Reviewerly/exHarmony huggingface dataset:
Cross-Cultural Analysis of Bilingualism in Preparinginternational Students for Training Professionally Oriented Disciplines

The practice of using two languages in teaching students, for whom at least one of them is not native, is a constant subject of teachers’ scientific discussions. The specificity of productive bilingualism in the educational environment, the need to understand, reproduce and produce foreign-language professionally oriented texts in two languages requires detailed consideration. The ratio of the two languages is another problem analysed by the authors. How the share of each language depends on the stage of training and the socio-cultural environment is the subject of research in this work. The authors consider cross-cultural differences in the initial educational training of students of the Preparatory faculty of the Financial University under the government of the Russian Federation and ways to improve their adaptation to the specificity of training in a Russian university. Particular attention is paid to the issues of language propaedeutics for mastering specialised terminology, in particular, the use of mathematical apparatus. As the authors imply, the functional and speech-dominant of students’ training is the Russian language for specific purposes, which becomes the language of a foreign student’s “survival” in the Financial University, providing him with the opportunity to study special subjects, passing tests and exams. If teaching is conducted in English, then in case of lexical difficulties, teachers of academic disciplines use Russian as a partner language, and such bilingual approach is justified, as it increases the efficiency of mastering disciplines. The article discusses the features of teaching international students mathematics through English as the language of international communication and Russian as the language of the host country. The authors analyse the program for teaching “Mathematics”, taking into account the fact that students have a different initial level of knowledge in mathematics and sometimes do not speak English or the language of the host country. The article presents a statistical analysis
Multimedia Approaches to Learning the Foundations of Library and Information Science

This paper presents a case study of two types of multimedia resources that were integrated as supplementary learning materials into the design and delivery of two different graduate courses on the historical foundations of library and information science (LIS): video and audio lectures from an online course on the history of information (integrated into a doctoral seminar), and a curated playlist of a weekly public radio broadcast on the history of ideas (integrated into a master’s course). It also considers some of the limitations of compiling LIS-related audiovisual materials from disparate online sources, with references to examples. By analyzing and critiquing these three applications of multimedia resources in LIS graduate courses, this paper attempts to answer the following research question: Beyond traditional pedagogical strategies such as lectures and text-based readings and assignments, how might students, practitioners, and the general public gain a sweeping understanding of our field? The paper aims to help LIS educators to diversify their pedagogical strategies and reach people outside their classrooms. By incorporating these kinds of multimedia resources into course designs, educators may help to empower students to actively and creatively apply what they learn in class to the analysis of historical events, biographies, and social movements, develop technical skills that will benefit their professional development, and produce deliverables that can be shared on public platforms to reach a wider audience beyond LIS classrooms.
Face Identification Based on Discrete Wavelet Transform and Neural Networks

The subject paper presents implementation of a new automatic face recognition system. To formulate an automated framework for the recognition of human faces is a highly challenging endeavor. The face identification problem is particularly very crucial in the context of today’s rapid emergence of technological advancements with ever expansive requirements. It has also significant relevance in the related engineering disciplines of computer graphics, pattern recognition, psychology, image processing and artificial neural networks. This paper proposes a side-view face authentication approach based on discrete wavelet transform and artificial neural networks for the solution of the problem. A subset determination strategy that expands on the number of training samples and permits protection of the global information is discussed. The authentication technique involves image profile extraction, decomposition of the wavelets, splitting of the subsets and finally neural network verification. The procedure exploits the localization property of the wavelets in both the frequency and spatial domains, while maintaining the generalized properties of the neural networks. The realization strategy of the methodology was executed using MATLAB, demonstrating that the performance of the technique is quite satisfactory.
Ask me: A Question Answering System via Dynamic Memory Networks

Most of the natural language processing problems can be reduced into a question answering problem. Dynamic Memory Networks (DMNs) are one of the solution approaches for question answering problems. Based on the analysis of a question answering system built by DMNs described in [1], this study proposes a model named DMN* which contains several improvements on its input and attention modules. DMN* architecture is distinguished by a multi-layer bidirectional LSTM (Long Short Term Memory) architecture on input module and several changes in computation of attention score in attention module. Experiments are conducted on Facebook bAbi dataset [2]. We also introduce Turkish bAbi dataset, and produce increased vocabulary sized tasks for each dataset. The experiments are performed on English and Turkish datasets and the accuracy performance results are compared by the work described in [1]. Our evaluation shows that the proposed model DMN* obtains improved accuracy performance results on various tasks for both Turkish and English.
Deep Interactive Segmentation of Uncertain Regions with Shadowed Sets

