Reviewerly/exHarmony huggingface dataset:
Classification of Ethnicity Using Efficient CNN Models on MORPH and FERET Datasets Based on Face Biometrics

Ethnic conflicts frequently lead to violations of human rights, such as genocide and crimes against humanity, as well as economic collapse, governmental failure, environmental problems, and massive influxes of refugees. Many innocent people suffer as a result of violent ethnic conflict. People’s ethnicity can pose a threat to their safety. There have been many studies on the topic of how to categorize people by race. Until recently, the majority of the work on face biometrics had been conducted on the problem of person recognition from a photograph. However, other softer biometrics such as a person’s age, gender, race, or emotional state are also crucial. The subject of ethnic classification has many potential uses and is developing rapidly. This study summarizes recent advances in ethnicity categorization by utilizing efficient models of convolutional neural networks (CNNs) and focusing on the central portion of the face alone. This article contrasts the results of two distinct CNN models. To put the suggested models through their paces, the study employed holdout testing on the MORPH and FERET datasets. It is essential to remember that this study’s results were generated by focusing on the face’s central region alone, which saved both time and effort. Classification into four classes was achieved with an accuracy of 85% using Model A and 86% using Model B. Consequently, classifying people according to their ethnicity as a fundamental part of the video surveillance systems used at checkpoints is an excellent concept. This categorization statement may also be helpful for picture-search queries.
Improved Upsampling Based Depth Image Super-Resolution Reconstruction

Constrained by current sensing technology, depth camera only acquires a low-resolution depth image that does not meet actual requirements. To solve this problem, this paper take a divide-and-conquer strategy to synthesize a high-resolution depth image from a low-resolution range image under the guidance of a registered high-resolution color image. Initially, the depth image is divided into planar areas and edge regions. For different zones, we exploit different methods to interpolate the missing depths. At planar area, the linear interpolation method is employed to perform upsampling. At edge region, a segmentation-separation upsampling method is used to interpolate the missing values. Then the upsampling result are refined on the Depth CNN that is built in this paper. We conduct extensive experiments on the benchmark database and real world data with various upsampling rates to illustrate the upsampling ability of our method. The comparison with classical super-resolution algorithms demonstrates that our upsampling algorithm achieves the best quality with fewer artifacts and our depth CNN outperforms the most state-of-the-art methods in terms of qualitative and quantitative evaluations.
A Neural Attention-Based Encoder-Decoder Approach for English to Bangla Translation

Machine translation (MT) is the process of translating text from one language to another using bilingual data sets and grammatical rules. Recent works in the field of MT have popularized sequence-to-sequence models leveraging neural attention and deep learning. The success of neural attention models is yet to be construed into a robust framework for automated English-to-Bangla translation due to a lack of a comprehensive dataset that encompasses the diverse vocabulary of the Bangla language. In this study, we have proposed an English-to-Bangla MT system using an encoder-decoder attention model using the CCMatrix corpus. Our method shows that this model can outperform traditional SMT and RBMT models with a Bilingual Evaluation Understudy (BLEU) score of 15.68 despite being constrained by the limited vocabulary of the corpus. We hypothesize that this model can be used successfully for state-of-the-art machine translation with a more diverse and accurate dataset. This work can be extended further to incorporate several newer datasets using transfer learning techniques. <!-- se va indica textul rezumatului-->
Research on the Correlation between National Economic Growth and Income Distribution Indicators (Gini Coefficient)

The issue of economic growth is the oldest topic in economics, and it is also a worldwide topic.Throughout the history of economic growth in various countries around the world, growth is often associated with income distribution, and differences in economic growth among countries have led to unequal distribution of income levels.At the same time, the disparity of income distribution also affects the economic growth of various countries.This paper firstly selects relevant statistical data from 1971 to 2018, establishes appropriate econometric models, and verifies and revises the models through various statistical testing methods.Then the relationship between economic growth and its related economic index (Gini coefficient) is quantitatively analyzed.Finally, through the analysis of the final regression model in economic significance, the relevant policy recommendations are put forward.
Information Literacy Education in the Context of Education Informatization

