Reviewerly/exHarmony huggingface dataset:
SIVDSR-Dhaze: Single Image Dehazing with Very Deep Super Resolution Framework and Its Analysis

Adverse climate conditions can affect digital photography and cause issues such as colour shifting, poor visibility, contrast reduction, and fainted appearance due to the scattering of atmospheric Particulate Matter (APM).Estimating an optimum transmission matrix is the key to success for any single image dehazing technique.The use of VDSR 20-weighted Layers ImageNet classifier within l earning based Super Resolution technique x allows improving any image resolution and leads to noise suppression.High Residual Learning gradient clipping ensures fast convergence of the algorithm followed by denoising and artifacts removal as a result of compression.This key introspection has been exercised in improving resolution of the hazy images with an optical image formation model.In addition, we evaluate the benchmark of established images and make results comparisons to the state-of-the-art methods that shows a consistent improvement in accurate scene transmission estimation resulting in clear, natural haze-free radiance.A plausible consistency between execution speed and processing speed has been achieved.
iNOUN Chatbot: Providing Support and Microlearning with a Web Based Conversational Smart Assistant

Innovative solutions based on Artificial Intelligence (AI) are transforming modern society. The goal of AI is to create computers or machines that imitate human abilities as closely as possible. Conversational interfaces, or chatbots, are considered a revolutionary step towards the next generation of digital experiences. Few studies, however, have examined how these tools can be used to increase access to education and content-driven support. This study describes and demonstrates an intelligent support personal bot that responds to simple questions on information on the university website. Furthermore, the chatbot can provide learners with small chunks of learning material via voice and text input. The iNOUN chatbot uses Drupal's content management system to serve content, Google's Dialog flow NLU tools to process text and voice input, and a chatbot interface to interact with users. The web-based system powered by Artificial Intelligence will offer personalized support to learners as well as serve as a teaching assistant capable of presenting small chunks of content to learners. As a result, equity, inclusion, and lifelong learning are likely to be promoted.
Leakage-Resilient Anonymous Multi-Recipient Signcryption Under a Continual Leakage Model

A multi-recipient signcryption (MRSC) scheme possesses the functionalities of both multi-recipient public-key encryption and digital signature to ensure both integrity and confidentiality of transmitted messages. Moreover, an anonymous MRSC (AMRSC) scheme retains the functionalities of an MRSC scheme while offering privacy-preserving, namely, a recipient’s identity or public key being hidden to other recipients. In the past, numerous MRSC and AMRSC schemes based on various public-key cryptographies (i.e., public key infrastructure (PKI)-based, identity (ID)-based and certificateless (CL)) were proposed. Recently, an attacker can realize side-channel attacks to acquire partial bits of private keys participated in cryptographic computations. However, up to date, no MRSC or AMRSC scheme can resist side-channel attacks so that these schemes might suffer from such attacks and could be broken. To resist such attacks under a continual leakage model, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> PKI-based leakage-resilient AMRSC (PKI-LR-AMRSC) scheme in this paper. In the proposed scheme, an attacker is permitted to continually acquire partial bits of private keys partook in computations of the PKI-LR-AMRSC scheme, and formal security proofs are given to show that the proposed scheme still retains the original security of AMRSC schemes. As compared with the relevant AMRSC schemes, our PKI-LR-AMRSC scheme not only resists side-channel attacks but also reduces the cost of executing the multi-signcryption and unsigncryption algorithms. In particular, the point is that the computational complexities of our scheme respectively require only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> ) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1) in executing the Multi-signcryption algorithm and the Unsigncryption algorithm, where t is the number of recipients.
A Huffman based short message service compression technique using adjacent distance array

The short message service (SMS) is a wireless medium of transmission that allows you to send brief text messages. Cell phone devices have an uttermost SMS capacity of 1,120 bits in the traditional system. Moreover, the conventional SMS employs seven bits for each character, allowing the highest 160 characters for an SMS text message to be transmitted. This research demonstrated that an SMS message could contain more than 200 characters by representing around five bits each, introducing a data structure, namely, adjacent distance array (ADA) using the Huffman principle. Allowing the concept of lossless data compression technique, the proposed method of the research generates character's codeword utilising the standard Huffman. However, the ADA encodes the message by putting the ASCII value distances of all characters, and decoding performs by avoiding the whole Huffman tree traverse, which is the pivotal contribution of the research to develop an effective SMS compression technique for personal digital assistants (PDAs). The encoding and decoding processes have been discussed and contrasted with the conventional SMS text message system, where our proposed ADA technique performs outstandingly better from every aspect discovered after evaluating all outcomes.
ANALISA PENURUNAN DAYA HISAP POMPA PENDINGIN MOTOR INDUK PADA KAPAL MV.BAHARI 22 DAN ALTERNATIF PEMECAHANNYA

