Reviewerly/exHarmony huggingface dataset:
Maximum <i>A Posteriori</i> Approximation of Hidden Markov Models for Proportional Sequential Data Modeling With Simultaneous Feature Selection

One of the pillar generative machine learning approaches in time series data study and analysis is the hidden Markov model (HMM). Early research focused on the speech recognition application of the model with later expansion into numerous fields, including video classification, action recognition, and text translation. The recently developed generalized Dirichlet HMMs have proven efficient in proportional sequential data modeling. As such, we focus on investigating a maximum a posteriori (MAP) framework for the inference of its parameters. The proposed approach differs from the widely deployed Baum-Welch through the placement of priors that regularizes the estimation process. A feature selection paradigm is also integrated simultaneously in the algorithm. For validation, we apply our proposed approach in the classification of dynamic textures and the recognition of infrared actions.
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency

Local SGD is a promising approach to overcome the communication overhead in distributed learning by reducing the synchronization frequency among worker nodes. Despite the recent theoretical advances of local SGD in empirical risk minimization, the efficiency of its counterpart in minimax optimization remains unexplored. Motivated by large scale minimax learning problems, such as adversarial robust learning and training generative adversarial networks (GANs), we propose local Stochastic Gradient Descent Ascent (local SGDA), where the primal and dual variables can be trained locally and averaged periodically to significantly reduce the number of communications. We show that local SGDA can provably optimize distributed minimax problems in both homogeneous and heterogeneous data with reduced number of communications and establish convergence rates under strongly-convex-strongly-concave and nonconvex-strongly-concave settings. In addition, we propose a novel variant local SGDA+, to solve nonconvex-nonconcave problems. We give corroborating empirical evidence on different distributed minimax problems.
Performance Analysis of Adaptive Bitrate Algorithms for Multi-user DASH Video Streaming

With the increasing video demand in daily network traffic, it is an urgent task to develop effective algorithms to facilitate high-quality content delivery service. Recently, numerous adaptive streaming algorithms have been proposed to improve the user perceived experience. However, these algorithms were mainly developed from the perspective of single user. There is not yet systematical evaluation and comparison of the bitrate adaptation methods for multi-user video streaming. Besides, the Quality of Experience (QoE) metrics were not unified.In this work, we propose a new mininet-based testbed framework which is able to conduct real-time video streaming emulation in various multi-user scenarios. Seven state-of-the-art adaptation methods are incorporated into the testbed. Meanwhile, ITU-T P.1203 model, the world's first standard for measuring QoE of HTTP adaptive streaming, is implemented to calculate the mean opinion scores of different methods. Using the developed testbed, the performance of current adaptation methods in multi-user network is analyzed and compared. A variety of experiments are carried out by changing the user number and network conditions, in which the QoE of different users are investigated. It is found that current algorithms perform inconsistently in various network scenarios. In the excessive user and limited bandwidth cases, machine learning and scheduling techniques show superiority in providing high and equal QoE for all users. While in the high-delay case, the buffer-based approaches show robust performance. Overall, the findings of this work give an insight for designing and choosing adaptive streaming strategies in different multi-user network conditions.
Design Ethics in Practice - Points of Departure

Recent years have seen an increased interest in ethically responsible design of technology in society at large as well as among design practitioners. While design ethics is a well-established research area, less research has enquired into how design practitioners understand ethics. Through semi-structured interviews we have explored practitioners' approaches to ethics in design. Our findings show noticing, reflecting and reacting as three ways that ethical matters are approached. This ties into dynamic and participatory views on ethics, rather than viewing ethics as a set of formal frameworks and deductive reasoning practices. Practitioners need approaches that tie in to existing work practices. Our results provide practice-led suggestions and points of departure of when and how it might be suitable to infuse design ethics into design practice.
Histoscope: A Web-Based Microscopy Tool for Oral Histology Education

