Reviewerly/exHarmony huggingface dataset:
Residual Learning of Video Frame Interpolation Using Convolutional LSTM

Video frame interpolation aims to generate intermediate frames between the original frames. This produces videos with a higher frame rate and creates smoother motion. Many video frame interpolation methods first estimate the motion vector between the input frames and then synthesizes the intermediate frame based on the motion. However, these methods rely on the accuracy of the motion estimation step and fail to accurately generate the interpolated frame when the estimated motion vectors are inaccurate. Therefore, to avoid the uncertainties caused by motion estimation, this paper proposes a method that implicitly learns the motion between frames and directly generates the intermediate frame. Since two consecutive frames are relatively similar, our method takes the average of these two frames and utilizes residual learning to learn the difference between the average of these frames and the ground truth middle frame. In addition, our method uses Convolutional LSTMs and four input frames to better incorporate spatiotemporal information. We also incorporate attention mechanisms in our model to further enhance the performance. This neural network can be easily trained end to end without difficult to obtain data such as optical flow. Our experimental results show that the proposed method without explicit motion estimation can perform favorably against other state-of-the-art frame interpolation methods. Further ablation studies show the effectiveness of various components in our proposed model.
A proposal for rapid grade control of gibbsitic bauxites using multivariate statistics on XRD data

Currently, traditional wet chemistry methods are used for quality control of bauxites. Such methods indirectly quantify the gibbsite and kaolinite content as available alumina (AvAl2O3) and reactive silica (RxSiO2), respectively, and it is very costly and time-consuming. In order to achieve a rapid and reliable method to estimate these parameters, as alternative to current wet chemistry methods, this study, evaluates the use of multivariate statistics – Partial Least Square Regression (PLSR) on XRD data of gibbsitic bauxites (Miltonia 3 plateau). The method was optimized through Principal Component Analysis (PCA) and factorial Design of Experiments (DOE). The results demonstrated that such method has a potential to be industrially applied to quality control of bauxites. In comparison with traditional method, the same precision is achieved for the ore samples, but faster, easier to implement and carry out the analyses, demands less laboratory space and manpower, and no reagent is necessary. In addition, the use of XRD for quality control makes it possible to track the mineralogy of the bauxite that feeds the Bayer process, and therefore, be aware of how it could impact the process.
The NiuTrans System for WNGT 2020 Efficiency Task

This paper describes the submissions of the Ni-uTrans Team to the WNGT 2020 Efficiency Shared Task.We focus on the efficient implementation of deep Transformer models (Wang et al., 2019;Li et al., 2019) using NiuTensor 1 , a flexible toolkit for NLP tasks.We explored the combination of deep encoder and shallow decoder in Transformer models via model compression and knowledge distillation.The neural machine translation decoding also benefits from FP16 inference, attention caching, dynamic batching, and batch pruning.Our systems achieve promising results in both translation quality and efficiency, e.g., our fastest system can translate more than 40,000 tokens per second with an RTX 2080 Ti while maintaining 42.9 BLEU on newstest2018.
Towards secure IoT programming in Haskell

IoT applications are often developed in programming languages with low-level abstractions, where a seemingly innocent mistake might lead to severe security vulnerabilities. Current IoT development tools make it hard to identify these vulnerabilities as they do not provide end-to-end guarantees about how data flows within and between appliances. In this work we present Haski, an embedded domain specific language in Haskell (eDSL) for secure programming of IoT devices. Haski enables developers to write Haskell programs that generate C code without falling into many of C’s pitfalls. Haski is designed after the synchronous programming language Lustre, and sports a backwards compatible information-flow control extension to restrict how sensitive data is propagated and modified within the application. We present a novel eDSL design which uses recursive monadic bindings and allows a natural use of functions and pattern-matching in Haskell to write Haski programs. To showcase Haski, we implement a simple smart house controller where communication is done via low-energy Bluetooth on Zephyr OS.
GAP: Genetic Algorithm Based Large-Scale Graph Partition in Heterogeneous Cluster

Graph is an important model to describe various networks, and its scale becomes larger and larger with the development of communication and information technology.The analysis of large-scale graphs requires distributed graph processing systems, and graph partition is the basis of these systems.The existing graph partitioning algorithms are almost proposed for homogeneous clusters, which don't consider the differences among computing nodes in heterogeneous clusters.This paper proposes GAP, a Genetic Algorithm based graph Partitioning algorithm to solve this problem.GAP aims to reduce the total processing time on a heterogeneous cluster by partitioning graphs according to the computing powers of computing nodes.GAP balanced partition the graph initially, and then utilizes genetic algorithm to transfer vertices to reduce cut edges.GAP can balance the processing time of computing nodes, and reduce the communication time among computing nodes.The experiments performed on a heterogeneous cluster demonstrate the outperformance of GAP than Hash.
Synthetic Sample Generation for Label Distribution Learning

Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than a single label or multiple labels. Current LDL methods have proven their effectiveness in many machine learning applications. As of the first formulation of the LDL problem, numerous studies have been carried out that apply the LDL methodology to various real-life problem solving. Others have focused more specifically on the proposal of new algorithms. The purpose of this article is to start addressing the LDL problem as of the data pre-processing stage. The baseline hypothesis is that, due to the high dimensionality of existing LDL data sets, it is very likely that this data will be incomplete and/or that poor data quality will lead to poor performance once applied to the learning algorithms. In this paper, we propose an oversampling method, which creates a superset of the original dataset by creating new instances from existing ones. Then, we apply already existing algorithms to the pre-processed training set in order to validate the effcacy of our method. The effectiveness of the proposed SSG-LDL is verified on several LDL datasets, showing significant improvements to the state-of-the-art LDL methods.
Rethinking Relation between Model Stacking and Recurrent Neural Networks for Social Media Prediction

Popularity prediction of social posts is one of the most critical issues for social media analysis and understanding. In this paper, we discover a more dominant feature representation of text information, as well as propose a singe ensemble learning model to obtain the popularity scores, for social media prediction challenge. However, most social media prediction techniques focus on predicting the popularity score of social posts based on a single model, such as deep learning-based or ensemble learning-based approaches. However, it is well-known that the model stacking strategy is a more effective way to boost the performance on various regression tasks. In this paper, we also show that the model stacking can be modeled as a simple recurrent neural network problem with comparable performance on predicting popularity scores. Firstly, a single strong baseline is proposed based on the deep neural network with a prediction branch. Then, the partial feature maps of the last layer of our strong baseline are used to establish a new branch with an isolated predictor. It is easy to obtain multi-prediction by repeating the above two steps. These preliminary predicted scores are then formed as the input of the recurrent unit to learn the final predicted scores, called Recurrent Stacking Model (RSM). Our experiments show that the proposed ensemble learning approach outperforms other state-of-the-art methods. Furthermore, the proposed RSM also shows the superiority over our ensemble learning approach, having verified that the model stacking problem can be transformed into the training problem of a recurrent neural network.
Hierarchical goal effects on center of mass velocity and eye fixations during gait

The purpose of this study was to determine the effect of hierarchical goal structure of a yet-to-be performed task on gait and eye fixation behavior while walking to the location of where the task was to be performed. Subjects performed different goal-directed tasks representing three hierarchical levels of planning. The first level of planning consisted of having the subject walk to a bookcase on which an object (a cup) was located in the middle of a shelf. The second level of planning consisted of walking to the bookcase and picking up the cup which was in the middle, on the right side, or on the left side of the bookcase shelf. The third level of planning consisted of walking to the bookcase, picking up the cup which was located in the middle of the bookcase shelf, and moving it to a higher shelf. Findings showed that hierarchal goals do affect center of mass velocity and eye fixation behavior. Center of mass velocity to the bookcase increased with an increase in the number of goals. Subjects decreased gait velocity as they approached the bookcase and adjusted their last steps to accommodate picking up the cup. The findings also demonstrated the important role of vision in controlling gait velocity in goal-directed tasks. Eye fixation duration was more important than the number of eye fixations in controlling gait velocity. Thus, the amount of information gained through object fixation duration is of greater importance than the number of fixations on the object for effective goal achievement.
Next Basket Recommendation Model Based on Attribute-Aware Multi-Level Attention

Next basket recommendation is a challenging problem, mainly due to the relationships among the items in a basket almost not being considered in current research. In this article, we address next basket recommendation with a novel deep learning architecture. In particular, we consider both the short-term user interests and the long-term user preferences, and we design a new attention that considers the relationships among the items in a basket. We extensively evaluated the proposed model on two benchmark data sets, the Ta-Feng and JingDong datasets. The experimental results show that the proposed model outperforms several state-of-the-art next basket recommendation models. In the experiments of the modules' effects, we also verify the effectiveness of each module. The model significantly improves the NDCG by 25.5 percentage points and 5 percentage points when compared with the uncompleted network model on the JingDong and Ta-Feng datasets, respectively; while in terms of the F1, the performance is improved by 14.6 and 23.4 percentage points, respectively.
Mixed Noise Removal by Residual Learning of Deep CNN

