Reviewerly/exHarmony huggingface dataset:
"The Socio-cultural Impacts of Globalization through the Perspective of Cordilleran Educators"

Culture is an integral part of the identity of people. Customs and traditions passed from generation to generation is a product of strong cultural identities and a rich cultural heritage. But change is something that is constant. Progress and technological advancements became a part of the day-to-day lives of people. Thus, this research aimed to investigate the socio-cultural impacts of globalization on the indigenous people through the perspective of six (5) teachers from the different provinces of the Cordillera Administrative Region (CAR). Qualitative phenomenological research was used to gather and interpret data. Results showed that there were two positive impacts of globalization: (1) Increased Social Awareness, (2), Financial Stability and Opportunity and two negative impacts of globalization: (1) Weakening of Identity, (2) Lesser Socialization. Through this study, the researchers would like to contribute to the preservation of culture and identity.
Mapping Japanese Language Proficiency Test Needs for Vocational Students Based on Industrial World Demand

Currently, it is hoped that the Indonesian government will be able to meet the industry needs for vocational graduates at the university level. This study aims to map the need for Japanese language proficiency tests for vocational students based on the demands of the industrial world. In addition, this study also investigates how the industry demands the Japanese language skills of vocational students. The data was collected through a survey conducted on eight foreign companies that employ employees with Japanese language skills. This research shows that the industry's need for Japanese language proficiency tests is still very high. Specifically, the industry requires graduates with business communication skills in Japanese and graduates who have mastery of the vocabulary of industrial terminology. It is a challenge for vocational graduates, so there is continuity between vocational graduates and the needs of the industrial world. In addition, English language and soft skills, the ability to work in teams, and high integrity are still needed in the industrial world.
Knowledge Distillation for Neural Transducers from Large Self-Supervised Pre-trained Models

Self-supervised pre-training is an effective approach to leveraging a large amount of unlabelled data to reduce word error rates (WERs) of automatic speech recognition (ASR) systems. Since it is impractical to use large pre-trained models for many real-world ASR applications, it is desirable to have a much smaller model while retaining the performance of the pre-trained model. In this paper, we propose a simple knowledge distillation (KD) loss function for neural transducers that focuses on the one-best path in the output probability lattice under both streaming and non-streaming setups, which allows a small student model to approach the performance of the large pre-trained teacher model. Experiments on the LibriSpeech dataset show that despite being 10 times smaller than the teacher model, the proposed loss results in relative WER reductions (WERRs) of 11.5% and 6.8% on the test-other set for non-streaming and streaming student models compared to the baseline transducers trained without KD using the labelled 100-hour clean data. With an additional 860 hours of unlabelled data for KD, the WERRs increase to 48.2% and 38.5% for non-streaming and streaming students. If language model shallow fusion is used for producing distillation targets, a further improvement in the student model is observed.
Color Enhancement Algorithm for Visual Communication Posters Based on Homomorphic Filtering

In order to increase the image quality of visual communication posters and to solve the problems of uneven pixel distribution, distortion, and poor visual effect caused by the lighting environment, an image filtering method, with features based on wavelet and block homomorphic, is proposed on the basis of homomorphic filtering. According to the component change defect of red, green, and blue (RGB) color space, the image color space that needs to be processed is converted into HSV form and then processed in blocks. At that moment, the wavelet transform method is applied to replace the traditional Fourier transform in the block homomorphic filtering to decompose the luminance component. Furthermore, subimages that are decomposed by the wavelet are divided into blocks and then subjected to high-pass filtering so as to obtain a clear image. The investigational outcomes express that the proposed algorithm has an obvious enhancement effect on the visual communication poster image under the variable illumination situation. Furthermore, the suggested technique effectively rectifies the overall brightness of the image and significantly advances the detail contrast of the visual communication poster image. The signal-to-noise (SNR) ratio of our algorithm at different bit rates is higher than that of the LICE and CIE methods. Similarly, the enhancement impact demonstrates that the image statistical parameters of our algorithm are substantially better than those of the LICIE and CIE methods.
Classification of Acute Lymphocytic Leukemia (ALL) using Ventral-Dorsal and bottleneck Attention-based Network(VDBAN)

