Reviewerly/exHarmony huggingface dataset:
Image Retrieval using Neural Network based Hash Encoding: A Survey

Analysis of image contents became one of the important subjects in modern life. In order to recognize the images with efficient way, several techniques appeared and periodically enhanced by the developers. Image retrieval becomes one of the main problems that face computer society inside the revolution of technology. To increase the effectiveness of computing similarities between images, hashing approaches became the focusing of the programmers. Also, deep learning in the past few years is considered the backbone of image analysis using a convolutional neural network (CNN). This paper is a survey of the most recent work performed in image retrieval field. Image retrieval techniques which use neural network-based hash encoding may be categorized into 3 classes: Supervised, Unsupervised, and Semi-Supervised techniques according to learning methods of each technique. Most important related works appeared in the literature have been reviewed and constructive comparisons have been done to show the strengths and limitations of various techniques.
Lattice-Based Weak Curve Fault Attack on ECDSA

ECDSA algorithm is usually used in ICT system to ensure the authenticity of communication. But the weaknesses in various implementations of ECDSA may make its security deviate from theoretical guarantee. This paper proposes a new lattice-based weak curve fault attack on ECDSA. Since the ECDLP is not required to be computationally practical on the whole group of $$\langle G \rangle $$ (G is the specified base point of ECDSA), our approach extends the existing attacks along this line. In detail, the proposed attack assumes a segment of consecutive bits of the curve parameter a in the Weierstrass equation of ECDSA can be disturbed randomly by fault injection and thus is changed into $$a'$$ . An analysis about the density of smooth numbers demonstrates the faulty parameter $$a'$$ can be used for our attack with high probability. Then we show $$a'$$ can be recovered by a dedicated quadratic residue distinguisher. Some reduced information about the nonce used in ECDSA signature generation can be obtained by solving the instances of ECDLP on the new curve about $$a'$$ . With the help of these information, we can construct a new model of lattice to recover the private key with the lattice basis reduction techniques. Further, we show the same strategy can defeat the nonce masking countermeasure if the random mask is not too long, and makes the commonly employed countermeasures ineffective. To our knowledge, the problem remains untraceable to the existing weak curve fault attacks. Thus the proposed approach can find more applications than the existing ones. This is demonstrated by the experimental analysis.
[A convolutional neural network based model for assisting pathological diagnoses on thyroid liquid-based cytology].

Objective: To develop a convolutional neural network based model for assisting pathological diagnoses on thyroid liquid-based cytology specimens. Methods: Seven-hundred thyroid TCT slides were collected, scanned for whole slide imaging (WSI), and divided into training and test sets after labeling the correct diagnosis (benign versus malignant). The extracted regions of interest after noise filtering were cropped into pieces of 512 × 512 patch on 10 × and 40 × magnifications, respectively. A classification model was constructed using deeply learning algorithms, and applied to the training set, then automatically tuned in the test set. After data enhancement and parameters optimization, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the model were calculated. Results The training set with 560 WSI contained 4 926 cell clusters (11 164 patches), while the test set with 140 WSI contained 977 cell clusters (1 402 patches). YOLO network was selected to establish a detection model, and ResNet50 was used as a classification model. With 40 epochs training, results from 10× magnifications showed an accuracy of 90.01%, sensitivity of 89.31%, specificity of 92.51%, positive predictive value of 97.70% and negative predictive value of 70.82%. The area under curve was 0.97. The average diagnostic time was less than 1 second. Although the model for data of 40× magnifications was very sensitive (98.72%), but its specificity was poor, suggesting that the model was more reliable at 10× magnification. Conclusions: The performance of a deep-learning based model is equivalent to pathologists' diagnostic performance, but its efficiency is far beyond. The model can greatly improve consistency and efficiency, and reduce the missed diagnosis rate. In the future, larger studies should have more morphology diversity, improve model's accuracy and eventually develop a model for direct clinical use.
Equivalent conditions for digital covering maps

It is known that every digital covering map p:(E,k) ? (B,?) has the unique path lifting property. In this paper, we show that its inverse is true when the continuous surjective map p has no conciliator point. Also, we prove that a digital (k,?)-continuous surjection p:(E,k)? (B,?) is a digital covering map if and only if it is a local isomorphism, when all digital spaces are connected. Moreover, we find out a loop criterion for a digital covering map to be a radius n covering map.
PELATIHAN PERAWATAN DAN PERBAIKAN PERALATAN LISTRIK RUMAH TANGGA BAGI MASYARAKAT RUKUN WARGA 09 KELURAHAN BUNULREJO – KOTA MALANG

