Reviewerly/exHarmony huggingface dataset:
Relation prediction in knowledge graph by Multi-Label Deep Neural Network

Knowledge graph will be usefull for the intelligent system. As the relationship prediction on the knowledge graph becomes accurate, construction of a knowledge graph and detection of erroneous information included in a knowledge graph can be performed more conveniently. The goal of our research is to predict a relation (predicate) of two given Knowledge Graph (KG) entities (subject and object). Link prediction between entities is important for developing large-scale ontologies and for KG completion. TransE and TransR have been proposed as the methods for such a prediction. However, TransE and TransR embed both entities and relations in the same (or different) semantic space(s). In this research we propose a simple architecture model with emphasis on relation prediction by using a Multi-Label Deep Neural Network (DNN), and developed KGML. KGML embeds entities only; given subject and object are embedded and concatenated to predict probability distribution of predicates. Since the output of KGML is the probability distribution in [0, 1], output can be classified as positive and negative by using the threshold of 0.5. Since the output of the existing method TransE is the score in [0, ∞), the threshold value must be calculated each time. Experimental results showed that predictions by KGML are more accurate than those by TransE and TransR. KGML is more accurate than DKRL which uses both KG triples and entity descriptions for learning. KGML is more accurate than PTransE in and its learning speed is faster than PTransE. The code of KGML is available at https://github.com/yo0826jp/KGML .
Science Mapping and Visualization Tools Used for Bibliometric and Scientometric Studies: A Comparative Study

In the fastest growing technological world of information and communication technology, scientific research and development, an overwhelming amount of information / data in various formats is generated directly or indirectly. As far as academic and scientific community is concerned, a large number of scholarly articles are being published on daily basis by research scholars and academician across the world. After the introduction of Computer Technology computerized data processing has become common among the researchers, this has been prompted to develop Bibliometric software. Some of them are open source software (Freeware) and others are commercial products. The study comprises to assess the potential value of data analysis of selected open source Bibliometric and Scientometric Packages (Tools). Keywords: Scientometric, Bibliometrics, Science Mapping and Visualization Tools, Bibexcel, Publish or Perish, Citespace II, CiteNetExplorer, VOSviewer. Cite this Article Ravindra S Bankar, Shalini R Lihitkar. Science Mapping and Visualization Tools Used for Bibliometric and Scientometric Studies: A Comparative Study. Journal of Advancements in Library Sciences. 2019; 6(Special Issue 1): 382s–394s.
Deep Architectures for Human Activity Recognition using Sensors

Human activity recognition (HAR) is a renowned research field in recent years due to its applications such as physical fitness monitoring, assisted living, elderly-care, biometric authentication and many more.The ubiquitous nature of sensors makes them a good choice to use for activity recognition.The latest smart gadgets are equipped with most of the wearable sensors i.e. accelerometer, gyroscope, GPS, compass, camera, microphone etc.These sensors measure various aspects of an object, and are easy to use with less cost.The use of sensors in the field of HAR opens new avenues for machine learning (ML) researchers to accurately recognize human activities.Deep learning (DL) is becoming popular among HAR researchers due to its outstanding performance over conventional ML techniques.In this paper, we have reviewed recent research studies on deep models for sensor-based human activity recognition.The aim of this article is to identify recent trends and challenges in HAR.
Using Gamification to Improve Information Security Behavior: A Password Strength Experiment

Information security emphasizes the importance of motivating end users to improve their security behavior towards protecting their private and organizational information assets. Password authentication is widely used as a user authentication method to safeguard information resources from unauthorized access. Despite its prevalence password best practice is not often followed and the use of weak passwords persist. Although password strength feedback mechanisms commonly aim to extrinsically motivate users to improve their password creating behavior, it is not yet clear how other methods, specifically gamification, influences security behavior regarding password creation behavior. The purpose of this study is to examine the effect gamification on user information security behavior, specifically regarding password creation. This study presents results from an online experiment of 232 respondents, who interacted with two different password strength feedback methods, namely a meter feedback method and a gamified feedback method using gamification points. A significant difference between the methods was found when measuring password strength using the number of guesses needed to crack the password, with the points method resulting in stronger passwords. The results of the study reveal that gamified feedback can lead to increased engagement and stronger password creation.
Pseudo-Marginal MCMC Sampling for Image Segmentation Using Nonparametric Shape Priors