In the era of educational informatization, the application of information technology in the field of education is becoming more and more extensive.In order to improve people's ability and literacy of autonomous learning, problem-solving and knowledge innovation , this study aims to provide reference for information literacy education and research.It hopes to innovate the way of information literacy education by exploring new ideas, new concepts and new models.This study used bibliometric method.It uses the Web of Science core data set as the data source, searches the literature related to information literacy in the past 20 years, selects the representative research literature, and analyzes the literature from the perspectives of publication year, leading countries, institutions and authors.It uses CiteSpace as a tool to conduct citation analysis of relevant literature, identify important node literature and research points, and grasp the research overview and development context of information literacy education.The content of information literacy education research has the diversity of theory and practice, and it is applied to various scenarios such as scientific research, study and life.This study can promote its reform and innovation, and make corresponding contributions to popularizing information literacy education.Information literacy education can promote the development of quality education.It helps people to rapidly improve their learning ability, scientific research ability, problem-solving ability and innovation ability.This study can provide reference for information literacy educators.
Research on the Integration and Development of Technology and Teaching Innovation

Under the background of education informatization 2.0, this paper conducts research on the integration and development of information technology and education and teaching innovation in Linyi University.Summarize the achievements of education and teaching innovation and development of Linyi University.Collect and organize educational and teaching achievements, summarize successful experiences, and establish a typical image through literature method, interview method and investigation method .Summarize development achievements and share successful experiences, so that other schools can establish a development plan that fully integrates information technology and education and teaching according to actual conditions and local conditions.Linyi University uses information technology to display Yimeng spiritual education in the classroom through holographic projection technology combining light and shadow, immersive VR technology and other channels, which deepens the impression and deepens the feelings.
The Attitude of Indonesian Senior High School Students towards Learning English

This study aims at investigating the senior high school students’ attitudes toward learning English. Therefore, a Mix-method was applied to this study in which quantitative data were collected by using questionnaire and qualitative data were collected through semi structured interview. Furthermore, 21 female and 15 male students were randomly selected based on the class's free time as the sample of this study. As results, the findings demonstrate that the students at the senior high school have a positive attitude toward learning English and the English language. Three factors shape the attitude: cognitive, affective, and behavioral. The students demonstrate a positive attitude by saying positive words about English and how they feel about English.
Computational Model for Image Processing in the Minds of People with Visual Agnosia using Fuzzy Cognitive Map

The Agnosia is a neurological condition that leads to an inability to name, recognize, and extract meaning from the visual, auditory, and sensory environment, despite the fact that the receptor organ is perfect.Visual agnosia is the most common type of this disorder.People with agnosia have trouble communicating between the mind and the brain.As a result, they cannot understand the images seen.In this paper, a model is proposed that is based on the visual pathway so that it first receives the visual stimulus and then, after understanding, the object is identified.In this paper, a model based on the visual pathway is proposed and using intelligent Fuzzy Cognitive Map will help improve image processing in the minds of these patients.First, the proposed model that is inspired by the visual perception pathway, is designed.Then, appropriate attributes that include the texture and color of the images are extracted and the concept of the seen image is perceived using Fuzzy Cognitive Mapping, the meaning recognition and the relationships between objects.This model reduces the difficulty of perceiving and recognizing objects in patients with visual agnosia.The results show that the proposed model, with 98.1% accuracy, shows better performance than other methods.
Normal vibration distribution search-based differential evolution algorithm for multimodal biomedical image registration

In linear registration, a floating image is spatially aligned with a reference image after performing a series of linear metric transformations. Additionally, linear registration is mainly considered a preprocessing version of nonrigid registration. To better accomplish the task of finding the optimal transformation in pairwise intensity-based medical image registration, in this work, we present an optimization algorithm called the normal vibration distribution search-based differential evolution algorithm (NVSA), which is modified from the Bernstein search-based differential evolution (BSD) algorithm. We redesign the search pattern of the BSD algorithm and import several control parameters as part of the fine-tuning process to reduce the difficulty of the algorithm. In this study, 23 classic optimization functions and 16 real-world patients (resulting in 41 multimodal registration scenarios) are used in experiments performed to statistically investigate the problem solving ability of the NVSA. Nine metaheuristic algorithms are used in the conducted experiments. When compared to the commonly utilized registration methods, such as ANTS, Elastix, and FSL, our method achieves better registration performance on the RIRE dataset. Moreover, we prove that our method can perform well with or without its initial spatial transformation in terms of different evaluation indicators, demonstrating its versatility and robustness for various clinical needs and applications. This study establishes the idea that metaheuristic-based methods can better accomplish linear registration tasks than the frequently used approaches; the proposed method demonstrates promise that it can solve real-world clinical and service problems encountered during nonrigid registration as a preprocessing approach.The source code of the NVSA is publicly available at https://github.com/PengGui-N/NVSA.
Waring numbers over finite commutative local rings

