Reviewerly/exHarmony huggingface dataset:
Adaptive Neuron-wise Discriminant Criterion and Adaptive Center Loss at Hidden Layer for Deep Convolutional Neural Network

A deep convolutional neural network (CNN) has been widely used in image classification and gives better classification accuracy than the other techniques. The softmax cross-entropy loss function is often used for classification tasks. There are some works to introduce the additional terms in the objective function for training to make the features of the output layer more discriminative. The neuron-wise discriminant criterion makes the input feature of each neuron in the output layer discriminative by introducing the discriminant criterion to each of the features. Similarly, the center loss was introduced to the features before the softmax activation function for face recognition to make the deep features discriminative. The ReLU function is often used for the network as an active function in the hidden layers of the CNN. However, it is observed that the deep features trained by using the ReLU function are not discriminative enough and show elongated shapes. In this paper, we propose to use the neuron-wise discriminant criterion at the output layer and the center-loss at the hidden layer. Also, we introduce the online computation of the means of each class with the exponential forgetting. We named them adaptive neuron-wise discriminant criterion and adaptive center loss, respectively. The effectiveness of the integration of the adaptive neuron-wise discriminant criterion and the adaptive center loss is shown by the experiments with MNSIT, FashionMNIST, CIFAR10, CIFAR100, and STL10.
Explore an Evolution of Physical Education Based on Virtual Reality Lab for Traditional Ethnic Minorities’ Sports

Purpose: Ten-year Plan for the Development of Education Informatization (2010–2020) was issued in China, which had its unique impact on improving the quality of education, promoting the evolution of educational concept, and cultivating the innovative talents with international competitiveness. This paper was to explore why and how we could establish virtual reality for traditional ethnic minorities’ sports, and aimed to teach traditional ethnic minorities culture, achieve visualization and retention with virtual reality and cloud computing technologies.Methods: We choose archery, firecrackers, and curling to construct 3D models of sports field, participants, costumes, and facilities of the three sports. Students putting on VR/AR gargles to immerse in and interact with virtual scenes.Results: 1. The construction of traditional ethnic minorities’ sports virtual reality is aiming to meet the need of P.E. development, apply the computer simulation technology, and realize the digitization, visualization, and operationalization of P.E. discipline. 2. The laboratory is consists of three sections: computer room, exploration zone, and observation zone. The processes of the lab were modeling 3D scene, constructing traditional ethnic minorities’ sports, interacting with virtual scenes, and evaluating the previous interaction.Conclusions: By the help of cloud computing and VR technology, MUC is aiming to protect and inherit the culture of traditional ethnic minorities’ sports, realize the visualization and operationalization of traditional ethnic minorities’ sports, and enhance students’ understanding of those sports and the culture of traditional ethnic minorities’ sports.
CatIO - A Framework for Model-Based Diagnosis of Cyber-Physical Systems

Diagnosing cyber-physical systems is often a challenge due to the complex interactions between its individual cyber and physical components. With CatIO (From 'Causarum Cognitio', Latin for "(seek) knowledge of causes"), we propose a framework that supports a designer in developing corresponding diagnostic solutions that utilize either abductive or consistency-based diagnosis for detecting and localizing faults at runtime. Employing an interface to tools of the modeling language Modelica, a designer is able to simulate a cyber-physical system's detailed behavior, and based on the observed data she can then assesses the diagnostic solution(s) under development and explore the trade-offs of individual solutions. For the abductive reasoning variant, CatIO supports also in coming up with the required abductive diagnosis model via an automated concept based on fault injection and the simulation of corresonding Modelica models.
The Talking Comic Strip: Technology-Enhanced Learning for English Communication

There have been challenges in educational fields which involve a diversity of learners from different countries because the majority of learning materials and resources are written in English. In nursing education, the comprehension of the written materials in English has a bilateral effect on the academic and clinical performance of the learners and the teaching capacity of the mentors. This chapter presents the development of technology-enhanced learning material—the talking comic strip—as an aid in improving English nursing communication. This learning material features the use of augmented reality technology that allows the convergence of real-world objects and digital objects. It allows learners to read the comic strip and hear its audio at the same time. A total of 39 nursing student-respondents completed a usability survey to evaluate the acceptability of the learning material. The majority of the respondents found the talking comic strip to be useful, easy to use, and easy to learn, and they were satisfied with using it.
Indonesian Millennial Segmentation to Increase Behavioral Intention to use Language Learning Apps

