Reviewerly/exHarmony huggingface dataset:
Unsupervised Exemplar-Based Learning for Improved Document Image Classification

Many recent state-of-the-art approaches for document image classification are based on supervised feature learning that requires a large amount of labeled training data. In real-world problem of document image classification, the available amount of labeled data is limited and scarce while a large amount of unlabeled data is often available at almost no cost. In this paper, we present an approach for learning visual features for document analysis in an unsupervised way, which improves the document image classification performance without increasing the amount of annotated data. The proposed approach trains a neural network model on an auxiliary task in which every training example is associated with a different label (exemplar) and expanded to multiple images through a data augmentation technique. Thus, the learned model, which is trained in an unsupervised way, is used to boost the document classification performance. In fact, this learned model has proved to be consistently efficient in two different settings: i) as an unsupervised feature extractor to represent document images for an unsupervised classification task (i.e., clustering); and ii) in the parameters initialization of a supervised classification task trained with a small amount of annotated data. We perform experiments on the Tobacco-3482 dataset and demonstrate the capability of our approach to improve i) the unsupervised classification accuracy up to 2.4%; and ii) the supervised classification accuracy by 1.5% without any extra data or by 5% when using 3000 additional not annotated samples.
hW-inference: A heuristic approach to retrieve models through black box testing

We present an efficient approach to retrieve behavioural models from reactive software systems in the form of Finite State Machines by testing them. The system is accessed in black box mode; thus, no source or binary code is needed. The novelty of the approach is that it does not require to reset the system between tests (queries) and does not require any knowledge of the system apart from its input domain. Experiments have shown that it can scale up to systems that may have thousands of states.
An Analysis of Theories Supporting Agile Scrum and the Use of Scrum in Systems Engineering

Agile methods have become popular in software development as they offer ways to adapt to constant change during development. Agile methods have begun to be employed on non-software systems. The methods used for development on any type of system should be derived from established theories to ensure the methods enable success in development. A better understanding of Agile methods and the theories behind them can aid engineering managers in the selection of an appropriate development process for engineered systems. In this article, theories supporting Agile and Model-Based Systems Engineering (MBSE) methodologies are reviewed and found to be a key enabler of Agile methods for systems engineering. A process for performing Agile systems engineering using a model-based approach for complex systems addressing steps identified in the INCOSE systems engineering handbook is presented.
A topic sentiment based method for friend recommendation in online social networks via matrix factorization

Data sparsity and prediction quality have been recognized as the crucial challenges in recommender system. With the expansion of social network data, social network analysis is becoming more and more important. Traditional Recommendation System assumes that users are independent and distributed equally, which ignores the social interaction or connection among users. In order to solve the prediction quality of friend recommendation in social networks, a user recommendation algorithm for social networks based on sentiment analysis and matrix factorization is proposed in this paper. This method is based on the traditional matrix factorization model. By integrating Sentiment (S), Important (I) and Objective (O) of user topic content in the social network, this paper proposes the approach base on sentiment analysis and matrix factorization to solve the poor prediction accuracy by employing social network. SIO model solves the problem that users in social networks can′t score the content of topics. User-topic matrix is constructed by SIO model. Combining the SIO model with matrix factorization, algorithm called SIO-TMF algorithm is proposed. Applying this method on social network, comparing with some traditional recommendation algorithms from four aspects: accuracy, diversity, novelty and coverage, the experimental results show that the proposed method improves the prediction quality of recommender system.
The Impact of Artificial Intelligence on the Activities of a Futurist

Artificial intelligence (AI) has surpassed the level of science fiction; it is increasingly being used as an analysis tool in modern scientific research. AI is able to classify and cluster large amounts of data in a short time, which could potentially save a lot of time and money. Algorithms can also recognize patterns that scientists might overlook. These advantages are currently hardly exploited in futures studies. This article will focus on the impact of AI on the activities of a futurist. A distinction is made between predicting, exploring, and creating the future. The aim of this article is to discuss the possibilities and challenges of using AI in futures studies. One of the biggest challenges of using AI in futures studies is the dependence of AI on large amounts of data which are not available about the future. We therefore have to work with historical data. I emphasize that it is crucial for futurists to take advantage of the opportunities that AI offers in futures studies, but to be well aware of its disadvantages and limitations.
Analysis of Techniques for Credit Card Fraud Detection: A Data Mining Perspective

