1 in which the result of students’ achievement test have met the criteria of success. Based on this finding, the researcher concluded that the implementation of SQ3R method in teaching and learning process can develop students’ reading skill on Islamic texts.Keywords: Implementation, Reading Skill, SQ3R method.
Microfaunal Assemblages and microfacies of Nummulitic Limestone of Naopurdan Group from Chwarta Area, Sulaimaniya Governorate, Northern Iraq

Thirty-one samples were collected from nummulitic limestone of the Naopurdan Shaly Group of Paleocene-Early Eocene at the northern part of Chwarta town. Thirty-three thin sections were prepared and analyzed under a binocular microscope. Four main depositional microfacies Packstone, Floatstone, Boundstone, and Wackestone have been recognized. Thirty-two species of benthic foraminifers and algae were recorded; most of them were identified for the first time. The new species of microfossil assemblages identified comprises Nummulites sp., N. kdjokdjokartae, N. globulus, N. mammilatus, N. subirregularis, Alveolina violae, A. stercusmuris, A. frumentiformis, A. fornasinii, A. cf. sakaryaensis, Assilina laminose, A. granulosa, Discocyclina ranikotensis, D. dispansa, Lepidocyclina sumatrensis, L. parva, Cuvillierina sireli, Thalmanitta madrugaensis, Nephrolepidina sp., Idalina sinjarica, Cymopolia ellongata, Quinqueloculina, Biloculina, Pseudohatigerina sp., Asterocyclina ferrandezi n. sp. and Eulepidina dilatata. Besides these species, nine levels of distribution of microfauna were established and based on the microfossil assemblages and microfacies, the new paleoenvironments of deposition from the subtidal to outer ramp have been determined. In addition, the age Late Paleocene (Selandian) to Early Eocene (Lower Lutetian) was confirmed.
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation

3D image segmentation is one of the most important and ubiquitous problems in medical image processing. It provides detailed quantitative analysis for accurate disease diagnosis, abnormal detection, and classification. Currently deep learning algorithms are widely used in medical image segmentation, most algorithms trained models with full annotated datasets. However, obtaining medical image datasets is very difficult and expensive, and full annotation of 3D medical image is a monotonous and time-consuming work. Partially labelling informative slices in 3D images will be a great relief of manual annotation. Sample selection strategies based on active learning have been proposed in the field of 2D image, but few strategies focus on 3D images. In this paper, we propose a sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation. Attention mechanism is used to improve segmentation accuracy and estimate the segmentation accuracy of each slice. The comparative experiments with three different strategies using datasets from the developing human connectome project (dHCP) show that, our strategy only needs 15% to 20% annotated slices in brain extraction task and 30% to 35% annotated slices in tissue segmentation task to achieve comparative results as full annotation.
Empirical Study of Extreme Overfitting Points of Neural Networks

In this paper we propose a method of obtaining points of extreme overfitting—parameters of modern neural networks, at which they demonstrate close to 100% training accuracy, simultaneously with almost zero accuracy on the test sample. Despite the widespread opinion that the overwhelming majority of critical points of the loss function of a neural network have equally good generalizing ability, such points have a huge generalization error. The paper studies the properties of such points and their location on the surface of the loss function of modern neural networks.
A Novel Two-Stage Selection of Feature Subsets in Machine Learning

In feature subset selection the variable selection procedure selects a subset of the most relevant features. Filter and wrapper methods are categories of variable selection methods. Feature subsets are similar to data pre-processing and are applied to reduce feature dimensions in a very large dataset. In this paper, in order to deal with this kind of problems, the selection of feature subset methods depending on the fitness evaluation of the classifier is introduced to alleviate the classification task and to progress the classification performance. To curtail the dimensions of the feature space, a novel approach for selecting optimal features on two-stage selection of feature subsets (TSFS) method is done, both theoretically and experimentally. The results of this method include improvements in the performance measures like efficiency, accuracy, and scalability of machine learning algorithms. Comparison of the proposed method is made with known relevant methods using benchmark databases. The proposed method performs better than the earlier hybrid feature selection methodologies discussed in relevant works, regarding classifiers’ accuracy and error.
Leveraging Meta Information in Short Text Aggregation

Analysing topics in short texts (e.g., tweets and new headings) is a challenging task because short texts often contain insufficient word co-occurrence information, which is important to learn good topics in conventional topic topics.To deal with the insufficiency, we propose a generative model that aggregates short texts into clusters by leveraging the associated meta information.Our model can generate more interpretable topics as well as document clusters.We develop an effective Gibbs sampling algorithm favoured by the fully local conjugacy in the model.Extensive experiments demonstrate that our model achieves better performance in terms of document clustering and topic coherence.
Algorithm of Obstacle Avoidance for Autonomous Surface Vehicles based on LIDAR Detection

This paper mainly introduces the algorithm of avoiding obstacles based on the detection of LIDAR and gives the results of simulation under environment of MATLAB. The image acquisition system based on laser radar can provide real-time location information to meet the requirements of image acquisition and processing of system. The VFH+ algorithm is used to update angle and speed of the ASV. A method of rapid expansion, corrosion processing of binary image and device were used for the process of laser image processing. Binary image has features of simple, less-space et al., so efficiency of image processing is improved by dealing with multiple pixels at one time. The innovative point of the laser image processing technology is combined with the classical algorithm VFH+ of avoiding obstacle to realize the autonomous navigation system of ASV. The proposed ASV algorithm of avoiding obstacle based on LIDAR detection is applied to the ASV, which has great value of research and application. For example, it was used for ASV with wind and water complementary, water quality monitoring and sampling, and cleaning up the surface of oil pollution.