Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of AES, ECC and Logistic Chaotic Map Algorithms in Image Encryption

I&#x0D; n these days of technology, the usage of images has become increasingly high especially now that almost everyone has access to internet. Also, image helps us to learn, grabs our attention, explains complicated concepts as well as inspires us. Sharing these images is essential and therefore image encryption algorithms are proposed to secure the transmission of these image from many types of attacks such as Man-in-the-middle attack (MITM). In this paper, we proposed a hybrid security system that consist of Elliptic Curve Cryptography (ECC) and Advanced Encryption System (AES). Where ECC is used to generate private/public keys and AES is for encryption and decryption of the image using the ECC generated keys. The system works as follows; it begins by inserting the image to be encrypted which is “lena.png” of size 256*256 and passing it to AES Algorithm along with the generated public key. Then, in AES algorithm, the public key is hashed then used to encrypt the image with AES encryption algorithm. On the other hand, the decryption algorithm works as follows; inserting the encrypted image then calculating the decryption key to use it to decrypt the image using AES decryption algorithm. Finally, our experimental results shows that the National Institute of Standards and Technology (NIST) test shows that the ECC generated keys have better randomness than using only AES generated keys. Also, the encrypted image histogram show that the image pixels values are well distributed across all three channels R, G and B. This shows that the hybrid system is a step further to get a more secure image encryption system against attacks with the generated ECC keys. To get further, Logistic chaotic map has been used to encrypt images for comparison purposes with AES and ECC generated images in terms of randomness, security and histogram
Active Control-Group Designs in Cognitive Rehabilitation Trials: A Qualitative Review of Computer-Based Rehabilitation in Patients with Acquired Brain Injury

Background and Objectives: To outline experimental variables of relevance to clinical trials employing active control condition (ACC) designs within cognitive rehabilitation after Acquired Brain Injury (ABI), and to qualitatively review the current methodology of trials within this field.Method: Firstly, experimental variables which evidently impact rehabilitation outcome were outlined, based on the authors' joint knowledge of the research field.Secondly, clinical trials within cognitive rehabilitation after ABI that employed ACC designs were identified and qualitatively evaluated based on the outlined variables.Due to the breadth of this research field, a focus on computer-based cognitive rehabilitation (CBCR) was chosen.Trials were identified through a systematic search in prespecified scientific databases using prespecified inclusion criteria.Results: 10 experimental variables were identified.Nine trials were included in this review.None of the included trials accounted for all experimental variables in their study design. Conclusion:There are methodological shortcomings in the design of trials using ACC's within the field of CBCR after ABI.Although this methodological review is limited to a subfield within cognitive rehabilitation research, the conclusions drawn here are believed to generalize to the overall field.The experimental variables outlined here may aid researchers in the design phase of future trials using ACC's.
KAJIAN EKONOMI PEMBANGKIT LISTRIK TENAGA MIKRO HIDRO (PLTMH) BENUIS

energy as a source of electrical energy, both solar, wind, hydro and other renewable energy sources, a potential source of uranium in Indonesia.What needs attention is how to design energy policies to use each type of energy optimally, so as to avoid over-exploitation of fossil energy on the one hand, and on the other hand prevent neglect of renewable energy sources. In an effort to overcome this, the central and regional governments have made new policies that encourage the use of renewable energy sources as an alternative to supplying electricity to rural communities.The aims and objectives of the Feasibility Study Preparation Activity for the Benuis Micro-hydro Power Plant (PLTMH) Benuis Village, Selimbau District are; Availability of technical documents that describe the detailed design of the PLTMH and the Budget Plan (RAB) for the Benuis PLTMH Benuis Village. As a proposal for the construction of network facilities and infrastructure electricity..
A Lightweight Super-resolution Network with Skip-connections