Abstract One of the diseases with a high mortality rate worldwide is leukemia or blood cancer. Therefore, a timely and reliable diagnosis of leukemia is essential. Today, Artificial Intelligence and Deep Learning methods can automatically, quickly, and cost-effectively identify many diseases. In this paper, the state-of-the-art Convolutional Neural Network (CNN)based on the proposed attention blocks is introduced for the automatic classification of Acute Lymphoblastic Leukemia (ALL) and normal (healthy) cell images. Before sending images to the network, the preprocessing operation, including data augmentation and White Blood Cells (WBCs, or Leuko cytes) segmentation by Circular Hough Transform (CHT), is performed. Then, the proposed network attention blocks are designed and implemented based on the ventral and dorsal pathways of the occipital lobe that process visual information in the brain. The proposed network is ResNet18, in which the VentralDorsal Attention Block (VDAB) is embedded inside its blocks and the Bottleneck Attention Block (BAB) between them. Therefore, it is called Ventral-Dorsal and Bottleneck Attention Network (VDBAN). We designed three networks, including the first model (ResNet18), the second model (ResNet18 + VDAB) and the proposed model ((ResNet18+ VDAB + BAB) or VDBAN), and trained and tested them with similar parameters on the ALL-IDB2 database. The simulation results showed that the proposed model had obtained the highest value in all performance metrics compared to the other two models in the test step.
Unsupervised video object segmentation: an affinity and edge learning approach

Abstract This paper presents a new approach, called TMNet, to solve unsupervised video object segmentation (UVOS) problem. The UVOS is still a challenging problem as prior methods suffer from issues like generalization errors in unseen test videos, over reliance on optic flow, and capturing fine details at object boundaries. These issues make the UVOS an ill-defined problem, particularly in presence of multiple objects. Our focus is to constrain the problem and improve the segmentation results by fusion of multiple available cues such as appearance and motion, as well as image and flow edges. To constrain the problem, instead of predicting segmentation directly, we predict affinities between neighbouring pixels for being part of the same object and cluster those to obtain category agnostic segmentation. To further improve the segmentation, we fuse multiple-sources of information through a novel Temporal Motion Attention (TMA) module that uses neural attention to learn powerful spatio-temporal features. In addition, we also design an edge refinement module (using image and optic flow edges) to refine and improve the accuracy of object segmentation boundaries. The overall framework is capable of segmenting and finding accurate objects’ boundaries without any heuristic post processing. This enables the method to be used for unseen videos. Experimental results on challenging DAVIS16 and multi object DAVIS17 datasets show that our proposed TMNet performs favorably compared to the state-of-the-art methods without post processing.
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction

Distant supervision assumes that any sentence containing the same entity pairs reflects identical relationships. Previous works of distantly supervised relation extraction (DSRE) task generally focus on sentence-level or bag-level de-noising techniques independently, neglecting the explicit interaction with cross levels. In this paper, we propose a hierarchical contrastive learning Framework for Distantly Supervised relation extraction (HiCLRE) to reduce noisy sentences, which integrate the global structural information and local fine-grained interaction. Specifically, we propose a three-level hierarchical learning framework to interact with cross levels, generating the de-noising context-aware representations via adapting the existing multi-head self-attention, named Multi-Granularity Recontextualization. Meanwhile, pseudo positive samples are also provided in the specific level for contrastive learning via a dynamic gradient-based data augmentation strategy, named Dynamic Gradient Adversarial Perturbation. Experiments demonstrate that HiCLRE significantly outperforms strong baselines in various mainstream DSRE datasets.
Parental experiences during remote education in Paraguay: a comparison between the beginning and the end of 2020

During 2020 education underwent significant changes due to the abrupt interruption of face-to-face instruction around the world due to the impact of the COVID-19 pandemic. This study presents a comparison of parent’s perspectives in Paraguay during two periods, at the beginning and at the end of the school year. A sample of 505 parents participated in the first phase, and 202 in the second phase. Data were collected through online questionnaires in both phases. The results show that parents were forced to purchase electronic devices, mainly computers, to respond to certain school activities, despite the economic recession of the pandemic moment. WhatsApp played a very important role at the beginning of remote classes, both to maintain communication and/or send academic assignments. However, by the end of 2020 the use of digital educational platforms was widespread as reported by parents. Support of school activities was carried out by mothers for the most part. Difficulties evidenced include the comprehension of tasks, motivation and the internet connectivity, which increased. significantly throughout the year. A majority of parents expressed satisfaction with the implementation of ICT in remote education, and the main reason noted was the fear of sending children to schools in-person.
Analysis of LCR Network Circuits with Exponential Sources by New Integral Transform Gupta Transform

In science and engineering, the analysis of Inductor, Capacitor and Resistor (LCR) network circuits is a principal course and is generally done by applying calculus or integral transforms. In this paper, we will analyze LCR network circuits with exponential excitation sources by applying the new integral transform ‘Gupta Transform’ and obtain their responses in the form of current or voltage. This paper presents the application of the ‘Gupta Transform’ and proves its applicability for analyzing the LCR network circuits with exponential excitation sources and concludes that ‘Gupta Transform’ like other methods or approaches is also an effective and simple tool.
A Survey on Underwater Image Processing Techniques