Segmenting images of low quality or with missing data is a challenging problem. In such scenarios, exploiting statistical prior information about the shapes to be segmented can improve the segmentation results significantly. Incorporating prior density of shapes into a Bayesian framework leads to the posterior density of segmenting shapes given the observed data. Most segmentation algorithms that exploit shape priors optimize a cost function based on the posterior density and find a point estimate (e.g., using maximum a posteriori estimation). However, especially when the prior shape density is multimodal leading to a multimodal posterior density, a point estimate does not provide a measure of the degree of confidence in that result, neither does it provide a picture of other probable solutions based on the observed data and the shape priors. With a statistical view, addressing these issues would involve the problem of characterizing the posterior distributions of the shapes of the objects to be segmented. An analytic computation of such posterior distributions is intractable; however, characterization is still possible through their samples. In this paper, we propose an efficient pseudo-marginal Markov chain Monte Carlo (MCMC) sampling approach to draw samples from posterior shape distributions for image segmentation. The computation time of the proposed approach is independent from the training set size. Therefore, it scales well for very large data sets. In addition to better characterization of the statistical structure of the problem, such an approach has the potential to address issues with getting stuck at local optima, suffered by existing shape-based segmentation methods. Our approach is able to characterize the posterior probability density in the space of shapes through its samples, and to return multiple solutions, potentially from different modes of a multimodal probability density, which would be encountered, e.g., in segmenting objects from multiple shape classes. We present promising results on a variety of synthetic and real data sets.
SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage

With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali.That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access.In this paper we present SARAL, an end-to-end cross-lingual information retrieval (CLIR) and summarization system for lowresource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed.The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations.
Collocation Classification with Unsupervised Relation Vectors

Lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words.In this paper, we explore to which extent the current distributional landscape based on word embeddings provides a suitable basis for classification of collocations, i.e., pairs of words between which idiosyncratic lexical relations hold.First, we introduce a novel dataset with collocations categorized according to lexical functions.Second, we conduct experiments on a subset of this benchmark, comparing it in particular to the well known DiffVec dataset.In these experiments, in addition to simple word vector arithmetic operations, we also investigate the role of unsupervised relation vectors as a complementary input.While these relation vectors indeed help, we also show that lexical function classification poses a greater challenge than the syntactic and semantic relations that are typically used for benchmarks in the literature.
Lossless Compression of Medical Image Sequences Using a Resolution Independent Predictor and Block Adaptive Encoding

The proposed block-based lossless coding technique presented in this paper targets at compression of volumetric medical images of 8-bit and 16-bit depth. The novelty of the proposed technique lies in its ability of threshold selection for prediction and optimal block size for encoding. A resolution independent gradient edge detector is used along with the block adaptive arithmetic encoding algorithm with extensive experimental tests to find a universal threshold value and optimal block size independent of image resolution and modality. Performance of the proposed technique is demonstrated and compared with benchmark lossless compression algorithms. BPP values obtained from the proposed algorithm show that it is capable of effective reduction of inter-pixel and coding redundancy. In terms of coding efficiency, the proposed technique for volumetric medical images outperforms CALIC and JPEG-LS by 0.70 % and 4.62 %, respectively.
Towards Unknown Traffic Identification Using Deep Auto-Encoder and Constrained Clustering

Nowadays, network traffic identification, as the fundamental technique in the field of cybersecurity, suffers from a critical problem, namely “unknown traffic”. The unknown traffic refers to network traffic generated by previously unknown applications (i.e., zero-day applications) in a pre-constructed traffic classification system. The ability to divide the mixed unknown traffic into multiple clusters, each of which contains only one application traffic as far as possible, is the key to solve this problem. In this paper, we propose the DePCK to improve the clustering purity. There are two main innovations in our framework: (i) It learns to extract bottleneck features via deep auto-encoder from traffic statistical characteristics; (ii) It uses the flow correlation to guide the process of pairwise constrained k-means. To verify the effectiveness of our framework, we make contrast experiments on two real-world datasets. The experimental results show that the clustering purity rate of DePCK can exceed 94.81% on the ISP-data and 91.48% on the WIDE-data [1], which outperform the state-of-the-art methods: RTC [20], and k-means with log data [15].
Reduction of Salt-and-Pepper Noise from Digital Grayscale Image by Using Recursive Switching Adaptive Median Filter