Pancreas segmentation is a challenging task in medical image analysis because of its large variations in texture, location, shape and size and the high similarity to the surrounding tissues especially around the boundary regions, which leads to the high segmentation uncertainty and makes the results inaccurate. Existing fully automatic segmentation methods rarely achieve sufficiently accurate and robust results. To tackle this problem, we propose a deep learning based interactive uncertain segmentation method which can involve the domain knowledge in the process of segmentation in an interactive and iterative way. Specially, the proposed method describes the uncertain regions of pancreatic CT images based on shadowed sets theory which are further corrected through interaction. The proposed method is evaluated on a challenging 3D pancreatic CT images dataset collected from the Changhai Hospital. The experimental results demonstrate that our proposed method outperforms the existing methods in terms of both the Dice similarity coefficient of 78% and the pixel-wise accuracy of 96%, which reveals the effectiveness and the potential of our method in clinical settings.
Structured Neural Motifs: Scene Graph Parsing via Enhanced Context

Scene graph is one kind of structured representation of the visual content in an image. It is helpful for complex visual understanding tasks such as image captioning, visual question answering and semantic image retrieval. Since the real-world images always have multiple object instances and complex relationships, the context information is extremely important for scene graph generation. It has been noted that the context dependencies among different nodes in the scene graph are asymmetric, which meas it is highly possible to directly predict relationship labels based on object labels but not vice-versa. Based on this finding, the existing motifs network has successfully exploited the context patterns among object nodes and the dependencies between the object nodes and the relation nodes. However, the spatial information and the context dependencies among relation nodes are neglected. In this work, we propose Structured Motif Network (StrcMN) which predicts object labels and pairwise relationships by mining more complete global context features. The experiments show that our model significantly outperforms previous methods on the VRD and Visual Genome datasets.
Reply to comments on “Dehydration of hot oceanic slab at depth 30–50 km: Key to formation of Irankuh-Emarat Pb-Zn MVT belt, Central Iran” by Mohammad Hassan Karimpour and Martiya Sadeghi” by

The Irankuh-Emarat Pb-Zn belt is situated within the forearc Urumieh-Dokhtar zone of western part of Iran and it is classified as Mississippi Valley TYPE Pb-Zn mineralization. According to Karimpour and Sadeghi (2018) deep-seated thrust faults played an important role in the upward migration of hydrothermal fluids from the basement to shallow depths. The style and shape of mineralization are controlled by the host rocks. Rajabi et al. (2019) commented on the model presented by Karimpour and Sadeghi (2018) but the proposed syngenetic MVT model presented by Rajabi et al. (2019) is not acceptable based on the age of the mineralization at Irankuh-Ahangharan belt and also the role of deep-seated thrust faults in the mineralization area and its spatial distribution and association with the main ore bodies was completely omitted. In addition, the work presented by Karimpour and Sadeghi (2018) did not explain metallogenic aspect of all Zn-Pb deposits in the area and other belts and the proposed model is applicable to Pb-Zn deposits in the Irankuh-Emarat belt and not necessary to explain other Pb-Zn mineralization elsewhere in Iran.
A GPU fully vectorized approach to accelerate performance of NSGA-2 based on stochastic non-domination sorting and grid-crowding

This work introduces an accelerated implementation of NSGA-2 on a graphics processing unit (GPU) to reduce execution time. Parallelism is achieved in the population level using vectorization. All the components of the algorithm are run on the device, minimizing communication overhead. New stochastic versions of both non-domination sorting and crowding are introduced in the article. They are designed to be efficiently vectorized on GPU, therefore, the proposed approach is finally limited by the sorting procedure O(nlog(n)), while the original algorithm was of order O(n2). This improvement is reflected on the speed-ups attained in the experiments. The results include metrics regarding solution quality to show there is not a significant trade-off between acceleration and performance. The possibilities to apply multi-objective evolutionary algorithms to real-time applications are discussed in the conclusions, where the possibility of devising restricted multi-objective evolutionary algorithms is concluded a possibility to implement faster methods.
Weakly-Supervised Completion Moment Detection using Temporal Attention