The pump is a machine for moving fluid. The pump moves fluid from a low pressure place to a place with a higher pressure, to overcome this pressure difference a power is needed. The cooling system is a system that serves to keep the engine temperature in ideal conditions.Seawater cooling pump used on the MV.BAHARI 22 ship, experienced a problem of decreased suction power analyzed using quantitative research methods in which research uses data taken directly on the ship. Then the analysis of this study uses a predetermined formula. Research findings and analyzed data are head loss which includes static head, head pressure, head speed, head major loss, head minor loss. &#x0D; The decrease in the pressure of the sea water coolant pump on the ship is caused by the total head of the system which exceeds the pump head where the head of the system according to the calculation is 61 m, while the maximum head of the pump is only 55 m so that the cavitation in the pump is ensured which causes impeller damage and damages the house the snail This can be proven from the calculation results where the NPSHr value = 5.49 meters far exceeds the NPSHa value which is only 2.52 m.
Multi-enhanced Adaptive Attention Network for RGB- T Salient Object Detection

Nowadays, Salient object detection (SOD) on RGB images has achieved remarkable success. However, the performance of this single-modal SOD will be considerably reduced when faced with complicated situations. To deal with these challenges, the fusion of RGB and thermal infrared images, termed as RGB- T SOD, becomes a new SOD research direction recently. Thermal images can supply the essential additional information to RGB because they are immune to illumination and weather conditions. Though in this area, existing methods don't take full advantage of multi-level encoded features to generate global context. In addition, these approaches feed unprocessed encoded features that contain interference such as background directly to the decoder and don't explicitly establish the correlation of two heterogeneous modalities. In this paper, we proposed a multi-enhanced adaptive attention network (MEAANet) to solve aforementioned problems. Specifically, we use a multi-modal multi-level feature fusion (MMFF) module to fuse low-level and high-level encoded features to enhance the global context. Then, we design the thermal adaptive attention module (TAAM) to enhance encoded features while reducing noise interference. Moreover, to explore the correlation between the two modalities, we utilize the cross-enhanced integration module (CIM) to learn the shared features of two modalities. The comprehensive experimental results demonstrate the effectiveness of our proposed approach against the state-of-the-art.
Wavelet Multi Resolution Analysis based Data Hiding with Scanned Secrete Images

Wavelet Multi Resolution Analysis (MRA) based data hiding with scanned secrete images is proposed for improvement of invisibility of the secrete images.Daubechies (biorthogonal basis was adopted as the wavelet, but it was demonstrated that the key image (or secret image data) information can be restored with the biorthogonal wavelet.Also, the information of what to adopt as the biorthogonal wavelet is hidden.Key image information can also be protected by doing so, that the horizontal biorthogonal wavelet of the image does not have to be the same as the vertical biorthogonal wavelet, and the insertion position of the secret image data can be freely selected.It is also possible to divide the bit string of the secret image data and insert it into an arbitrary high frequency component, that the information hiding capability changes depending on the number of bit strings (information amount) of the secret image data, and the secret image in the public image data.Random scanning is effective for improving the visibility of data, selection of scanning method type, random number initial value It was shown that sharing only among parties is useful for improving confidentiality, resistance to noise, resistance to data compression, and resistance to tampering with data.
TEACHING ENGLISH SPEAKING THROUGH STORY TELLING METHOD (A FIELD RESEARCH AT SDN CENGKARENG BARAT 03 PAGI)

Abstract&#x0D; The object of this research is to apply storytelling method in teaching English speaking. The research was conducted at the fourth grade SDN Cengkareng Barat 03 Pagi in academic year 2017/2018. In process teaching, writer used theory by Fikriah which describes about procedures of teaching used storytelling method to improved speaking skill. Writer used picture as media and also students experience to learn English speaking. The writer analyses the process of teaching and collecting data through; field research, library research, and internet. There are some difficulties in English speaking that found at SDN Cengkareng Barat 03 pagi, such as students’ vocabulary still less, lack of ideas to tell, and students afraid to speak louder. During process teaching run, there are some advantages of using storytelling method, these was : (1) Improving students’ activeness and imagination in speaking, especially in the implementing or telling a story (2) students brave to speak in front of class to tell the story (3) by practice directly students more understood the material given. Thus, it can be said that storytelling method could improve students’ speaking skill
PENGEMBANGAN TUGAS MATEMATIKA DARING BERBASIS GOOGLE FORM PADA MATERI STATISTIKA DI KELAS VIII SMP/MTs