Digital images may suffer from impulse noise, including salt-and-pepper noise. One of the common methods to deal with this noise is by using median filter, which is a type of non-linear filter. Standard median filter includes noisy pixels in calculating the median value for the restoration process. However, this will lead to an inaccurate result, where the noisy pixel values may be selected for the restoration. Another approach is by using recursive median filter, where the calculation for the median value is also based on the previous outputs. Therefore, in this paper, we investigate the feasibility of improving the performance of recursive median filter, by adapting it to switching and adaptive approaches. This scheme is called as Recursive Switching Adaptive Median Filter. As the switching median filter is used, the method is divided into two stages, which are noise detection and noise restoration stages. In the noise detection stage, salt-and-pepper pixel candidates are identified. Then, in the restoration stage, an adaptive method is used for the restoration. The size of the filter is expanding until there are at least eight noise-free pixel candidates defined by the window. As the recursive method is used, the noise mask is updated every time the restoration is done. The experimental results show that this scheme has good performance in terms of mean square error and structural similarity index measure, as compared to six other median filtering approaches. However, the scheme does not perform well at high level of corruption, especially when the level of corruption is more than 80%.
Multi-Criteria Chinese Word Segmentation with Transformer

Different linguistic perspectives cause many diverse segmentation criteria for Chinese word segmentation (CWS). Most existing methods focus on improving the performance of single-criterion CWS. However, it is interesting to exploit these heterogeneous segmentation criteria and mine their common underlying knowledge. In this paper, we propose a concise and effective model for multi-criteria CWS, which utilizes a shared fully-connected self-attention model to segment the sentence according to a criterion indicator. Experiments on eight datasets with heterogeneous segmentation criteria show that the performance of each corpus obtains a significant improvement, compared to single-criterion learning.
Morphological, thermoelectrical, geochemical and isotopic anatomy of auriferous pyrite from the Bagrote valley placer deposits, North Pakistan: Implications for ore genesis and gold exploration

The Bagrote valley in North Pakistan, belonging to the Kohistan island arc, is well-known for regional placer gold mining. However, no economically feasible in situ hydrothermal gold deposits have been discovered in this region due to rugged terrain and remote nature of its location in the western Himalaya. The streams draining the Main Karakoram Thrust (MKT)/Shyoke suture zone carry placer gold in sediments as well as old river terraces, although the primary source remains unknown. In this paper, we employ a multiparametric approach including, X-ray Diffraction (XRD) analysis, thermoelectricity, major and trace element geochemistry and isotopic characteristics of pyrite associated with placer gold with a view to identify the nature of the unknown deposits and ore forming fluids on the catchment of the Bagrote valley. Pyrite in the Bagrote valley placers is euhedral to subhedral indicating the proximal gold sources. The high rate of occurrence of N-type thermoelectric coefficients (89%) with low P-type (11%) and crystallization temperature (290 °C−380 °C) combined with chemical features indicate that the pyrite was derived from porphyry or epithermal type of magmatic hydrothermal gold deposits from the hinterlands of the Bagrote valley. The X-Ray elemental maps show that Fe, As, Mo and Ni are homogenously distributed from core to rim suggesting stable crystallization condition without any alteration by later fluids. The calculated chemical formula of pyrite of our samples is [Au0.0006Fe] S2.004], plots of Au-As and Au-Fe shows that gold occurs in pyrite as micro to nano inclusion as Au0. The δ34 SV-CDT values of pyrite range from – 0.6‰ to 0.9‰ with an average of −0.02‰, indicating the derivation of sulfur from a homogeneous magmatic source. The Pb isotope data indicates that the Pb was sourced from orogenic-type source, with minor contribution of lower crust. The narrow variations in 206Pb/204Pb and 208Pb/204Pb values suggest a single lead source. The low, medium and high Mo/Ni ratios reflect a mixed provenance for the auriferous pyrite. The average value of γ (71.8%), of pyrite computed from thermoelectric parameters (XnP), suggests that the dominant part of the primary source that contributes to the placers might have already been eroded. However, the proximal source and with high content of gold in the pyrite grains (up to 1160 ppm) suggest the possibility of significant economic mineralisation below the present erosion level of the deposits in the hinterlands of Bagrote Valley.
On Distributed Merlin-Arthur Decision Protocols

In a distributed locally-checkable proof, we are interested in checking the legality of a given network configuration with respect to some Boolean predicate. To do so, the network enlists the help of a prover—a computationally-unbounded oracle that aims at convincing the network that its state is legal, by providing the nodes with certificates that form a distributed proof of legality. The nodes then verify the proof by examining their certificate, their local neighborhood and the certificates of their neighbors. In this paper we examine the power of a randomized form of locally-checkable proof, called distributed Merlin-Arthur protocols, or $${\textsf {dMA}}$$ for short. In a $${\textsf {dMA}}$$ protocol, the prover assigns each node a short certificate, and the nodes then exchange random messages with their neighbors. We show that while there exist problems for which $${\textsf {dMA}}$$ protocols are more efficient than protocols that do not use randomness, for several natural problems, including Leader Election, Diameter, Symmetry, and Counting Distinct Elements, $${\textsf {dMA}}$$ protocols are no more efficient than standard nondeterministic protocols. This is in contrast with Arthur-Merlin ( $${\textsf {dAM}}$$ ) protocols and Randomized Proof Labeling Schemes (RPLS), which are known to provide improvements in certificate size, at least for some of the aforementioned properties.
Using Ensemble Methods to Solve the Problem of Pulsar Search