Objectives Histology, the study of tissue structure under a microscope, is one of the most essential yet least engaging topics for health professional students. Understanding tissue microanatomy is crucial for students to be able to recognize cellular structures and follow disease pathogenesis. Traditional histology teaching labs rely on light microscopes and a limited array of slides, which inhibits simultaneous observation by multiple learners, and prevents in-class discussions. We have developed an interactive web-based microscopy tool called âHistoscopeâ for oral histology in this context. Methods Good quality microscope slides were selected for digital scanning. The slides were scanned with multiple layers of z-stacking, a method of taking multiple images at different focal distances. The digital images were checked for quality and were archived on Histoscope. The slides were annotated, and self-assessment questions were prepared for the website. Interactive components were programmed on the website to mimic the experience of using a real light microscope. Results This web-based tool allows users to interact with histology slides, replicating the experience of observing and manipulating a slide under a real microscope. Through this website, learners can access a broad array of digital oral histology slides and self-assessment questions. Conclusions Incorporation of Histoscope in a course can shift traditional teacher-centered histology learning to a collaborative and student-centered learning environment. This platform can also provide students the flexibility to study histology at their own pace. Keywords: Histology, Education, Web-Based Microscope, Self-Assessment, Dental Education
Countermeasure for security loophole caused by asymmetric correlations of reference frame independent quantum key distribution with fewer quantum states

One of the challenging issues in free-space quantum key distribution (QKD) is the requirement of active compensation of the reference frame between the transmitter and receiver. Reference frame independent (RFI) QKD removes active compensation, but it requires more quantum states. A recent proposal can effectively reduce the required quantum states, but this can be achieved assuming the correlations defined in RFI QKD are symmetric. In a real QKD system, such symmetric correlations cannot always be satisfied owing to the device imperfections and optical misalignment. We theoretically analyze the effect of asymmetric correlations. Consequently, we report that the asymmetry causes security loopholes and provide a countermeasure to prevent them. Furthermore, we provide the experimental results of a free-space RFI QKD system to verify the countermeasure for the aforementioned problem. In conclusion, our work provides feasibility of the practical RFI QKD system with fewer quantum states by effectively preventing the security loophole.
Predicting Breast Cancer using Linear Kernel Support Vector Machine

Breast cancer now a days has become a quite common problem in women. It starts when cells of breast grows tremendously. These cells generally form a tumor, which is felt like a lump. The tumor can be malignant or benign. Malignant is cancerous tumor while benign is non-cancerous. Benign can be treated easily but malignant treatment depends on the stage. Therefore, it is very important to understand for a women to type of lump and stage. This study is an effort towards the prediction of type breast cancer. For the same, linear kernel Support Vector Machine (SVM) algorithm is being used. Experimental results shown 90.3% accuracy in prediction.
Evaluation of online learning for mathematics education students

Abstract The Covid-19 pandemic has affected most countries in the world, including universities in Medan, Indonesia. The spread of Covid-19 through social interaction requires the omission of face-to-face learning activities. Based on UNESCO data, 850 million students around the world, including Indonesia, study at home. For the mathematics education students in a private university in Medan, Indonesia, studying at home is a new phenomenon. Students and lecturers must change the learning method without preparation due to the sudden pressure. This article examined this phenomenon. The method used was descriptive qualitative to describe the process of implementing distance learning, the media, and evaluation used in e-learning during the Covid-19 pandemic for mathematics education students. Data collection was conducted by a questionnaire administered to 200 lecturers, interviews, and direct observation. The results show that Group Chat via WhatsApp was the most widely used online communication media, especially the Baby Boomers and Generation X, but millennials used online multimedia more frequently. The implementation has several shortcomings: the lack of interaction between instructors and students, students being asked to learn independently and do independent projects, teachers using more than one media not following the teaching-load hours or spending less time, and many experiencing technical obstacles.
Study on Local to Global Radiometric Balance for Remotely Sensed Imagery

Due to the difference of factors, such as lighting conditions, shooting environments, and time, there is compound brightness difference between adjacent images, which includes local brightness difference and radiometric difference. This paper proposed a method to eliminate the compound brightness difference of adjacent images after mosaicking, named local to global radiometric balance. It includes the brightness compensation model and brightness approach model. Firstly, the weighted average value of each row and column of image are calculated to express the brightness change; secondly, according to weighted average value, the brightness compensation model is built; thirdly, combined with the blocking method, the brightness compensation model is applied to image. Based on the value after above process, the brightness approach model is established to make the gray value of adjacent images approach to the mosaic line. In the paper, the standard deviation, MSE (mean square error) and mean value are used as the measure indices of the effect of brightness balance. The three groups of experimental results show that compared with the brightness stretch method, the histogram equalization method and the radiometric balance method, the local to global radiometric balance method not only realizes compound brightness balance, but also has better visual effects than others.
Instances as Queries

