Reviewerly/exHarmony huggingface dataset:
Sharing and Privacy in PHRs: Efficient Policy Hiding and Update Attribute-based Encryption

Personal health records (PHRs) is an electronic medical system that enables patients to acquire, manage and share their health data.Nevertheless, data confidentiality and user privacy in PHRs have not been handled completely.As a fine-grained access control over health data, ciphertext-policy attribute-based encryption (CP-ABE) has an ability to guarantee data confidentiality.However, existing CP-ABE solutions for PHRs are facing some new challenges in access control, such as policy privacy disclosure and dynamic policy update.In terms of addressing these problems, we propose a privacy protection and dynamic share system (PPADS) based on CP-ABE for PHRs, which supports full policy hiding and flexible access control.In the system, attribute information of access policy is fully hidden by attribute bloom filter.Moreover, data user produces a transforming key for the PHRs Cloud to change access policy dynamically.Furthermore, relied on security analysis, PPADS is selectively secure under standard model.Finally, the performance comparisons and simulation results demonstrate that PPADS is suitable for PHRs.
Blink identification with eye tracking: a software processing program

Currently in the UK, a national trial to test the effect of a transition from traditional Full Field Digital Mammography (FFDM) to Digital Breast Tomosynthesis (DBT) is being conducted. DBT, having a higher sensitivity and specificity as compared to FFDM alone, could be a better modality in national breast cancer screening. However, its incorporation in the incredibly busy and detailed UK screening program is difficult. Reading times in DBT have been shown to be longer and strenuous (Connor et al, 2012). Therefore, much research needs to be completed to develop recommendations for its efficiency. One key factor in DBT reading is the progression of fatigue, as both a cause and effect of prolonged reading times. We aimed to develop a program to process real time raw eye tracking data to identify a change in fatigue-state through blink detection. Our focus was on analysing the whole data set and defining blinks through observed events. Two real time signals which the eye tracker generates, namely the left and right ‘Eyelid Opening’ value, were considered. Through assessment of these signals, blinks of varying duration were identified. Additional parameters such as recorded frame sequences and time stamps were added to the processing to delineate the exact occurrence of these blinks during the reading process. We aim to analyse past and future large DBT eye tracked files, with our processing software, to identify the point of fatigue onset in a DBT reading session.
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation

Designing proper loss functions is essential in training deep networks. Especially in the field of semantic segmentation, various evaluation metrics have been proposed for diverse scenarios. Despite the success of the widely adopted cross-entropy loss and its variants, the mis-alignment between the loss functions and evaluation metrics degrades the network performance. Meanwhile, manually designing loss functions for each specific metric requires expertise and significant manpower. In this paper, we propose to automate the design of metric-specific loss functions by searching differentiable surrogate losses for each metric. We substitute the non-differentiable operations in the metrics with parameterized functions, and conduct parameter search to optimize the shape of loss surfaces. Two constraints are introduced to regularize the search space and make the search efficient. Extensive experiments on PASCAL VOC and Cityscapes demonstrate that the searched surrogate losses outperform the manually designed loss functions consistently. The searched losses can generalize well to other datasets and networks. Code shall be released.
RETRACTED: A Study on English Teaching Model under the Background of Big Data

Abstract With the development of society and the application of information technology, computer big data is gradually developing in the mainstream. Under big data, college education is constantly innovating and developing, teaching methods and teaching means are constantly changing, and modern English teaching based on the Internet has become the mainstream. College English education with the help of computer big data, continuous innovation and development. Modern education relies on the Internet, changes the educational idea, the way, constructs the modern education way, through the Internet technology, the student studies more convenient, fast, reduces the traditional education restriction factor, relies on the Internet technology, with the Internet rich resources, the fast information, the efficient auxiliary teaching, promotes the education goal, the task realization. The times are constantly developing, changing the previous teaching model [1] , with the modern big data education model, anytime, anywhere learning concept. Under the current computer big data, college English education in China is gradually getting rid of the previous single education mode, and the diversified college English education mode is gradually entering the college English teaching classroom.
Eye tracking in digital pathology: identifying expert and novice patterns in visual search behaviour

