Reviewerly/exHarmony huggingface dataset:
Application Innovation of Educational Measurement Theory, Method, and Technology in China’s New College Entrance Examination Reform

China’s new college entrance examination (the new gaokao) reform provides an opportunity for researchers and practitioners of educational measurement to directly participate in the reform. Therefore, conducting in-depth research on the characteristics of the new gaokao and the issues it faces, and finding corresponding solutions theoretically, methodologically, and technically will not only help to deploy the education examination reform smoothly, but also expand and enrich the research and application of educational measurement. This article provides discussions and suggestions on some issues related to the new gaokao reform, including the stability issue of the examination brought by the scoring methods and subject selection, the equating issue of test scores due to biannual tests or cross-year comparisons, and the issue of giving feedback to basic education based on the analysis of the gaokao data.
A Q-Learning Based Hyper-Heuristic for Generating Efficient UAV Swarming Behaviours

The usage of Unmanned Aerial Vehicles (UAVs) is gradually gaining momentum for commercial applications. These however often rely on a single UAV, which comes with constraints such as its range of capacity or the number of sensors it can carry. Using several UAVs as a swarm makes it possible to overcome these limitations. Many metaheuristics have been designed to optimise the behaviour of UAV swarms. Manually designing such algorithms can however be very time-consuming and error prone since swarming relies on an emergent behaviour which can be hard to predict from local interactions. As a solution, this work proposes to automate the design of UAV swarming behaviours thanks to a Q-learning based hyper heuristic. Experimental results demonstrate that it is possible to obtain efficient swarming heuristics independently of the problem size, thus allowing a fast training on small instances.
KBCNMUJAL@HASOC-Dravidian-CodeMix-FIRE2020: Using Machine Learning for Detection of Hate Speech and Offensive Code-Mixed Social Media text.

This paper describes the system submitted by our team, KBCNMUJAL, for Task 2 of the shared task Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), at Forum for Information Retrieval Evaluation, December 16-20, 2020, Hyderabad, India. The datasets of two Dravidian languages Viz. Malayalam and Tamil of size 4000 observations, each were shared by the HASOC organizers. These datasets are used to train the machine using different machine learning algorithms, based on classification and regression models. The datasets consist of tweets or YouTube comments with two class labels offensive and not offensive. The machine is trained to classify such social media messages in these two categories. Appropriate n-gram feature sets are extracted to learn the specific characteristics of the Hate Speech text messages. These feature models are based on TFIDF weights of n-gram. The referred work and respective experiments show that the features such as word, character and combined model of word and character n-grams could be used to identify the term patterns of offensive text contents. As a part of the HASOC shared task, the test data sets are made available by the HASOC track organizers. The best performing classification models developed for both languages are applied on test datasets. The model which gives the highest accuracy result on training dataset for Malayalam language was experimented to predict the categories of respective test data. This system has obtained an F1 score of 0.77. Similarly the best performing model for Tamil language has obtained an F1 score of 0.87. This work has received 2nd and 3rd rank in this shared Task 2 for Malayalam and Tamil language respectively. The proposed system is named HASOC_kbcnmujal.
A Novel Image Encryption Using Parity Based Visual Cryptography

The current era is mainly focused on secured data transmission and every organization takes preventive measures to protect network’s private data. Among different techniques visual cryptography is a prominent one that that encrypts the visual information and decrypts secret using mechanical operations without any computation, but each share need pixel expansion. In the current work, we propose an Image encryption technique using (n, n) Visual cryptography based on simple operations without pixel expansion. The proposed novel technique gives an image encryption using visual cryptography based on Least significant bit (LSB) technique in spatial domain and parity mechanism using Exclusive-OR(XOR) operation. developed for encrypting grey scale image. Image encryption and decryption uses simple Boolean operations. The technique provides better quality of shares and recovers without any loss.
Deep Learning for Digital Asset Limit Order Books

This paper shows that temporal CNNs accurately predict bitcoin spot price movements from limit order book data. On a 2 second prediction time horizon we achieve 71% walk-forward accuracy on the popular cryptocurrency exchange coin-base. Our model can be trained in less than a day on commodity GPUs which could be installed into co-location centers allowing for model sync with existing faster order-book prediction models. We provide source code and data at https://github.com/Globe-Research/deep-order-book.
A Framework for Leveraging Image Security in Cloud with Simultaneous Compression and Encryption Using Compressive Sensing

