Reviewerly/exHarmony huggingface dataset:
Lite Hourglass Network for Multi-person Pose Estimation

Recent multi-person pose estimation networks rely on sequential downsampling and upsampling procedures to capture multi-scale features and stacking basic modules to reassess local and global contexts. However, the network parameters become huge and difficult to be trained under limited computational resource. Motived by this observation, we design a lite version of Hourglass module that uses hybrid convolution blocks to reduce the number of parameters while maintaining performance. The hybrid convolution block builds multi-context paths with dilated convolutions with different rates which not only reduces the number of parameters but also enlarges the receptive field. Moreover, due to the limitation of heatmap representation, the networks need extra and non-differentiable post-processing to convert heatmaps to keypoint coordinates. Therefore, we propose a simple and efficient operation based on integral loss to fill this gap specifically for bottom-up pose estimation methods. We demonstrate that the proposed approach achieves better performance than the baseline methods on the challenge benchmark MSCOCO dataset for multi-person pose estimation.
Performance Evaluation of Video Segmentation Metrics

With the recent advancement of video data and daily creation of a large number of digital videos, the field of video indexing and retrieval is becoming an active area. For proper indexing and retrieval of the data, content-based video retrieval (CBVR) is a fundamental step for video segmentation. Content-based video retrieval is used for manipulating a video into frames. Shot detection is the fundamental step for automated indexing and content-based video retrieval. In this paper, the traditional metrics likelihood ratio, chi-square test, wavelet-based method are evaluated on datasets for detecting shot boundaries and measured in terms of recall, precision, and F1 measure. It has been determined that these results are affected by disturbance due to illumination and fire flicker.
Proposal of Wearable Force Feedback Device Using Compressed Air Jetting

Force feed back devices have been studied. Many of them are desktop type fixed to a heavy object such as desks, and they can not be used for applications with movement. Wearable devices have been developed, however, other problems such as discomfort caused by the reaction force of the device at another part of the body are occured. In this research, a force feed back method using compressed air jetting for wearable force feed back device is proposed. In this paper, concept of the method is introduced and the effectiveness is verified with prototype.
Processing and Understanding Mixed Language Data

Multilingual communities exhibit code-mixing, that is, mixing of two or more socially stable languages in a single conversation, sometimes even in a single utterance. This phenomenon has been widely studied by linguists and interaction scientists in the spoken language of such communities. However, with the prevalence of social media and other informal interactive platforms, code-switching is now also ubiquitously observed in user-generated text. As multilingual communities are more the norm from a global perspective, it becomes essential that code-switched text and speech are adequately handled by language technologies and NUIs.Code-mixing is extremely prevalent in all multilingual societies. Current studies have shown that as much as 20% of user generated content from some geographies, like South Asia, parts of Europe, and Singapore, are code-mixed. Thus, it is very important to handle code-mixed content as a part of NLP systems and applications for these geographies.In the past 5 years, there has been an active interest in computational models for code-mixing with a substantive research outcome in terms of publications, datasets and systems. However, it is not easy to find a single point of access for a complete and coherent overview of the research. This tutorial is expecting to fill this gap and provide new researchers in the area with a foundation in both linguistic and computational aspects of code-mixing. We hope that this then becomes a starting point for those who wish to pursue research, design, development and deployment of code-mixed systems in multilingual societies.
Individual variation in mathematics performance: Behavioral and cognitive profiles

18th Biennial Conference of the European Association of Research on Learning and Instruction (EARLI 2019) (Aken, Germany, 12-16 August, 2019)
Archives of the academy of sciences as repository of the historical memory of Russia

The paper presents a brief history and description of collections of the oldest archives formed by the Russian Academy of Sciences (RAS) in St. Petersburg for almost 300 years of its history. Attention is focused on the main problems of the archives, which emerged after the reform of the RAS in 2013, and prospects for their solution are outlined.
Sentiment Analysis for Movie Reviews Based on Four Machine Learning Techniques

The spread of Internet and social media led to be sentiment analysis an open research area.Social media is used so the people can be state their opinions and attitudes on blogs, Tweets, and forums.Sentiment analysis deals with identifying and extracting people's opinions and attitudes from texts on the internet.The classification of the text which is based upon sentiment is differ from topical text classification because it has recognition based on an opinion on a topic.This research studying the ability to apply TF-IDF feature selection approach for sentiment analysis and examines the performance for classification by 4 machine learning methods (naïve Bayes, KNN, J48, and logistic regression) with regard to recall, precision and F1-measure.This research included a comparison between the selected ML methods.The results show the naïve Bayes over performed on other classification methods with precision about 94.0%.
APLIKASI WEB MOBILE CALON PENERIMA JAMBAN SEHAT BAGI KELUARGA KURANG MAMPU METODE TOPSIS

