Reviewerly/exHarmony huggingface dataset:
Towards Real-Time Automatic Portrait Matting on Mobile Devices

We tackle the problem of automatic portrait matting on mobile devices. The proposed model is aimed at attaining real-time inference on mobile devices with minimal degradation of model performance. Our model MMNet, based on multi-branch dilated convolution with linear bottleneck blocks, outperforms the state-of-the-art model and is orders of magnitude faster. The model can be accelerated four times to attain 30 FPS on Xiaomi Mi 5 device with moderate increase in the gradient error. Under the same conditions, our model has an order of magnitude less number of parameters and is faster than Mobile DeepLabv3 while maintaining comparable performance. The accompanied implementation can be found at \url{https://github.com/hyperconnect/MMNet}.
A Hierarchical Spatial Fuzzy C Means Algorithm for Mammographic Mass Segmentation

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Bayesian Neural Networks at Finite Temperature

We recapitulate the Bayesian formulation of neural network based classifiers and show that, while sampling from the posterior does indeed lead to better generalisation than is obtained by standard optimisation of the cost function, even better performance can in general be achieved by sampling finite temperature ($T$) distributions derived from the posterior. Taking the example of two different deep (3 hidden layers) classifiers for MNIST data, we find quite different $T$ values to be appropriate in each case. In particular, for a typical neural network classifier a clear minimum of the test error is observed at $T&gt;0$. This suggests an early stopping criterion for full batch simulated annealing: cool until the average validation error starts to increase, then revert to the parameters with the lowest validation error. As $T$ is increased classifiers transition from accurate classifiers to classifiers that have higher training error than assigning equal probability to each class. Efficient studies of these temperature-induced effects are enabled using a replica-exchange Hamiltonian Monte Carlo simulation technique. Finally, we show how thermodynamic integration can be used to perform model selection for deep neural networks. Similar to the Laplace approximation, this approach assumes that the posterior is dominated by a single mode. Crucially, however, no assumption is made about the shape of that mode and it is not required to precisely compute and invert the Hessian.
New Evidence on Why Many African Firms Perform Poorly

This study examines a potential explanation for the poor performance of many African countries: African firms generally produce low value‐added and less ‘complex’ products due to a weak legal system and widespread corruption. Using data on manufacturing firms from Kenya, Ghana and Tanzania, this study finds that: (i) corruption and weak legal system are associated with lower efficiency among firms that produce more complex products and (ii) corruption matters more for firm efficiency than legal system. These results remain unchanged under various scenarios and may have important policy implications.
A Multi-task Learning Approach for Named Entity Recognition using Local Detection

Named entity recognition (NER) systems that perform well require task-related and manually annotated datasets. However, they are expensive to develop, and are thus limited in size. As there already exists a large number of NER datasets that share a certain degree of relationship but differ in content, it is important to explore the question of whether such datasets can be combined as a simple method for improving NER performance. To investigate this, we developed a novel locally detecting multitask model using FFNNs. The model relies on encoding variable-length sequences of words into theoretically lossless and unique fixed-size representations. We applied this method to several well-known NER tasks and compared the results of our model to baseline models as well as other published results. As a result, we observed competitive performance in nearly all of the tasks.
Density based Clustering Algorithm for Distributed Datasets using Mutual k-Nearest Neighbors

Privacy and security have always been a concern that prevents the sharing of data and impedes the success of many projects.Distributed knowledge computing, if done correctly, plays a key role in solving such a problem.The main goal is to obtain valid results while ensuring the non-disclosure of data.Density-based clustering is a powerful algorithm in analyzing uncertain data that naturally occur and affect the performance of many applications like location-based services.Nowadays, a huge number of datasets have been introduced for researchers which involve high-dimensional data points with varying densities.Such datasets contain data points with highdensity regions surrounded by data points with sparse density.The existing clustering approaches handle these situations inefficiently, especially in the context of distributed data.In this paper, we design a new decomposable density-based clustering algorithm for distributed datasets (DDBC).DDBC utilizes the concept of mutual k-nearest neighbor relationship to cluster distributed datasets with different density.The proposed DDBC algorithm is capable of preserving the privacy and security of data on each site by requiring a minimal number of transmissions to other sites.
A Classifier Based on a Decision Tree with Temporal Cuts