Maintenance and repair of household electrical appliances for the Rukun community Residents 09 Bunulrejo Village / Kec. Blimbing - Malang City, gives additional the skills and motivation for the Rukun Community of 09 Bunulrejo Village for practice maintenance and repair of household electrical appliances for the Rukun community Residents 09 Bunulrejo Village. Besides that it can also improve skills addition to the community of Rukun Warga 09 Kelurahan Bunulrejo in the field of care and repair of household electrical appliances for the 09 Rukun Warga community in the Village Bunulrejo that applies in this era.
On Training Knowledge Graph Embedding Models

Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner. The key to success of these models is their ability to learn low-rank vector representations for knowledge graph entities and relations. Despite the rapid development of KGE models, state-of-the-art approaches have mostly focused on new ways to represent embeddings interaction functions (i.e., scoring functions). In this paper, we argue that the choice of other training components such as the loss function, hyperparameters and negative sampling strategies can also have substantial impact on the model efficiency. This area has been rather neglected by previous works so far and our contribution is towards closing this gap by a thorough analysis of possible choices of training loss functions, hyperparameters and negative sampling techniques. We finally investigate the effects of specific choices on the scalability and accuracy of knowledge graph embedding models.
Development of Economic E-Module on Money and Banking Material For Student

This research was implemented because of the burnout and boredom Economics students to receive lessons because teachers only use the lecture method without any other study variation and only rely on one textbook as a source of learning. Besides, many students are reluctant to read the textbook because the book is less interesting and too many posts. SMA Negeri 1 Panggul Trenggalek have been had some enough electronic facilities, but it not used optimally by the teachers in the learning process. The E-Module research and development model used is ADDIE model with five steps, that is analysis, design, development, implementation, and evaluation. In the development of this Economics E-Module involves the validation of material expert and media experts from Economics professors and lecturers who are competent in the learning media. Besides, the subject of a trial in this research is all students in Tenth Grade A that about 32 students. The result of the research and validation of material expert and media expert shows that the Economics E-Module on Money and Banking Material is fit for use. Based on the result of the pre-test and post-test that was given to students before and after using the Economic E-Module indicates that E-Module is effectively used to improve the students learning outcomes. Keyword s Developing, Economics E-Module, Money and Banking
Comprehensive Analysis of Flow Incorporated Neural Network based Lightweight Video Compression Architecture

The increasing video content over the internet motivated the exploration of novel approaches in the video compression domain.Though neural network based architectures have already emerge as de-facto in the field of image compression and analytics, their application in video compression also result in promising outputs.Adaptive and efficient compression techniques are required for video transmission over varying bandwidth.Several deep learning based techniques and enhancements were proposed and experimented but they didn't exhibit full optimal behavior and are not end to end trained and optimized.In the zest of a pure and end to end trainable compression technique, a deep learning based video compression architecture has been proposed comprises of frame autoencoder, flow autoencoder and motion extension network for the reconstruction of predicted frames.The video compression network has been designed incrementally and trained with random emission steps strategy.The proposed work results in significant improvement in visual perception quality measured in SSIM and PSNR when compared to some state-of-art techniques but in trade-off with frame reconstruction time sheet.
DNN Design for Object Detection in Airport Runway Operations

In this project, we attempted to solve the problem of object detection in airport runway environments by leveraging DNNs. The DNN model developed will be able to classify objects and also accurately localize objects of different classes. The major aspect here is the task of object detection is going to be considered as a regression problem to object bounding box masks. It would be a multi-scale inference procedure that can generate high-resolution object detection at a minimal cost. Here we were able to test our custom dataset on three object detection models. The models considered were (1) Faster-RCNN with VGG-16, (2) YOLOv3 with darknet53, and (3) SSD Inception V2(coco) from the TensorFlow object detection API. All the above algorithms were trained using the Tensorflow framework. The paper gives a brief comparison in the performance of the above-mentioned algorithms when trained and tested while keeping the main goal to be object detection in airport runways. We trained the YOLOv3 with darknet53 on our custom dataset and were able to obtain the classifier accuracy for the bounding boxes to be 74.00% which was the best of the three algorithms and has outperformed many previous works. The YOLOv3 could be considered a fast training algorithm when the model is trained on a powerful GPU and could play a major role in the field in terms of real-time object detection scenarios.
Tuning to Optimize SVM Approach for Breast Cancer Diagnosis with Blood Analysis Data

