Reviewerly/exHarmony huggingface dataset:
Tied gender condition for facial expression recognition with deep random forest

Facial expression recognition (FER) in an uncontrolled environment is difficult due to changes in occlusion, illumination, noise, and personal attributes. A deep learning enhanced gender conditional random forest (G_DRF) is proposed for FER in an uncontrolled environment. In order to reduce the influence of occlusion, illumination, low image resolution, etc., our method extracts robust facial features by deep multi-instance learning. Then a G_DRF is devised to address the facial personal attributes’ influence, such as gender variation by conditional RF. A large number of experiments were conducted on the public CK+, BU-3DEF, and LFW face databases. The experimental results showed that the proposed method had better performance and robustness than the state-of-the-art methods. The recognition rates of CK+, BU-3DEF, and LFW were 98.83%, 90%, 60.58%, respectively. Compared with other advanced deep learning methods that require a large number of training samples, the proposed method needs a small number of training samples and achieves better results.
Information and Communication Technologies and Education

The main goal of this article is the way of implementing the new technologies of informing and communicating in education. The advantages of using the new technologies in education are multiple: the reduction of time consumption, the possibility of adapting personal education programs, the possibility of fast accommodation with the changes and the new knowledge from various fields, extended interdisciplinary education opportunities. The didactic means have evolved and diversified over time from printed course support to learning programs with direct transmission or video recording on TV, to interactive multimedia information in real time with the help of the internet. In the educational field, the necessity for information and communication is realized through the registered progress that creates new opportunities and advantages in the training process. The informational and communicational technology impact over education is remarkable and it seems that this development will lead to positive effects, but will also lead additional issues. The process of replacing the real life with components, the aspects brought by the media can be called virtualization. In education, this phenomenon is manifesting when computers are used to substitute the learning experiences that were previously made in contact with the teacher or to give the chance to learn what in the past wouldn’t have been available to anyone. The specificity of the management of these structures emerges from the particularities brought by the new realities: moving the focus of the broadcasting institution from the savoir determination to the educator’s autonomy to conquer and build, through new technologies of communicating and informing. After some analysts, the introduction of new informational technologies in education has in subsidiary hidden ideologies, designed to position the social actors to the actual world. “The valorization of NTIC in the educational field dreams less of modernizing the school, than producing of a new conception of an institution where the finalities would not consist in the social emancipation of subjects, but in their incorporation in the technical, economical and capitalist complex.” (Thellen, 2002). Through the codification and standardization systems, through the promotion policies and the advertising strategies, through the fair competition, through the subsequent legal frameworks, the NTIC promoters are creating bridges for access to benefits, which, far from being accessible to anyone, are creating and deepening inequalities. Because of that, one of the preconditions of virtualization of training is to reduce gaps in creating the premises of equal access to such instruments.
Invariant Feature Extraction for Component-based Facial Recognition

This paper proposes an alternative invariant feature extraction technique for facial recognition using facial components.Can facial recognition over age progression be improved by analyzing individual facial components?The individual facial components: eyes, mouth, nose, are extracted using face landmark points.The Histogram of Gradient (HOG) and Local Binary Pattern (LBP) features are extracted from the individually detected facial components, followed by random subspace principal component analysis and cosine distance.One of the preprocessing steps implemented is the facial image alignment using angle of inclination.The experimental results show that facial recognition over age progression can be improved by analyzing individual facial components.The entire facial image can change over time, but appearance of some individual facial components is invariant.
How to measure digital literacy?: a case of Croatian adult learners