Monitoring the progression of an action towards completion offers fine grained insight into the actor's behaviour. In this work, we target detecting the completion moment of actions, that is the moment when the action's goal has been successfully accomplished. This has potential applications from surveillance to assistive living and human-robot interactions. Previous effort required human annotations of the completion moment for training (i.e. full supervision). In this work, we present an approach for moment detection from weak video-level labels. Given both complete and incomplete sequences, of the same action, we learn temporal attention, along with accumulated completion prediction from all frames in the sequence. We also demonstrate how the approach can be used when completion moment supervision is available. We evaluate and compare our approach on actions from three datasets, namely HMDB, UCF101 and RGBD-AC, and show that temporal attention improves detection in both weakly-supervised and fully-supervised settings.
Beyond Literal Visual Modeling: Understanding Image Metaphor Based on Literal-Implied Concept Mapping

Existing ultimedia content understanding tasks focus on modeling the literal semantics of multimedia documents. This study explores the possibility of understanding the implied meaning behind the literal semantics. Inspired by human's implied imagination process, we introduce a three-step solution framework based on the mapping from literal to implied concepts by integrating external knowledge. Experiments on self-collected metaphor image dataset validate the effectiveness in identifying accurate implied concepts for further metaphor understanding in controlled environment.
A Novel Deep Learning Approach for Left Ventricle Automatic Segmentation in Cardiac Cine MR

Cardiac magnetic resonance imaging provides a way for heart's functional analysis. Through segmentation of the left ventricle from cardiac cine images, physiological parameters can be obtained. However, manual segmentation of the left ventricle requires significant time and effort. Therefore, automated segmentation of the left ventricle is the desired and practical alternative. This paper introduces a novel framework for the automated segmentation of the epi- and endo-cardial walls of the left ventricle, directly from the cardiac images using a fully convolutional neural network similar to the U-net. There is an acute class imbalance in cardiac images because left ventricle tissues comprise a very small proportion of the images. This imbalance negatively affects the learning process of the network by making it biased toward the majority class. To overcome the class imbalance problem, we propose a novel loss function into our framework, instead of the traditional binary cross entropy loss that causes learning bias in the model. Our new loss maximizes the overall accuracy while penalizing the learning bias caused by binary cross entropy. Our method obtained promising segmentation accuracies for the epi- and endo-cardial walls (Dice 0.94 and 0.96, respectively) compared with the traditional loss (Dice 0.89 and 0.87, respectively).
Analysis of Pre-Weighted and Post-Weighted Association Rule Mining

Association Rule Mining (ARM) is an important task of data mining that finds interesting patterns and relationships among items in the huge amount of data stored. While widely used in many different fields, the classical ARM assumes that all items have the same significance. Weighted Association Rule Mining (WARM) overcomes this problem by assigning weights to items with the goal of reflecting their importance to the mining results. In this case, the main question is whether weighting process should be applied before ARM or after it. To answer this question, this paper analyzes and compares two alternative approaches: Pre-Weighted Association Rule Mining (PreWARM) and Post-Weighted Association Rule Mining (PostWARM). It is shown by experimental studies that PostWARM produces more compact rules with higher information content and PreWARM finds more meaningful rules than standard rule generation methods.
Object Retrieval in Microscopic Images of Rocks Using the Query by Sketch Method

This paper presents the retrieval method of geological images or their fragments using Query by Sketch method. The sketch can be created manually, for instance using a graphics editor, and may show the shape of objects or their distribution within an image. This query is then used to search the image database for objects showing the greatest similarity. As an example of the proposed method, the detection of porosity in microscopic images of carbonate rock and sandstone was presented. An approach was described which is founded on the designation of parameters of selected properties of the query image and images in databases, as well as on the conformity analysis of these parameters. Two methods were proposed: the first one searches for the most similar object in the image database with respect to the set criteria. The second method performs a search based on a sketch of images which are similar in terms of object distribution (i.e., porosity). The presented research results confirm that database search using the query by sketch method forms an interesting and modern approach and may constitute one of the functionalities of IT systems intended for use in geology and mining industry.
Deep Reinforcement Learning Methods in Match-3 Game

A large number of methods are being developed in the deep reinforcement learning area recently, but the scope of their application is limited. The number of environments does not always allow for a comprehensive assessment of a new agent training algorithm. The main purpose of this article is to present another environment for Match-3 game that could be expanded, which would have a connection with the real business. The results for the most popular deep reinforcement learning algorithms are presented as a baseline.
Robot perceptual adaptation to environment changes for long-term human teammate following