As one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers. It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time. This paper intents the scope of the biomarker that can be used to predict the breast cancer from the routine Blood Analysis Data. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for breast cancer diagnosis, this paper proposed a novel cancer classification algorithm based on optimizes the relevant parameters of SVM classifier through an intelligent algorithm using Grid Search Algorithm These parameters are: Gaussian radial basis function (GRBF) kernel parameter g and C penalty parameter of SVM classifier. Our experiment showed that SVM parameter optimization using grid search always finds near optimal parameter combination within the given ranges to evaluate the performance of the proposed model, Breast Cancer Coimbra Dataset used taken from UCI library. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used. The performance of the proposed method is compared with that of other methods on this data sets. The obtained results show the improvement over state of-the-art algorithms with improved performance parameters e.g. disease prediction accuracy, sensitivity and better F1 score etc..Funding Statement: The authors stated that this research received no external funding.Declaration of Interests: The authors declare no conflict of interest.Ethics Approval Statement: Not required.
The Ability of 10th-Grade Students to Use Past Tenses in Writing Narrative Text in SMA Kartika Jaya 1-4 Pematangsiantar

ABSTRACTThis paper aims to investigate how well the 10th-grade students in SMA Kartika Jaya 1-4 Pematangsiantar able to use past tenses in writing a narrative text. This paper uses descriptive quantitative method in which the data is the students narrative text consisting 20 verbs in past tense and past perfect tense. Based on the investigation, it is found that the 10th-grade students in SMA Kartika Jaya 1-4 Pematangsiantar are able to use past tenses in writing narrative text and their ability is in the medium level. There is one student in the highest level or 2.86%, the students who are able at the medium level ability are thirty students or 85.72%, and the students who are able at the lowest level ability are four students or 11.42%. The mean is 67.57 and standard deviation is 14.46 where the validity is 0.79 and the reliability is 0.88. By conducting this study, it is expected the teachers get insight about measuring students ability using past tenses in writing a narrative text.Keywords: writing, narrative text, past tense, past perfect tense
A GRAPHICAL PASSWORD AUTHENTICATION SYSTEM

Authentication is the approach of giving individuals get entry to to gadget item based totally on user's distinctiveness.If the code suit, the method could be done and consumer gets the approval to get admission to the system.textual content-based totally password scheme follows the guidelines together with as a minimum 8 characters lengthy, must combine higher case and lower-case and digits.person have problem to do not forget their complicated password over the years because of the obstacle of human brain, consumer tend to neglect about their password.user have a tendency to apply the same password for all kind of account.So, if one account is hacked, the possibility for different account to be hack is high.apart from that, choos-ing the simple textual-based password may additionally increase its vulnerability for assaults or intrusions.consequently, graphical password authentication via the usage of passpoints scheme has been added on this mission.Graphical password authentication by way of using passpoints scheme is a version to become aware of the most probably regions for consumer to click with a purpose to create graphical password.The operation of the purposed scheme is easy and clean to examine for person seeing that they acquainted with textual graphical password scheme.In conclusion, this graphical password scheme will make it easier for user to do their authentication process because it is easy to take into account and difficult to guess by way of others.
A Method of Obtaining Catchment Basins with Contour Lines for Foam Image Segmentation

Foam image segmentation, represented by watershed algorithm, is wildly used in the extraction of bubble morphology features. H-minima transformation was proved to be effective in locating the catchment basins in the traditional watershed segmentation method. To further improve the accuracy of watershed segmentation, method of top-bottom-cap filters and method of morphological reconstruction were implied to marking the catchment basins. In this paper, instead of H-minima transformation, a method of contour lines is specially proposed to obtain the catchment basins for foam image segmentation by using top-bottom-cap filters and less morphological reconstruction. Experimental results in foam segmentation show that the proposed method is equally accurate but more efficient than the method of H-minima plus morphological reconstruction, and equally efficient but more accurate than the method of H-minima plus top-bottom-cap filters.
Challenging the Teaching Excellence Framework

A book review of A. French, and K. Carruthers Thomas (eds) (2020) Challenging the Teaching Excellence Framework: diversity deficits in higher education evaluations. Bingley: Emerald Publishing.
Analisis Runtun Waktu Untuk Memprediksi Jumlah Mahasiswa Baru Dengan Model Random Forest

