Reviewerly/exHarmony huggingface dataset:
Simulation-Based Approach to Efficient Commonsense Reasoning in Very Large Knowledge Bases

Cognitive systems must reason with large bodies of general knowledge to perform complex tasks in the real world. However, due to the intractability of reasoning in large, expressive knowledge bases (KBs), many AI systems have limited reasoning capabilities. Successful cognitive systems have used a variety of machine learning and axiom selection methods to improve inference. In this paper, we describe a search heuristic that uses a Monte-Carlo simulation technique to choose inference steps. We test the efficacy of this approach on a very large and expressive KB, Cyc. Experimental results on hundreds of queries show that this method is highly effective in reducing inference time and improving question-answering (Q/A) performance.
LENA: Locality-Expanded Neural Embedding for Knowledge Base Completion

Embedding based models for knowledge base completion have demonstrated great successes and attracted significant research interest. In this work, we observe that existing embedding models all have their loss functions decomposed into atomic loss functions, each on a triple or an postulated edge in the knowledge graph. Such an approach essentially implies that conditioned on the embeddings of the triple, whether the triple is factual is independent of the structure of the knowledge graph. Although arguably the embeddings of the entities and relation in the triple contain certain structural information of the knowledge base, we believe that the global information contained in the embeddings of the triple can be insufficient and such an assumption is overly optimistic in heterogeneous knowledge bases. Motivated by this understanding, in this work we propose a new embedding model in which we discard the assumption that the embeddings of the entities and relation in a triple is a sufficient statistic for the triple’s factual existence. More specifically, the proposed model assumes that whether a triple is factual depends not only on the embedding of the triple but also on the embeddings of the entities and relations in a larger graph neighbourhood. In this model, attention mechanisms are constructed to select the relevant information in the graph neighbourhood so that irrelevant signals in the neighbourhood are suppressed. Termed locality-expanded neural embedding with attention (LENA), this model is tested on four standard datasets and compared with several stateof-the-art models for knowledge base completion. Extensive experiments suggest that LENA outperforms the existing models in virtually every metric.
An investigative design of optimum stochastic language model for bangla autocomplete

&lt;p class="Abstract"&gt;Word completion and word prediction are two important phenomena in typing that have extreme effect on aiding disable people and students while using keyboard or other similar devices. Such autocomplete technique also helps students significantly during learning process through constructing proper keywords during web searching. A lot of works are conducted for English language, but for Bangla, it is still very inadequate as well as the metrics used for performance computation is not rigorous yet. Bangla is one of the mostly spoken languages (3.05% of world population) and ranked as seventh among all the languages in the world. In this paper, word prediction on Bangla sentence by using stochastic, i.e. &lt;em&gt;N&lt;/em&gt;-gram based language models are proposed for autocomplete a sentence by predicting a set of words rather than a single word, which was done in previous work. A novel approach is proposed in order to find the optimum language model based on performance metric. In addition, for finding out better performance, a large Bangla corpus of different word types is used.&lt;/p&gt;
Malaysian Speaking Proficiency Assessment Effectiveness for Undergraduates Suffering from Minimal Descriptors

This study was primarily aimed at developing an English-speaking proficiency test and analytic rubrics designed to measure speaking proficiency of Malaysian undergraduates.On the basis of Littlewood's Methodological Framework and Long's Interaction Hypothesis, the researchers derived three speaking tasks from four sources: (a) syllabus of the English language courses at the relevant university, (b) Kathleen Bardovi-Harlig's operationalizing conversation speech acts, (c) IELTS part B speaking test, and (d) task B speaking section of Malaysian University English Test (MUET).A total of 96 undergraduates with four levels of the language proficiency (i.e., low performers, intermediate performers, upperintermediate performers, and high performers) from a public university in Malaysia voluntarily participated in the study.While two TESOL experts were invited to validate the content of the tasks and the rubrics, two raters rated students' test scores.Construct validity was established through known-group validity (construct validity) for a known-group comparison of the task performance at the three difficulty levels namely, elementary, intermediate and advanced.The test scores, having good internal consistency (a= .89)and inter-rater reliability (ICC= .84),yielded speaking proficiency descriptors.This result showed that the test is reliable and valid to diagnose speaking proficiency of Malaysian undergraduates in pursuit of improvement.
Analysis of Explicit Parallelism of Image Preprocessing Algorithms—A Case Study

