Reviewerly/exHarmony huggingface dataset:
THE DEVELOPMENT HANDOUT Excoecaria agallocha (Buta Buta) IN MANGROVE AREA COASTAL OF SUNGAI RASAU AS ENHANCEMENT FOR BIODIVERSITY CONCEPT IN SENIOR HIGH SCHOOL

Teaching materials based on local potential are teaching materials that use the surrounding environment as a learning resource, one of which is a handout. Buta Buta plants are regional potentials that can be utilized by educators as material for enriching biodiversity that students learn in high school in the subject of Biology.The research and development aim to analyze the development of the Blinding Tree (Excoecaria Agallocha) handout in in the Sungai Rasau Mangrove Area as an EnrichmentMaterial for the Concept of Biodiversity in HighSchool. The development model uses Tessmer (1998) to the stage of self-evaluation and expert review. The research and development method used the development steps of the handout, the data analysis of the validity of the Blinding tree (Excoecaria agallocha) plant handout by using the validity score formula which includes 3 aspects of feasibility, namely aspects of content feasibility, presentation feasibility and language assessment. The results of the assessment of the validity of the handouts that were developed were stated to be very valid by obtaining an average score on the aspect of content feasibility, namely 87.5%, the average score in the aspect of feasibility of presentation was 93.33%, and the average score on the aspect of language assessment was 90.37% so that the handouts that have been developed can be used as material for enriching the concept of Biodiversity in High School.
A Study on Cloud Storage Security Method Using Data Classification

Cloud computing is a new model for providing diverse services of software and hardware. This paradigm refers to a model for enabling on-demand network access to a shared pool of configurable computing resources, that can be rapidly provisioned and released with minimal service provider interaction .It helps the organizations and individuals deploy IT resources at a reduced total cost. However, the new approaches introduced by the clouds, related to computation outsourcing, distributed resources and multi-tenancy concept, increase the security and privacy concerns and challenges. It allows users to store their data remotely and then access to them at any time from any place .Cloud storage services are used to store data in ways that are considered cost saving and easy to use. In cloud storage, data are stored on remote servers that are not physically known by the consumer. Thus, users fear from uploading their private and confidential files to cloud storage due to security concerns. The usual solution to secure data is data encryption, which makes cloud users more satisfied when using cloud storage to store their data. Motivated by the above facts; we have proposed a solution to undertake the problem of cloud storage security. In cloud storage, there are public data that do not need any security measures, and there are sensitive data that need applying security mechanisms to keep them safe. In that context, data classification appears as the solution to this problem. The classification of data into classes, with different security requirements for each class is the best way to avoid under security and over security situation. The existing cloud storage systems use the same Journal of University of Shanghai for Science and Technology ISSN: 1007-6735 Volume 23, Issue 9, September – 2021 Page-1105 key size to encrypt all data without taking into consideration its confidentiality level. Treating the low and high confidential data with the same way and at the same security level will add unnecessary overhead and increase the processing time. In our proposal, we have combined the K-NN (K Nearest Neighbors) machine learning method and the goal programming decision-making method, to provide an efficient method for data classification. This method allows data classification according to the data owner security needs. Then, we introduce the user data to the suitable security mechanisms for each class. The use of our solution in cloud storage systems makes the data security process more flexible, besides; it increases the cloud storage system performance and decreases the needed resources, which are used to store the data.
Research on Image Feature Extraction Method Fusing HOG and Canny Algorithm

research-article Research on Image Feature Extraction Method Fusing HOG and Canny Algorithm Share on Authors: Li Wang Hainan University and Hainan University, China Hainan University and Hainan University, ChinaView Profile , Taijun Li Hainan University and Hainan University, China Hainan University and Hainan University, ChinaView Profile Authors Info & Claims DSIT 2021: 2021 4th International Conference on Data Science and Information TechnologyJuly 2021 Pages 208–211https://doi.org/10.1145/3478905.3478947Online:28 September 2021Publication History 0citation16DownloadsMetricsTotal Citations0Total Downloads16Last 12 Months16Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Unsupervised Deep Video Denoising