Recently, query based object detection frameworks achieve comparable performance with previous state-of-the-art object detectors. However, how to fully leverage such frameworks to perform instance segmentation remains an open problem. In this paper, we present QueryInst (Instances as Queries), a query based instance segmentation method driven by parallel supervision on dynamic mask heads. The key insight of QueryInst is to leverage the intrinsic one-to-one correspondence in object queries across different stages, as well as one-to-one correspondence between mask RoI features and object queries in the same stage. This approach eliminates the explicit multi-stage mask head connection and the proposal distribution inconsistency issues inherent in non-query based multi-stage instance segmentation methods. We conduct extensive experiments on three challenging benchmarks, i.e., COCO, CityScapes, and YouTube-VIS to evaluate the effectiveness of QueryInst in instance segmentation and video instance segmentation (VIS) task. Specifically, using ResNet-101-FPN backbone, QueryInst obtains 48.1 box AP and 42.8 mask AP on COCO test-dev, which is 2 points higher than HTC in terms of both box AP and mask AP, while runs 2.4 times faster. For video instance segmentation, QueryInst achieves the best performance among all online VIS approaches and strikes a decent speed-accuracy trade-off. Code is available at \url{this https URL}.
Attacks on a Privacy-Preserving Publish-Subscribe System and a Ride-Hailing Service

A privacy-preserving Context-Aware Publish-Subscribe System (CA-PSS) enables an intermediary (broker) to match the content from a publisher and the subscription by a subscriber based on the current context while preserving confidentiality of the subscriptions and notifications. While a privacy-preserving Ride-Hailing Service (RHS) enables an intermediary (service provider) to match a ride request with a taxi driver in a privacy-friendly manner. In this work, we attack a privacy-preserving CA-PSS proposed by Nabeel et al. (2013), where we show that any entity in the system including the broker can learn the confidential subscriptions of the subscribers. We also attack a privacy-preserving RHS called lpRide proposed by Yu et al. (2019), where we show that any rider/driver can efficiently recover the secret keys of all other riders and drivers. Also, we show that any rider/driver will be able to learn the location of any rider. The attacks are based on our cryptanalysis of the modified Paillier cryptosystem proposed by Nabeel et al. that forms a building block for both the above protocols.
Evaluation of eyestrain with vertical electrooculogram

Eyestrain has been increasingly severe in our lives and works as the progress of computers and smartphones. Evaluating eyestrain helps to prevent and relieve eyestrain. Our study aimed to evaluate eyestrain by analyzing vertical electrooculogram (VEOG). 21 young subjects were asked to watch a video on the computer for a totally 120 minutes each, during which the VEOG signal was acquired using only three electrodes, and the questionnaire was answered every 30 minutes. The VEOG signal was divided into four 30-minute phases, from which VEOG signal power probability (VEOGSPP) features and blink features were extracted. The blink features include the changes of blink number (BN), group blinks number (GBN) and ratio (GBR), mean blink amplitude (Mean_BA) and duration (Mean_BD), mean blink duration at 50% (Mean_BD50), mean closing duration (Mean_CD) and opening duration (Mean_OD), mean opening duration at early 50% (Mean_ODE50) and late 50% (Mean_ODL50), mean blink maximum rising slope (Mean_BMRS) and falling slope (Mean_BMFS). The results showed that the VEOGSPP in the high-frequency band (0.8-6.3Hz), BN, GBN, and GBR significantly increased while the VEOGSPP in the low-frequency band (0.1-0.4Hz), Mean_BA, Mean_OD, and Mean_ODL50 significantly decreased with eyestrain ( P <0.05). In conclusion, eyestrain induced by watching videos for a long time could be well evaluated by analyzing the VEOG signal.
A Multi-Threshold Ant Colony System-based Sanitization Model in Shared Medical Environments

During the past several years, revealing some useful knowledge or protecting individual’s private information in an identifiable health dataset (i.e., within an Electronic Health Record) has become a tradeoff issue. Especially in this era of a global pandemic, security and privacy are often overlooked in lieu of usability. Privacy preserving data mining (PPDM) is definitely going to be have an important role to resolve this problem. Nevertheless, the scenario of mining information in an identifiable health dataset holds high complexity compared to traditional PPDM problems. Leaking individual private information in an identifiable health dataset has becomes a serious legal issue. In this article, the proposed Ant Colony System to Data Mining algorithm takes the multi-threshold constraint to secure and sanitize patents’ records in different lengths, which is applicable in a real medical situation. The experimental results show the proposed algorithm not only has the ability to hide all sensitive information but also to keep useful knowledge for mining usage in the sanitized database.
Recognition of Handwritten Devanagari Character using Convolutional Neural Network

