Reviewerly/exHarmony huggingface dataset:
Reforming the Public Universities Financing Scheme: The Case of Ghana’s Higher Education Transformation Agenda

Public higher education institutions in Ghana are confronted with unending financing constrains every academic year thus affecting the financial health of these institutions. The financial sustainability of these institutions has become increasingly critical due to the persistent reported funding gaps and the weak funding allocation regime. Countries the world over have begun implementing reform programmes to deal with issues of financial sustainability of higher education. The study employed quantitative research methodology with a well-validated research instrument. This correlational study attempts to measure the relationship between the financing scheme variables and financial sustainability. The outcome of the study revealed that there was statistically significant relationship between the combined effects of the variables while three variables out of the seven were found to be significant in predicting best fit equation for financial sustainability. The study recommends to the Ministry of Education (MOE) and the National Council for Tertiary Education (NCTE), a review of the funding policy direction with a precise focus on addressing Ghana’s higher education infrastructural deficit, skills gap, improving research, science and technology.
Core Competencies and Core Themes of Historical Studies in the Era of the Fourth Industrial Revolution

This paper studied the core competencies and core topics of history education that should be emphasized in the era of the fourth industrial revolution.BR In the 4th Industrial Revolution, humanities, especially history and history education, should be studied and educated at the natural and cosmic levels beyond the center of human nature.BR As suggested by WEF, OECD, UNESCO, DeSeCo, and P21, education that develops creative capabilities should be more critical in the era of the fourth industrial revolution, emphasizing only competency-oriented education. However, some limitations do not mention how competence is formed and developed and how the student development process and competency building relate. It should be very vigilant, as there will be many risks of subordinating competency education if the provision of future societies determines core competencies.BR Competent scholar Charles Fadel et al. mentioned 4C in 2009 in the 21st Century Skills, which is essential to accept. It is because not only core competencies but also traditional knowledge bases are needed. This knowledge base is the core theme, the traditional historical knowledge, the present writer means. History education should also be taught not only by the core capabilities required by a future society based on the educational ideology and human image that the future requires but also by the core facts, which are traditional historical facts based on existing knowledge.
Deep Learning in Medical Image Processing and Computer-Aided Diagnosis

Deep learning is a versatile, powerful framework that can acquire image-processing and analysis functions through training with image examples. On the other hand, various ML with image input techniques were proposed before the introduction of the term “deep learning.” In the field of signal and image processing, supervised nonlinear filters based on a modified MLP model, called neural filters, have been proposed. The MTANN is trained with input images and the corresponding “teaching” images for enhancement of specific patterns and suppression of other patterns in images. The edge enhancement performance of the NEE was superior to that of conventional edge detectors in this challenging segmentation problem. The segmentation by the NEE agreed almost perfectly with the “gold-standard” manual segmentation by an experienced cardiologist. In other words, classification between a particular pattern and other patterns is made by enhancement of the particular pattern, which may be referred to as “classification by enhancement.”.
CONVINCED ENHANCEMENTS IN FEATURE DIVERSITY FOR DATA MINING AND ITS APPLICATION IN OPINION MINING

Opinion Mining (OM), which is also known as Sentiment classification or a Polarity classification, is that binary classification of task labelling of an opinionated text/document that expresses either a positive or an overall opinion that is negative. Several technique of analysis of a subjective information in the classification of sentiment is available. The extraction of feature chooses sufficient features for characterizing the opinion. The feature selection (FS) that is also known as the variable selection, subset selection or attribute reduction available in machine learning. It does select an input of dataset which closely defines its specific outcome. Here in this research a feature selection mechanism that is wrapper based is used based on a heuristic algorithm. The experiments demonstrate that the proposed method improves the efficiency of the classifiers and achieve higher accuracy.
Exploiting ROLLO’s constant-time implementations with a single-trace analysis

ROLLO, for Rank-Ouroboros, LAKE and LOCKER, was a candidate to the second round of the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography (PQC) standardization process. In the lastest update in April 2020, there was a key-encapsulation mechanism (ROLLO-I) and a public-key encryption scheme (ROLLO-II). In this paper, we propose a side-channel attack to recover the syndrome during the decapsulation process of ROLLO-I. From this syndrome, we explain how to recover the private key. We target two constant-time implementations: the C reference implementation and a C implementation available on GitHub. By capturing power measurements during the execution of the Gaussian elimination function, we are able to extract from a single trace each element of the syndrome. This attack can also be applied to the decryption process of ROLLO-II. Finally, we give countermeasures based on masking and randomization to protect future implementations. We also provide their impact regarding the execution time.
Clustering-Based Interpretation of Deep ReLU Network