The expeditious development of World Wide Web tradition has derived the situation where online shopping and other payment services become most popular among people. They always buy goods and services online or off-line and use their credit or debit card for payment. With the swipe card employment, the fraud rate is also swelling day by day. Hence, the credit card has evolved as the standardized method of payment and the fraud associated with credit card is also expanding exponentially. As we know, many modern data mining techniques have been deployed for the detection of fraud in the domain of credit cards, such as Hidden Markov model, fuzzy logic, K-nearest neighbor, genetic algorithm, Bayesian network, artificial immune system, neural network, decision tree, support vector machine, hybridized method, and ensemble classification. The purpose addressed in this paper is to consolidate various data mining approaches used for finding credit card frauds by researchers to carry out research in the domain and has a state-of-the-art view of the financial domain.
Foreign Students’ Study in the “Flagship Universities” of the Russian Federation within the Context of “Soft Power” Implementation

The article discusses the mechanisms, models, best practices of the organization foreign students’ study in the cohort of “flagship universities” of the Russian Federation according to the policy of “soft power”. The purpose of this article is to determine the possibility and feasibility of using international cooperation of flagship universities as a tool of “soft power” implementation. The universities extend their partnership activities with the long-term goals to promote a positive image of our country, to ensure their development as well as the development of RF regions of their localization. The article is aimed at the elaboration of recommendations addressed to universities and education authorities. The statistical basis for the analysis was the monitoring data on the effectiveness of Russian universities (published data for 2017 and preliminary data for 2018). Factual information is based on the results of the development programs of flagship universities for 2017–2018. It should be noted that such an analysis is carried out for the first time and it is the first step towards the consideration of the international activities of flagship universities within the concept of “soft power”.
Why face recognition accuracy varies due to race

The varying accuracy of face recognition across race and gender has attracted a good deal of media attention. Publications ranging from the New York Times to Wired have carried headlines like ‘Facial Recognition Is Accurate, if You're a White Guy’ and ‘The best algorithms still struggle to recognize black faces equally’ 1, 2, 3 . Press releases by organisations like the American Civil Liberties Union (ACLU) and Georgetown Law Center on Privacy &amp; Technology 4, 5 have spurred some of this media coverage – which at times has been more sensationalised than well-informed about the technology.
Auto Tuning of RNN Hyper-parameters using Cuckoo Search Algorithm

Long Short Term Memory based Recurrent Neural Network (LSTM+RNN) with additional capability of learning long term dependencies in sequential data is an effective model for analyzing time series data. The hyper-parameters play a crucial role in obtaining an optimized learning model for dataset in hand. The comprehensive studies conducted on selecting the network hyper-parameters have tried limited number of combinations and mark this as a limitation. Further, once optimized for a dataset in hand, the model may not be effective for another dataset. This requires auto tuning methods for selecting hyper-parameters. In this paper, meta-heuristic algorithm named Cuckoo Search has been utilized to find suitable heuristics for auto tuning the hyper-parameters of a RNN+LSTM network. The average accuracy of the models optimized for the experimental datasets are 96.3%.
A Text Analysis of Data-Science Career Opportunities and US iSchool Curriculum

Data-science employment opportunities of varied complexity and environment are in growing demand across the globe. Data science as a discipline potentially offers a wealth of jobs to prospective employees, while traditional information science–based roles continue to decrease as budgets get cut across the United States. Since, historically, data are related closely to information, this research will explore the education of US iSchool professionals and compare it to traditional data-science roles being advertised within the job market. Through a combination of latent semantic analysis of over 1,600 job postings and iSchool course documentation, the aim of the article is to explore the intersection of library and information science and data science. It is hoped that these research findings will guide future directions for library and information science professionals into data science–driven roles, while also examining and highlighting the data-science techniques currently driven by the education of iSchool professionals. In addition, the aim is to understand how data science could benefit from a mutually symbiotic relationship with the field of information science, since, statistically, data scientists spend too much time working on data preparation and not nearly enough time conducting scientific inquiry. The results of this examination will potentially guide future directions of iSchool students and professionals toward more cooperative data science roles and guide future research into the intersection between iSchools and data science and possibilities for partnership.
Automatic Border Surveillance Using Machine Learning in Remote Video Surveillance Systems