The use of a Convolutional Neural Network to recognize Devanagari characters is explored in this paper. Any pattern recognition task’s classification stages and feature extraction are responsible for accurately defining the patterns. Deep learning surmounts the part of highlight extraction and does so consequently, encouraging the weight on software engineers. Deep learning is slowly but steadily displacing other pattern recognition methods. Deep learning is the best option to tackle the complexities involved in applications like character recognition, which require vast quantities of data and uncertainty in the data. Devanagari consonants have a recognition accuracy of 98 %, vowels have a recognition accuracy of 97.56 %, and Devanagari numerals have a recognition accuracy of 99 %.
Design of English reading and learning management system in college education based on artificial intelligence

English reading learning in college education is an efficient means of English learning. However, most of the current English reading learning platforms in colleges and universities only put different English books on the platform in electronic form for students to read, which leads to blindness of reading. Based on artificial intelligence algorithms, this paper builds model function modules according to the needs of English reading and learning management in college education and implements system functions based on artificial intelligence algorithms. Moreover, according to the above design principles of personalized learning model and the characteristics of personalized network learning, this paper designs a personalized learning system based on meaningful learning theory. In addition, this article verifies and analyzes the model performance. The research results show that the model proposed in this paper has a certain effect.
Media Paradigms and Programmed Code of Values Orientation in Adolescence

Many studies on media show that apart from family, school and peer groups media get imposed as agents of socialization influencing models of behavior, life styles establishment and young people’s code of values formation. This research is dedicated to the range of adolescents’ interests, i.e. the influence of informative, educational and distractive media contents. The research findings show that the use of television and the internet occupies everyday activities of the subjects in their spare time, and that it remarkably influences their attitudes and code of values formation. Modern media paradigms promote competences over competitions, diminish codes of values and blur the border line between the good and bad, animal and humanistic. The manipulative media features are getting more dynamic, natural and tougher.
Chinese Sentiment Classification Model based on Pre-Trained BERT

In order to solve the problems of low accuracy, less training data and poor training results of traditional machine learning algorithm in Chinese sentient classification task, this paper proposes a Chinese sentient classification model based on pre trained BERT. After pre training for Bert using large-scale unlabeled corpus, the Bert model can extract the text abstract features of a single Chinese character based on the context semantic relationship. Then we combine the text abstract features with the whole sentence's semantic vector and send them to the softmax classifier to form a Chinese sentient classification model for training and fine tuning. The model performs well in multiple open source Chinese sentient classification datasets, which indicates that the model can complete Chinese sentient classification task well.
Racist Tweets-based Sentiment Analysis Using Individual and Ensemble Classifiers

In this time of a global pandemic, as our society is pushing forward to a more digital culture of working, living, studying, and earning, the Internet’s significance is unprecedented, and inevitably so, the use of various social media platforms has seen a boom. This paper focused on hate speech racism and sexism and built a refined classifier that detects racist and sexist comments from the tweets. Investigating accessible data from Internet-based existence may produce interesting findings and bits of information about virtually every object, organization, or individual inside the universe of general feelings. Opinion mining is the statistical action behavior of suppositions, notions, and subjectivity of content from a corpus that integrates natural language processing (NLP) and machine learning (ML) to classify a range of emotions. As such, sentiment analysis remains a widely researched and ever-evolving topic in the field of NLP. XGBoost with word2vec gave us the most promising results, so we refined it further by fine-tuning. Furthermore, we observed that the F1 score was 0.690285. Thus, we were able to reach an accuracy of 69% in our classifier.
Academic Records Verification Platform Based on Blockchain Technology

Recently, blockchain technology has received substantial attention from researchers and developers. Although some research explores the uses of the blockchain and practice it in the real world, blockchain has not yet been applied widely in the education area. The goal of this research is to present an academic records verification platform which is cost-effective and secure based on the blockchain. Previous research on the applications of the blockchain and the RSA algorithm lays a strong foundation for this study. The decentralized nature of the blockchain is utilized to testify the academic honesty of students, and RSA algorithm protects the privacy of individuals. Plus, the demands of different groups of people contribute to constructing a user-friendly platform. Next, the framework of the platform is presented, including the procedures of verification and query, and the traditional platform of student record verification and this platform are compared. Lastly, the conclusion analyzes the efficiency and explains the way how the platform is useful to people and a few limitations existing in the practice of the platform. This research studys established papers to analyze novel technology, the demands of people and the analysis of traditional platform for proposing a novel framework of the student record verification platform. The result shows that the platform is more cost-effective and convenient than conventional way of verification.