Images taken underwater generally suffer from various forms of degradation due to the effects of absorption, light scattering due to suspended particles, presence of background light, low light, et cetera. Over the years, researchers have put forth various underwater image enhancement and image restoration techniques to improve the conditions of these images to assist in the examination and analysis of marine lifeforms and underwater objects. This paper aims to provide a summary and analysis of a few recent existing underwater image enhancement and image restoration techniques that were put forth in the past six years. Also, the performance of a few of the methods was evaluated both subjectively and objectively.
Utilization of Cloud Computing in the field of LIS

Distributed computing is a procedure that gives benefits to virtual machines. Distributed computing administrations are changing how organizations and open establishments use data innovation. Today cloud administrations are accessible to meet most any Information Technology (IT) needs. The primary point of Cloud registering is to get adaptability the framework, because of which Central Processing Unit (CPU) and memory will be completely used. Utilization of distributed computing in libraries is generally new territory when contrasted with its applications in business and corporate division. Libraries everywhere throughout the world are moving towards distributed computing usage to utilize the highlights and administrations of it to improve their frameworks and administrations. This article talks about the nuts and bolts of distributed computing alongside its attributes, types, favourable circumstances, needs and applications in the field of libraries. This article talks about both the positive and negative part of distributed computing and it additionally tells the accepted procedures for the execution in the library condition to get its most extreme advantage.
ScarfNet: Multi-scale Features with Deeply Fused and Redistributed Semantics for Enhanced Object Detection

Convolutional neural network (CNN) has led to significant progress in object detection. In order to detect the objects in various sizes, the object detectors often exploit the hierarchy of the multi-scale feature maps called feature pyramid, which is readily obtained by the CNN architecture. However, the performance of these object detectors is limited since the bottom-level feature maps, which experience fewer convolutional layers, lack the semantic information needed to capture the characteristics of the small objects. In order to address such problem, various methods have been proposed to increase the depth for the bottom-level features used for object detection. While most approaches are based on the generation of additional features through the top-down pathway with lateral connections, our approach directly fuses multi-scale feature maps using bidirectional long short term memory (biLSTM) in effort to generate deeply fused semantics. Then, the resulting semantic information is redistributed to the individual pyramidal feature at each scale through the channel-wise attention model. We integrate our semantic combining and attentive redistribution feature network (ScarfNet) with baseline object detectors, i.e., Faster R-CNN, single-shot multibox detector (SSD) and RetinaNet. Our experiments show that our method outperforms the existing feature pyramid methods as well as the baseline detectors and achieve the state of the art performances in the PASCAL VOC and COCO detection benchmarks.
The Role of Education in Social Change

It is education that takes society on the path of progress and enriches it. Such education plays a very important role in societal needs and change. Therefore, changes in education are made in the interest of society. People have known since ancient times that education is not for imparting knowledge, but the real purpose of education should be to teach a man higher quality and make him a better man. Education and teaching methods have also changed as education in Tolkapiyam, education in the Sangam era, education in the Gurukulam era, education in the Pallavar era, education in the Nayakkar era, education that was imparted in porches, education in the British era, today's education, computer-based education, professional education, distance education, technical education, and online education. Learned people are always held in high esteem and respect in society.
Efektivitas Pembelajaran Blended Learning Menggunakan Google Meet Pada Pelajaran Bahasa Indonesia di SDN 75 Palembang

The problem in this study is that the teacher only uses whatsapp but it has not been effective because the use of whatsapp is only limited to absences, has not used the right method in delivering Indonesian subject matter and students who have smartphones have not used smartphones properly when online learning takes place. This study aims to determine the effectiveness of Blended Learning using Google Meet in Indonesian subject for fourth grade students at SDN 75 Palembang. The learning method is an experimental method with a True Experimental research design in the form of a Pretest-Posttest Control Group Design consisting of a control class group and an experimental class group. The sample in this study there are two classes, namely class IV A as the experimental class and class IV B as the control class, each class consists of 30 students. The data collection technique in this study used a test instrument in the form of multiple choice test questions. The data analysis technique used the related sample t-test test. Based on the results of this study, the average value of Indonesian learning outcomes for the control class was 82.5, while the average value of Indonesian learning outcomes for the experimental class was 91 so that there was an increase in student learning outcomes. The results of the t-test analysis showed that t_count = 3.638 with a significant level of 0.05, t_table = 2.017, where t_count &gt; t_table (3.638 &gt; 2.017). So it can be stated that H_(a ) is accepted, so it can be concluded that the use of the blended learning learning method using the google meet application is effective on learning outcomes in Indonesian class IV students at SDN 75 Palembang.
comparative study to assess the impact of school culture on psychosocial attributes of vedic and non-vedic school students