Border Surveillance and security are of prime concern for any country. The utilization of modern smart technologies empowers strong border security. It is an imperative need to advance these technologies for better security. In this research, we employ machine learning techniques in Remote Video Surveillance for real-time threat level detection and classification of targets crossing borders. The algorithm used for the machine learning based detection of objects in the videos in this research is the Viola-Jones algorithm. This algorithm requires a training set of both positive and negative images, for the purpose of which, a collection of positive and negative images was used for the training of the algorithm for objects such as humans, vehicles, and handguns. A threat level classifier and alert warning system were also added to classify and annotate the videos in real-time for each frame. The threat level classifier performs four-fold categorization of the real-time video into—safe, low, medium, and high (danger). The alert warning system specifies the type of warning based on the type of intrusion (human, vehicle, or weapon) detected. For the algorithm proposed in this work, the accuracy for the human detection is an average of 94.93%, the accuracy for the vehicle detection is an average 95.2%, and the accuracy for the weapons detection is an average of 97.67%. The accuracy of our proposed method (97%) was much higher than that of the compared previously published method (64%) for object detection.
A Comparative Analysis of Human and Automatic Query Variants

We present an in-depth comparative analysis of the effectiveness distributions of sets of human-created and automatically-created query variations used to represent the same information need. The automatic variations are generated using Bing's click graph. Experiments performed with TREC datasets show that using automatic variations for retrieval can result in similar effectiveness to that of using human variations, although the two types of variations can be appreciably different in several important respects --- e.g., their similarities and corresponding retrieved lists.
Piloting Multimodal Learning Analytics using Mobile Mixed Reality in Health Education

Mobile mixed reality has been shown to increase higher achievement and lower cognitive load within spatial disciplines. However, traditional methods of assessment restrict examiners ability to holistically assess spatial understanding. Multimodal learning analytics seeks to investigate how combinations of data types such as spatial data and traditional assessment can be combined to better understand both the learner and learning environment. This paper explores the pedagogical possibilities of a smartphone enabled mixed reality multimodal learning analytics case study for health education, focused on learning the anatomy of the heart. The context for this study is the first loop of a design based research study exploring the acquisition and retention of knowledge by piloting the proposed system with practicing health experts. Outcomes from the pilot study showed engagement and enthusiasm of the method among the experts, but also demonstrated problems to overcome in the pedagogical method before deployment with learners.
Detection of Anomalies in Human Action Using Optical Flow and Gradient Tensor

In this work, we present a tensor-based motion descriptor by combining both optical flow (OF) and histogram of oriented gradient (HOG) information from the video data to detect anomalous events. New combined aggregation method is proposed based on tensor descriptors. In video, motion is represented by polynomial coefficient and these coefficients approximate the optical flow (OF) and histogram of gradient (HOG) of video also used to represent the accumulated data. The coefficients are generated by projecting the motion vector on Legendre polynomials, and then sequence of coefficients are combined by using orientation tensors. In this paper, we have combined both tensor descriptors OF and HOG to capture the moving patterns in the video. We have trained the sequence of video containing only normal events by using SVM, and in testing phase, moving pattern of each region of the frame is compared with trained video to detect any types of anomaly events in the video. The proposed motion descriptor is evaluated on UCSD anomaly action dataset using SVM classifier and shows interesting results with very good accuracy.
An Infrastructure for Service Accountability based on Digital Identity and Blockchain 3.0

Nowadays, achieving strong accountability for two or more parties that interact each other within heterogeneous and distributed business processes using a trustable and decentralised approach has become a very important issue. In this paper a solution is proposed that consists of an IT infrastructure which uses blockchain and digital identity technologies as the building blocks. In particular, the notion of accountability node, a special case of blockchain 3.0 masternode, is identified as the key element of the devised infrastructure which acts as the main interface to cooperating services and relieves the users from the burden of having to directly interact with the blockchain.
Multi-Agent non-Overlapping Pathfinding with Monte-Carlo Tree Search