The need for the image processing algorithm is inevitable in the present era as every field involves the use of images and videos. The performance of such algorithms can be improved using parallelizing the tasks in the algorithm. There are different ways of parallelizing the algorithm like explicit parallelism, implicit parallelism, and distributed parallelism. The proposed work shows the analysis of the performance of the explicit parallelism of an image enhancement algorithm named median filtering in a multicore system. The implementation of explicit parallelism is done using MATLAB. The performance analysis is based on primary measures like speedup time and efficiency.
Hybrid Self-Organizing Map Approach for Traveling Salesman Problem

As efficient planning and design of transportation route can reduce transportation cost of an organization, the development of an effective algorithm for solving this problem can be used as a guideline for developing the route and transportation cost. Currently, several studies focus on the study and development of the Traveling Salesman Problem (TSP), which is largely similar to transportation route. One of the algorithms that helps solve this type of problem quickly is the Self-Organizing Map (SOM), which is adapted from the Artificial Neural Networks algorithm. However, using SOM to solve TSP problem does not provide the shortest path, nor is it the optimal solution. Therefore, this study aims to develop the solution based on SOM together with Local Search algorithm to provide better quality solution. The results of this research, both in terms of processing time and quality of solution, were compared to the results of previous research. The findings indicated that the solution from the proposed algorithm improved the processing time when compared to every problem in previous research.
Approximation of Densities on Riemannian Manifolds

Finding an approximate probability distribution best representing a sample on a measure space is one of the most basic operations in statistics. Many procedures were designed for that purpose when the underlying space is a finite dimensional Euclidean space. In applications, however, such a simple setting may not be adapted and one has to consider data living on a Riemannian manifold. The lack of unique generalizations of the classical distributions, along with theoretical and numerical obstructions require several options to be considered. The present work surveys some possible extensions of well known families of densities to the Riemannian setting, both for parametric and non-parametric estimation.
An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm

In this paper, we study the multi-sensor multi-target tracking problem in the formulation of random finite sets. The Gaussian Mixture probability hypothesis density (GM-PHD) method is employed to formulate the sequential fusing multi-sensor GM-PHD (SFMGM-PHD) algorithm. First, the GM-PHD is applied to multiple sensors to get the posterior GM estimations in a parallel way. Second, we propose the SFMGM-PHD algorithm to fuse the multi-sensor GM estimations in a sequential way. Third, the unbalanced weighted fusing and adaptive sequence ordering methods are further proposed for two improved SFMGM-PHD algorithms. At last, we analyze the proposed algorithms in four different multi-sensor multi-target tracking scenes, and the results demonstrate the efficiency.
A Randomized Clustering Forest Approach for Efficient Prediction of Protein Functions

With the advances in genetic sequencing technology, the automated assignment of protein function has become a key challenge in bioinformatics and computational biology. In nature, many kinds of proteins consist of a variety of structural domains, and each domain almost holds its own function independently or implements a new function in cooperation with neighbors. Thus, a multi-domain protein function prediction problem can be converted into multi-instance multi-label (MIML) learning tasks. In this paper, we propose a novel ensemble MIML algorithm called multi-instance multi-label randomized clustering forest (MIMLRC-Forest) for protein function prediction. In MIMLRC-Forest, we develop a set of hierarchical clustering trees and conduct a label transfer mechanism to identify the relevant function labels in learning process. The clustering tree with a hierarchical structure can handle the multi-label problem by exploiting more discriminable label concepts at higher-level nodes and by transferring less discriminable labels into the lower-level nodes. Then, the label dependency can be computed by aggregating tree labels for protein function prediction. Extensive experiments on five real-world protein data sets show the effectiveness of the proposed algorithm compared with several state-of-the-art baselines, including MIMLSVM, MIMLNN, MIML-kNN, EnMIMLNN, and M3MIML.
A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination

Most of the existing clustering algorithms are often based on Euclidean distance measure. However, only using Euclidean distance measure may not be sufficient enough to partition a dataset with different structures. Thus, it is necessary to combine multiple distance measures into clustering. However, the weights for different distance measures are hard to set. Accordingly, it appears natural to keep multiple distance measures separately and to optimize them simultaneously by applying a multiobjective optimization technique. Recently a new clustering algorithm called ‘multiobjective evolutionary clustering based on combining multiple distance measures’ (MOECDM) was proposed to integrate Euclidean and Path distance measures together for partitioning the dataset with different structures. However, it is time-consuming due to the large-sized genes. This paper proposes a fast multiobjective fuzzy clustering algorithm for partitioning the dataset with different structures. In this algorithm, a real encoding scheme is adopted to represent the individual. Two fuzzy clustering objective functions are designed based on Euclidean and Path distance measures, respectively, to evaluate the goodness of each individual. An improved evolutionary operator is also introduced accordingly to increase the convergence speed and the diversity of the population. In the final generation, a set of nondominated solutions can be obtained. The best solution and the best distance measure are selected by using a semisupervised method. Afterwards, an updated algorithm is also designed to detect the optimal cluster number automatically. The proposed algorithms are applied to many datasets with different structures, and the results of eight artificial and six real-life datasets are shown in experiments. Experimental results have shown that the proposed algorithms can not only successfully partition the dataset with different structures, but also reduce the computational cost.
An Image-Constrained Particle Filter for 3D Human Motion Tracking

Tracking 3D human motion from monocular video sequences has aroused great interest in recent years. Among these human motion tracking methods, the particle filter is considered as an effective approach. However, the current approaches based on particle filter still have some limitation such as many particles are obviously not consistent with the observed image due to they are independent of the image information. In this paper, we present an image-constrained particle filter approach to track 3D human motion from monocular video clips with the assistance of a pre-captured motion library. We propose two novel particle filtering criteria and design a hierarchical likelihood function. The top layer of the function consists of the particle filtering criteria, and the bottom layer consists of the likelihood functions based on image contours and edge features. We remove those particles that do not match the image significantly at the top level, and the remaining particles are evaluated using the underlying likelihood function. The experimental results show that our method can effectively improve the accuracy of motion tracking and constrain the estimation of human body position.
Sexual Abuse Detection Through Drawing Workshop: E-Learning Contribution

Sexual abuse is a common worldwide phenomenon, with severe physical and mental consequences. Yet its detection remains challenging due to a variety of factors. Sexual abuse detection through drawing shows promising results for the detection of sexual abuse. This study aims to evaluate the contribution to practice of a sexual abuse detection through drawing workshop, and to compare online and face-to-face versions of this program in term of skills and knowledge. The results of the study show that the detection of sexual abuse through drawing was improved through both the online and face-to-face formats of the workshop. There was no difference between the two formats of the workshop. Long-term contributions to practice were moderate. There was a decline in skills and knowledge in the three-month follow-up reports, emphasizing the need for further practice. The study concludes with the advantages of the e-learning format to sexual abuse detection training.
Preliminary review of the geology of the hornblende biotite gneisses of Obudu Plateau Southeastern Nigeria

The Obudu Plateau, southeastern Nigeria is dominantly underlain by gneissose rocks with migmatitic characteristics, one of which is the hornblende-biotite gneiss.The gneisses are banded with quartz and plagioclase as the major minerals in the leucosome while hornblende and biotite occur in the palaeosome.Geochemically, the rocks are enriched in silica and alumina with an average of 71.6wt% and 14wt% respectively.Geochemical plots indicate that they are calc-alkaline to tholeiitic in character and derived from protholiths of igneous origin with minor sedimentary input.
Corporate Social Responsibility and Sustainable Development: A Study of Commercial Banks in Bangladesh

This paper investigates the practices of Corporate Social Responsibility (CSR) by commercial banks in Bangladesh and the achievement of sustainable development through this practice. The motive of this descriptive research is to look at the framework of CSR areas and contribution by the state-owned and private commercial banks to those areas during 2011-2017. This study reveals that commercial banks contribution in CSR is increasing every year and new CSR areas are being concentrated on by commercial banks as part of their responsibility to the society and more and more focus is being given to important CSR areas to achieve sustainable development. This study also shows that there is a positive relationship between CSR and sustainable development. This research effort suggests that a precise guideline by the government about CSR expenditure, large corporation’s timely involvement in CSR activities and CSR disclosure in the bank’s annual report will fulfill the desired objectives of the business organizations. These will also ensure the successful and sustainable development of the country.These will also ensure successful and sustainable development of the country.
Semi-Supervised Feature Selection with Adaptive Discriminant Analysis