In the contemporary era, technological innovations like cloud computing and Internet of Things (IoT) pave way for diversified applications producing multimedia content. Especially large volumes of image data, in medical and other domains, are produced. Cloud infrastructure is widely used to reap benefits such as scalability and availability. However, security and privacy of imagery is in jeopardy when outsourced it to cloud directly. Many compression and encryption techniques came into existence to improve performance and security. Nevertheless, in the wake of emergence of quantum computing in future, there is need for more secure means with multiple transformations of data. Compressive sensing (CS) used in existing methods to improve security. However, most of the schemes suffer from the problem of inability to perform compression and encryption simultaneously besides ending up with large key size. In this paper, we proposed a framework known as Cloud Image Security Framework (CISF) leveraging outsourced image security. The framework has an underlying algorithm known as Hybrid Image Security Algorithm (HISA). It is based on compressive sensing, simultaneous sensing and encryption besides random pixel exchange to ensure multiple transformations of input image. The empirical study revealed that the CISF is more effective, secure with acceptable compression performance over the state of the art methods.
Personal Data Protection in the Internet of Things

Recent growth of the information and communication technologies (ICT) created enormous opportunities for the economic and technical growth.However, they also opened the Pandora's box of cybercrime, stealing personal information and identity, spying on people's working and private lives as well as other related issues.Internet of Things (IoT) that combines a myriad of devices connected together for a two-way flow of information represents another important challenge, since it collects and stores zillions of personal data that can be abused by the third parties.Our paper analyses the recent developments of IoT in the light of data protection and tackling cybercrime.We show that since the concept is relatively young, very few legislative norms exist for its regulation and persecution of violators.It is also clear that new laws and rules should be introduced but the fragile balance between personal data protection and total "Big Brother-like" control from the law enforcement agencies and the governments should be carefully maintained and secured.
Multi-Task FaceBoxes: A Lightweight Face Detector Based on Channel Attention and Context Information

In recent years, convolutional neural network (CNN) has become the primary method for face detection.But its shortcomings are obvious, such as expensive calculation, heavy model, etc.This makes CNN difficult to use on the mobile devices which have limited computing and storage capabilities.Therefore, the design of lightweight CNN for face detection is becoming more and more important with the popularity of smartphones and mobile Internet.Based on the CPU real-time face detector FaceBoxes, we propose a multi-task lightweight face detector, which has low computing cost and higher detection precision.First, to improve the detection capability, the squeeze and excitation modules are used to extract attention between channels.Then, the textual and semantic information are extracted by shallow networks and deep networks respectively to get rich features.Finally, the landmark detection module is used to improve the detection performance for small faces and provide landmark data for face alignment.Experiments on AFW, FDDB, PASCAL, and WIDER FACE datasets show that our algorithm has achieved significant improvement in the mean average precision.Especially, on the WIDER FACE hard validation set, our algorithm outperforms the mean average precision of FaceBoxes by 7.2%.For VGA-resolution images, the running speed of our algorithm can reach 23FPS on a CPU device.
Culinary Works Come in Three Ontological Flavours

When investigating the nature of culinary works, it is easy to take for granted that they all share the same ontology. This paper argues that, on the contrary, the ontology of culinary works is really threefold. Some culinary works are edible concrete particulars, or dishes, as many of us may first assume. But others are types, or multiply realisable abstract entities. And, while some of these types are determined by one recipe, others are rather chased after by their indefinitely many recipes. So, there are really three kinds of culinary works; only those belonging to one of the three are edible per se; and, each kind has a very different relationship to recipes. Indeed, it is very doubtful that culinary works consisting in edible concrete particulars are suitable to have one or more recipes: by exploring what are the requirements for being a recipe, the paper also examines under what necessary conditions there is a recipe for preparing a culinary work qua concrete particular.
The Influence of Organizational Justice on Job Commitment in the Jordanian Ministries

The aim of this study was to investigate the impact of organizational justice on job commitment in Jordanian ministries.Data were collected through a questionnaire distributed to 450 employees in three Jordanian ministries, namely, the Ministry of Education; Ministry of Health; and Ministry of Awqaf Islamic Affairs and Holy Places.Data were analyzed via descriptive, simple linear regression analysis, and stepwise multiple linear regression analysis.The hypotheses of the research were tested and the probabilities of their acceptance were rejection.Findings revealed a statistically significant impact between organizational justice and job commitimnt.Results also indicated that informational justice has the highest influential effect on employee job commitmint, while procedural justice had the lowest impact on it.Furthermore, this study reports differences between the dimensions of the (OJ) in their impacts on the dimensions of job commitment.Moreover, the study found that the three dimensions of organizational justice (information justice, personal justice, and distributive justice) combined had a higher predictive capacity of job commitment than that of any independent dimension or two dimensions of organizational justice.In view of the results, the researcher gives some recommendations to the decision makers in Jordanian ministries that may guide them to promote the concept and practice of organizational justice in their ministries such as the need for increasing the levels of organizational justice, especially the distributive justice, and focusing on compatibility of income of the employee with their academic level and practical experience, in addition to necessity of improving the system of rewards in the Jordanian ministries because the rewards have a significant impact on the employees' perception of justice and on her/his interest in supporting her/his colleagues when doing additional work in the ministry.In other respects, this study has practical implications for future research such as attracting attention to the need to study job satisfaction and job engagement as mediatingvariables of association of employee's job commitment with organizational justice.This is in addition to conducting studies similar to the present one in different environments inside and outside Jordan.
A Cross-Tenant RBAC Model for Collaborative Cloud Services