Amongst others, the adoption of Rectified Linear Units (ReLUs) is regarded as one of the ingredients of the success of deep learning. ReLU activation has been shown to mitigate the vanishing gradient issue, to encourage sparsity in the learned parameters, and to allow for efficient backpropagation. In this paper, we recognize that the non-linear behavior of the ReLU function gives rise to a natural clustering of the input space when the pattern of network’s active neurons is considered. This observation helps to deepen the learning mechanism of the network; in fact, we demonstrate that, within each cluster, the network can be fully represented as an affine map. The consequence is that we are able to recover an explanation, in the form of feature importance, for the predictions done by the network to the instances belonging to a specific cluster. The methodology we propose is able to increase the level of interpretability of a fully connected feedforward ReLU neural network, downstream from the fitting phase of the model, without altering the structure of the network. A simulation study and the empirical application to the Titanic dataset, show the capability of the method to bridge the gap between the algorithm optimization and the human understandability of the black box deep ReLU networks.
3D Human Reconstruction: A Comparison Between Images-Based and Point Cloud-Based Method

Nowadays, Human Reconstruction is an important part of 3D Reconstruction because it enables us to acquire human information. In reality, Human Reconstruction is applied more and more widely in various domains of people's life such as healthcare, education, sport, entertainment, fashion, anthropometry…especially in the Covid-19 pandemic situation. Technically, the digitalization of human shape and pose is being implemented by two main solutions. The first solution aims to re-create 3D shape and pose of people by using a set of 2D images from different view angles. The second method focuses on handling with the point cloud which is acquired from a set of depth cameras. Both of them have their own advantages and disadvantages. The current trend of 3D Human Reconstruction is to try recreating human shape and pose not only from various available sources (images, point cloud) but also from natural sources (surveillance camera). However, the accuracy of current solutions is significantly sensitive to the working environment. Therefore, this work gives an attempt to develop two human reconstruction methods and evaluate their performance in various conditions as synthetic, laboratory, and real-world environments. The results show a comparison of human reconstruction's accuracy due to the working environment and method of approach. In the synthetic environment, the accuracy is highest for both two methods because of the perfection of input. The accuracy of these methods is lower in the remaining environment. Besides, the result also proves that point cloud-based method is more sensitive than image-based method due to the limitation of hardware devices.
Survey on VLSI Architecture for Lifting based Discrete Wavelet Transform in Image Processing Applications

Nowadays all over the world follow the high resolution images and videos in domestic and commercial applications. In this high resolution applications  memory usage is very high especially 3D images for transmitting and receiving and also bandwidth requirements are very high. These type of processing required more processing time and also required more power. The Discrete Wavelet Transform(DWT) is one of the technique to solve the problems in image processing.. This paper is a survey on VLSI Architecture for image compression through Lifting Based Discrete wavelet transform. The more number of techniques are proposed on DWT in early years among them lifting scheme is more efficient compared to conventional DWT in power and area considerations These lifting architectures give a perfect solution with efficient hardware utilization and less memory usage.
Students’s acceptance toward the use of mobile learning in fractions

Abstract Most of the fifth-grade students have been familiar with mobile technology and commonly use it to play games. Accordingly, the school do not allow fifth-grade students to bring or operate mobile technology while they are at school in order to keep students’ attention on learning. However, during the pandemic COVID-19 condition, the government demands students to learn from home. Thus, one solution is using mobile technology to maintain communication and interaction between students and teachers during teaching and learning activities from home. The present study developed a mobile application, called U-Fraction, to assist students in the mobile learning process. The developed application was associated with authentic learning so that students could have stronger understanding of the real-world problems. Students’ perception toward the mobile learning supported by U-Fraction were measured using TAM with partial least square analysis. The result shows that students tended to use this mobile application for mobile learning. Students also believed that the use of U-Fraction for mobile learning could help their learning process.
Cloud Computing as a Strategic Asset: European Union Regulatory Inititatives