A new method of decision tree construction from temporal data is proposed in the paper. This method uses the so-called temporal cuts for binary partition of data in tree nodes. The novelty of the proposed approach is that the quality of cuts is calculated not on the basis of the discernibility of o bjects (related to time points), but on the basis of the discernibility of time windows labeled with different decision classes. The paper includes results of experiments performed on our data sets and collections from machine learning repositories. In order to evaluate the presented method, we compared its performance with the classification results of a local discretization decision tree, and other methods well known from literature. Our new method outperforms these known methods.
TO ACCEPT, OR NOT TO ACCEPT: PREREQUISITES TO ENSURE QUALITY TEACHER TRAINING

The research focuses on the problem of acceptability of applicants with the minimal level of the Unified State Exam (the USE) to teacher training programs, and coinage of language teachers with under-developed language competency, the latter being fated to turn insufficient for preparing school students for the USE. In the article the author answers the following two research questions: what level of language competency should be demonstrated by a teacher who works in a Russian regular secondary school; what the initial level of language mastery of applicants of teacher training programmes, measured with the USE and demonstrated with its results, would be adequate for them to be academically and linguistically agile for passing the exam which is known as the Certificate in Advanced English (CAE). The methods of the research included the content analysis of official documents regulating the requirements towards teacher competency and examination procedures and assessment; longitudinal observations on language competency development (330 students were observed throughout six years); comparative analysis of examination results. The outcomes of this research may be of high value to teacher training institutions in terms of the determination of categories of programme applicants who have stronger or weaker chances to graduate as competent (job market relevant) teachers.
Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation

Story composition is a challenging problem for machines and even for humans. We present a neural narrative generation system that interacts with humans to generate stories. Our system has different levels of human interaction, which enables us to understand at what stage of story-writing human collaboration is most productive, both to improving story quality and human engagement in the writing process. We compare different varieties of interaction in story-writing, story-planning, and diversity controls under time constraints, and show that increased types of human collaboration at both planning and writing stages results in a 10-50% improvement in story quality as compared to less interactive baselines. We also show an accompanying increase in user engagement and satisfaction with stories as compared to our own less interactive systems and to previous turn-taking approaches to interaction. Finally, we find that humans tasked with collaboratively improving a particular characteristic of a story are in fact able to do so, which has implications for future uses of human-in-the-loop systems.
Teaching Physics at School and Colleges

The Physics EducatorVol. 01, No. 01, 1950003 (2019) Research ArticleNo AccessTeaching Physics at School and CollegesMikhail ShifmanMikhail ShifmanIda Cohen Fine Professor of Theoretical Physics, William I. Fine Theoretical Physics Institute, University of Minnesota, 116 Church Street SE, Minneapolis, MN 55455, USAhttps://doi.org/10.1142/S2661339519500033Cited by:1 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail References 1. R. Feynman, The Value of Science, Engin. Sci. 19, December 1955 Issue, page 13. Google Scholar2. Kvantik, https://kvantik.com (in Russian). Kvantik’s older “brother,” the magazine Kvant intended for curious young people age 17 and older also exists. On website http://kvant.mccme.ru one can find its archive including all issues from January 1970 till present. Google Scholar FiguresReferencesRelatedDetailsCited By 1Something SpecialA. V. Smilga30 March 2021 | The Physics Educator, Vol. 03, No. 01 Recommended Vol. 01, No. 01 Metrics History Received 9 February 2019 Accepted 19 February 2019 PDF download
ATAM: Arabic Traffic Analysis Model for Twitter

Harvesting Twitter for insight and meaning in what is called sentiment analysis (SA) is a major trend stemming from computational linguistics and AI.Industry and academia are interested in maximizing efficiency while mining text to attain the most currently available data and crowdsourcing opinions.In this study, we present the ATAM model for traffic analysis using the data available on Twitter.The model comprises five components that start with data streaming and collection and ends with the road incident prediction through classification.The classification of data is done using a lexiconbased method.The predicted classes are as follows: safe, needs attention, dangerous, and neutral.The data were collected for three months in the city of Riyadh, Saudi Arabia.The model was applied on 10k tweets with an overall accuracy of the model classifying all four classes of 82%.
An Optimization Scheme for Non-Negative Matrix Factorization Under Probability Constraints