Tenants in the cloud computing environment share various services, including storage, network, computing, and applications. For better use of services in the cloud computing environment, tenants collaborate in tasks, resultin... | Find, read and cite all the research you need on Tech Science Press
Atom: A Stream Cipher with Double Key Filter

It has been common knowledge that for a stream cipher to be secure against generic TMD tradeoff attacks, the size of its internal state in bits needs to be at least twice the size of the length of its secret key. In FSE 2015, Armknecht and Mikhalev however proposed the stream cipher Sprout with a Grain-like architecture, whose internal state was equal in size with its secret key and yet resistant against TMD attacks. Although Sprout had other weaknesses, it germinated a sequence of stream cipher designs like Lizard and Plantlet with short internal states. Both these designs have had cryptanalytic results reported against them. In this paper, we propose the stream cipher Atom that has an internal state of 159 bits and offers a security of 128 bits. Atom uses two key filters simultaneously to thwart certain cryptanalytic attacks that have been recently reported against keystream generators. In addition, we found that our design is one of the smallest stream ciphers that offers this security level, and we prove in this paper that Atom resists all the attacks that have been proposed against stream ciphers so far in literature. On the face of it, Atom also builds on the basic structure of the Grain family of stream ciphers. However, we try to prove that by including the additional key filter in the architecture of Atom we can make it immune to all cryptanalytic advances proposed against stream ciphers in recent cryptographic literature.
Stimulation, Detection, and Early Intervention of Growth of Children Aged 1-5 Years Old for Healthcare Center's Cadre and Parents at Kenarilang Health Center, Alor

Background:The growth and development of children will vary from one child to another.Development focuses on changes that occur gradually from the lowest level to the highest and most complex levels through the process of maturation and learning.Development relates to changes in quality, including an increase in individual capacity to function which is achieved through a process of growth, maturation, and learning.Subjects and Method: This study aims to determine knowledge, change attitudes and behavior of parents regarding Stimulation, Detection, and Early Intervention for children aged 1-5 years at Kenarilang Health Center, Alor.Descriptive research data collection based on cross sectional approach.The population taken in this study was all healthcare center's cadres and mothers who have children aged 1-5 years who live in the working area of Kenarilang Health Center, Alor, totaling 252 people.The technique for determining the sample is proportional random stratification.The number of samples used in this study was 50 samples.Results: Most of the respondent's as many as 27 people (54.0%) had a lack of ability to stimulate growth and development, as many as 42 people (84.0%) had a good ability to detect growth and development, and 46 people (92.0%) had good abilityat interventions for growth and development.Conclusion: Most of the respondents have less ability to stimulate growth and development.Most of the respondents have less ability to detect growth and development.Most of the respondents have a good ability in conducting growth and development interventions.
Data Sharing Goals for Nonprofit Funders of Clinical Trials

Sharing clinical trial data can provide value to research participants and communities by accelerating the development of new knowledge and therapies as investigators merge data sets to conduct new analyses, reproduce published findings to raise standards for original research, and learn from the work of others to generate new research questions. Nonprofit funders, including disease advocacy and patient-focused organizations, play a pivotal role in the promotion and implementation of data sharing policies. Funders are uniquely positioned to promote and support a culture of data sharing by serving as trusted liaisons between potential research participants and investigators who wish to access these participants’ networks for clinical trial recruitment. In short, nonprofit funders can drive policies and influence research culture. The purpose of this paper is to detail a set of aspirational goals and forward thinking, collaborative data sharing solutions for nonprofit funders to fold into existing funding policies. The goals of this paper convey the complexity of the opportunities and challenges facing nonprofit funders and the appropriate prioritization of data sharing within their organizations and may serve as a starting point for a data sharing toolkit for nonprofit funders of clinical trials to provide the clarity of mission and mechanisms to enforce the data sharing practices their communities already expect are happening.
Lessons from the GDPR in the COVID-19 era