This study aims to present the rationale, the instrument and the main findings of the assessment of digital literacy of adult learners in Croatia, as a part of the project Implementation of EU Agenda for Adult Learning 2017Learning -2019, by Ministry of Science and Education.The concept of digital literacy is at the heart of EU development policies and encompasses skills, competences, and dispositions for using a wide range of technologies, from personal computers to smartphones, vending machines, and ATMs.The digital technologies are also being used for different purposes (work, leisure, communication, health, news, etc.) and differently by social groups (by age, gender socioeconomic status, etc.).Thus, testing digital literacy in the population is of great importance given the pervasiveness of digital technologies and the apparent inadequacy of self-reported measures of digital literacy.An original test was developed, testing the usage of Windows, Word, Excel, PowerPoint, internet browser and Google Maps.The test was implemented through the LimeSurvey software with the additional files available on the participant's computer desktop.The questions were devised in a way that a participant had to solve a specific problem using the specific software, thus including the cognitive component of digital literacy.The difficulty level of the questions is approximate of those of the levels "Below Level 1" and "Level 1" of PSTRE PIAAC research.Additionally, the participants answered the question about their socio-economic status, the frequency of technology usage, and their self-perception of digital skills.The test was administered to 92 attendees of the specialization and qualification programs in seven adult learning facilities throughout Croatia (Knin, Karlovac, Zagreb, Split, Čakovec, Koprivnica, and Virovitica).The results indicate relatively low levels of digital literacy of the tested sample and the different patterns of technology usage by different socio-economic groups.Also, the questionnaire's metric characteristics show that it can be successfully used for testing digital literacy outside the laboratory setting.Conclusion: The low levels of digital literacy among the adult attendees of the specialization and qualification programs implicate the need to include the digital literacy curriculum in their learning programs, according to individual needs and experiences.Also, this research is one of the very few that did not assess only the self-perceptions of the skills or test the skills in the research facility setting, but rather in the learning facilities of adult education.Since the questionnaire performed well under these circumstances, it can be modified to be implemented in multiple environments.
Study on Software Vulnerability Characteristics and Its Identification Method

A method for identifying software data flow vulnerabilities is proposed based on the dendritic cell algorithm and the improved convolutional neural network to effectively solve the transmission errors in software data flow. In this method, we first gave the software data flow propagation model and constructed the data propagation tree structure. Secondly, we analyzed the running characteristics of the software, took the interaction among indexes into account, and identified data flow vulnerabilities using the dendritic cell algorithm and the improved convolutional neural network. Finally, we conducted an in-depth study on the performance of this method and other algorithms through mathematical simulation. The results show that this method has better advantages in detection time, storage cost, and software code size.
AR/VR is this year's hot ticket for jobs: But growth in demand for blockchain developers stutters - [Careers]

It's a good time to be an engineer specializing in augmented reality or virtual reality. That's the conclusion of the latest report by the job site Hired, which released its annual state of software engineers report in February. To compile its data, Hired reviewed 420,000 interview requests from 10,000 companies made to 98,000 job seekers throughout 2019.
Supervised Machine Learning and Feature Selection for a Document Analysis Application

Over the past three decades large amounts of information have been converted to image formats from paper documents.Though in digital form, extracting the information, usually textual, from these documents requires complex image processing and optical character recognition techniques.The processing pipeline from the image to information typically includes an orientation correction task, document identification task, and text analysis task.When there are many document variants the tasks become difficult requiring complex subanalysis for each variant and quickly exceeds human capability.In this work, we demonstrate a document analysis application with the orientation correction and document identification task carried out by supervised machine learning techniques for a large, international airline.The documents have been amassed over forty years with numerous variants and are mostly black and white, typically consist of text and lines, and some have extensive noise.Low level symbols are extracted from the raw images and separated into partitions.The partitions are used to generate statistical features which are then used to train the classifiers.We compare the classifiers for each task (e.g.decision tree, support vector machine, and random forest) to choose the most appropriate.We also perform feature selection to reduce the complexity of the document type classifiers.These parsimonious models result in comparable accuracy with 80% or fewer features.
Estimation of gamma-corrected exposure time ratio in multi-exposure images for removal of moving objects

The constructed high-dynamic-range image from merging standard low-dynamic-range images with different camera exposures contains ghost-like artifacts caused by moving objects in the scene. We present a method to utilize the gamma-corrected exposure time ratio between multi-exposure images for removal of moving objects. Between each consecutive image pair in multi-exposure images, the ratio of their exposure times is computed and raised to the power gamma, and this value is used as a cue to detect the pixels corresponding to the moving objects. We propose a method to estimate this ratio from the observed image intensity values, in case the exposure time information or gamma value is unknown. Then the moving objects in multi-exposure images are removed by replacing the intensity values of the detected moving pixels with their expected background values. Experimental results show that the proposed method could remove fast-moving objects from the original multi-exposure images and construct a ghost-free high-dynamic-range image.
Improving Quality of Higher Education Using Academic Information System as a Public Administration Service: The Case of Indonesia