The National Health Service (NHS) in the United Kingdom (UK) is on the verge of transitioning into the use of digital pathology. Technological advancements such as whole slide imaging (WSI), whereby microscopy slides are scanned using a high throughput device to produce digital copies of the slides, has enabled such revolution possible. These digital images enable viewing using image viewer systems on computer monitors, hence making eye tracking studies feasible. The Digital Pathology trial has been set up to evaluate the use of virtual microscopy in the UK. As part of this trial, we conducted two pilot studies as our initial evaluation prior to larger scale studies. In this study, we present the initial findings from our latest pilot study which aims to characterise visual search patterns in pathologists of varying experience. This study was conducted with three pathologists at different stages of their careers, looking at core needle biopsy of breast specimens. We found that the more experienced pathologist had shorter reading duration and had lesser number of fixations consistent with our previous pilot study findings. However, this study did not reproduce any significant finding on magnification pattern. Further work would require a larger sample size to reach more certainty.
Summary of Recommendation Algorithm Research

Abstract In order to solve the problem of information overload caused by the rapid expansion of information, it is particularly difficult for users to select the information they are interested in when faced with massive amounts of information. Traditional search has some drawbacks and cannot solve the problem of information overload. The recommendation algorithm provided by the recommendation system is a good strategy to deal with. This article mainly introduces two recommendation algorithms and their principles, content-based recommendation algorithms and collaborative filtering-based recommendation algorithms. And their advantages and disadvantages are compared.
Klasifikasi Ujaran Kebencian pada Media Sosial Twitter Menggunakan Support Vector Machine

Nowadays social media has become a place for peoples to express their opinions, there are many ways that can be done to express both positive and negative opinions. Hate speech is one of the problems that we find quite a lot in cyberspace, that things can be detrimental to many parties. Twitter as one of social media, can be used as a source of analysis about people's behavior in cyberspace. Many of our society that unconsciously act of hate speech on social media, therefore this study finds out how people's behavior patterns in cyberspace and the main issue of hate speech on a particular topic and time period by classify it into five classes, namely ethnicity, religion, race, inter-groups and neutral using Support Vector Machine. In this study also compares three kernel that common to use and the result is the system can classify hate speech by using RBF kernel and got the highest result with 93% accuracy on 700 data train and 300 data test.
Research on the Attribute Extraction Algorithm of People and Vehicles Based on Video Structure

Abstract With the development of sensor technology and computer vision, the speed of video generation is increasing exponentially. The workload of video browsing and the amount of stored data are increasing. In order to achieve fast browsing and efficient storage, video structure technology is increasingly important. This paper focuses on the video structure technology in the construction of smart cities, outlines the system process of video structure, and studies the extraction algorithm of human and vehicle target attributes based on deep learning. And combined with the actual video monitoring scene, the experiment is carried out. Compared with traditional image processing methods, the video structure target attribute extraction model based on deep learning has high detection accuracy and strong algorithm generalization ability. The intelligent analysis and processing of data by video structure technology is conducive to quickly increasing the speed of target search, reducing storage capacity, solving the problem of long-term storage, and realizing full automation of surveillance video.
The Ajax Dilemma in Authorship

Department of Plastic Surgery, Inha University School of Medicine, Incheon, South Korea. Address correspondence and reprint requests to Kun Hwang, MD, PhD, Department of Plastic Surgery, Inha University School of Medicine, 27 Inhang-ro, Jung-gu, Incheon, 22332, South Korea; E-mail: [email protected] Received 15 July, 2020 Accepted 5 January, 2021 This study was supported by a grant from National Research Foundation of Korea (NRF-2020R1I1A2054761). The author report no conflicts of interest.
Eye and head movements while looking at rotated scenes in VR.

Video stream: https://vimeo.com/356859979 &#x0D; Production and publication of the video stream was sponsored by SCIANS Ltd http://www.scians.ch/&#x0D; We examined the extent to which image shape (square vs. circle), image rotation, and image content (landscapes vs. fractal images) influenced eye and head movements. Both the eyes and head were tracked while observers looked at natural scenes in a virtual reality (VR) environment. In line with previous work, we found a horizontal bias in saccade directions, but this was affected by both the image shape and its content. Interestingly, when viewing landscapes (but not fractals), observers rotated their head in line with the image rotation, presumably to make saccades in cardinal, rather than oblique, directions. We discuss our findings in relation to current theories on eye movement control, and how insights from VR might inform traditional eyetracking studies. - Part 2: Observers looked at panoramic, 360 degree scenes using VR goggles while eye and head movements were tracked. Fixations were determined using IDT (Salvucci &amp; Goldberg, 2000) adapted to a spherical coordinate system. We then analyzed a) the spatial distribution of fixations and the distribution of saccade directions, b) the spatial distribution of head positions and the distribution of head movements, and c) the relation between gaze and head movements. We found that, for landscape scenes, gaze and head best fit the allocentric frame defined by the scene horizon, especially when taking head tilt (i.e., head rotation around the view axis) into account. For fractal scenes, which are isotropic on average, the bias toward a body-centric frame gaze is weak for gaze and strong for the head. Furthermore, our data show that eye and head movements are closely linked in space and time in stereotypical ways, with volitional eye movements predominantly leading the head. We discuss our results in terms of models of visual exploratory behavior in panoramic scenes, both in virtual and real environments.
On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior.