Perception is one of the several fundamental abilities required by robots, and it also poses significant challenges, especially in real-world field applications. Long-term autonomy introduces additional difficulties to robot perception, including short- and long-term changes of the robot operation environment (e.g., lighting changes). In this article, we propose an innovative human-inspired approach named robot perceptual adaptation (ROPA) that is able to calibrate perception according to the environment context, which enables perceptual adaptation in response to environmental variations. ROPA jointly performs feature learning, sensor fusion, and perception calibration under a unified regularized optimization framework. We also implement a new algorithm to solve the formulated optimization problem, which has a theoretical guarantee to converge to the optimal solution. In addition, we collect a large-scale dataset from physical robots in the field, called perceptual adaptation to environment changes (PEAC), with the aim to benchmark methods for robot adaptation to short-term and long-term, and fast and gradual lighting changes for human detection based upon different feature modalities extracted from color and depth sensors. Utilizing the PEAC dataset, we conduct extensive experiments in the application of human recognition and following in various scenarios to evaluate ROPA. Experimental results have validated that the ROPA approach obtains promising performance in terms of accuracy and efficiency, and effectively adapts robot perception to address short-term and long-term lighting changes in human detection and following applications.
A machine learning driven approach for multivariate timeseries classification of box punches using smartwatch accelerometer sensordata

The objective of this study was the automatic classification of boxing punches to help athletes improving their punch performance. Our research proposes a system consisting of a smartwatch as the measuring device and serversided machine learning classification models. An app running on the wearable device measures the acceleration of the punches and sends the raw data to the backend where the classifcation is done. The classification result is announced to the athlete via a speech feedback. The system can classify four different classes of punch moves: frontal punch, hook, upper-cut, no-action. Additionally we classify which hand was used to punch and the athlete that performed the punch. In order to train our machine learning models, we created a dataset of more than 7600 punches performed by 8 different athletes. Using standard preprocessing and feature extraction methods the created algorithms reached an accuracy of 98.68% for the punch type classification and an accuracy of 90.80% for the hand classification and even an 80.54% accuracy classifing the athletes name. Via a developed dashboard web application the athletes can see their trainings statistics and use the system to optimize their punch performance. The main contributions of this paper are:1) We provide the first public available box punch dataset recorded with a standard smartwatch (https://kaggle.com/smartpunchteam/boxpunch-dataset) 2) We treat box punch classification as machine learning problem and investigate different approaches on our dataset 3) We provide the whole open source project on GitHub (https://github.com/smartpunch).
Online detection of anomaly behaviors based on multidimensional trajectories

In the surveillance domain, timely detection of anomaly behaviors is very important and is a great challenge to human operators due to information overload, fatigue and inattention. Many anomaly detection algorithms based on trajectories have been proposed for this problem. However, these algorithms generally have problems such as complex parameter setting, unfaithful statistical model, not well-calibrated false alarm rate, poor ability of online learning and sequential anomaly detection, etc. The theory of conformal prediction was introduced to solve these problems by constructing the sequential Hausdorff nearest neighbor conformal anomaly detector. Yet, it only considers position information of the targets and is not sensitive to velocity and course anomaly behaviors. And the run times are increasing as the increase of the data size, which is not appropriate for early warning surveillance application. In order to solve these problems, sequential multi-factor Hausdorff nearest neighbor conformal anomaly detector (SMFHNNCAD) and sequential multi-factor Hausdorff nearest neighbor inductive conformal anomaly detector (SMFHNNICAD) based on multidimensional trajectories are proposed in this paper. Experiments in both simulated military scenario and realistic civilian scenario show the presented algorithm has a good performance to online detect anomaly behaviors and would have a wide prospect in early warning surveillance systems.
Twitter Sentiment Analysis on Airline Tweets in India Using R Language

Abstract Every day, passengers from all parts of the world are carried over to various destinations in about 100,000 flights. And their experience in the journey is almost unknown to the airlines. Gaining knowledge on customer opinions on the airlines is very crucial as it is very difficult for the Indian Airlines to collect customer feedback. Also, the understanding of the degree to which customers are satisfied is vital for the strategy development for the company. And hence, twitter data is utilized for the same purpose. Twitter is the best possible to way to obtain such information. This paper focuses mainly on the analysis of customer views on Indian Airlines services. Data visualization is used here for displaying customer feelings which can be anger, fear, anticipation, trust, surprise, sadness, joy and disgust. Whole together their opinion can be sentiments such as positive and negative.
Formal Modeling of Multi-Level Security and Integrity Control Implemented with SELinux

The paper presents formal security model of Linux distributions provided by Bazealt SPO, which integrates multi-level security (MLS) and mandatory integrity control (MIC) implemented on the base of SELinux framework. The model also includes information flows analysis framework described as an extended Take-Grant model. The main novelty of the model is the integration of MLS and MIC that can be implemented in SELinux. The model is specified in a hierarchical manner on Event-B language, its security properties are represented as invariants and formally proved.