Decision making using multiple attribute has an advantage in determining alternative weight values. In determining to determine the condition of latrines declared healthy or unhealthy using six criteria, including: Knowledge, the number of people using latrines, Institutional Monitoring, Regulations, Cleanliness of Public Facilities, Community Behavior. In this study, to determine healthy latrines, community samples were used in Pringsewu District. The TOPSIS method is applied to determine the priority of unhealthy latrine conditions. The research aims to design a Decision Support System for the feasibility of latrines in public housing in Pringsewu District to realize Pringsewu Open Defecation Free. Testing results are combined with using web mobile to facilitate the Health Office in calculating weight values and following up on the results of calculations as an evaluation material for the service to prepare policy directives to achieve the goal of Open Defecation Free. From the Test Results the system describes the results of calculations and accurate research in accordance with the manual tests conducted. Which alternative 3 has the highest value than the other alternatives while alternative 4 has the lowest value.
Designing of a mobile app for the development of pervasive games

We face a technology growth period that promotes STEM (Science, Technology, Engineering and Mathematics) as basic set of competences in diversified professional backgrounds. However, the process of teaching this areas is complex and abstract. We propose using game-based learning (GBL) for increased engagement and efficacy. More specifically, we use pervasive games as the drive of the learning process, promoting challenges in the context of the learner. To develop pervasive games, we require mobile devices (for location-based games) and a set of functionalities regarding location, time, and context of the user. In this article, we present the design of a mobile app to implement pervasive games. The perks of the mobile devices will be used to get unique and engaging challenges, to stimulate learning. Therefore, the learning process will become more enjoyable, and it will accomplish the goal of learning “anytime anywhere”. This work sets upon previous work developed in the H2020 BEACONING project and the mobile app presented will allow other developers to create pervasive games for distinct areas of learning in a more engaging and effective way.
A Prediction Method of Node Attack Path based on Optimized-AG

Abstract An optimization algorithm for reducing the scale of attack graphs and an attack path prediction method are presented herein to cover the difficulty of predicting the attack path in traditional attack graphs. Firstly, a new distance cost attack graph model is generated and converted into a marked attack graph via the algorithm. Secondly, the original attack graph is optimized by introducing an attack distance weight value and a path redundancy coefficient in order to determine the possible attack paths and calculate the cost of path attack behavior. Finally, the preferred attack path is determined based on an evaluation function. According to the experimental results, the above scheme eliminated the path redundancy of attack graphs and achieved comprehensive prediction of attack paths.
Low-complexity 1D-convolutional Neural Network for Super Resolution

This paper proposes a method for accelerating deep learning based super-resolution technology. In order to alleviate the complexity of the deep learning based super-resolution technology, the proposed method extracts the horizontal and vertical high-frequency signals separately using one-dimensional filters. Then, the final super-resolution image is obtained by the proposed network from the horizontal and vertical high-frequency signals and the low-resolution input image. The proposed method requires a low computational complexity by using only onedimensional filters due to a smaller number of weights. The proposed method in the high-resolution image restoration experiment shows that the average visual quality in PSNR and SSIM is comparable to ones of VDSR. However, the average speed performance is accelerated by 86.57%.
Cyber terrorism: definitional problems [News article]

Cyber terrorism is an emerging problem. Defining and categorise a diverse set of politically-motivated online crimes is a complicated task. When is a group classified as a terrorist and when is the online activity defined as terrorism? This article discusses some of the challenges defining cyber-terrorism and politically-motivated cybercrime.
On the Relationship between City Mobility and Blocks Uniformity

The spatial organization and the topological organization of cities have a great influence on the lives of their inhabitants, including mobility efficiency. Entropy has been often adopted for the characterization of diverse natural and human-made systems and structures. In this work, we apply the exponential of entropy (evenness) to characterize the uniformity of city blocks. It is suggested that this measurement is related to several properties of real cities, such as mobility. We consider several real-world cities, from which the logarithm of the average shortest path length is also calculated and compared with the evenness of the city blocks. Several interesting results have been found, as discussed in the article.
Compression of convolutional neural networks for high performance imagematching tasks on mobile devices.