The existence of language learning apps answers the needs and behavior trends of Indonesian millennial. However, the behavioral intention to use language learning apps has not been maximized. The purpose of this study is to explore the factors of Indonesian millennial behavioral intention to use language learning apps, then find out whether those factors influence Indonesian millennial behavioral intention to use language learning apps significantly, and finally identify the Indonesian millennial segmentation in language learning apps industry based on the factors of behavioral intention to use. This research uses both qualitative and quantitative methods. The results of the research show that there are 13 variables that affect Indonesian millennial behavioral intention to use language learning apps; then based on those 13 variables, 3 millennial segments are formed, namely The Thirst Learners Skill segment, The Ease-comfort Seekers segment, and The Worth-it Strivers with different characteristics.
VIDEM 2020

Currently, both understanding and developing interactive visual interfaces become ever more challenging, since different visual solutions exist that involve large interdisciplinary teams and deal with massive amounts of data, a wide range of interaction techniques, and domain-specific aspects. At the same time, traditional design methods become obsolete with more and more resources and knowledge that needs to be acquired. The proposed workshop provides a forum for discussing experimental and theoretical techniques, frameworks, and prototyping methods to design visual interfaces in different domains such as data visualization, tangible and embedded interaction, extended and mixed reality, multi-modal interfaces, and others.
Extended depth-of-field lensless camera using a radial amplitude mask

We propose a novel lensless camera equipping a radial amplitude mask for realizing extended depth-of-field imaging. Here we report simulation results of depth invariant point spread function and extended depth-of-field imaging.
A Guided Learning Approach for Generative Adversarial Networks

In this paper, we propose a novel technique for training Generative Adversarial Networks (GANs) using autoencoders. GANs, in recent years, have emerged as one of the most popular generative models. Despite their success, there are several challenges in maintaining the trade-off between diversity and quality of the generated distribution. Our idea stems from the fact that deeper layers of an autoencoder contain high-level feature representation of the input data distribution. Reusing these layers provides GAN with information about the representative characteristics of real data and hence can guide its adversarial training. We call our model Guided GAN since the autoencoder (guiding network) provides a direction to train the GAN (generative network). Guided GAN also minimizes both the forward and reverse Kullback-Leibler (KL) divergence in a single model, exploiting the complementary statistical properties of the two. We conduct extensive experiments and use various metrics for assessing the quality, diversity of generated images and convergence of the model. Our model is evaluated on two standard datasets: CIFAR-10 and CelebA demonstrating either superior or competitive performance compared to baseline GANs, especially in the earlier training stages. Our guided training procedure has been tested on different baseline GANs without any changes to their hyper-parameter configuration or architecture.
A Novel Global Motion Estimation and Compensation Framework in Compressed Domain for Sign Language Videos

In this paper, we have proposed a novel framework for Global Motion Estimation (GME) and Global Motion Compensation (GMC) in compressed videos. The framework employs the motion vectors (MVs) partially decoded from the compressed videos which are inherent in the videos. GME is a challenging task in the presence of moving objects in a video as the estimation of the Global Motion (GM) parameter vector is affected by these moving regions. In this framework, we have proposed a novel method based on the histogram of magnitudes of MVs for removing the moving regions from a video. The MVs corresponding to background Motion Blocks (MBs) are selected for the estimation of Global Motion (GM) parameter vector from the bin corresponding to the peak value of the histogram. After obtaining the GM, finally compensation of the GM is done by removing this GM from the original MV field. We have tested our algorithm for American Sign Language (ASL) videos encoded with H.264/AVC JM encoder and results are found to be satisfactory in presence of various outliers.
A new idea in response to fast correlation attacks on small-state stream ciphers

In the conference “Fast Software Encryption 2015”, a new line of research was proposed by introducing the first small-state stream cipher (SSC). The goal was to design lightweight stream ciphers for hardware applications by going beyond the rule that the internal state size must be at least twice the intended security level. Fast correlation attack (FCA) was successfully applied to all proposed SSCs which can be implemented by less than 1000 gate equivalents in hardware. It is possible to increase the security of stream ciphers against FCA by exploiting more complicated functions for the nonlinear feedback shift register and the output function, but we use lightweight functions to design the lightest SSC in the world while providing more security against FCA. Our proposed cipher provides 80-bit security against all types of Time-memory-data trade-off (TMDTO) attacks, while Lizard and Plantlet provide only 60-bit and 58-bit security against TMDTO distinguishing attacks, respectively. Our main contribution is to propose a lightweight round key function with a very long period that increases the security of SSCs against FCA.
Evaluation of heavy metals in coastal aquifers and seawater