Non-negative matrix factorization (NMF) has been recognized as an effective method of dimensionality reduction over matrices and has been successfully applied for wide variety of tasks in different domains, such as text, image, and audio. In many cases, such matrices represent probabilities of the modeled objects while standardized optimization algorithms for NMF did not take into account the probabilistic constraints. In this paper we propose a novel optimization algorithm for NMF under probability constraints, thereby achieving better performance in terms of accuracy without losing efficiency. More precisely, our theoretical analysis of our algorithm shows that: (1) our method monotonically decreases the objective function; (2) the output matrices satisfy the probability constraint; (3) its learning rate is the largest while among satisfying (1) and (2); and (4) the computational time complexity remains the same as the existing optimization method. We conduct experimental evaluations using a real-world dataset by applying the proposed method to different tasks. The results show that, when applying it to topic modeling, the accuracy of clustering and topic detection is better than the ordinary NMF. When applying it to an algorithm for community detection and attribute-value clustering over multi-attributed graphs (CARNMF [1]), it also achieved higher accuracy compared to the original NMF optimization.
국어교육과 “교과 논리 및 논술” 교과목의 교육 내용 체계화를 위한 기초 연구 ­ - 2009~2018년 강의계획서에 대한 텍스트 마이닝을 중심으로 - ­

This study investigated the perception of university professors in syllabus of logic and essay by applying text mining and searched for the direction of contents that should be taught to pre-service Korean language arts teachers.As a result of the analysis, the contents of the argument elements were detailed in the existing syllabus, and the textbook supporting this (Williams & Colomb, 2007/2008) was structured relatively systematically; however, the content of the epistemic cycle of essay writing related to multiple document literacy (multiple document comprehension, discourse synthesis), teaching and learning and evaluation appears to form a relatively less specific or isolated network of forms, which could be seen as a further development item. Theoretical and educational implications were discussed based on results.
교사와 학생 간 협력을 통한 전문 교육용 학습 자료 개발

Colleges of technology in Japan are characterized by specialized education starting from the first grades aged fifteen, making it particularly...
Blockchain and Social Concepts: Exposure of the Problem Field

The article studies the influence of blockchain technology on the formation of semantic structures in the social world. The feature of this influence is that the usage of a decentralized distributed database challenges traditional forms of social interactions, serving as a catalyst for constituting social values without analogues in the social world. Because the concepts developed in classical social theory are used to describe these values, this requires an actualization of its thesaurus. In particular, the article studies the concepts of trust, responsibility, time, and mining in the context of the introduction of blockchain technology. Because the content of these concepts is open and developing, they are given the status of social concepts. The study uses the аctor-network theory as a theoretical and methodological basis. It lets us abandon conventions of common knowledge, and shows that these concepts are semantic nodes which emerge as trails (or links to trails) of the configured actions of actor-actants. In conclusion, the studied concepts are proposed to be used with the following meanings: (1) trust is confidence in algorithmized certainty processes and the actions of communication participants, and is based on the guarantees provided by decentralized information systems and on restricting freedom by subjecting anonymous collective identities; (2) responsibility is the personal willingness of participants to take actions in conditions of uncertainty, and to compensate risks; (3) block-time is the own-time of the technology of blocking, which characterizes the state of the protocol for the external observer. For the internal observer, block-time is chronological, since it fixes the timeless stability of the algorithm which connects blocks with each other; (4) mining is a kind of economic activity based on the synthesis of cryptographic technologies and the perception of archaic ideas about risk, luck, and wealth.
Regularization Activation Function for Extreme Learning Machine

Extreme Learning Machine (ELM) algorithm based on single hidden layer feedforward neural networks has shown as the best time series prediction technique.Furthermore, the algorithm has a good generalization performance with extremely fast learning speed.However, ELM facing overfitting problem that can affect the model quality due to the implementation using empirical risk minimization scheme.Therefore, this study aims to improve ELM by introducing an Activation Functions Regularization in ELM called RAF-ELM.The experiment has been conducted in two phases.First, investigating the modified RAF-ELM performance using four types of activation functions are: Sigmoid, Sine, Tribas and Hardlim.In this study, input weight and bias for hidden layers are randomly selected, whereas the best neurons number of hidden layer is determined from 5 to 100.This experiment used UCI benchmark datasets.The number of neurons (99) using Sigmoid activation function shown the best performance.The proposed methods has improved the accuracy performance and learning speed up to 0.016205 MAE and processing time 0.007 seconds respectively compared with conventional ELM and has improved up to 0.0354 MSE for accuracy performance compare with state of the art algorithm.The second experiment is to validate the proposed RAF-ELM using 15 regression benchmark dataset.RAF-ELM has been compared with four neural network techniques namely conventional ELM, Back Propagation, Radial Basis Function and Elman.The results show that RAF-ELM technique obtain the best performance compared to other techniques in term of accuracy for various time series data that come from various domain.
Novel Similarity Measurements for Reassembling Fragmented Image Files