Deep neural networks have demonstrated state-of-the-art performance for feature-based image matching through the advent of new large and diverse datasets. However, there has been little work on evaluating the computational cost, model size, and matching accuracy tradeoffs for these models. This paper explicitly addresses these practical metrics by considering the state-of-the-art HardNet model. We observe a significant redundancy in the learned weights, which we exploit through the use of depthwise separable layers and an efficient Tucker decomposition. We demonstrate that a combination of these methods is very effective, but still sacrifices the top-end accuracy. To resolve this, we propose the Convolution-Depthwise-Pointwise(CDP) layer, which provides a means of interpolating between the standard and depthwise separable convolutions. With this proposed layer, we can achieve an 8 times reduction in the number of parameters on the HardNet model, 13 times reduction in the computational complexity, while sacrificing less than 1% on the overall accuracy across theHPatchesbenchmarks. To further demonstrate the generalisation of this approach, we apply it to the state-of-the-art SuperPoint model, where we can significantly reduce the number of parameters and floating-point operations, with minimal degradation in the matching accuracy.
Northern Bald Ibis Algorithm-Based Novel Feature Selection Approach

Emails have become one of the popular and flexible web or mobile-based applications that enables users to communicate. For decades, the most severe problem identified in email applications was unwanted emails. Electronic spam is also referred as spam emails, in which unsolicited and unwanted mails are Received. Making an email mailbox clean by detecting and eliminating all the spam mails is a challenging task. Classification-based email filtering is one of the best approaches used by many researchers to deal with the spam email filtering problem. In this work, the NOA optimization algorithm and the SVM classifier are used for getting an optimal feature subset of the Enron-spam dataset and classifying the obtained optimal feature subset. NOA is a recently developed metaheuristic algorithm which is driven by mimicking the energy saving flying pattern of the Northern Bald Ibis (Threskiornithidae). The performance comparisons have been made with other existing methods. The superiority of the proposed novel feature selection approach is evident in the analysis and comparison of the classification results.
Construction of a multiplicatively weighted diagram of a crow with fuzzy parameters

An algorithm for constructing a multiplicatively weighted Voronoi diagram in the presence of fuzzy parameters with optimal location of a finite number of generator points in a bounded set of n-dimensional Euclidean space En is proposed in the paper. The algorithm is based on the formulation of a continuous set partitioning problem from En into non-intersecting subsets with a partitioning quality criterion providing the corresponding form of Voronoi diagram. Algorithms for constructing the classical Voronoi diagram and its various generalizations, which are based on the usage of the methods of the optimal set partitioning theory, have several advantages over the other used methods: they are out of thedependence of En space dimensions, which containing a partitioned bounded set into subsets, independent of the geometry of the partitioned sets, the algorithm’s complexity is not growing under increasing of number of generator points, it can be used for constructing the Voronoi diagram with optimal location of the points and others. The ability of easily construction not only already known Voronoi diagrams but also the new ones is the result of this general-purpose approach. The proposed in the paper algorithm for constructing a multiplicatively weighted Voronoi diagram in the presence of fuzzy parameters with optimal location of a finite number of generator points in a bounded set of n-dimensional Euclidean space En is developed using a synthesis of methods for solving optimal set partitioning problems, neurofuzzy technologies and modifications of the Shor’s r-algorithm for solving non-smooth optimization problems.
Teaching Reflection and the Improvement of Teaching Ability of New Teachers in Colleges

This paper aims to explore effective ways to improve the teaching ability of new teachers in colleges. Through extensive research, this paper analyzes problems of new teachers in teaching reflection. On the basis of studying the importance and necessity of teaching reflection on the improvement of teaching ability of new teachers [1-3], this paper creatively introduces teaching reflection trilogy. Applying this teaching reflection trilogy to daily teaching, as shown in Figure 1, new teachers can improve their teaching ability step by step. Teaching practice shows, teaching reflection trilogy is conducive to improve the teaching ability of new teachers from multiple dimensions as shown in Figure 3.
A Heuristic Approach to Parameter Tuning in a SMOTE-Based Preprocessing Algorithm for Imbalanced Ordinal Classification