In this paper we study Waring numbers gR(k) for (R,m) a finite commutative local ring with identity and k∈N with (k,|R|)=1. We first relate the Waring number gR(k) with the diameter of the Cayley graphs GR(k)=Cay(R,UR(k)) and WR(k)=Cay(R,SR(k)) with UR(k)={xk:x∈R⁎} and SR(k)={xk:x∈R×}, distinguishing the cases where the graphs are directed or undirected. We show that in both cases (directed or undirected), the graph GR(k) can be obtained by blowing-up the vertices of GFq(k) a number |m| of times, with independence sets the cosets of m, where q is the size of the residue field R/m. Then, by using the above blowing-up, we reduce the study of the Waring number gR(k) over the local ring R to the computation of the Waring number g(k,q) over the finite residue field R/m≃Fq. In this way, using known results for Waring numbers over finite fields, we obtain several explicit results for Waring numbers over finite commutative local rings with identity.
A Study on the Influence of Sensors in Frequency and Time Domains on Context Recognition

Adaptive AI for context and activity recognition remains a relatively unexplored field due to difficulty in collecting sufficient information to develop supervised models. Additionally, building a dataset for human context activities “in the wild” demands time and human resources, which explains the lack of public datasets available. Some of the available datasets for activity recognition were collected using wearable sensors, since they are less invasive than images and precisely capture a user’s movements in time series. However, frequency series contain more information about sensors’ signals. In this paper, we investigate the use of feature engineering to improve the performance of a Deep Learning model. Thus, we propose using Fast Fourier Transform algorithms to extract features from frequency series instead of time series. We evaluated our approach on the ExtraSensory and WISDM datasets. The results show that using Fast Fourier Transform algorithms to extract features performed better than using statistics measures to extract features from temporal series. Additionally, we examined the impact of individual sensors on identifying specific labels and proved that incorporating more sensors enhances the model’s effectiveness. On the ExtraSensory dataset, the use of frequency features outperformed that of time-domain features by 8.9 p.p., 0.2 p.p., 39.5 p.p., and 0.4 p.p. in Standing, Sitting, Lying Down, and Walking activities, respectively, and on the WISDM dataset, the model performance improved by 1.7 p.p., just by using feature engineering.
SPEECH/TEXT TO INDIAN SIGN LANGUAGE USING NATURAL LANGUAGE PROCESSING

Sign language is a way of communication that helps people exchange information by using hand and arm gestures, commonly used by individuals who have difficulty hearing.However, sign language isn't universal, because impaired individuals from different countries use their corresponding sign languages.Using sign language, it allows us to communicate with impaired individuals including our loved ones, students in mainstream/deaf schools/colleges, locals and company owners, etc.Studies say learning sign language makes it simpler for a person to grasp lip-reading along with their native language.Most research has been done on Sign Language Translation/Recognition; different sign languages are translated into a common spoken language.However, the inverse is less, meaning limited research has been done on converting spoken languages to sign languages.Focusing on this matter, this study aims to translate speech/text into Indian Sign Language using the basics of Natural Language Processing.
Automated Type 2 Diabetes Case and Control Identification from the MIMIC-IV Database.

Phenotyping for Type 2 Diabetes (T2DM) is needed due to the increasing demand for T2DM research on electronic health records (EHRs). eMERGE is a reliable and interpretable rule-based algorithm for the identification of T2DM cases and controls in EHRs. MIMIC-IV, an extension of MIMIC-III, contains more than 520,000 hospital admissions and has become a valuable EHR database for secondary medical research. However, there was no prior work to extract T2DM cases and controls from MIMIC-IV, which requires a comprehensive knowledge of the database. Our work provided insight into the structure and data elements in MIMIC-IV and adapted eMERGE to accomplish the task. The results included MIMIC-IV's data tables and elements used, 12,735 cases and 9,828 controls of T2DM, and summary statistics of the cohorts in comparison with those on other EHR databases. They could be used for the development of statistical and machine learning models in future studies about the disease.
Secure Data Sharing for Cross-domain Industrial IoT Based on Consortium Blockchain