Urbanization along the coastal line tends to increase the concentration of heavy metals like Fe, Pb, Zn, Cr, Cd, Cu, Mn, and Ni in seawater. In this study, seawater samples collected around the coast of Calicut beach in Kozhikode district, Kerala, India have been analyzed for heavy metal contamination and its influence on the coastal aquifer system was studied. The concentration of heavy metals was analyzed using inductively coupled plasma mass spectrometer Arithmetic means were calculated for each heavy metal following the order Fe > Cu > Zn > Mo > V > Ni > Cr > Mn > Pb > As > Se > Co > Cd > Sb > Be in the case of seawater, and Fe > Mn > Zn > Cu > Pb > Cr > Ni > V > Co > As > Mo > Se > Sb > Cd > Be in the case of groundwater. The mean heavy metal pollution index (HPI) for collected groundwater samples were calculated by comparing with Bureau of Indian Standards (BIS) guidelines and they fall under the low HPI category. The Landsat 8 imagery of the study area has used for deriving the correlation between heavy metal concentration and reflectance values. Further, the correlation with principal bands obtained from principal component analysis of the Landsat image was also studied. Results derived from the present study will be useful to evaluate the groundwater quality along coastal areas and will provide a clear idea about the measures that should have been taken to overcome the contamination, if any.
Choosing TCP Variants for Cloud Tenants – A Measurement based Approach

Cloud computing has become the de-facto paradigm for fulfilling the computing needs of a myriad of applications like streaming, e-commerce, data analytics, etc. While a significant amount of work exists on how cloud providers can improve the networking performance of their cloud platforms, very little has been done to explore how a cloud tenant can achieve the best performance for their applications. In this paper, we study how the choice of the TCP variant impacts the performance achieved by tenant applications. We present a generic measurementbased approach to identify the best TCP variant for any given application in a given cloud environment. Our approach is comprised of first measuring several performance metrics including throughput, latency, and loss in the given cloud platform for several TCP variants, and then identifying the best TCP variant based on three things: observations from the measurements, nature of the traffic of the given application, and application requirements such as high throughput or low latency. We study the effectiveness of our approach by implementing it in two large public clouds, Amazon's AWS and Google's GCP, and present our observations from several case studies using three common cloud applications, namely streaming, distributed input-output, and sort, and four common TCP variants, namely Cubic, New Reno, BBR, and DCTCP. From our observations, we found that just by changing the TCP variant that an application uses, the average throughput can be increased by up to 13.7% and the round trip time can be decreased by up to 5 times.
Intelligent system for sports movement quantitative analysis

Action is the key to sports and the core factor of standardization, quantification, and comprehensive evaluation. However, in the actual competition training, the occurrence of sports activities is often fleeting, and it is difficult for human eyes to identify quickly and accurately. There are many existing quantitative analysis methods of sports movements, but because there are many complex factors in the actual scene, the effect is not ideal. How to improve the accuracy of the model is the key to current research, but also the core problem to be solved. To solve this problem, this paper puts forward an intelligent system of sports movement quantitative analysis based on deep learning method. The method in this paper is firstly to construct the fuzzy theory human body feature method, through which the influencing factors in the quantitative analysis of movement can be distinguished, and the effective classification can be carried out to eliminate irrelevantly and simplify the core elements. Through the method of human body characteristics based on fuzzy theory, an intelligent system of deep learning quantitative analysis is established, which optimizes the algorithm and combines many modern technologies including DBN architecture. Finally, the accuracy of the method is improved by sports action detection, figure contour extraction, DBN architecture setting, and normalized sports action recognition and quantification. To verify the effect of this model, this paper established a performance comparison experiment based on the traditional method and this method. The experimental results show that compared with the traditional three methods, the accuracy of the in-depth learning sports movement quantitative analysis method in this paper has greatly improved and its performance is better.
Distance learning in the aspect of labor psychology of teachers and students

The article analyzes the experience of distance learning in conditions of self-isolation, obtained in the spring of 2020 in higher education institutions. A survey of students in the first days of transition to online training and after three months of working in this mode revealed the most common difficulties manifested in the subjective assessments of cadets and teachers of higher education organizations. Four groups of difficulties are identified that, according to students, reduce not only the productivity of work in the process of forced distance learning, but also the satisfaction of this form of training: difficulties associated with a decrease in motivation, creativity, and the value of professional training; difficulties in understanding and completing tasks; user difficulties when working with a computer; technical difficulties associated with unsatisfactory computer or Internet performance. Quantitative characteristics of each group of difficulties are given, their probable causes and ways to support students are discussed. Some data from the survey of students and teachers after three months of training in self-isolation are presented. A number of data on the psychological causes of subjective difficulties of students is provided.
Multi-paragraph Reading Comprehension with Token-level Dynamic Reader and Hybrid Verifier