Reassembling fragmented image files is a useful technique to seize image evidence in digital forensics. A key problem of reassembly is how to measure the similarity between the fragments. Most of the measurements are based on the local similarity of the images. We analyze the impact of similarity patterns on the judgment of the adjacency of the fragments and conclude that the horizontal similarity has little help. According to this conclusion, we improve the median edge detector by replacing the horizontal similarity with the left and right diagonal similarity. Furthermore, we improve the sum of differences and Euclidean distance by replacing the mean/sum used in the two measurements with the median. Experimental results verify the analysis of similarity patterns and the improvements.
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning

Image quality plays a big role in CNN-based image classification performance. Fine-tuning the network with distorted samples may be too costly for large networks. To solve this issue, we propose a transfer learning approach optimized to keep into account that in each layer of a CNN some filters are more susceptible to image distortion than others. Our method identifies the most susceptible filters and applies retraining only to the filters that show the highest activation maps distance between clean and distorted images. Filters are ranked using the Borda count election method and then only the most affected filters are fine-tuned. This significantly reduces the number of parameters to retrain. We evaluate this approach on the CIFAR-10 and CIFAR-100 datasets, testing it on two different models and two different types of distortion. Results show that the proposed transfer learning technique recovers most of the lost performance due to input data distortion, at a considerably faster pace with respect to existing methods, thanks to the reduced number of parameters to fine-tune. When few noisy samples are provided for training, our filter-level fine tuning performs particularly well, also outperforming state of the art layer-level transfer learning approaches.
Fuzzy type theory with partial functions

This paper is a study of fuzzy type theory (FTT) with partial functions. Out of several possibilities we decided tointroduce a special value ”∗” that represents ”undefined”. In the interpretation of FTT, this value lays outside of thecorresponding domain. In the syntax it can be naturally represented by the description operator acting on the empty(fuzzy) set, because choosing an element from its kernel gives no result. Among many results, we will show that if, in atheory T, ”∗” is defined or provable then T is contradictory. We will also show completeness in the sense any consistenttheory of FTT with partial functions has a model.
F10-SGD: Fast Training of Elastic-net Linear Models for Text Classification and Named-entity Recognition

Voice-assistants text classification and named-entity recognition (NER) models are trained on millions of example utterances. Because of the large datasets, long training time is one of the bottlenecks for releasing improved models. In this work, we develop F10-SGD, a fast optimizer for text classification and NER elastic-net linear models. On internal datasets, F10-SGD provides 4x reduction in training time compared to the OWL-QN optimizer without loss of accuracy or increase in model size. Furthermore, we incorporate biased sampling that prioritizes harder examples towards the end of the training. As a result, in addition to faster training, we were able to obtain statistically significant accuracy improvements for NER. On public datasets, F10-SGD obtains 22% faster training time compared to FastText for text classification. And, 4x reduction in training time compared to CRFSuite OWL-QN for NER.
Source parameters, focal mechanisms and stress tensor inversion from moderate earthquakes and its relationship with subduction Zone

Nine earthquakes occured between 2005 and 2009, with magnitudes between 4.9 and 5.5, at the zone that correspond to the triple junction of the Cocos, Rivera and North America plates were relocated. Records from RESCO seismic stations were used together with records from SSN seismic network. Source parameters were estimated: source dimension, seismic moment, stress drop, average displacement and source time.The seismic moment tensor inversion was used to estimate focal mechanism and seismic moment to compare then whit short and long period estimation of these parameters.A stress tensor inversion was also performed in order to evaluate stress state of the area of study. The results indicate a compressive stress regime, which is characteristic of a subduction zone. It is interesting however, that plunge of ?1 goes from a moderate angle in the northwest to sub-horizontal in the southeast. The existence of normal and strike-slip mechanisms is also interesting in a subduction zone. Their existence was interpreted in terms of the interaction of Cocos and Rivera plates in this tectonically complicate zone.