Given the tremendous increase of activity in the digital domain, whether government related activity or digital economy activity, it was important that this sphere would be regulated and that stakeholders were subjected to regulation that would allow democratic the democratic principles to prevail. The paper presents the policy steps taken by the EU in relation to the Data-based Economy, mainly exploring the five projects led by the EU and that are related to Cloud Computing as a new space for solidifying the single market. It also discusses the challenges that EU-based business enterprises experience when they use facilities and cloud computing storage services situated outside the EU, thus challenging the efficacy of the related existing sets of EU rules. It also analyses the effectiveness of specific sets of rules that have been proposed in respect to the five strategies that are currently in the focus.
Budaya Literasi Melalui Gesigeli (Gerakan Siswa Gemar Literasi)

Reading an interest in the student must be done in a manner continuing when not done on a regular basic and discliplined it would not have formed a good literary culture. When reading culture is assimilated into students, it builds up an optimal literative character. Developing attitudes and characteristics often require the support of all parties of government, school, teachers, parents, and environment and yourself. If all parties can collaborate and assist in establishing an orderly attitude and custom, then is a virtue.
A Domain-Adaptable Heterogeneous Information Integration Platform: Tourism and Biomedicine Domains

In recent years, information integration systems have become very popular in mashup-type applications. Information sources are normally presented in an individual and unrelated fashion, and the development of new technologies to reduce the negative effects of information dispersion is needed. A major challenge is the integration and implementation of processing pipelines using different technologies promoting the emergence of advanced architectures capable of processing such a number of diverse sources. This paper describes a semantic domain-adaptable platform to integrate those sources and provide high-level functionalities, such as recommendations, shallow and deep natural language processing, text enrichment, and ontology standardization. Our proposed intelligent domain-adaptable platform (IDAP) has been implemented and tested in the tourism and biomedicine domains to demonstrate the adaptability, flexibility, modularity, and utility of the platform. Questionnaires, performance metrics, and A/B control groups’ evaluations have shown improvements when using IDAP in learning environments.
PENERAPAN JARINGAN SYARAF TIRUAN PREDIKSI KEBUTUHAN ALAT LAMPU PENERANGAN JALAN UMUM (LPJU) DENGAN METODE BACKPROPAGATION

Street lights or also known as Public Street Lighting (LPJU) are lights that are used for street lighting at night so that it makes it easier for pedestrians, cyclists and vehicle drivers to see more clearly the road / terrain to be traversed at night, so as to improve safety traffic and safety of road users from criminal activities / actions. The data used in the Artificial Neural Network analysis process using the back propagation method is the data on the number of PJU equipment needs starting from January to December. Then the data is used as training data, training targets and test data. The artificial neural network system can recognize training data and target data with a 786 iteration target error of 0.9 and a leraning rate of 0.2 so as to produce a prediction of the total number of LPJU equipment needs predicted in 2020, namely with the results of the need for LPJU equipment, some have experienced an increase, some have decreased.
Touchless Modalities of Human-Computer Interaction in Hospitals

This paper aims to identify conceptual and practical findings in new modalities of interactions in hospital settings. A Systematic Literature Review was carried out through database research and the snowballing technique. From the review, 46 papers were extracted. The reviewed literature describes the main modalities and technology issues to the medical specialty or hospital department where they are used. Results obtained can help readers identify better solutions for a specific department hospital requiring special conditions. Moreover, we identify that the touchless interface has a relevant contribution since it can be implemented with low-cost technology and provides system user interaction that is more adaptable to the hospital environment's health team activities.
Properties of Module Notions and Atomic Decomposition

Various properties of ontology modules have been studied, such as coverage, self-containment, depletingness, monotonicity, preservation of justifications. These properties are important from a theoretical and practical point of view because they ensure, e.g., that modules have meaningful interfaces, can be used for ontology debugging, or are suitable for computing a meaningful modular structure of an ontology, such as via atomic decomposition (AD). Given one of the many existing module notions, it is not always obvious whether it satisfies a given property, particularly when the module extraction procedure is based on normalization. We investigate several module properties from an abstract point of view with an emphasis on properties relevant for AD. We examine their interrelations, their relation with iterated module extraction, their preservation in normalization-based module notions, and the adjustment of the latter to the requirements of AD. As a case study, we apply our results to modules based on Datalog reasoning (DBMs), which comprise a large family of normalization-based module notions that provide logical guarantees of varying strengths and are thus suitable to a wide range of use cases. This makes DBMs ready to be used for AD and thereby opens AD to new applications.
Space-Time Crop & Attend: Improving Cross-Modal Video Representation Learning