Human reading behavior is tuned to the statistics of natural language: the time it takes human subjects to read a word can be predicted from estimates of the word's probability in context. However, it remains an open question what computational architecture best characterizes the expectations deployed in real time by humans that determine the behavioral signatures of reading. Here we test over two dozen models, independently manipulating computational architecture and training dataset size, on how well their next-word expectations predict human reading time behavior on naturalistic text corpora. We find that across model architectures and training dataset sizes the relationship between word log-probability and reading time is (near-)linear. We next evaluate how features of these models determine their psychometric predictive power, or ability to predict human reading behavior. In general, the better a model's next-word expectations, the better its psychometric predictive power. However, we find nontrivial differences across model architectures. For any given perplexity, deep Transformer models and n-gram models generally show superior psychometric predictive power over LSTM or structurally supervised neural models, especially for eye movement data. Finally, we compare models' psychometric predictive power to the depth of their syntactic knowledge, as measured by a battery of syntactic generalization tests developed using methods from controlled psycholinguistic experiments. Once perplexity is controlled for, we find no significant relationship between syntactic knowledge and predictive power. These results suggest that different approaches may be required to best model human real-time language comprehension behavior in naturalistic reading versus behavior for controlled linguistic materials designed for targeted probing of syntactic knowledge.
Real-Time Image Stitching For Multiple Camera Panoramic Video Shoot: A Case Study in Football Matches

In this study, a real-time image stitching method is proposed for cost-effective high resolution wide angle video shooting. In the first stage, the images taken from more than one camera with fixed position are stitched with a classical algorithm. After the parameters calculated in the first stage are stored, the image pixels are mapped using the stored parameters and ArUco markers. The mapping process is used for real-time panoramic video shooting after it has been calculated for that particular camera setup once. The images taken from the multi camera are combined by remapping with a GPU based approach. Therefore, registered mapping can be used in different environments without changing the position and lenses of the cameras. As a case study, real-time panoramic video is shot with two cost-effective cameras in football matches. Deep-learning based autonomous pilot video shooting is then performed on the high resolution panoramic video obtained. In experiments, 36 FPS speed has been reached by using a standard desktop computer and it has been seen that image quality measurements are at reasonable levels.
The Development of an e-Workload Distribution System: A Focus on the Fair Distribution of Teaching Workloads of Lecturers

Studies have shown excessive teaching workloads of teachers and lecturers have become a contentious issue in the academic realm, making them physically and emotionally strained. Therefore, this study was carried out to develop a workload distribution management system called e-WLOAD to help the management of universities in distributing workloads that are fair and acceptable to all lecturers. In this study, the researchers used the Evolutionary Prototyping method for the development of e-WLOAD. A series of interviews involving a head of a department, a faculty dean and an academic registrar as well as the analysis of the Academic Workload Guidance Document of the Malaysian Qualifications Agency (MQA) and the academic Annual Performance Assessment Report were carried out to reveal the relevant criteria for the distribution of lecturers’ workloads, namely Status, Minimum and maximum credits, Lecturer classification, Current Teaching Status, and Teaching Load Requirements. After running the system, the results of the functionality test indicated that the prototype had fulfilled all system requirements successfully based on such distribution criteria.&#x0D; &#x0D; Keywords: Evolutionary Prototyping, Lecturers’ teaching workloads, Workload distribution criteria, Workload distribution system
Performance Comparison of Various Algorithms During Software Fault Prediction

Producing software of high quality is challenging in view of the large volume, size, and complexity of the developed software. Checking the software for faults in the early phases helps to bring down testing resources. This empirical study explores the performance of different machine learning model, fuzzy logic algorithms against the problem of predicting software fault proneness. The work experiments on the public domain KC1 NASA data set. Performance of different methods of fault prediction is evaluated using parameters such as receiver characteristics (ROC) analysis and RMS (root mean squared), etc. Comparison is made among different algorithms/models using such results which are presented in this paper.
Dynamical properties of quantum Fisher information of a two-level atoms interacting with two-mode superposition coherent state