Industrial Internet of Things (IIoT) is considered one of the most revolutionary technologies that can significantly improve manufacturing efficiency and realize intelligent production. With the increasing complexity of industrial manufacturing, the manufacturing process of a product often involves several different IoT domains (e.g., factories). To achieve a common production goal, devices from various domains share their data for cooperative work, which raises privacy and security concerns for cross-domain communication. Most existing data-sharing schemes rely on a trusted third party. Hence, the privacy and security issues in cross-domain data sharing are still challenging research directions. This paper proposes CBDS, a consortiumblockchain-based cross-domain IIoT data-sharing mechanism. Specifically, we introduce consortium blockchain to construct trust among different domains in IIoT. A group signature is presented to ensure each device’s privacy to achieve anonymous authentication. In addition, the collected data should be stored in the ciphertext. The smart contract and proxy re-encryption are utilized in the CBDS to realize secure cross-domain data sharing. The experimental results demonstrate the effectiveness and efficiency of the proposed CBDS.
The Use of Design Thinking for Reflection in Recruitment and Selection of IT Professionals

Technology companies increasingly need to recruit and select qualified Information Technology professionals, as the technology market is fast-paced, and there is a shortage of these professionals. We used Design Thinking supported by remote communication technologies to reflect on this problem and generate solutions. In this context, we seek to answer: What are the opportunities and challenges when using Design Thinking to support the creation of an innovative solution for recruiting and selecting Information Technology professionals suitable for the market’s demands? We found that the main opportunities are to identify and integrate different profiles of professionals in the same environment in the selective process. The main challenges were contacting and recruiting participants during the COVID19 pandemic, developing solutions quickly, and applying the ideas generated in building a prototype for recruiting Information Technology professionals to meet market demand.
The Journal Observatory – Connecting information on scholarly communication

As the scholarly communication community is quickly moving towards more diverse and transparent modes of working, there is an increasing need for reliable information on different platforms’ policies. To address this need, we present the outcomes of the Journal Observatory project. This project aims to connect information from a scattered landscape of resources, thereby building toward systematic high-quality information on scholarly communication platforms.
Deep Active Recognition through Online Cognitive Learning

Deep models need a large number of labeled samples to be trained. Furthermore, in practical application settings where objects’ features are added or changed over time, it is difficult and expensive to get enough labeled samples in the beginning. Cognitive learning mechanism can actively raise the deep models’ proficiency online with a few training labels gradually. In this paper, inspired by human being’s cognition procedure to acquire new knowledge stage by stage, we develop a novel deep active recognition framework based on the analysis of models’ cognitive error knowledge to fine-tune the deep models online. The transformation of the cognitive errors is defined, and the corresponding knowledge is obtained to identify the models’ cognitive information. Based on the cognitive knowledge, the sensitive samples are selected to finely tune the models online. To avoid forgetting the previous learned knowledge, the selected prior training samples are used as the refreshening samples at the same time. The experiments demonstrate that the sensitive samples can benefit the target recognition and the cognitive learning mechanism can boost the deep models’ performance efficiently. The characterization of cognitive information can restrain the other samples’ disturbance to the models’ cognition effectively and the online training method can save mass of the time evidently. In conclusion, we introduce this work to provide a trial of thought about the cognitive lifelong learning used in deep learning scenarios.
Multi-scale Convolutional Feature Approximation for Defocus Blur Detection

Deep learning technology has promoted the performance of defocus blur detection. However, blur detectors suffer from background clutter, scale ambiguity and blurred boundaries of the defocus blur regions. To conquer these issues, previous methods propose to use multi-scale image patches or images for blur detection, which costs much computation time. In this paper, we propose a deep neural network that takes a single-scale image as input to generate robust defocus blur detection. Specifically, we first extract multi-scale convolutional features by a feature extraction network. And then we resize the convolutional features of each layer by a fixed ratio to approximate convolutional features that extracted from a resized image with the same ratio. By approximation, it not only generates features extracted from a scaled image but also reduces the computation of feature extraction from multi-scale images. We concatenate the features extracted from the original image with the approximated features at the corresponding layers by convolutional layers to increase the blur distinguish ability. We gradually fuse the convolutional features from top-to-bottom by Conv-LSTMs to refine the blur predictions. We compare our method with nine state-of-the-art defocus blur detectors on two defocus blur detection benchmark datasets. Experiment results demonstrate the effectiveness of our proposed defocus blur detector.