Reviewerly/exHarmony huggingface dataset:
IoT-Inspired Monitoring Framework for Real-Time Stereotypic Movement Analysis

Physical activity recognition has become one of the prominent domain of research in the field of smart healthcare. The identification of physical activities through wearable sensors provide significant information about the functional ability and lifestyle of an individual. In this article, a smart physical activity-assisted behavior prediction framework is presented to recognize different stereotypical motor movements of the individual suffering from autism spectrum disorder in real time. This article employs the data acquisition efficiency of Internet of Things (IoT) assisted smart wearable sensors to capture the physical activities of an individual. To predict the physical activities, a deep learning-inspired multilayered convolutional neural network (CNN) + long short-term memory network is proposed. Furthermore, the utility of the framework is increased by presenting an effective fog-assisted alert-based decision-making module. The decision-making module notifies the concerned caretaker or medical specialist about the performed irregular physical activity in real time. Moreover, the process of cloud-based record generation also enhances the significance of the proposed solution. The proposed approach is validated by comparing the prediction performance with traditional handcraft-based and modern deep learning-based state-of-the-art approaches such as <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$k$</tex></formula> -nearest neighbors, hidden Markov model, and CNN, where it outperformed by achieving the overall accuracy of 91.88%.
Smart Surveillance System by Face Recognition and Tracking Using Machine Learning Techniques

With the recent technological advancements, the theft and other such unnecessary activities can be controlled. At present, many traditional security systems are available to just record the situation, but the unnecessary activities will be already taken. The main objective of this research work is to control and stop the theft by tracking the burglar face or the stranger face image and send the notification to the owners. This system will not only alert the owners, and it can also give access to the door lock with the face recognition feature. In this way, the proposed system replaces the traditional security system with the feature like RFID, PIN method, etc. The accuracy of the proposed system is high, and it works in all the situations, where it can be operated with both battery and main power system. The system has its own database to recognize the person, and the person with registered facial image can only be given access to the door. The developed system has a low powered device with high accuracy system. Moreover, it also helps to reduce the unnecessary activities.
Some Conditional Cube Testers for Grain-128a of Reduced Rounds

In this paper, a new strategy, maximum last round, is proposed to select cubes for cube attacks. This strategy considers the cubes in a particular round where the probability of its superpoly to be 1 is at most, where is a very small number. A heuristic method to find a number of suitable cubes using this strategy and the previously used strategies (i.e., maximum initial zero, maximum last zero) are proposed. To get a bias at the higher rounds, the heuristic, too, imposes conditions on some state bits of the cipher to make the non-constant superpoly of a cube as zero for the first few rounds. Some cube testers are formed by using those suitable cubes to implement a distinguishing attack on Grain-128a of reduced KSA (or initialization) rounds. We present a distinguisher for Grain-128a of 191 (out of 256) KSA round in the single key setup and 201 (out of 256) KSA round in the weak key setup by using the cubes of dimension 5. The number of rounds is the highest till today, and the cube dimension is smaller than the previous results. Further, we tested our algorithm on Grain-128 and achieved good results by using small cubes.
Revisiting the Fairness and Randomness of Delegated Proof of Stake Consensus Algorithm

There are many disparate conceptualizations to secure a cryptocurrency network. Bitcoin relies on the proof-of-work mechanism and heterogeneous altcoins use proof-of-stake. Delegated Proof-of-Stake (DPoS), known to be a fast, efficient, decentralized, and highly flexible blockchain design, further offers some interesting reshaping that are well worth deliberating. In DPoS consensus, a panel of trusted parties (called committee of producers in the paper) has to be established, with all of its members eligible to create blocks and prevent non-trusted parties from participating. Deterministic selection of block producers allows transactions to be confirmed in an expected average of 1 second. The paper revisits the security properties of DPoS and reinforces the fairness and randomness for the algorithm without sacrificing its performance. We first scrutinize the limitations of the DPoS consensus, including predictability of producers, bribing producers, and lack of task incentivizing. We then introduce a cascade progressive-like ranking to judge the behaviour of the producers, and the producer who does not contribute to the network at the current interaction will be committed to a lower chance of being qualified for the coming involvement. More specifically, we conceptualize the tweaking parameters and the weights for the producers and bring in the weighted committee to measure the contributions of the producers. The weighted committee is a multiset derived from the committee and the weights specify the multiplicities of the elements (the producers). A pseudo-random process is also formulated to squeeze out the producer probabilistically from the weighted committee for creating the blocks. This unpredictability of the producers amplifies the randomness of the network and gives rise to a more secure and reliable cryptocurrency system. The proposal aspires to be beneficial for practical applications and the practitioners are as well encouraged to customize case-by-case the tweaking parameters and the pseudo-random process.
Are parallel translation tasks parallel in difficulty? an eye-tracking study