Math's student’s assignment based on Google Form is one of the products that help students study by themselves. It is adapted from Learning Activities in the Grade 8 Mathematics Student Book. This product provides direct feedback to help students understand and avoid conceptual errors. This research aims to develop a valid and practical Google Form-based online math assignment on statistics material in class VIII SMP/MTs. The model used for this development research is Plomp model which consists of the Preliminary, Prototyping and Assessment Phases. The preliminary stage consists of needs analysis, student analysis, curriculum analysis and concept analysis. At the prototyping stage, the task is designed to produce prototype 1, then self-evaluation is carried out to obtain prototype 2. Furthermore, an expert review is carried out to get the validity of the product until a prototype 3 is obtained. Then a one-to-one evaluation is carried out to a group of students and the results are revised to obtain a prototype 4. Finally, prototype 4 was tested with a small group evaluation of nine students. The product can be accessed at https://linktr.ee/tugasmtk8statistika. The results showed that the product has a level of validity in the valid category and has a level of practicality in the practical category. The conclusion of the resulting product is valid and practical. Keywords __ Student Assignment, Google Form, Online, Feedback, Student Book
Multilayer Perceptron Generative Model via Adversarial Learning for Robust Visual Tracking

Visual tracking is an open and exciting field of research. The researchers introduced great efforts to be close to the ideal state of stable tracking of objects regardless of different appearances or circumstances. Owing to the attractive advantages of generative adversarial networks (GANs), they have been a promising area of research in many fields. However, GAN network architecture has not been thoroughly investigated in the visual tracking research community. Inspired by visual tracking via adversarial learning (VITAL), we present a novel network to generate randomly initialized masks for building augmented feature maps using multilayer perceptron (MLP) generative models. To obtain more robust tracking these augmented masks can extract robust features that do not change over a long temporal span. Some models such as deep convolutional generative adversarial networks (DCGANs) have been proposed to obtain powerful generator architectures by eliminating or minimizing the use of fully connected layers. This study demonstrates that the use of MLP architecture for the generator is more robust and efficient than the convolution-only architecture. Also, to realize better performance, we used one-sided label smoothing to regularize the discriminator in the training stage and the label smoothing regularization (LSR) method to reduce the overfitting of the classifier in the online tracking stage. The experiments show that the proposed model is more robust than the DCGAN model and offers satisfactory performance compared with the state-of-the-art deep visual trackers on OTB-100, VOT2019 and LaSOT datasets.
A Novel Hand Gesture Recognition Method Using sEMG-Based PSODE-BPNN

Aim to improve the deficiencies of slow convergence speed and prone to trapped into local minimum for back propagation neural network (BPNN) in sEMG-based hand gesture, a novel hand gesture recognition method associating particle swarm optimization (PSO) with differential evolution (DE) is proposed to optimize BPNN, namely PSODE-BPNN. Firstly, initial parameters of weights and biases are adjusted and updated to yield optimal values, via PSO and DE incorporation with the gradient descent method of BPNN. Secondly, the optimal values are utilized as the initial parameters to train the BPNN. Then the proposed method is evaluated on sEMG-based hand gesture dataset Ninapro DB4. The performance for the PSODE-BPNN is compared with the PSO-BPNN, the BPNN, and other related competing methods in literatures. The experimental results show that the PSODE-BPNN improves the inadequacy of slow convergence rate and local minimum of BPNN, and achieves higher gesture recognition accuracy than other comparison listed methods. The effectiveness and superiority of the proposed method is significantly validated.
Transforming Social Behavior in Society through the Implementation of Digital Literacy

Globalization is a necessity that cannot be avoided, as well as the rapid flow of technology. So, people must be able to react wisely. This is because, if people are not digital literate and blind to digital literacy, the easy situation will actually become a boomerang for themselves and others, such as the occurrence of various crime cases in online and offline media. Digital literacy is predicted to be able to overcome problems regarding various ignorance in utilizing digital which of course affects various aspects. For this reason, the researcher wants to find out more about the transformation of social behavior in society through digital implementation. The method used is descriptive quantitative by selecting respondents through purpose sampling which is adjusted to the research objectives, then the process of obtaining data using surveys which are distributed to all respondents. In addition to surveys, observation and interviews are also used to increase the validity of the research results. The results of the study show that the transformation of social behavior goes from bad to better which is seen from the mindset and culture and then forms social behavior in the community that seems to have changed. Where they can think more critically, improve the quality of culture and not be careless when they behave, they also help to harmonize society through the social behavior displayed.
Speed-Reading Technique to Boost English as Foreign Language Learners’ Text Comprehension