Information Communication and Technology has made a lot of investments to the rapid transformation of modern society. The goal of the study was to establish a highly customized student information and accounting system in Indonesia to promote the enrolment and accounting phase and to meet the needs of all clients and employees in the provision of line services. This study applied the context of Computer Science Research for Information Systems while describing the problems faced in the enrolment and accounting phase, specifying the analysis purpose, planning and implementing the program, installing and testing it, and reporting the results of the study. The digitalized enrolment system works in numerous computer units over a system with a shared database for data storage. It has distinct embedded features that promote the needs of frontline service providers and customers. The aggregate feature of the enrolment system has increased the efficiency of the frontline providers, as most of the processes are automated and computerized. The results of the survey, along with service quality, the precision of documents and records and promptness, demonstrate that the tine enrolment system is a significant and effective instrument for delivering front-line services. Hence, improving the quality service of higher education institutions in Indonesia through system development will provide better services offered to clienteles.
Synthesis and Properties of Magnetic-Semiconductor Fe₃O₄/TiO₂ Heterostructure Nanocomposites for Applications in Wastewater Treatment

In this research, a simple method has been presented to synthesize the magnetic - semiconductor Fe₃O₄/TiO₂ heterostructure nanocomposites via three steps: firstly, synthesis of Fe₃O₄ nanoparticles by co-precipitation method; thereafter, formation of Fe₃O₄/TiO₂ composites by sol-gel method; finally, annealing to form Fe₃O₄/TiO₂ anatase (denoted as Fe₃O₄/TiO₂-A) and Fe₃O₄/TiO₂ rutile (denoted as Fe₃O₄/TiO₂-R) heterostructure nanocomposites, respectively. The results of X-ray diffraction, transmission electron microscopy, and energy-dispersive X-ray spectroscopy show that the Fe₃O₄/TiO₂-A and Fe₃O₄/TiO₂-R heterostructure nanocomposite samples contain both magnetite (Fe₃O₄) and semiconductor TiO₂ (anatase or rutile phase, respectively). The asprepared nanocomposite samples exhibit superparamagnetic properties at room temperature with high saturation magnetization (MSUBs/SUB) above 19.5 emu/g at the applied magnetic field of 11 kOe. Moreover, the Fe₃O₄/TiO₂-A and Fe₃O₄/TiO₂-R heterostructure nanocomposites with a low band gap energy of 2.89 eV and 2.81 eV, respectively, are promising to enhance the performance of photocatalytic activities in the visible light region for application in wastewater treatment.
0.54 pJ/bit, 15Mb/s True Random Number Generator Using Probabilistic Delay Cell for Edge Computing Applications

This paper presents a true random number generator utilizing the probabilistic delay of the cross-coupled latch during start up as an entropy source. The start-up signal is used as a slow and jittered clock to sample a fast ring oscillator to harvest random bit stream. The tunable jitter delay range and all-digital design ensure high quality random bits generation across a wide range of supply voltages. The test chip consumes 0.54 pJ/bit with a bit rate of 15Mb/s at 0.5 V supply voltage while occupying only 18% area of the state of the art. The design's compact area, simple interface, and high energy efficiency make it a well suited choice for edge computing applications.
ORGANIZATION OF RESEARCH ACTIVITIES OF PEDAGOGICAL UNIVERSITY STUDENTS USING ELEMENTS OF THE COACHING APPROACH

The article deals with the issues of attracting students of pedagogical universities to independent research.The problem stated in the article is related to the fact that it is difficult to attract students to scientific work, since research is performed on an independent basis and is not evaluated in mid-term or continuous assessment.In the course of theoretical and practical analysis, it is concluded that the management of independent research activities of students requires a special approach.It is revealed that the most effective approach is the implementation of coaching, which corresponds to the specifics of interaction between the teacher and the student when performing independent research activities.The author designed and implemented an experimental model for managing research work of students of a pedagogical university using elements of coaching.The article describes a step-by-step work plan to manage students' independent research using coaching methods and techniques integrated with the traditional stages of scientific research: introductory, exploratory, and final.During the implementation of the experimental model, it has been established that the elements of individual coaching contribute to the development of students' interest and motivation for scientific research, and provide immersion in the problem under study.As a result of applying elements of the coaching approach, students have developed research competencies required for future teaching activities, and the performance of students' scientific work has improved.
Monitoring and early warning of new cyber-telecom crime platform based on BERT migration learning