In this work, we propose a novel implementation of Monte-Carlo Tree Search (MCTS) algorithm to solve a multiagent pathfinding (MAPF) problem. We employ an optimization of MCTS with low time-complexity and acceptable reliability to approach the MAPF problems with no time constraint. To examine the efficiency and performance of the proposed approach, the NumberLink problem as a MAPF is investigated. We show that the addressed problem could be characterized as multi-agent pathfinding problem with no overlapping paths for the agents. Furthermore, we define this problem to be a simplified and special case of Multi-commodity flow problem (MCFP). Our MCTS solution utilizes a modified search-tree structure to efficiently solve the problem based on a 2-dimensional search space which performs in quadratic time complexity (O(m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) where input size is m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and linear memory complexity (O(m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )). To evaluate our algorithm, we investigate the efficiency of the proposed solution for the well-known Flow Free puzzle. Our implementation solves a large 40 × 40 Numberlink puzzle in 21 minutes. To the best of our knowledge, there is no other efficient solution for this puzzle where the size of the problem is considerably large.
Design Quality Metrics to Determine the Suitability and Cost-Effect of Self-* Capabilities for Autonomic Computing Systems

Every software in the universe requires maintenance and management during its life cycle. The manual management of software is costly and sometimes error-prone. The other solution is autonomic computing that induces self-management capabilities, “self-*services”, in software systems with the help of autonomic managers. The design quality of a self-management capability affects the computing infrastructure regarding processing load, the memory requirement, data channel demand and performance of perturbation restore. It is critical to assess the design quality of a self-management capability to determine its effect over the computing infrastructure when it gets invoke against some anomaly or perturbation. Moreover, there are two possible host environments for an autonomic manager to offer a self-management capability as a self-* service: the local environment and the cloud environment. A criterion is needed to decide which environment is more suitable and cost-effective to run the service. However, the literature lacks in the assessment of the design quality metrics on self-management capabilities and the suitability and cost-effectiveness of the execution environment. In this work, we have proposed a suite of design quality metrics to determine the design quality of self-management capabilities. We validate the proposed metrics with a stock trade & forecasting system that was designed as an autonomic computing system with self-management capabilities. The proposed metrics were applied to define functions that identify the suitable and cost-effective execution environment for the self-* service. The results proved that these metrics are useful in determining the design quality, suitability, and cost-effectiveness of a self-* capability for an autonomic computing system. The proposed metrics can be used to compare differently designed autonomic solutions for complexity, efficiency, performance, understandability, and maintainability.
Word Similarity Datasets for Thai: Construction and Evaluation

Distributional semantics in the form of word embeddings are an essential ingredient to many modern natural language processing systems. The quantification of semantic similarity between words can be used to evaluate the ability of a system to perform semantic interpretation. To this end, a number of word similarity datasets have been created for the English language over the last decades. For Thai language few such resources are available. In this work, we create three Thai word similarity datasets by translating and re-rating the popular WordSim-353, SimLex-999 and SemEval-2017-Task-2 datasets. The three datasets contain 1852 word pairs in total and have different characteristics in terms of difficulty, domain coverage, and notion of similarity (relatedness vs. similarity). These features help to gain a broader picture of the properties of an evaluated word embedding model. We include baseline evaluations with existing Thai embedding models, and identify the high ratio of out-of-vocabulary words as one of the biggest challenges in the evaluation process. All datasets, evaluation results, and a tool for easy evaluation of new Thai embedding models are available to the NLP community online.
Generating Fillers Based on Dialog Act Pairs for Smooth Turn-Taking by Humanoid Robot

In spoken dialog systems for humanoid robots, smooth turn-taking function is one of the most important factors to realize natural interaction with users. Nakanishi, Ryosuke Speech collisions often occur when a user and the dialog system speak simultaneously. Inoue, Koji This study presents a method to generate fillers at the beginning of the system utterances to indicate an intention of turn-taking or turn-holding just like human conversations. Nakamura, Shizuka To this end, we analyzed the relationship between a dialog context and fillers observed in a human-robot interaction corpus, where a user talks with a humanoid robot remotely operated by a human. Takanashi, Katsuya At first, we annotated dialog act tags in the dialog corpus and analyzed the typical type of a sequential pair of dialog acts, called a DA pair. Kawahara, Tatsuya It is found that the typical filler forms and their occurrence patterns are different according to the DA pairs. Then, we build a machine learning model to predict occurrence of fillers and its appropriate form from linguistic and prosodic features extracted from the preceding and the following utterances. The experimental results show that the effective feature set also depends on the type of DA pair.