Nowadays, many English as Foreign Language (EFL) learners still have problems comprehending the texts they are reading. This article aims at reviewing the speed-reading technique as one of the techniques to boost EFL learners’ text comprehension. A qualitative descriptive research design was applied for the present study. The data were obtained through a documentation study and used state-of-the-art sources in the related studies. Furthermore, the literature review was used to elucidate the findings from the previous studies and related to the present review. The data were analyzed qualitatively by describing how the speed-reading technique can boost learners’ text comprehension. It initiates with information about a brief of speed reading, the development, and trends of speed reading, then describes the speed-reading purposes and movement. The speed-reading techniques to be acquired by the EFL learners are described thoroughly. Besides, the benefits and several implications of speed reading for the Indonesian EFL learners in the classroom practice are highlighted. Finally, this article provides some conclusions and recommendations on what teachers, students, and government can do to boost EFL learners’ text comprehension.
Joint Data and Model Driven Channel-Free Signal Detection based Learned Factor Graph

We propose a learned factor graph based on convolutional neural network (CNN) and Bi-directional Long Short Term Memory (BiLSTM) to realize signal detection under the scenario of no channel model. It can solve the inevitable over-reliance on channel state information (CSI) of model-based signal detection methods and avoid the shortcomings of large training scale of general data-driven methods by using relatively small training samples. The proposed method uses a network of CNN-BiLSTM structure with strong learning capabilities to determine the statistical relationship of the channel model which is what traditional model-based methods rely on. Based on above, the parameter estimation (Gaussian mixture model considering Akaike information criterion) and non-parametric estimation (adaptive kernel density) are adopted to learn a factor node together. The simulations show that, the proposed method can guarantee the accuracy of signal detection and robustness to the training of imperfect CSI.
TransNet: Shift Invariant Transformer Network for Side Channel Analysis

Deep learning (DL) has revolutionized Side Channel Analysis (SCA) in recent years. One of the major advantages of DL in the context of SCA is that it can automatically handle masking and desynchronization countermeasures, even while they are applied simultaneously for a cryptographic implementation. However, the success of the attack strongly depends on the DL model used for the attack. Traditionally, Convolutional Neural Networks (CNNs) have been utilized in this regard. This work proposes to use Transformer Network (TN) for attacking implementations secured with masking and desynchronization. Our choice is motivated by the fact that TN is good at capturing the dependencies among distant points of interest in a power trace. Furthermore, we show that TN can be made shift-invariant which is an important property required to handle desynchronized traces. Experimental validation on several public datasets establishes that our proposed TN-based model, called TransNet, outperforms the present state-of-the-art on several occasions. Specifically, TransNet outperforms the other methods by a wide margin when the traces are highly desynchronized. Additionally, TransNet shows good attack performance against implementations with desynchronized traces even when it is trained on synchronized traces.
Compliance Incentive Models in Criminal Procedure

As a new type of corporate management, corporate compliance aims to avoid compliance risks and prevent, monitor and remedy illegal and criminal activities. However, it is meaningless for a company to establish a compliance system on paper merely. The effectiveness of this management relies on its associated administrative and criminal incentive mechanism. Setting the rules of leniency for compliance in criminal law can urge enterprises suspected of committing crimes to implement compliance programs. For enterprises that have promised to establish or improve a compliance management system, the procuratorates set a supervision period and decide whether to prosecute them based on the effectiveness of their compliance system. More and more countries have established this rule. So far, the United States has established deferred prosecution and non-prosecution systems, while the United Kingdom, France, Australia, Canada, Singapore and other countries have imitated the United States and established the deferred prosecution system.
Relationship between subjective well-being and self-care among caregivers in pandemic times

Self-care refers to disease prevention and health maintenance practices. Self-care capacity can be an important factor in different conditions and contexts and when associated with aspects of an individual's health it can provide direct benefits to this person. During the Pandemic (COVID-19) this ability may be diminished or impaired, and even hindered with affective aspects, influencing their subjective well-being, that is, affecting their assessment of their own well-being. Thus, this study aimed to evaluate the relationship between self-care capacity and the affective attribution (positive or negative) of male and female adults who act as caregivers. The instruments used were a sociodemographic questionnaire, the Positive and Negative Affect Schedule and the Scale to Assess Self-Care Capabilities. After analyzing the frequency of the variables, Pearson's correlation analysis was performed and through it, moderate associations (medium high) were revealed between the measure of positive affect and self-care (r=0.62; p=0.000) and negative affect and self-care, in this case, with a negative coefficient (r=-0.42; p=0.000); thus, this indicates that people tend to have more self-care, have better positive affects and less negative affects, respectively. It was possible to infer the idea that well-being and quality of life are intertwined with the affective attribution of the individual.