The network is a major platform for implementing new cyber-telecom crimes. Therefore, it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms, which will lay the foundation for the establishment of prevention and control systems to protect citizens' property. However, the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks. For instance, the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling. Therefore, a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed. This method first identifies the text data and their tags, and then performs migration training based on a pre-training model. Finally, the method uses the fine-tuned model to predict and classify new cyber-telecom crimes. Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method, compared with the deep-learning method.
The Use of LSTM Neural Networks to Implement the NARX Model. A Case Study of EUR-USD Exchange Rates

The paper focuses on financial data forecasting in terms of one-step-ahead nonlinear model with exogenous inputs.The main aim is the development of a methodology to forecast the exchange rate between EURO and US Dollar.The prediction task is carried out by two recurrent neural networks, the standard NARX neural network and a LSTM-based approach.The exogenous inputs consist of historical trading data and three widely used technical indicators, namely a variant of moving average, the Upper Bollinger Frequency Band and the Lower Bollinger Frequency Band.In order to obtain accurate forecasting algorithms, the exogenous inputs are filtered using the well-known Gaussian low-pass filter.The quality of each method is evaluated in terms of both quantitative and qualitative metrics, namely the root mean squared error, the mean absolute percentage error, and the prediction of change in direction.Extensive experiments point out that the most suited forecasting method is based on the proposed LSTM neural network for NARX model.
Analysis of Final-year Students Self-Awareness Using Reference Management Software

Background of the study: Academic environments such as universities have never been separated from the activities of scientific writing. In scientific writing, there is information that needs to be studied through various literature as a basis for writing. Literature as reference material in scientific papers needs to be managed so that it can be included in the bibliographic list according to the applicable rules. Management of bibliographic lists with various writing styles can be helped by using software assistance. Observations made by the authors at the Universitas Gadjah Mada (UGM) Graduate School Library, many of theses from students are still not appropriate in the procedure for writing the bibliography. So, it becomes a question of how the final level self-awareness of students in the use of citation/reference software. Purpose: Therefore, in this pilot study, the aim was to see and provide an initial description of the final-year students of self-awareness in the use of citations/reference management software. Method: Quantitative methods were used in this study and surveys were conducted to obtain data by adopting and modifying the Self-Awareness Outcomes Questionnaire (SAOQ) to measure student self-awareness. Findings: The results of the survey that have been carried out, the final level students of the Master of Culture and Media Study Program, the Graduate School of UGM have self-awareness in the use of citation/reference software. Conclusion: This researchcan be used as an initial step in further research and a reflection for libraries to always assist students.
Performance Predict Method Based on Neural Architecture Search

Deep learning has granted remarkable breakthroughs on various tasks over the past few years, such as image segmentation, speech recognition, and nature language processing. One vital aspect of progress is the emergence of advanced neural architectures. However, Currently used architectures have frequently been developed manually by human experts, which is a time-consuming and laborious process. Because of this, more and more research is now involved in automated neural architecture search techniques. This paper studies the process of Neural Architecture Search (NAS) technology, summarizes the previous work in this research field and classifies it according to three aspects: search space, search strategy, and acceleration method. In addition, this study selects the performance prediction method in the NAS acceleration strategy as a breakthrough direction and inherits the works of MetaQNN network structure and the sequential regression prediction model (SRMs), which were proposed by the previous research. Firstly, based on our hypothesis, we successfully use the idea of the N-grams model of natural language processing to extract the sequence features belonging to the chain neural network. Then, based on the extracted network structure features, referring to the steps of SRMs, we give a new recipe for predicting the accuracy score of neural network models on the training set. Finally, through experiments and comparison, we prove the accuracy of this prediction model and the effectiveness of accelerating the neural architecture search process.