Due to the huge difference of noise distribution, the result of a mixture of multiple noises becomes very complicated. Under normal circumstances, the most common type of mixed noise is to add impulse noise (IN) and then white Gaussian noise (AWGN). From the reduction of cascaded IN and AWGN to the latest sparse representation, a great deal of methods has been proposed to reduce this form of mixed noise. However, when the mixed noise is very strong, most methods often produce a lot of artifacts. In order to solve the above problems, we propose a method based on residual learning for the removal of AWGN-IN noise in this paper. By training, our model can obtain stable nonlinear mapping from the images with mixed noise to the clean images. After a series of experiments under different noise settings, the results show that our method is obviously better than the traditional sparse representation and patch based method. Meanwhile, the time of model training and image denoising is greatly reduce
Automatic Face Recognition Using Enhanced Firefly Optimization Algorithm and Deep Belief Network

Face recognition is an emerging research area in biometric identification systems, which attracted many researchers in pattern recognition and computer vision.Since, the human facial images are high dimensional in nature, so a new optimization algorithm was proposed in this paper to reduce the dimension of facial images.At first, the input facial images were collected from ORL, YALE, and FAce Semantic SEGmentation (FASSEG) databases.Then, feature extraction was then performed by using Local Ternary Pattern (LTP) and Binary Robust Invariant Scalable Key points (BRISK) to extract the features from the face images.In addition, an enhanced fire-fly optimizer was used to reject the irrelevant features or to reduce the dimension of the extracted features.In enhanced fire-fly optimization algorithm, Chi square distance measure was used to find the distance between the fire-flies and also it uses only a limited number of feature values for representing the data that effectively reduces the "curse of dimensionality" concern.Finally, Deep Belief Network (DBN) was used to classify the individual's facial images.The experimental outcome showed that the proposed work improved recognition accuracy up to 7-20% compared to the existing work.
Distributed Ledger Technologies for IoT and Business DApps

The scope of the existing distributed ledger technologies is divergent in terms of technological features, as well as in their acceptance among the user and developer communities. In this paper, we propose a set of diverse criteria for the evaluation of a distributed ledger ecosystem, which does not focus only on the technological aspects of viable solutions. They also consider decentralized application development perspective and sustainability of its use. We provide a brief overview of some of the alternative distributed ledger ecosystems for the development and deployment of business and IoT decentralized applications. The set of investigated technologies and networks includes Ethereum, Hyperledger, Hedera Hashgraph, EOS, Corda, IOTA, and Multichain.
Does Bitcoin dominate the price discovery of the Cryptocurrencies market? A time-varying information share analysis

Using a time-varying vector error correction model (VECM), we examine the dynamic information shares of the top four Cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and Litecoin (LTC) over 1/1/2016–31/12/2019. Although steadily decreasing, the information share of BTC is still the largest as of end-2019. The individual dominances of market capitalization and trading volume can explain 20% of variations of the BTC information share but only 6% of those for ETH.
Human-centered Design Approach toward the Physical Activity Initiative for Work-from-Home Workers during the COVID-19 Outbreak

Physical activity levels have been declining since the COVID-19 outbreak due to social distancing and working from home With respect to the modifiable risk factor of work-from-home (WFH) workers' physical inactivity, the present study seeks to develop a physical activity initiative for them during this global pandemic Using the human-centered design (HCD) approach with the online involvement of twenty-two WFH workers, the results of this study presented the assessment and insights from the sequential HCD process regarding WFH workers' workstation in the home environment, views on received and tracked daily physical activities during the outbreak, and feedback from a five-day live physical activity prototyping This study discusses the systematic development of the HCD approach in designing a physical activity initiative However, a low number of participants may limit the conclusions drawn from this research In conclusion, the HCD approach provided an opportunity to engage WFH workers and create and design with them to promote physical activity during a pandemic After being developed through design development, being examined through hands-on use, and tested on the target group in pandemic settings, this present study informs and provides insights to design practitioners to consider the practical design of health workers during a pandemic © Common Ground Research Networks, Juhri Selamet, Some Rights Reserved (CC BY-NC-ND 4 0)
Secret Image Communication Scheme Based on Visual Cryptography and Tetrolet Tiling Patterns

Visual cryptographic scheme is specially designed for secret image sharing in the form of shadow images. The basic idea of visual cryptography is to construct two or more secret shares from the original image in the form of chaotic image. In this paper, a novel secret image communication scheme based on visual cryptography and Tetrolet tiling patterns is proposed. The proposed image communication scheme will break the secret image into more shadow images based on the Tetrolet tiling patterns. The secret image is divided into 4×4 blocks of tetrominoes and employs the concept of visual cryptography to hide the secret image. The main feature of the proposed scheme is the selection of random blocks to apply the tetrolet tilling patterns from the fundamental tetrolet pattern board. Single procedure is used to perform both tetrolet transform and the scheme of visual cryptography. Finally, the experimental results showcase the proposed scheme is an extraordinary approach to transfer the secret image and reconstruct the secret image with high visual quality in the receiver end.
Influence of new interval-valued pre-aggregation function on medical decision making