In some hospitals in remote areas, due to the lack of MRI scanners with high magnetic field intensity, only low-resolution MRI images can be obtained, hindering doctors from making correct diagnoses. In our study, higher-resolution images were obtained through low-resolution MRI images. Moreover, as our algorithm is a lightweight algorithm with a small number of parameters, it can be carried out in remote areas under the condition of the lack of computing resources. Moreover, our algorithm is of great clinical significance in providing references for doctors' diagnoses and treatment in remote areas.We compared different super-resolution algorithms to obtain high-resolution MRI images, including SRGAN, SPSR, and LESRCNN. A global skip connection was applied to the original network of LESRCNN to use global semantic information to get better performance.Experiments reported that our network improved SSMI by 0.8% and also achieved an obvious increase in PSNR, PI, and LPIPS compared to LESRCNN in our dataset. Similar to LESRCNN, our network has a very short running time, a small number of parameters, low time complexity, and low space complexity while ensuring high performance compared to SRGAN and SPSR. Five MRI doctors were invited for a subjective evaluation of our algorithm. All agreed on significant improvements and that our algorithm could be used clinically in remote areas and has great value.The experimental results demonstrated the performance of our algorithm in super-resolution MRI image reconstruction. It allows us to obtain high-resolution images in the absence of high-field intensity MRI scanners, which have great clinical significance. The short running time, a small number of parameters, low time complexity, and low space complexity ensure that our network can be used in grassroots hospitals in remote areas that lack computing resources. We can reconstruct high-resolution MRI images in a short time, thus saving time for patients. Our algorithm can be biased towards practical applications; however, doctors have affirmed the clinical value of our algorithm.
An entropy based unsupervised visual vocabulary optimization for visual loop closure detection

In the task of Simultaneous Localization and Mapping (SLAM), a mobile robot should be able to recognize the location that it has previously visited. This is termed as the loop closure detection. Current loop closure detection is implemented by matching of image features extracted from present image and previous images, where good feature representation is highly required. Bag-of-visual Words (BoW) is known to be an effect feature representation for image and video matching task. However, to build the visual feature vocabulary in the unsupervised manner is still challenging. In this paper, we are aiming at evaluating the representability of the visual words and generating the discriminative vocabulary representation. We first present an entropy ranking based scheme to improve the spectral clustering algorithm by leveraging eigenvector selection and feature embedding simultaneously. We then propose to evaluate the representative ability of the visual vocabulary quantitatively based on the complexity of the decomposition of similarity matrix in the unsupervised manner. We perform experiments on the loop closure detection datasets. which contains strong perceptual aliasing in both indoor and outdoor scenarios. Results demonstrate that the proposed BoW based feature representation has strong representation ability in measuring the similarity of loop closure events and differentiating new visited places.
FAIR CHARACTERISTIC- PRIMARILY BASED TOTALLY ENCRYPTION SCHEME FOR DATA SHARING IN BLOCKCHAIN

In this project, a proposed secure DNA checking out the procedure for the already gift venture using the set of rules AES (Advance Encryption Standard) and blockchain procedure.Some situations would be needed for DNA checking.For example, some crime analyzing purposes also would be needed and disease analysis purposes also be needed.So, all situations need a secure way of processing.The mixture of AES and blockchain techniques put into force this challenge.Encrypted DNA blockchain is an emerging technology that combines the fields of genetics, cryptography, and distributed ledger systems.It leverages the unique properties of DNA, such as its immense data storage capacity and inherent encryption capabilities, to create a secure and tamperresistant platform for storing and sharing genomic information.SHA-256 is a cryptographic hash function designed to ensure the integrity and security of digital data.It takes an input message of any size and produces a unique 256-bit hash value, which serves as a digital fingerprint of the input data.The SHA-256 algorithm employs a series of logical and arithmetic operations, including bitwise operations, modular addition, and logical functions, to process the input data and generate the hash value.The resulting hash is highly sensitive to even the slightest changes in the input data, making it ideal for verifying data integrity and detecting tampering or unauthorized modifications.
Logisim circuit similarity algorithm based on maximum connected subgraph

Logisim is a cross-platform educational tool for designing and simulating digital logic circuits, and it is widely used in experiments for courses such as computer organization in universities. As Logisim is widely used, tasks such as plagiarism detection require the calculation of Logisim circuit similarity, and the Logisim circuit similarity algorithm becomes very important. Logisim can represent circuits as text in XML format or render circuits as RGB images, which means that Logisim circuit similarity can be calculated using both text similarity algorithms and image similarity algorithms. However, due to the characteristics of Logisim circuits, text similarity algorithms and image similarity algorithms cannot achieve good results. In this paper, we propose a Logisim circuit similarity algorithm based on the maximum connected subgraph. We first represent the Logisim circuit as a collection of maximum connected subgraphs, then calculate the proportion of nodes with the same type, similar location and isomorphic local subgraphs in maximum connected subgraphs as the similarity of maximum connected subgraphs, and finally calculate the proportion of maximum connected subgraphs whose maximum similarity exceeds a threshold as the similarity of the collection of maximum connected subgraphs (i.e., Logisim circuits). We construct two Logisim similar circuit datasets based on real-world data. The experimental results on these datasets show that our method outperforms well-known text similarity algorithms and image similarity algorithms.
LIVE CLASS MONITORING SYSTEM (FACE EMOTION DETECTION)