Admission of new students is an important process in educational institutions such as tertiary institutions which is useful for screening accepted prospective students according to the criteria determined by the college. The purpose of this study is to predict the number of new students using the Random Forest model with the new student admissions dataset of XYZ University. The Random Forest Model is a machine learning algorithm that is excellent at solving classification and regression problems. Based on the research results, it was found that the resulting model has an accuracy rate of 99.8% with MSE and MAE values of 0.02% in predicting new students. The best parameter of the model with a maxnodes value of 100 and ntree 900 and a decreasing trend in the number of students for the next few years.
Keyword extraction for film reviews based on social network analysis and natural language technology

At present, there are many movie reviews appear on main stream websites, and these evaluations are quite different to the same movie. As a customer, how to choose your favorite movie and television program? To solve this problem, this study attempts to use the semantic analysis of word vectors (Word2vec) semantic analysis in machine learning as a research tool to mine a large number of movie reviews. The research shows that most movie reviews have a certain theme cohesion and their semantic network has quite connected. Through the use of social network analysis and the use of Word2vec word vector technology in natural language processing, it is possible to present a streamlined movie review based on movie review network semantics and keyword extraction, thus helping to select the favorite movie review.
DIAGNOSTIC TOOLS FOR EVALUATING THE UNIVERSITY STUDENTS LEADERSHIP QUALITIES

Changes in the economy associated with the emergence of various types and forms of economic activity, economic freedom, stimulate a creative and innovative approach to professional activity, a professional with leadership qualities is in demand. University complexes as centers of education, science and culture of the region accumulate and transmit innovative pedagogical experience from Central universities to branches, creating a modern educational space for the multidimensional development of personal qualities of students. Analysis of scientific research in the field of sociology, psychology, and leadership pedagogy allowed us to specify a set of qualities that characterize the future leader in various areas of management-industrial, sports, tourism, and educational. The formation and development of students ‘ leadership qualities includes a multi-aspect diagnostics of leadership. Diagnostic tools are characterized by a significant variety, due to the ambiguity of the idea of a set of personal qualities needed by the leader. Integrative assessment of leadership qualities requires the use of a representation of the profile of leadership qualities. To select diagnostic methods, a critical analysis of tools for diagnosing students ‘ leadership qualities was carried out. A matrix of conformity of the diagnosed quality and the methodology used to assess its development was created. The presented tools were used to determine the main directions of work on the development of these qualities in students. A computer program for individual assessment of a student’s leadership profile has been developed, which functions through a personal account on the University’s website. Diagnostics of students from different universities allowed us to track the dynamics of changes in the development of leadership qualities of graduates, to assess the effectiveness of the proposed pedagogical measures, and differences in the mentality of young people in large and small cities.
Training of the future vocational education teachers in the conditions of student-centred learning and teaching

The article reveals the theoretical aspects of the future vocational education teachers' training in the conditions of student-centred learning and teaching, which is based on the principles of the flexibility of individual educational trajectories and different methods usage, students' autonomy support, mutual respect of teachers and students and the respect to the diversity of students and their needs. The purpose of the article is to highlight and analyze the peculiarities of preparation of future vocational education teachers in the conditions of student-centred learning and teaching. In accordance with the purpose and the set objectives, such theoretical methods of research were applied as an analysis, a synthesis and the generalization of scientific-pedagogical literature and normative-legal documents. The author believes that the future vocational education teachers’ training in a student-centred setting is a complex task according to the ESG recommendations. The study identifies the positive features of a student-centred approach, as well as the possible risks; characterizes and analyzes the key principles of the European Standards and Guidelines (ESG) on Quality Assurance in the European Higher Education Area (EHEA) in terms of the specificities of the training system for future vocational education teachers. Moreover, it defines that providing sufficient feedback from students, forming assessment criteria based on the creative display of acquired knowledge and skills, rational lesson planning, adherence to the “lifelong learning” principle, and promoting self-examination among teachers and students are the key components of implementing the student-centred learning and teaching principles. The author made a conclusion that student-centred learning and teaching contributes to the realization of intellectual, cultural and emotional potential, creates the conditions for students’ self-expression and self-identification, promotes the democratic and humanistic values of institutions of higher education (IHE) as important public institutions and, having a positive impact on the quality of training, creates a springboard for realizing the educational process participants’ potential on the basis of proactivity and common opinion.