In this paper, we propose a novel Adaptive Discriminant Analysis for semi-supervised feature selection, namely SADA. Instead of computing fixed similarities before performing feature selection, SADA simultaneously learns an adaptive similarity matrix S and a projection matrix W with an iterative method. In each iteration, S is computed from the projected distance with the learned W and W is computed with the learned S. Therefore, SADA can learn better projection matrix W by weakening the effect of noise features with the adaptive similarity matrix. Experimental results on 4 data sets show the superiority of SADA compared to 5 semisupervised feature selection methods.
Equivalence checking of quantum finite-state machines

In this paper, we introduce the model of quantum Mealy machines and study the equivalence checking and minimisation problems of them. Two efficient algorithms are developed for checking equivalence of two states in the same machine and for checking equivalence of two machines. As an application, they are used in equivalence checking of quantum circuits. Moreover, the minimisation problem is proved to be in PSPACE.
An Executable Specification of Map-Join-Reduce Using Haskell

The Map-Join-Reduce programming model effectively supports the join operation among different heterogeneous data sets by adding the Join module and processes the multiway joining directly. In this paper, we propose a rigorous description of the Map-Join-Reduce that performs as an executable specification. First, this paper briefly introduces the differences between the Map-Join-Reduce and the MapReduce. Then, we use Haskell to specify each module of the Map-Join-Reduce programming model and analyze the structure and function of each module. Finally, we test the specification by analyzing an example of the mall sales records. The executable specification contributes to helping the developers to unscramble the relationship between the MapReduce and the Map-Join-Reduce, which may serve as a basis for further development of the theory of related programming model design. Furthermore, the most important function of an executable specification is guaranteeing the target informal or semi-formal model with interesting properties. This paper is a forward step to prepare for verifying related properties and, even, providing verified prototypes.
Detecting Current Job Market Skills and Requirements Through Text Mining

Abstract Recent research exists that utilizes machine learning techniques to analyze the underlying patterns in the job market. In this paper, Skill Miner System (SMS) is presented. SMS utilizes text mining algorithms to identify these skills and qualifications employers seek for in STEM fields. In addition, SMS generates a skill demand index (SDI), which is used to determine the demand for particular skills. This study focuses on Industrial Engineering but can be easily adaptable to other fields. Data for this study was collected by scrapping various job postings for Industrial Engineers. The data used to develop SMS consisted of more than 5,000 jobs. The underlying pattern of the job market is compared to a public database of various occupational information, O*NET. O*NET, sponsored by the U.S. Department of Labor, is one of the most comprehensive publicly accessible databases of occupational requirements for skills, abilities and knowledge. However, by itself the information in O*NET is not enough to characterize the distribution of occupations required in a given market or region. SMS is different from O*NET as it detects skills required in the job market on a more frequent basis and provides a metric to determine the importance of a skill. This paper shows that SMS is able to detect skills that are required by the job market and are not mentioned in O*NET. SMS sets a weight to portray the demand for each skill. This is beneficial for institutions and organizations to remain competitive in the job market. At the University of Illinois at Chicago, senior engineering undergraduates in the Department of Mechanical and Industrial Engineering are required to take a professional development course (PDC) to assist in their career development. PDC employs SMS routinely to help each student cater to various job positions. In addition, resumes are improved by using additional relevant keywords employers seek, which are detected by SMS. SMS has assisted in increasing the number of students that graduate with a job offers and in the course's goal of helping students obtain careers. The analysis presented in this paper shows that SMS can benefit various stakeholders, such as universities, students, employers, and recruiting firms. Universities will have a better understanding of the job market and will be able to improve the education of their students with the evolving job market. Students will be more qualified and better prepared for the job market. Employers and recruiting firms will be able to remain competitive in the job market.
Certainty-Driven Consistency Loss for Semi-supervised Learning

One of the successful approaches in semi-supervised learning is based on the consistency regularization. Typically, a student model is trained to be consistent with teacher prediction for the inputs under different perturbations. To be successful, the prediction targets given by teacher should have good quality, otherwise the student can be misled by teacher. Unfortunately, existing methods do not assess the quality of the teacher targets. In this paper, we propose a novel Certainty-driven Consistency Loss (CCL) that exploits the predictive uncertainty in the consistency loss to let the student dynamically learn from reliable targets. Specifically, we propose two approaches, i.e. Filtering CCL and Temperature CCL to either filter out uncertain predictions or pay less attention on them in the consistency regularization. We further introduce a novel decoupled framework to encourage model difference. Experimental results on SVHN, CIFAR-10, and CIFAR-100 demonstrate the advantages of our method over a few existing methods.