Deep convolutional neural networks (CNNs) for video denoising are typically trained with supervision, assuming the availability of clean videos. However, in many applications, such as microscopy, noiseless videos are not available. To address this, we propose an Unsupervised Deep Video Denoiser (UDVD), a CNN architecture designed to be trained exclusively with noisy data. The performance of UDVD is comparable to the supervised state-of-the-art, even when trained only on a single short noisy video. We demonstrate the promise of our approach in real-world imaging applications by denoising raw video, fluorescence-microscopy and electron-microscopy data. In contrast to many current approaches to video denoising, UDVD does not require explicit motion compensation. This is advantageous because motion compensation is computationally expensive, and can be unreliable when the input data are noisy. A gradient-based analysis reveals that UDVD automatically adapts to local motion in the input noisy videos. Thus, the network learns to perform implicit motion compensation, even though it is only trained for denoising.
A Process for Building Domain Specific Thesauri for Query Expansion to Mine SW Documents Repositories within an Industrial Environment

In large Software Companies, retrieving relevant artifacts from indexed repositories with thousands of textual documents depends strongly on the quality of the query. Although existing search engines work well to some degree, they face problems with words matching. Often, only synonyms of words in the queries are present in the indexed documents. However, the coverage of Information Retrieval systems can be improved by Queries Expansion operation, which adds new (correlated) terms to the original query. Usually these words are obtained from a dictionary of synonyms (Thesaurus). Our work proposes a process for the automatic construction of domain thesauri based on documents available from SW Companies' local repositories. The aim is to avoid ambiguous or non-correlated words found in generic thesauri. The implemented system was used to generate a domain thesaurus based on private documents from a real world Company. The created thesaurus was used for query expansion to improve the performance of two local documents retrieval systems, showing very satisfactory results. This work was conducted within the context of a research cooperation project between Motorola Mobility (a Lenovo Company) and Centro de Informática (CIn-UFPE).
Towards constant-time probabilistic root finding for code-based cryptography

In code-based cryptography, deterministic algorithms are used in the root-finding step of the decryption process. However, probabilistic algorithms are more time efficient than deterministic ones for large fields. These algorithms can be useful for long-term security where larger parameters are relevant. Still, current probabilistic root-finding algorithms suffer from time variations making them susceptible to timing side-channel attacks. To prevent these attacks, we propose a countermeasure to a probabilistic root-finding algorithm so that its execution time does not depend on the degree of the input polynomial but on the cryptosystem parameters. We compare the performance of our proposed algorithm to other root-finding algorithms already used in code-based cryptography. In general, our method is faster than the straightforward algorithm in Classic McEliece. The results also show the range of degrees in larger finite fields where our proposed algorithm is faster than the Additive Fast Fourier Transform algorithm.
Global Perspective of Cyber Crimes and Related Laws

With the rapid development of computer technology and internet over the years, the problem of cyber crime has assumed gigantic proportions and emerged as a global issue. It has created an entirely new set of problems for law enforcement agencies all over the world. It has equally become cause of serious concern for the legal fraternity to find effective ways and means to combat cyber criminality because of its worldwide devastating effect.
Conversion of Summer STEM Program from In-Person to Virtual Learning Offers Unexpected Positives and Pitfalls

an annual summer STEM program called Gains in the Education of Mathematics and Science (GEMS) in which rising 7th-12th graders are mentored by undergraduate STEM majors (near-peer mentors -NPMs) who facilitate hands-on, inquiry-
Object–Part Registration–Fusion Net for Fine-Grained Image Classification

Classifying fine-grained categories (e.g., bird species, car, and aircraft types) is a crucial problem in image understanding and is difficult due to intra-class and inter-class variance. Most of the existing fine-grained approaches individually utilize various parts and local information of objects to improve the classification accuracy but neglect the mechanism of the feature fusion between the object (global) and object’s parts (local) to reinforce fine-grained features. In this paper, we present a novel framework, namely object–part registration–fusion Net (OR-Net), which considers the mechanism of registration and fusion between an object (global) and its parts’ (local) features for fine-grained classification. Our model learns the fine-grained features from the object of global and local regions and fuses these features with the registration mechanism to reinforce each region’s characteristics in the feature maps. Precisely, OR-Net consists of: (1) a multi-stream feature extraction net, which generates features with global and various local regions of objects; (2) a registration–fusion feature module calculates the dimension and location relationships between global (object) regions and local (parts) regions to generate the registration information and fuses the local features into the global features with registration information to generate the fine-grained feature. Experiments execute symmetric GPU devices with symmetric mini-batch to verify that OR-Net surpasses the state-of-the-art approaches on CUB-200-2011 (Birds), Stanford-Cars, and Stanford-Aircraft datasets.
Performance task as instrument for evaluation among grade vi pupils in public elementary schools