This study explores translation task equivalence by comparing parallel translation test tasks in levels of difficulty, using eye-tracking, as well as NASA-TLX and retrospection. It also examines the effectiveness of measures of translation difficulty when differences in translation difficulty may be minor. Fifty-two participants completed two Chinese-to-English parallel translation test tasks, ‘Airplane’ and ‘Subway’, in a randomly assigned, counter-balanced order, with their eyes tracked. Upon completion of each task, participants were asked questions regarding translation difficulty. They were also required to fill in the NASA-TLX questionnaire. Paired samples t-tests showed significant differences in eye-tracking metrics, temporal measures, subjective ratings, and translation quality, indicating that ‘Airplane’ was more difficult. The retrospective interviews confirmed these differences. Temporal measures were found more sensitive to task change than eye-tracking measures. The target text and source text eye-tracking measures were similar in sensitivity. The effectiveness of the measures is explained in relation to what is actually measured or elicited, translation directionality, translators, and task. This study has implications for validating parallel translation tests, translation task development, understanding differences and similarities in translation difficulty, and measuring translation difficulty.
Interpretable Clustering of Students’ Solutions in Introductory Programming

In introductory programming and other problem-solving activities, students can create many variants of a solution. For teachers, content developers, or applications in student modeling, it is useful to find structure in the set of all submitted solutions. We propose a generic, modular algorithm for the construction of interpretable clustering of students’ solutions in problem-solving activities. We describe a specific realization of the algorithm for introductory Python programming and report results of the evaluation on a diverse set of problems.
A novel dissimilarity metric based on feature‐to‐feature scatter frequencies for clustering‐based feature selection in biomedical data

Abstract Filter feature selection methods have been extensively used for dimensionality reduction in biomedical data analysis. In this article, a novel dissimilarity metric based on feature‐to‐feature (F2F) scatter frequencies is proposed for clustering‐based filter feature selection. The proposed metric is computed by obtaining the feature‐level ranks of samples and identifying the features which assign close ranks to each sample. The order of ranking is determined for each feature by class labels. Samples are represented as a set of affinity sets containing features having rank differences less than a predefined proximity window size. The F2F dissimilarity of a pair of features is computed using the frequency of their appearance in different affinity sets. Features are then clustered into distinct groups using F2F dissimilarity metric. From each cluster, the feature having the highest relevance score is selected. The experiments conducted on ten biomedical datasets confirmed the effectiveness of the proposed method in improving classification performance. Notably, the proposed method outperforms the widely used schemes in terms of both classification under ROC curve and stability.
Trimming Mobile Applications for Bandwidth-Challenged Networks in Developing Regions

Despite continuous efforts to build and update mobile network infrastructure, mobile devices in developing regions continue to be constrained by limited bandwidth. Unfortunately, this coincides with a period of unprecedented growth in the sizes of mobile applications. Thus it is becoming prohibitively expensive for users in developing regions to download and update mobile apps critical to their economic and educational development. Unchecked, these trends can further contribute to a large and growing global digital divide. Our goal is to better understand the source of this rapid growth in mobile app code size, whether it is reflective of new functionality, and identify steps that can be taken to make existing mobile apps more friendly to bandwidth constrained mobile networks. We hypothesize that much of this growth in mobile apps is due to poor resource/code management, and do not reflect proportional increases in functionality. Our hypothesis is partially validated by mini-programs, apps with extremely small footprints gaining popularity in Chinese mobile platforms. Here, we use functionally equivalent pairs of mini-programs and Android apps to identify potential sources of “bloat,” i.e., inefficient uses of code or resources that contribute to large package sizes. We analyze a large sample of popular Android apps and quantify instances of code and resource bloat. We develop techniques for automated code and resource trimming, and successfully validate them on a large set of Android apps. We hope our results will lead to continued efforts to streamline mobile apps, making them easier to access and maintain for users in developing regions.
Sentiment Analysis of Short Texts Based on Parallel DenseNet

Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks (CNNs).However, most of these CNN models focus only on learning local features while ignoring global features.In this paper, based on traditional densely connected convolutional networks (DenseNet), a parallel DenseNet is proposed to realize sentiment analysis of short texts.First, this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural network.Second, this paper solves the problem of ignoring global features in traditional CNN models by combining the original features with features extracted by the parallel feature extraction block, and then sending the combined features into the final classifier.Last, a model based on parallel DenseNet that is capable of simultaneously learning both local and global features of short texts and shows better performance on six different databases compared to other basic models is proposed.
Length of Stay Prediction in Acute Intensive Care Unit in Cardiothoracic Surgery Patients

The goal of this study was to apply machine learning (ML) methods to predict the Length of Stay in an Intensive Care Unit (LOS-ICU) based on preoperative factors. To optimize the capacity of the ICU in surgery department, the prediction of a long stay (more than 2 days) can support the clinical decision making on accepting or delaying a patient intervention, considering the ICU occupancy. A database with records from 7364 patients that were operated in the Cardiothoracic surgery department of a public Portuguese hospital was used as the base of ML algorithms training. Regarding the risk of the patients to be in the group of long LOS-ICU, we compared five machine learning algorithms including Gradient Boosting, Random Forest, Support Vector Machine (SVM), Adaboost and Logistic Regression. We studied the classifier performance to adjust the sensitivity of a long stay classification, in order to reduce the potential of long LOS-ICU classification being miss classified as a short LOS-ICU.
To Get People to Adopt Tracing Applications, Minimize the Probability They Will Regret It

Experimental and field studies of repeated decisions document high sensitivity to the probability of regret; experience leads people to underweight rare risks and select the actions that provide the best outcomes in most cases. We clarify the implications of this pattern to policies designed to combat the spread of coronavirus. Our analysis questions the value of policies that encourage adherence to physical distancing guidelines without continuous enforcement. Yet, it highlights the potential of policies that encourage the use of voluntary health code applications, which estimate the risk a person is infectious. To be effective, these applications should gently enforce physical distancing, and the policies should ensure that the common experience from using the applications is better than the common experience from not using them.
Estimation of 4-DoF manipulator optimal configuration for autonomous camera calibration of a mobile robot using on-board templates

Camera calibration is one of the important tasks in the field of robotics and computer vision. It enables to increase the accuracy of metric measurements in photogrammetry applications and provides higher performance in computer vision algorithms such as stereo matching and motion estimation. It is known that regardless of the calibration method used variation of given estimated camera parameters their is inevitable due to multiple reasons: varying weather conditions, temperature fluctuations or severe operation mode of a robotic system. In order to alleviate the aforementioned issues recalibration is required. In this work we employed 4-DoF manipulator Servosila Engineer mobile robotic system equipped with an on-board camera. In order to automate the process of calibration an algorithm is developed which enables to estimate optimal manipulator joint configurations. Forward kinematics is solved on a discretized set of joint angles, subsequently estimating possible camera positions for its further calibration. Experiments on real robot demonstrate the possibility of usage of the developed algorithm to find optimal joint configurations for automatic camera calibration.
Prostate Cancer Prognosis-a comparative approach using Machine Learning Techniques

Recently machine learning have been a field of interest for many researchers. Machine learning holds applications in tremendous sectors like healthcare, e-commerce and so on. It plays a vital role in the field of healthcare by their significant contribution through its ability to read from the historic/ past data especially in the diagnosis/predicting of diseases. Many classification algorithms have been proposed for the prediction process. The problem with classifiers is their vast variety and difficulty in choosing an appropriate method for the particular problem. To overcome the issue of selecting the appropriate method a comprehensive study on various classifiers should be done. This paper focuses on the working of various classifiers for prediction of prostate Cancer in calculating the level of efficiency in prediction and this helps in selecting the best method.
A Bank Card Number Recognition Method Based on Convolutional Neural Network