This paper gives you a current method on facial emotion stages, techniques and datasets.With the rapid increase in online and offline and online classes simultaneously so, there is urgent need to develop technology that can be accurately detect emotions from student expression, which can help in monitoring and engagement of students during the live class on going on.This paper can also be used in healthcare, paper provides proper examination usability of facial emotion and expression detection in both online and offline classes that will enhance the teaching and learning experience.And this paper also deal with issues and challenges comes in detecting emotion and expression with educational contexts and proposes valuable solution to how to deal with those challenges.
Multi-level feature networks for out-of-distribution image detection

The out-of-distribution detection task is a hot problem in image processing, aiming to detect samples that have never been trained by a deep learning model, thus ensuring the safety of the model for use in real-world environments. Currently widely used out-of-distribution detection methods suffer from single-layer representation dependence and poor adaptability. In order to overcome these problems, this paper proposes a Multilevel Feature Detection Network (MFDNet), which detects different representations of different out-of-distribution examples with the help of intermediate layer features. At the same time, a grouping module is introduced in the final layer of MFDNet to help the model reduce the decision boundaries between categories and enhance the adaptability of the model. A comparison with several common out-of-distribution detection methods on the out-of-distribution evaluation datasets validates that MFDNet can effectively handle the out-of-distribution detection task.
Study of connection function technology standard system for intelligent and connected vehicle

Automotive connection function is an important part of the Intelligent and Connected Vehicle (ICV) industry and an important support for the realization of automated driving. This paper summarizes the automotive connection function technology standards and regulations and compares and analyzes the typical connection function application scenarios related to driving safety developed by leading automotive industrial counties and international organizations. Combined with correlation between technology and typical working conditions, in terms of technology maturity, technology cost, product market plan, etc., use statistical analysis, get the result of priority ranking of automotive connection function application scenarios related to driving safety. Based on the research, proposes suggestions for standard system construction.
Data enhancement method based on attention activation map

Overfitting is a major problem faced by deep learning, and data enhancement is an effective means to mitigate it. Erasure-based data enhancement improves model generalization by generating new training samples, but these methods lead to over-erasure and under-erasure problems due to the random nature of erasure. To alleviate these problems, this paper proposes a new attention activation map-based image erasure method: Aag-dem, selectively erase the relevant regions of the image to generate high-quality training data and finally a large number of experiments are done to prove the effectiveness and generality of the method, and the performance of the baseline is improved to some extent.
Design of an intelligent massager based on the Cortex-M0 kernel microcontroller

Aiming at the blindness of human body massagers to muscle tension curve monitoring, this paper innovatively designs an intelligent massager which can detect, record, and display human body muscle elasticity curves. The intelligent massager stimulates muscle tissue and observes the changes in muscle tension, which can provide effective physical therapy effect evaluation basis for physiotherapists, help physiotherapists develop physical therapy programs, and improve the late physical therapy programs.
Research and application of atomic capability platform

Atomic capabilities are common elements or links supporting various applications and businesses, such as voice capabilities, SMS capabilities, RPA, computing capabilities, storage capabilities, etc. The enterprises formed through the encapsulation of internal capabilities and the introduction of external capabilities can be provided independently and have the characteristics of being integrated (standardized), reusable, pricing, extensible, authorized, and having a wide range of common needs. The current situation of China Telecom's atomic capability building is as follows: 1) The capabilities are invisible, untraceable and cannot be conveniently invoked. 2) Lack of a unified platform for capability deposition and display: various capability centers, professional companies, integration and general service systems and provincial companies in the whole group have accumulated certain capabilities during the implementation of DICT projects but failed to display and manage them in a unified way, and other units of the group cannot obtain relevant capability information. 3) The process of capability transfer is not smooth: it is difficult to transfer, long cycle, and difficult to settle, which makes it difficult to transfer and settle. The capability development unit cannot obtain benefits, and the enthusiasm of capability output is relatively low. 4) Lack of iteration of capability: the original DICT project or scientific research capability is lack of iteration and update, the project delivery and settlement are the completion of the project, and the project and capability are lack of long-term iteration and update and application mechanism. 5) The introduction of external ecological innovation capacity is slow: there is a gap between some of the current self-owned capacity and the external ecological innovation capacity, leading to weak competitiveness and development of some projects. Limited by processes and mechanisms, external ecological innovation and scientific and technological power cannot be quickly introduced and applied to internal and external projects, Lack of mechanisms and platforms for capacity exchange and introduction. In order to solve the above problems, this paper provides an open platform for building atomic capabilities, which can be checked, inquired and experienced. Provide standardized interface, real-time experience, fast delivery, 7*24 full-time service. Direct transaction between capability suppliers and front-end market demanders; Mobilize enthusiasm and enhance competitiveness. Provide an internal innovation platform to mobilize the enthusiasm of employees. The market speaks for product retention and value.
Multi-label feature selection algorithm based on HSIC-Lasso