This study aimed at determining the status of implementation and usefulness of Performance Task Evaluation (PTE) as an evaluation tool among Grade-VI pupils of Ramon District for the School Year 2018-2019. In this study, the implementation and usefulness of PTE were analyzed using quantitative research. In addition, related factors in implementing the evaluation model were determined. Then, based on the findings, programs and interventions were developed to improve the academic achievement of the Grade VI pupils through PTE. The respondents believed that PTE is being implemented in terms of practices. Therefore, the implementation of the PTE should be considered as a tool of evaluation. The respondents believed that PTE is useful in terms of academics, attitude and instructions. Pupils believed that PTE is useful in providing learning skills necessary for the education since PTE develops the skills of every pupil. Besides, through PTE pupil enhances their skills and potential using the updated and reliable assessment and evaluation tool. The faculty and pupils should attend seminars that will gain knowledge for the implementation of PTE to easily develop a solution or action for its effective implementation. Besides, improved the monitoring system of the implementation of PTE through seminars, trainings, workshop and additional assessment examination. Moreover, the Department of Education Ramon District and recognized pupil government organizations may provide co-curricular activities that would allow maximum participation of the pupils to experience different challenges in education towards a better appreciation of their currently enrolled in elementary through PTE.
A portable and compact decoy-state QKD sender

Quantum Key Distribution (QKD) enables secure key exchange, based on fundamental laws of quantum mechanics [1] . There exist various protocols which are mathematically proven to be secure, however, for an actual implementation vulnerabilities, due to non-ideal components, must be considered. For example, the usage of faint lasers instead of single-photon sources undermines the security, as even for mean photon numbers per pulse µ ≪ 1 there is a non-vanishing probability of emitting multiple photons. The decoy protocol [2] , [3] offers a countermeasure. When randomly switching between two or more different pulse intensity levels, by exploiting their non-orthogonality, the so-called photon number splitting (PNS) attack can be revealed.
Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting

In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work.
A modification of the Cayley-Purser algorithm

Cayley- Purser Algorithm is a public key algorithm invited by Sarah Flannery in 1998. The algorithm of Cayley-Purser is much faster than some public key methods like RSA but the problem of it is that it can be easily broken especially if some of the private key information is known. The solution to this problem is to modify this algorithm to be more secure than before so that it gives its utilizers the confidence of using it in encrypting important and sensitive information. In this paper, a modification to this algorithm based on using general linear groups over Galois field $GF(p^n)$, which is represented by $GL_m(GF(p^n))$ where $n$ and $m$ are positive integers and $p$ is prime, instead of $GL_2(Z_n)$ which is General linear set of inverted matrices $2 times 2$ whose entries are integers modulo $n$. This $GL_m(GF(p^n))$ ensures that the secret key of this algorithm would be very hard to be obtained. Therefore, this new modification can make the Cayley-Purser Algorithm more immune to any future attacks.
Estimating Attention Level from Blinks and Head Movement

In this paper, we develop a real-time method for estimating the level of attention while performing a task. This method uses only a low frame rate video from a standard camera so that it can be available even on a small computer. Eye blinks and head movements are calculated from a video by using landmarks. Existing blink detection methods use standard frame rate videos, making them difficult to process on a computer with low processing performance. One obvious solution is to use videos with a reduced frame rate. We investigate the error caused by reducing the frame rate, and then to overcome the error, we further develop a new method that uses the head movements calculated from the reduced frame rate videos. Then we demonstrate the error is within acceptable ranges by using the method, and show it is effective to estimate the attention level. Since this method uses only landmark information obtained from facial images, it reduces the mental burden on the user, and also partially protects personal information. In this paper, we explain the details of the method and report the experimental results.
Anomaly Detection Collaborating Adaptive CEEMDAN Feature Exploitation with Intelligent Optimizing Classification for IIoT Sparse Data

IIoT (Industrial Internet of Things) has gained considerable attention and has been increasingly applied due to its ubiquitous sensing and communication. However, the sparse characteristic of sensing data in distributed IIoT networks may bring out tremendous challenges to implement the security protection measures. Based on the design of centralized data gathering and forwarding, this paper proposes a novel anomaly detection approach for IIoT sparse data, which can successfully collaborate the adaptive CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) feature exploitation with one intelligent optimizing classification. Furthermore, in the adaptive CEEMDAN feature exploitation, the CEEMDAN energy entropy based on adaptive IMF (Intrinsic Mode Function) selection is designed to extract the sensing features from IIoT sparse data; in the intelligent optimizing classification, one effective OCSVM (One-Class Support Vector Machine) classifier optimized by the IABC (Improved Artificial Bee Colony) swarm intelligence algorithm is introduced to detect various abnormal sensing features. The experimental results show that, not only does the CEEMDAN energy entropy based on adaptive IMF selection accurately describe the change of industrial production by analyzing the probability distribution and energy distribution of sparse sensing data, but also the proposed IABC-OCSVM classifier has higher detection efficiency compared with the OCSVM classifiers optimized by other swarm intelligence algorithms.
Building Teacher Community During a Summer of Crisis: STEAM Professional Development in 2020