Multi-paragraph reading comprehension requires the model to infer answers of arbitrary user-generated questions by reasoning cross-passage information. Previous work usually generates answer by directly employing a pointer network to predict the start and end position of the answer. However, span-level reading is insufficient since intermediate words may matter more. In this paper, we propose a novel unified network that includes a selector, a Token-level dynamic reader, and a Hybrid verifier (TH-Net). The core of token-level dynamic reader is a gate mechanism which dynamically selects important intermediate words according to boundary words. We decide the reader score from each token being both the boundary and the content. Moreover, we adopt a hybrid network verifier considering semantic answer-answer and entailment question-answer relationships to robust the model in case of being fooled by adversarial answers. Our experiments on SQuAD-document, SQuAD-open, and Trivia-wiki datasets show significant and consistent improvement as compared to other baselines and achieve the state-of-the-art performance on two of them.
Multi-level Visual Fusion Networks for Image Captioning

Image captioning is a multi-modal complex task in machine learning. Traditional methods focus only on entities in visual strategy networks, and can't reason about the relationship between entities and attributes. There are problems of exposure bias and error accumulation in language strategy networks. To this end, this paper proposes a multi-level visual fusion network model based on reinforcement learning. In the visual strategy network, multi-level neural network modules are used to transform visual features into feature sets of visual knowledge. The fusion network generates function words that make the description more fluent, and is used for the interaction between the visual strategy network and the language strategy network. The self-criticism strategy gradient algorithm based on reinforcement learning in language strategy networks is used to achieve end-to-end optimization of visual fusion networks. We evaluated our model on the Flickr 30K and MS-COCO datasets, and verified the accuracy of the model and the diversity of model learning subtitles through experiments. Our model achieves better performance over state-of-the-art methods.
A New Approach to the Berlekamp-Massey-Sakata Algorithm: Improving Locator Decoding

We study the problem of the computation of Groebner basis for the ideal of linear recurring relations of a doubly periodic array. We find a set of indexes such that, along with some conditions, guarantees that the set of polynomials obtained at the last iteration in the Berlekamp-Massey-Sakata algorithm is exactly a Groebner basis for the mentioned ideal. Then, we apply these results to improve locator decoding in abelian codes.
Solving for muscle blending using data

Modeling of the human face is a challenging yet important problem in computer graphics. Building accurate muscle models for physics-based simulation of the face is a problem that either requires a lot of manual effort or drastic over-parameterization of the muscles to achieve desirable results. In this work, we reduce the number of parameters required to build personalized muscle models by taking into account the blending of the fine muscles and passive tissue when we solve for the muscle activations. We begin by adapting an anatomical template model to a neutral scan of a subject. Then, we solve an inverse physics problem using several scans simultaneously to solve for both the muscle activations and the geometry matrix representing blending of the muscles. Finally, we demonstrate that this geometry matrix can be used on new, previously unseen scans to solve for only the muscle activations. This greatly reduces the number of parameters that must be solved for compared to previous works while requiring no additional manual effort in constructing the muscles.
A Relativistic Study on Recent Clustering Algorithms

Ever-increasing volumes of data necessitate novel algorithms for extracting inherent information from voluminous data. Generally, clustering is adopted for voluminous and intricate data for determining groups and classifying stimulating categories in the data. Most of the available clustering algorithms are suitable for identifying spherical-shaped clusters and are not capable of handling outliers efficiently. In the present work, a relativistic study of two clustering algorithms, namely BIRCH and CURE algorithms, is carried out. These algorithms are reported to be pertinent for large databases, thus address the problems of traditional clustering techniques. Experimentations are conducted to study the relative efficiency of BIRCH and CURE. Experimental results reveal that CURE is much faster and can efficiently detect the significant outliers and identify non-spherical-shaped clusters with a wide range of size than BIRCH.
Towards a Chatbot Based Smart Pervasive Healthcare Medical Emergency Cases

Many people face death everyday because of severe medical emergencies, however a basic knowledge of how to deal with such situations could save several human lives. To avoid deterioration of a victim's condition and maintaining his/her physical integrity, this paper aim at developing a chatbot based smart pervasive healthcare to help victims or incident witnesses properly perform first-aid in a medical emergency situation until the aid arrives. Therefore, even a person with no first aid skills, could help a victim to survive by performing first aid support as suggested by the virtual assistant. This assistance make ordinary people more confident to become a rescuer.