With the popularity of online transactions and big data platforms, bank card digital recognition can save people from manually entering a long list of card numbers. Traditional optical way and machine learning methods have low identification accuracy and are susceptible to background environment interference. To solve this problem, this paper proposes a bank card number recognition method, which is a cooperation of convolutional neural network and character positioning. Firstly, various bank card pictures shot from different angles are collected as our input data; then EAST network algorithm is used to realize character positioning; finally, the image after character positioning segmentation is input into the convolutional neural network and vgg16 is used for transfer learning to realize card number recognition. Experimental results show that this method has better recognition accuracy and robustness.
A Comprehensive Study of Machine Translation Tools and Evaluation Metrics

In this article, the ideas of statistical and neural machine translation approaches were explored. Various machine translation tools and machine translation evaluation metrics were also investigated. Nowadays, machine translation plays a key role in the society where different languages are spoken as it removes the language barrier and digital divide in the society by providing access to all information in the local language which a person can understand. There were different phases of machine translation as its evolution is concerned, and different approaches were followed in different phases some requiring an enormous amount of parallel corpus which is considered a crucial element of machine translation. In the proposed system, some of the parameters were examined to carry the analysis of several translation tools, and evaluation metrics are also available for accessing the quality of machine translation.
Predicting Sex and Age using Swipe-Gesture Data from a Mobile Device

Swipe-gestures are by far the most common way to interact with mobile devices such as phones, tablets, and even some computers. As touch-screen technology has improved, the possibility of obtaining high-quality swipe-gesture data from touch-screen devices has become more and more prevalent. This has led to the exploration of its use in further improving authentication systems, and more recently, as the basis for soft biometrics prediction. This paper discusses the process of using swipe-gesture data for prediction of sex and age of individuals using mobile devices. The software used to obtain the data is presented, the features collected from the swipe data are detailed, and the machine learning classifiers are displayed in a way that the experiment can be replicated. During this experiment, a total of ten well-known classifiers have been used. The results of this analysis have further confirmed the possibility of predicting sex, obtaining an accuracy rate of 79% for a single classifier as well as a group average of almost 70%. Moreover, in the prediction of age category, the results are even more encouraging, obtaining an accuracy rate of nearly 80% on average as well as several of the classifiers performing well above the average.
Research on Urban Landscape Image Enhancement under the Background ofBig Data Visualization

At present, the lack of urban landscape image is common in most cities in China. This article uses the principle of big data visualization and the combination of urban landscape image to discuss the application of urban landscape image enhancement research in China. Starting from multiple dimensions such as Internet questionnaire data, big data software visualization, and urban landscape image enhancement steps, we explores how big data visualization can promote urban landscape image, and draws up countermeasures.
Fourier-Analysis-Based Form of Normalized Maximum Likelihood: Exact Formula and Relation to Complex Bayesian Prior

Normalized maximum likelihood (NML) distribution of probabilistic model gives the optimal code length function in the sense of minimax regret. Despite its optimal property, the calculation of NML distribution is not easy, and existing efficient methods have been focusing on its asymptotic behavior, or on specific models. This paper gives an efficient way to calculate NML by integral on parameter domain, not on data domain, showing that NML distribution is a Bayesian predictive distribution with a complex prior, based on our novel Fourier expansion approach. Our results provide an integrated way to calculate NML for exponential family and also include a non-asymptotic version of previous work on asymptotic behavior for general cases. The applications of our methodology are not limited to but also include normal distribution, Gamma distribution, Weibull distribution, and von Mises distribution.
Fast hybrid-MixNet for security and privacy using NTRU algorithm

Security and privacy-enhancing techniques are developed in order to provide strong protection over the Internet. These techniques aim to enable the users to keep their identities secret during the communication when they are forwarding an email, making payments online, and web browsing or posting to newsgroups. MixNet is the most practical solution for concealing the identities of messages and senders. The anonymous channel contains numerous routing protocols that consist of anonymous messages forwarding between nodes. The structure of MixNet is also based on this way to achieve anonymity. After the proposed work of Chaumian MixNet, many practical implementations of MixNet using many approaches and techniques are accomplished until now. In this paper, we propose the hybrid MixNet using NTRU asymmetric cryptosystem. We designed a mechanism for anonymous communication in which more people can participate in their secure communication. It hides the relation between input and output in each phase of Mix servers. The performance of Hybrid MixNet using NTRU is better than Hybrid Mixnet using ElGamal and ECC. In our proposed system, Hybrid MixNet using NTRU takes average time 16.4 milliseconds while Hybrid MixNet using ElGamal and ECC take 93.4 milliseconds and 182.2 milliseconds respectively.