The GoSTEAM program promotes authentic integration of the arts into PreK-12 computer science, engineering, and invention instruction.STEM and arts teachers come together to form STEAM Innovation Teams in collaboration with university-based coaches and creative Innovators-in-Residence. Starting with a STEAM professional development summer institute and continuing throughout the year, the teams come together to design and implement novel STEAM lessons and initiatives in their schools that integrate learning goals from both the STEM and the art disciplines.This type of transdisciplinary collaboration between colleagues from vastly different fields is new to most teachers and presents unique challenges.A primary goal of the GoSTEAM professional development is therefore to create safe, interdisciplinary spaces where meaningful, cross-disciplinary collaborations can occur.In 2019, this was accomplished through an intensive, 120hour face-to-face summer professional institute that incorporated many community building activities and collaborative planning sessions.In 2020, due to the COVID-19 pandemic, the summer institute changed to an online format and faced the challenge of providing teachers with a personally meaningful STEAM experience during a summer of crisis.Results show that the 2020 institute successfully supported the teachers, energized them, and provided them with tools to augment their virtual instruction.This paper describes the program adaptations due to COVID-19.
Facies Analysis, Depositional Environments and Geochemistry of the Late Ladinian-Early Carnian Ashin Formation, (Central Iran)

The present study aims to investigate the petrographic, geochemical features, and depositional facies of the Late Triassic Ashin Formation (Nakhlak Group, central Iran). For this purpose, 100 thin sections, and 13 samples of fine-grained sedimentary rocks were analyzed for their petrographic and geochemical characteristics, taken from a 330-m thick section of this formation. The petrographic types identified in this formation include sandstone, siltstone and limestone. This study suggests limestone facies are deposited in upper parts of the distal submarine fan towards the shore, sandstone facies are deposited in the middle part of the distal submarine fan, and the shale facies are deposited in lower parts of the distal submarine fan towards the abyssal plain by turbidity currents. The plotting of petrographic data on ternary diagrams for compositional classification illustrate their composition as litharenites, sub-litharenites, and a few litharenite-feldspathic and shales. Discrete diagrams refer to a tectonic setting of a continental arc complex and the active continental margin. The results of the modal analysis and geochemical data indicate the orogenic re-cycling for these deposits. CIA and CIW indexes indicate moderate weathering of the source area under semi-arid to semi-humid climates.
3, 3 Visual Cryptography for Online Certificate Authentication

Today organizations face a challenge while recruiting candidates, who provide forged mark sheets in order to get a job. To prevent wrong hiring a detailed and thorough approach is needed to verify the authentication of both the candidate and the marks obtained by him/her. There are so many modern cryptographic protocols available which can be used for authenticating individual’s academic achievement certificates. Visual Cryptography is a simple and secure way to allow the secret sharing of images without any cryptographic computations or the use of encryption or decryption keys. The novelty of the visual secret sharing scheme is in its decryption process where human visual system (HVS) is employed for decryption of secret shares. In this paper we have discussed (3, 3) visual cryptography scheme which can be used to generate shares and distributes them among three parties, i.e. the Job Seeker, Certificate Issuance Authority and the Organization conducting Job interview. Secret message can be decrypted only if all the three shares are available. Every certificate carries a unique number which is encrypted using visual cryptography and without handshaking of all the parties it is impossible to decrypt, thus ensuring full proof authentication.
Water Surface Object Detection Based on Neural Style Learning Algorithm

In order to detect the objects on the water surface, a neural style learning algorithm is proposed in this paper. The algorithm uses the Gram matrix of a pre-trained convolutional neural network to represent the style of the texture in the image, which is originally used for image style transfer. The objects on the water surface can be easily distinguished by the difference in their styles of the image texture. The algorithm is tested on the dataset of the Airbus Ship Detection Challenge on Kaggle. Compared to the other water surface object detection algorithms, the proposed algorithm has a good precision of 0.925 with recall equals to 0.86.