The quality of the image representations obtained from self-supervised learning depends strongly on the type of data augmentations used in the learning formulation. Recent papers have ported these methods from still images to videos and found that leveraging both audio and video signals yields strong gains; however, they did not find that spatial augmentations such as cropping, which are very important for still images, work as well for videos. In this paper, we improve these formulations in two ways unique to the spatio-temporal aspect of videos. First, for space, we show that spatial augmentations such as cropping do work well for videos too, but that previous implementations, due to the high processing and memory cost, could not do this at a scale sufficient for it to work well. To address this issue, we first introduce Feature Crop, a method to simulate such augmentations much more efficiently directly in feature space. Second, we show that as opposed to naive average pooling, the use of transformer-based attention improves performance significantly, and is well suited for processing feature crops. Combining both of our discoveries into a new method, Space-time Crop & Attend (STiCA) we achieve state-of-the-art performance across multiple video-representation learning benchmarks. In particular, we achieve new state-of-the-art accuracies of 67.0% on HMDB-51 and 93.1% on UCF-101 when pre-training on Kinetics-400.
An Experimental Evaluation of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs

The task of inferring missing links or predicting future ones in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature and show good prediction performance in many realworld graphs though they are heuristic. On the other hand, graph embedding approaches learn lowdimensional representation of nodes in graph and are capable of capturing inherent graph features, and thus support the subsequent link prediction task in graph. This paper studies a selection of methods from both categories on several benchmark (homogeneous) graphs with different properties from various domains. Beyond the intra and inter category comparison of the performances of the methods, our aim is also to uncover interesting connections between Graph Neural Network(GNN)- based methods and heuristic ones as a means to alleviate the black-box well-known limitation.
Improved Utilization Methodology of BERT Specialized in Text Classification

Recent language models are pre-trained to generate universal word representations. This study proposes a BERT-Triplet model and its utilization methodology to generate word representations specialized for the text classification task. Specifically, we use class information of the data in the pre-training stage of the proposed BERT-Triplet model to closely distribute the embedding vectors of words or sentences with a high probability of being classified into the same class in the vector space, unlike existing language models. The proposed methodology obtains improvement of the classification performance and is expected to be used in various sub-fields of text classification and in language models other than BERT.
THE INFLUENCE OF MALL (MOBILE ASSITED LANGUAGE LEARNING) ON ENGLISH AS FOREIGN LANGUAGE STUDENTSâ?? READING COMPREHENSION

Technological developments with the adoption of Mobile Multimedia devices and applications have translated into huge opportunities for English as a foreign Language (EFL). The mobile phone can be easily used everyone. Mobile phone have some applications with great advantage for user. Consequently, this study probes the influence of Mobile Assisted Language Learning (MALL) on EFL learnersâ?? reading comprehension. The population of this study was vocational studentÂ  EFL students (15-17 years old) at SMK Perwari Tulungagung. The material of reading comprehension (englishforeveryone.org) was used to have almost homogenous groups. After administrating the reading from reading comprehension test, 60 students who were randomly and equally assigned to the experimental and control groups (30 students in each group) were selected as the sample of this study. To see the impact of MALL on EFL studentsâ?? reading comprehension, TOEIC reading comprehension test in form of multiple choice and some open ended questions was used as a pre â?? test and post â?? test to assess the participantsâ?? reading comprehension in both control and experimental group.
The Learning of Writing Experience Stories for Elementary School Students in Banyumas Regency (Exploratory Study)

Literacy is one’s ability to digest information through listening, speaking, reading, and writing skills. A child is expected to master the four skills and be able to implement them in everyday life. The four language skills are a unity that is closely interconnected. A person’s language reflects his thoughts. The more skillful one uses language, the clearer one’s mind is. That is why cultural literacy needs to be developed to acquire language skills. From the initial survey result, it is known that the ability of the elementary school students in Banyumas Regency in writing personal experience stories is still low compared to other literacy skills. Therefore, the development of effective learning model to improve writing skill is needed. The development of learning model needs to be done because not all learning models can be effectively used in class. One of the learning models of writing experience stories that can be developed is a learning model of writing dialogue-based stories, uses a scientific approach, and contains edutainment elements. With this model, the learning of writing experience stories is easier to be applied by teachers, interesting, and fun for students.