The Hilbert-Schmidt Independence Criterion (HSIC) was originally design-ed to measure the statistical dependence of distribution-based Hilbert spaces embedding in statistical inference. In recent years, due to the validity and efficiency of this standard, it has been witnessed that this criterion can tackle a large number of learning problems owing to its effectiveness and high efficiency<sup>[1]</sup>. Unlike traditional binary classifications or multi-class single label problems, one goal of multi-label problems may be related to multiple labels. The rich relationship between labels makes the analysis of multilabel problems more complex. To solve the problem of how to make use of the relationship between features and labels, a multi-label feature selection algorithm based on HSIC is presented. This method uses Lasso (the least absolute shrinkage and selection operator) to solve a non-convex HSIC problem, and converts it to solve a lasso optimization problem, which can effectively calculate the global optimal solution. Finally, experiments show that our algorithm can improve the performance of multi-label classification.
Research on relation extraction model of overlapping entity based on attention mechanism

Relation extraction refers to get the triple structure composed of semantic relation entity pairs from unstructured text, which is an important part of tasks such as knowledge graphs. At present, the joint extraction model is in common used to avoid the impact of overlapping entities, but there are the following problems. First, the dependencies between text words are not fully considered, and the recognition performance of entities with long spans is low. Insufficient utilization of information makes it difficult to fully extract implicit relationships. In order to address these issues, this text proposes an improved joint learning model, which builds text semantic representation through BERT pre-training, obtains relation type representation as an additional mapping through a multi-label classification method, and sequentially uses multi-layer BiLSTM combined with highway network to obtain semantic information, and combine The attention mechanism obtains the entity location score, and the pointer network is used to obtain the entity location. The experiments of this method on the common dataset of relation extraction task is effective.
A method for extracting correlative features of power grid big data based on improved deep learning

With the continuous maturity of big data, artificial intelligence, Internet of Things and other technologies, the rapid development of smart grid has been helped, but at the same time, the increasing line loss power has also attracted widespread attention. In the process of building a smart grid, each link of the grid operation generates a large number of multi-source heterogeneous data, including line loss data and line loss cause related data, which constitutes the big line loss data. First of all, considering the mining efficiency in big data, FP growth algorithm in association rule learning is selected to search the frequent item set of line loss features. Support, confidence and lift are used as evaluation indicators to analyze the association relationship between the causes of line loss; Secondly, a line loss prediction model based on deep learning is established. By eliminating the influence of line loss characteristics in turn, the correlation contribution of line loss causes to line loss is calculated to quantify the line loss caused by line loss causes. After verification, the depth confidence network and BP depth neural network as the prediction model of the depth learning method are superior to the shallow artificial neural network model in the prediction effect, and the prediction accuracy means the reliability of the contribution calculation. Finally, combined with the above two aspects of analysis, the causes of line loss in the substation area are comprehensively evaluated, and guidance suggestions are given to assist power enterprises in decision-making.
Comprehensive evaluation of the effectiveness of power grid engineering cost management based on the improved AHM-cloud material element algorithm

In order to improve the accuracy of power grid engineering investment, this paper first sorts out the power grid project cost management process. According to the main problems of each responsible subject in the project construction, the cost management effectiveness evaluation model was established. Then, the hierarchical attribute model (AHM) was used to power evaluation indicators. According to the strong subjectivity of the judgment matrix, a parabolic ordered weighted operator was used to preprocess the matrix values. The p-value-finding algorithm in this operator was also studied. Finally, this paper took some power grid projects as examples. The improved cloud-material element algorithm was used to evaluate the overall cost management effectiveness level. According to the evaluation results, the optimization strategy of cost management was put forward, which provided a reference for improving the cost-efficiency of power grid investment and the level of lean control.