Datasets are the backbone for data mining and knowledge engineering field to build an excellent classification model. However, the learning model usually follows a significantly biased distribution of classes. It is known as a class imbalance. The class imbalance problem exists in many real-time datasets. The main objective of this research is to fix an adaptive threshold based on class data and fit that data into models that can be understood and utilized by ordinal multiclass imbalanced scenario for improving the predictive accuracy. The methodology utilizes Laplace-Gauss based SMOTE method for synthesizing sophisticated objects of minority classes. Dynamic parameters are adapted for SMOTE algorithm by utilizing the underlying class information. On the whole, the dataset is divided into training and test data. Training dataset is updated with new synthetic patterns. The experimental analysis is performed on testing dataset to check the efficiency of the proposed methodology by comparing it with the existing methodology. The performance evaluation is conducted in terms of the measures called Mean Absolute Error (MAE), 154Maximum Mean Absolute Error (MMAE), Geometric Mean (GM), Kappa, and Average Accuracy. The Experimental results prove that the proposed methodology can produce authentic synthetic patterns than the existing method. The proposed method can synthesize the new effective patterns with the help of dynamic parameter setting. It upgrades the global precision and class-wise precision especially preserves rank order of the classes.
Artificial Intelligence Diagnosys System of Ribbon Diseases using forward Chaining Method (Case study of Pringsewu District)

The large number of buffaloes in the Pringsewu Regency is not matched by the number of veterinarians. Department of Fisheries and Animal Husbandry (Dinkanak) of Pringsewu District recorded 961 cases of buffalo sick with 39 types of buffalo disease in 2016, plus the cost of bringing in doctors to treat buffalo sick was still expensive for farmers. To overcome this problem, this study aims to create a website-based expert diagnosis of Buffalo disease using the Forward Chaining method to help Buffalo farmers identify Buffalo disease and its treatment. For the development of expert systems this research uses the RAD (Rapid Application Development) method. To ensure the quality of the expert system, testing using three approaches of Validation comparing the results of the Expert System diagnosis with the doctor where the accuracy of the expert system diagnosis is 85% that the application can help to diagnose Buffalo disease and the expert system is quite easy to operate.
Reversible Data Hiding Scheme Using Turbo Code

A reversible data hiding scheme provides security for a secret message by embedding it in a cover image. After extracting the secret message at the receiver, the scheme restores the original message and the cover image without any distortion. However, data transmitted by the reversible data hiding scheme is vulnerable to the noise existing on the communications channel. A reversible data hiding scheme using turbo code is proposed in this paper to address that problem. The proposed reversible data hiding integrates modified matrix embedding, turbo code, and syndrome embedding to provide improved embedding capacity, security, and error correction. Modified matrix embedding hides the secret message in the cover image. The resulting stego-image is then encrypted and encoded by the turbo code. Turbo code deploys a secret key interleaver that shuffles the incoming bits using elliptic curve arithmetic and a secret key. The exact recovery of the cover image and the secret message by the receiver depends on the user-specific secret key. Modified matrix embedding hides a large number of secret message bits, which distorts the stegoimage. However, encrypting the stego-image with the secret key interleaver makes the embedding invisible. The proposed syndrome embedding transmits the locations of the modified bits in the cover image, and enables the receiver to reverse the effects of embedding. Simulation results with the proposed scheme demonstrate a high embedding capacity and improved error correction performance of 10-4 for an additive white Gaussian noise (AWGN) channel at an SNR of 2dB. It also successfully recovers the exact cover image and the secret message, compared to existing schemes such as matrix embedding and reversible data hiding in the encrypted domain. Moreover, the results depict high resistance to brute force attacks, statistical attacks, noise attacks, and cropping attacks.
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We need three types of knowledge to support reading science fiction at least. Ontology knowledge is not only the lever to mobilize the other two kinds of knowledge to participate in reading, but also the key to avoid misreading, including stylistic knowledge such as concept, type, elements and boundary determination. In terms of type, science fiction is divided into two categories , in addition to the theory of “soft and hard”, the eight types determined from the perspective of subject matter are generally recognized by researchers. “Scientific elements”, “strange environment”, “humanistic thinking” and “logical self-consistency” are necessary elements of science fictions. Together with the literary attributes and scientific boundaries of science fictions, they define the creative territory and reading vision of this kind of novels.
New ideas and strategies for eliminating obstacles to the integration of industry and education in vocational education

The integration of industry and education in vocational education is related to the direction of vocational education reform and the quality of personnel training. Nowadays, there are institutional obstacles to the integration of industry and education in vocational education in China in terms of government power boundary, governance system, governance model, safeguard measures and environmental atmosphere. Therefore, the author makes an analysis and discussion on the new ideas and strategies for eliminating the obstacles to the integration of industry and education in vocational education. The study found that during the social transition period, the survival and development of vocational and technical education became crucial. And the results of the research show that the integration of production and education in vocational education is an inevitable requirement for the establishment of a modern vocational education system. It is not an activity under the administrative instruction, but an inherent requirement for the development of vocational education.
High rate detection of entangled photon pairs opens the door to quantum applications

An efficient way to generate and detect high rates of polarization-entangled photon pairs.