Digital surveillance has played a key role in containing the COVID-19 outbreak in China, Singapore, Israel, and South Korea. Google and Apple recently announced the intention to build interfaces to allow Bluetooth contact tracking using Android and iPhone devices. In this article, we look at the compatibility of the proposed Apple/Google Bluetooth exposure notification system with Western privacy and data protection regimes and principles, including the General Data Protection Regulation (GDPR). Somewhat counter-intuitively, the GDPR’s expansive scope is not a hindrance, but rather an advantage in conditions of uncertainty such as a pandemic. Its principle-based approach offers a functional blueprint for system design that is compatible with fundamental rights. By contrast, narrower, sector-specific rules such as the US Health Insurance Portability and Accountability Act (HIPAA), and even the new California Consumer Privacy Act (CCPA), leave gaps that may prove difficult to bridge i...
О геологическом и изотопном возрасте золоторудных месторождений на примере золото-серебряного месторождения Кубака (Северо-Восток России)

Information on the geological and isotopic age of the Kubaka gold-silver deposit in the Omolon middle massif in the North-East of Russia is presented. It has been established that the Kubaka deposit geological age lies in between the Late Devonian age of the Kedon series volcanites, containing the gold-silver mineralization, and the Early Carboniferous age of the Korbinsky suite terrigenous rocks, overlapping the volcanites and the mineralization. The post-ore nature of the Omolon complex dykes, which produce no significant impact on the distribution of gold mineralization in ore bodies, is shown. According to isotope dating, the following stages of the Kubaka deposit formation are distinguished: the accumulation of the Kubaka suite tuffs (369 Ma); the introduction of subvolcanic intrusions (344 and 337 Ma); the formation of ore metasomatites (335±5 Ma); the formation of gold-silver mineralization (330 and 334 - 324 Ma); the introduction of post-ore dikes (179±8 - 176±10 Ma).
Learning in Networks During the Covid-19 Pandemic

In the period of Pandemic Novel Corona Virus Disease 2019 (Covid-19) several online learning models are applied to the educational world. This study aims to determine 1) student responses easy to access integration of WAG learning media and Zoom Application 2) understanding of material in learning using a combination of WAG learning media and Zoom Application, 3) the effectiveness of the combination of WAG learning media and Zoom Application in online learning. The method used in this study is a descriptive qualitative research method through survey techniques. The research instrument used was in the form of questionnaires and online interviews given to 375 Elementary School Teacher Education Study Program students (PGSD). The results of the study showed that as many as 71,14% of students agreed in the ease of access to the combination of WAG learning media and Zoom Application, as many as 71,25% of students agreed in understanding the material in learning by using a combination of WAG learning media and Zoom Application, as many as 72% of students agreed on the integration WAG learning media and Zoom Application in effective online learning. It can be concluded that the combination of WAG and zoom applications in online learning was received by students, this was seen by students' responses through the instruments that were delivered and agreed.
The nearest polyhedral convex conic regions for high-dimensional classification

In the nearest-convex-model type classifiers, each class in the training set is approximated with a convex class model, and a test sample is assigned to a class based on the shortest distance from the test sample to these class models.In this paper, we propose new methods for approximating the distances from test samples to the convex regions spanned by training samples of classes.To this end, we approximate each class region with a polyhedral convex conic region by utilizing polyhedral conic functions (PCFs) and its extension, extended PCFs.Then, we derive the necessary formulations for computing the distances from test samples to these new models.We tested the proposed methods on different high-dimensional classification tasks including face, digit, and generic object classification as well as on some lower-dimensional classification problems.The experimental results on different datasets show that the proposed classifiers achieve either the best or comparable results on high-dimensional classification problems compared to other nearest-convex-model classifiers, which shows the superiority of the proposed methods.
Implementasi Pembelajaran Sinkronus pada Mata Kuliah Fisika 1 Menggunakan Discord Dipadukan dengan Google Jamboard dan Powerpoint

COVID-19 virus have affected educational activities to be carried out through e-learning. Discord and Google Jamboard offer advantages in delivering synchronous e-learning. The purpose of this research is to implement e-learning of Physics 1 course using Discord integrated with Google Jamboard and Powerpoint. In this research, we used qualitative-descriptive method which conducted in several steps, i.e: (1) Discord installation and Physics 1 Class preparation; (2) Virtual Tablet Server installation and its testing for mobile phone; (3) Learning materials preparation in Powerpoint; (4) Implementation of Physics 1 Class using Discord integrated with Google Jamboard and Powerpoint; (5) Evaluation of the results and (6) Surveying of student’s opinion about e-learning using using Discord integrated with Google Jamboard and Powerpoint. Discord can be used as an alternative e-learning tool because of its practical use as well as more powerful when integrated with Google Jamboard and Powerpoint, especially for mathematical-analytics subject such as Physics. This is supported by the results of a survey in which 34 out of 39 students said they wanted to use Discord and Google Jamboard for other mathematical-analytics subjects.