A quantum cavity filled with a nonlinear medium (Kerr like medium) and contains a 2-mode field interacting with time dependence of 2-qubit is investigated. Part of the two qubits interact in the form of an amplifier, while the other is in the form of a ladder. The constants of motion are calculated by using the Heisenberg differential equation. Moreover, the analytical solution is calculated by solving the Schrodinger differential equations. Some statistical quantities are calculated to identify the features of the proposed model. The effective of the nonlinear term on the phenomena of collapse and revival are discussed through atomic population. The amount of entanglement between the quantum subsystem, whether it is between the 2-qubit and the cavity field, or between the 2-qubit together are calculated using the linear entropy and the concurrence. The results indicated that there is a superstructure between the atomic population and the linear entropy, concurrence. The intensity of oscillations decreases for the entanglement between the field and the two qubits when the Kerr like medium of the Hamiltonian system is included. The nonclassical distribution of the quantum system decreases after taking Kerr like medium into account.
Analysis on User Experience Using UEQ Method in the Information System of Web-Based Child Development Monitoring

nformation system refers to one of systems widely needed by every organization today in data management. Posyandu (Integrated Service Post) is one of non-governmental organizations in Indonesia functioned to monitor the child development. The data resulted from the activities of Posyandu is very important; hence, an information system is deemed essential to manage the data. The information system used certainly must have the user interface that is simple to be used. This paper discusses how the interface built in the information system of the child development monitoring meets the rules determined. The rating was conducted to the user experience to figure out the experience of the users for the facilities and efficiency when using the application. The rating method was conducted using UEQ distributed to the cadres of Posyandu. 30 respondents were participated as the target of this research. The results obtained in this research showed the average rating value of Attractiveness of 1,383, Perspicuity of 1,817, Efficiency of 1,619, Dependability of 1,617, Stimulation of 1,742, and Novelty of 1,825. The average values for all rating scales showed the positive results meaning that the interface can be accepted by the users
Intensity-dependent and multi-photon Hamiltonian of two two-level atoms and two-mode field in a Kerr medium solved by virtue of supersymmetric unitary transformation

We consider a generalized multi-photon interaction of two collectively two-level atoms with two-mode of electromagnetic field in the presence of Kerr medium and intensity-dependent coupling. We show that this atomic system possesses supersymmetric structure. We solved this system by virtue of supersymmetric unitary transformation. The supersymmetric generators of this atomic system are constructed. The diagonalization of the corresponding Hamiltonian is performed by introducing a supersymmetric unitary transformation. Accordingly, the eigenvalues and eigenfunctions of the Hamiltonian of the atomic system are obtained. The time evolution of the atom–field wave functions is derived in an exact form for two cases of the initial states of the atoms and the field modes. Some quantum effects such as the second-order correlation function, cross-correlation, purity and Husimi Q-function are investigated. The effects of the Kerr medium, detuning parameter, intensity-dependent coupling and multi-photon transition on the evolution of these quantum effects are examined. We conclude that the supersymmetric unitary transformation method is very simple and can be applied to a variety of atomic systems which possess a supersymmetric structure.
Classification of human development index using particle swarm optimization based on support vector machine algorithm

Abstract Human Development Index (HDI) is the comparative of life expectancy, education and standard of living to all countries. Human Development Index used as an indicator to assess the quality of the construction. Human Development Index may also be used to classify the city or state whether a country of developed countries, developing countries, or the state of underdeveloped and as well as to gauge the effects of economic policy on the quality of life. In the study is done capability of classifications Human Development Index in Indonesia. Methods Used to know Classifications of Human Development Index this is using Support Vector Machines algorithms base Particle Swarm Optimization. Support Vector Machine algorithms based Particle Swarm Optimization is a-method that has the capacity for the Classification of Human Development Index data. In research was built model of Support Vector Machines algorithms based Particle Swarm Optimization. Results from the human development index is in the country or province 24 96 data not be in class, very high with human development index is at the high is 952, data while in the medium is 2018 data and there are at the level of low 201 data with an accuracy to 96.26 %, weighted mean recall 97.74 %, weighted mean precision 99.09.