Youth plays an exceptional part in the advancement of a nation. They turn over political and social structure of their land. United Nations Alliance of Civilization (2013) likewise called youth as the “Pivotal Pool of Talent, Ideas and Energy”. They represent the most rigid fragment of any nation’s population. In a more extensive sense, youth are the pillars of any nation’s future as expressed by the former Indian president. Nowadays, with rapid westernization and urbanization youth are becoming more fixated to superficiality as well as negativity which cause many psychological as well as mental issues like: depression, migraine, insomnia etc. Such a situation is alarming for the individual as well as to its society. Globally, the incidents and intensity of emotional health problems among early adolescents is at a steep rise. The negative energies like stress, depression, laziness, betrayal etc. are getting dominant and if left untouched then it may lead to serious consequences in future by negatively affecting school performance, social and emotional life. It is an experimental intervention study which shows that the Vedic school culture plays an important role in developing positive psychosocial attributes.
IMPLEMENTATION SQ3R STRATEGY IN TEACHING READING COMPREHENSION IN MA AL-KHAIRIYAH WALANTAKA SERANG-BANTEN

The purpose of this research to describe the implementation of SQ3R in teaching reading comprehension and to identify students’ responses toward the implementation of SQ3R. This research used a qualitative case study conducted at MA Al-Khairiyah Pipitan. It involved one class of the eleventh grade which consists of 17 students. The data were collected through observation, interview, and documentation. The data from the three instruments were analyzed qualitatively. The results of this research showed that the implementation of SQ3R helped the students to comprehend English texts. The students also responded the implementation of SQ3R positively. They focused while reading English texts by using SQ3R. They were also interested and enthusiastic in doing the tasks. In addition n, SQ3R helped them to write questions from the texts, to rewrite the information by their own words, to write a report or a summary about the texts, and to enrich their vocabularies. In conclusion, SQ3R helped the students in reading English texts. Therefore, SQ3R is recommended to be implemented in teaching reading comprehension.
IMPROVING STUDENT’S SPEAKING ABILITY THROUGH MOBILE SOCIAL NETWORKING (Case Study : First Year Students of Stikes Yarsi Mataram

The aimed of this research was to find out whether the implanting of mobile social networking can be able to improve the student’s ability in speaking term of accuracy and fluency at first year students’ of Sikes Yarsi Mataram. This research was conducted through pre-experimental method. The population of this research consisted of one class of first year students’ of Sikes Yarsi Mataram which consisted of 31 students at Sikes Yarsi Mataram academic year 2020/2021. In collecting data, the researcher gave a speaking test, the speaking test help into two steps, there were pre-test and post-test, while to analyzed data , the researcher used (t)test. The findings of this research showed that the mean score in pre-test in term of pronunciation was (3,06) and post-test was (5,65). The mean score in pretest in term smoothness was ( 3,39) and post-test was (5,74). And the total result of pronunciation and smoothness or X1 was (3,22) and the post-test or X2 was (5,7). So, this is showed students got improvement in their speaking after being taught through mobile social networking, the students ability in speaking English became better than before
THE REALIZATION OF SEMANTIC MAPPING STRATEGY IN READING OF ENGLISH FOR FOREIGN LANGUAGE CLASS IN SMK BARDAN WASALAMAN BATANG

The purpose of this study is to gain a better understanding of the effectiveness of Semantic Mapping strategies in teaching reading comprehension in second grade students of SMK Bardan Wasalaman Batang. The research question is whether Semantic Mapping strategies are effective in Reading of English for Foreign language Class. Forty-two eleventh grade of in SMK Bardan Wasalaman Batang was chosen as a subject and divided equally into two groups; one group is the experimental group and the other is the control group. There are several procedures carried out in data collection to answer research questions; first, a pre-test is given before the experiment is performed to ascertain whether there are no significant differences in English proficiency in the two subject groups of lesson. Subjects are required to complete a reading comprehension test consisting of forty-five multiple-choice tests within ninety minutes. Second, after the experiment, a post-test was carried out using the same test for the pre-test. Finally, the t-test was used to determine whether there were significant differences in the achievement of reading comprehension between the two groups. The data showed that there were significant differences in the achievement of reading comprehension between the experimental groups - treated with Semantic Mapping strategies and the control group taught with KWL strategy. It means that the Semantic Mapping Strategy is more effective than KWL Strategy. The data indicate that there is significant difference in reading comprehension achievement between the experimental group - treated and Semantic Mapping strategy, and the control group taught with KWL strategy. It means that Semantic Mapping Strategy is more effective than KWL Strategy.