In this contribution the new concept of the operator in the interval-valued fuzzy sets, which is a pre-aggregation function, and their application in medical diagnosis is presented. Taking into account the widths of the intervals the type of interval operator measures are proposed in decision making problem. We suggest to apply the new interval-valued operator in decision model of medical diagnosis support.
Why Does Digital Learning Matter? Digital Competencies, Social Justice and Critical Pedagogy in Initial Teacher Education

Digital tools and spaces are becoming prevalent in schools across the world requiring the development of digital skillsets for student-teachers. Digital technology, in enabling education to extend beyond the space and time boundaries of the conventional classroom (Seifert, T., Sheppard, B. Wakeham, M., 2015) , brings the digital landscape into the classroom and firmly into the frame of reference for those preparing student-teachers to enter the profession. For Initial Teacher Education (ITE) programmes which foreground social justice, the digital (technology which is linked to the internet) goes far beyond a skillset or a discrete subject. Engaging with digital learning encompasses the 21st century context - both local and global - in which student-teachers and their future pupils are situated.&#x0D; Developing a critical pedagogic approach involves understanding the context in which one lives and enabling learners to challenge or change it (Freire, 1996) . For those working in ITE a postdigital lens provides a means to understand the context in which they are situated. Critical pedagogy enables student-teachers to understand that context and challenge the inequities which persist, preparing them not simply to navigate the digital landscape, but to engage with it critically. Reflecting on student-teacher learning this article explores the digital dimension, highlighting the importance of digital learning when engaging with critical pedagogy and social justice in ITE.
Prediction of Septic Shock Onset in ICU by Instantaneous Monitoring of Vital Signs

Septic Shock is a critical pathological state that affects patients entering the intensive care unit (ICU). Many studies have been directed to characterize and predict the onset of the septic shock, both in ICU and in the Emergency Department employing data extracted from the Electronic Health Records. Recently, machine learning algorithms have been successfully employed to help characterize septic shock in a more objective and automatic fashion. Only a few of these studies employ information contained in the continuously recorded vital signs such as electrocardiogram and arterial blood pressure. In particular, we have devised a novel feature estimation procedure able to consider instantaneous dynamics related to cardiovascular control. This work aims at developing a short-term prediction algorithm for identifying patients experiencing septic shock among a population of 100 septic patients extracted from the MIMIC-III clinical and waveform database. Among all the results obtained from several trained machine learning models, the best performance reached an AUC on the test set equal to 0.93 (Accuracy=0.85, Sensitivity=0.89 and Specificity=0.82).
A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks

We present a framework for compactly summarizing many recent results in efficient and/or biologically plausible online training of recurrent neural networks (RNN). The framework organizes algorithms according to several criteria: (a) past vs. future facing, (b) tensor structure, (c) stochastic vs. deterministic, and (d) closed form vs. numerical. These axes reveal latent conceptual connections among several recent advances in online learning. Furthermore, we provide novel mathematical intuitions for their degree of success. Testing various algorithms on two synthetic tasks shows that performances cluster according to our criteria. Although a similar clustering is also observed for gradient alignment, alignment with exact methods does not alone explain ultimate performance, especially for stochastic algorithms. This suggests the need for better comparison metrics.
Web Vulnerability Finder (WVF): Automated Black- Box Web Vulnerability Scanner

Today the internet has become primary source of communication among people because it holds limitless space and pool of various web applications and resources.The internet allows us to communicate in a fraction of second with another people who is sitting in the other part of the world.At present, the internet has become part of our daily life and its usage is increasing exponentially, therefore it accumulates a number of web applications on daily basis on Web podium.Most of the web applications exist with few weaknesses and that weaknesses give room to several bad buys (hackers) to interrupt that weak part of code in web applications.Recently, SQL Injection, Cross Site Scripting (XSS) and Cross Site Request Forgery (CSRF) seriously threaten the most of the web applications.In this study, we have proposed a black box testing method to detect different web vulnerabilities such as SQL Injection, XSS and CSRF and developed a detection tool i.e.Web Vulnerabilities Finder (WVF) for most of these vulnerabilities.Our proposed method can automatically analyze websites with the aim of finding web vulnerabilities.By applying the tool to some websites, we have found 45 exploitable XSS, SQL Injection 45, Directory Discloser 38 and Local/remote file inclusion 40 vulnerabilities.The experimental results show that our tool can efficiently detect XSS, SQL Injection, Directory Discloser and LFI/RFI vulnerabilities.