Ensemble methods are a machine learning technique that combines several base models in order to produce one optimal predictive model. In this paper, we compare accuracy metric of three ensemble methods: Bagging, Random Forest, and Boosting. Then, We use the “CARET package”, implemented in R language, to experiment the Time Resolution Universe (HTRU2) dataset, obtained from UCI Machine Learning Repository.
Autonomous Intentionality in Computationally Creative Systems

We consider the problem of building an artificial system that exhibits creativity. We begin by noting that the attribution of creativity is influenced by perceptions about the system and that perhaps the most difficult hurdles to overcome in this respect are those of system intentionality and system autonomy. We discuss ways in which these difficulties might be addressed and cite examples from the DARCI system that already exhibit these characteristics to some extent. We also discuss the difficult problem of evaluation and present several useful methods, as well as examples of their application to results produced by DARCI.
Recovering dropped pronouns in

Jingxuan Yang, Jianzhuo Tong, Si Li, Sheng Gao, Jun Guo, Nianwen Xue. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Optimal human navigation in steep terrain: a Hamilton–Jacobi–Bellman approach

Author(s): Parkinson, Christian; Arnold, David; Bertozzi, Andrea L; Chow, Yat Tin; Osher, Stanley | Abstract: We present a method for determining optimal walking paths in steep terrain using the level set method and an optimal control formulation. By viewing the walking direction as a control variable, we can determine the optimal control by solving a Hamilton-Jacobi-Bellman equation. We then calculate the optimal walking path by solving an ordinary differential equation. We demonstrate the effectiveness of our method by computing optimal paths which travel throughout mountainous regions of Yosemite National Park. We include details regarding the numerical implementation of our model and address a specific application of a law enforcement agency patrolling a nationally protected area.
UTFPR at SemEval-2019 Task 5: Hate Speech Identification with Recurrent Neural Networks

In this paper we revisit the problem of automatically identifying hate speech in posts from social media.We approach the task using a system based on minimalistic compositional Recurrent Neural Networks (RNN).We tested our approach on the SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (HatEval) shared task dataset.The dataset made available by the HatEval organizers contained English and Spanish posts retrieved from Twitter annotated with respect to the presence of hateful content and its target.In this paper we present the results obtained by our system in comparison to the other entries in the shared task.Our system achieved competitive performance ranking 7 th in sub-task A out of 62 systems in the English track.
Perspective-based search: a new paradigm for bursting the information bubble

Nowadays, individuals heavily rely on search engines for seeking information. The presence of information bubbles (filter bubbles and echo chambers) can threaten the effectiveness of these systems in providing unbiased information and damage healthy civic discourse and open-minded deliberation. In this paper, we propose a new paradigm for search that aims at mitigating the information bubble in the search. The paradigm, which we call perspective-based search (PBS), is based on the intuition that in a fair search the user should not be limited to the results corresponding to a specific perspective of the search topic. Briefly, in PBS, different perspectives of the search topic are identified and presented to the user and the user can select a perspective for the search results. In this paper, we focus on the paradigm itself, why it is an appropriate solution, and how it differs from other solutions. We raise new questions and call for research on the paradigm and on providing solutions for implementing its required components. We do not aim at providing any specific implementation for it, although we provide some hints on implementing it. We also provide a survey of the related concepts and methods and discuss their differences with PBS.
P3O: Policy-on Policy-off Policy Optimization

On-policy reinforcement learning (RL) algorithms have high sample complexity while off-policy algorithms are difficult to tune. Merging the two holds the promise to develop efficient algorithms that generalize across diverse environments. It is however challenging in practice to find suitable hyper-parameters that govern this trade off. This paper develops a simple algorithm named P3O that interleaves off-policy updates with on-policy updates. P3O uses the effective sample size between the behavior policy and the target policy to control how far they can be from each other and does not introduce any additional hyper-parameters. Extensive experiments on the Atari-2600 and MuJoCo benchmark suites show that this simple technique is effective in reducing the sample complexity of state-of-the-art algorithms. Code to reproduce experiments in this paper is at this https URL.