Reviewerly/exHarmony huggingface dataset:
Measuring Self-Supervised Representation Quality for Downstream Classification using Discriminative Features

Self-supervised learning (SSL) has shown impressive results in downstream classification tasks. However, there is limited work in understanding their failure modes and interpreting their learned representations. In this paper, we study the representation space of state-of-the-art self-supervised models including SimCLR, SwaV, MoCo, BYOL, DINO, SimSiam, VICReg and Barlow Twins. Without the use of class label information, we discover discriminative features that correspond to unique physical attributes in images, present mostly in correctly-classified representations. Using these features, we can compress the representation space by up to 40% without significantly affecting linear classification performance. We then propose Self-Supervised Representation Quality Score (or Q-Score), an unsupervised score that can reliably predict if a given sample is likely to be mis-classified during linear evaluation, achieving AUPRC of 91.45 on ImageNet-100 and 78.78 on ImageNet-1K. Q-Score can also be used as a regularization term on pre-trained encoders to remedy low-quality representations. Fine-tuning with Q-Score regularization can boost the linear probing accuracy of SSL models by up to 5.8% on ImageNet-100 and 3.7% on ImageNet-1K compared to their baselines. Finally, using gradient heatmaps and Salient ImageNet masks, we define a metric to quantify the interpretability of each representation. We show that discriminative features are strongly correlated to core attributes and, enhancing these features through Q-score regularization makes SSL representations more interpretable.
Adherence to Therapeutic Regimen for People with Diabetes Through the Implementation of Continuous Quality Improvement Projects – Scoping Review

Introduction: Diabetes mellitus is one of the major global health problems, estimated worldwide as one of the four groups of chronic diseases responsible for the high mortality rates in the age group between 30 and 69 years of age. The coordination of care in the management of symptom control and risk reduction, in order to minimize long-term complications, requires an imperative structuring and restructuring of care in order to involve not only the person with diabetes, but also health professionals, and it is pertinent to think about the development of projects for continuous quality improvement programs. Goals: To analyze the scientific evidence about adherence to the therapeutic regimen of people with diabetes as part of a continuous quality improvement project. Methods: Scoping review, with 3 steps: 1) an initial search in CINAHL and MEDLINE; 2) a broader search, using the same keywords and search terms, in the remaining databases of the EBSCOHost platform; 3) search in the bibliographic references of selected articles. The selected time limit covered the period between 2016 and 2021. Results: 21 articles were included; most were retrospective evaluations of continuous quality improvement programs in the area of diabetes mellitus in an outpatient and hospital setting. Conclusions: The implementation of continuous quality improvement programs in the area of diabetes mellitus significantly improves the adherence to the therapeutic regimen for people with diabetes mellitus, promoting self-care, knowledge and experience of their disease.
Dual Stream Computer-Generated Image Detection Network Based On Channel Joint And Softpool

With the development of computer graphics technology, the images synthesized by computer software become more and more closer to the photographs. While computer graphics technology brings us a grand visual feast in the field of games and movies, it may also be utilized by someone with bad intentions to guide public opinions and cause political crisis or social unrest. Therefore, how to distinguish the computer-generated graphics (CG) from the photographs (PG) has become an important topic in the field of digital image forensics. This paper proposes a dual stream convolutional neural network based on channel joint and softpool. The proposed network architecture includes a residual module for extracting image noise information and a joint channel information extraction module for capturing the shallow semantic information of image. In addition, we also design a residual structure to enhance feature extraction and reduce the loss of information in residual flow. The joint channel information extraction module can obtain the shallow semantic information of the input image which can be used as the information supplement block of the residual module. The whole network uses SoftPool to reduce the information loss of down-sampling for image. Finally, we fuse the two flows to get the classification results. Experiments on SPL2018 and DsTok show that the proposed method outperforms existing methods, especially on the DsTok dataset. For example, the performance of our model surpasses the state-of-the-art by a large margin of 3%.
INTERVENTION MATERIAL FOR LEAST LEARNED COMPETENCIES IN SELECTED TOPICS IN PHYSICS 10

The study was conceptualized to address the need for the development and validation of an intervention material for least learned competencies in selected topics in Physics 10.Moreover, it is also focused on understanding how the intervention material will help improve the learners' mastery of concepts and the perceived effectiveness of the intervention material to the learners.The research design used in this study is the Quasi-Experimental method of research.The present study concerns with the development and implementation of an intervention material for least learned competencies in selected topics in Physics 10.Thus, quasi-experiment was used.The respondents of the study were the Grade 10 learners in Magdalena Integrated National High School.The area of investigation was limited to four (4) areas of investigation: 1) what is the level of performance of the Grade 10 students in Physics?, 2) what is the level of acceptability and validity of the intervention material as rated by teachers and students?, 3) if there is a significant difference between the student's performance in terms of pretest and posttest?, and 4) if there is a significant relationship between the level of acceptability as rated by the teachers and students and the mean scores of the students.The findings of the study were the difference in the learners' academic performance it was noted that there was a substantial increase in the students' performance.Shown by a mean difference of 11.13, it was found that there is a significant difference in the students' performance, based on their pre-test and post-test scores (t=-27.518,p=0.00).In addition, the difference in the level of acceptability as rated by the two groups of respondents, both the students and the teachers assessed the acceptability of the intervention material in terms of adaptability as highly adaptable.No mean difference was noted in their ratings about its adaptability.Therefore, there is no significant difference in ratings given by the students and the teachers with regard to the adaptability of the supplemental material (t=0.309,p=0.764).It was concluded that the adaptability, content and usefulness of the intervention materials were acceptable to the teachers and students as intervention to the least learned competencies in selected topics in Physics 10 and the students' performance pretest and posttest were evident that the result of the posttest increased thus, the intervention materials were effective to the least learned competencies, they increased their academic performance after they use the intervention learning materials.
Penerapan Certainty Factor pada Diagnosis Penyakit Tanaman Tomat

Tomato (Solanum lycopersicum) is a horticultural crop that has important economic value as a vegetable or fruit. Many problems arise in tomato production, causing a decrease in production yields, namely pests and diseases. Handling tomato disease is often constrained due to delays in diagnosing the disease. To overcome this problem, a system was designed that was used to diagnose tomato plant diseases by applying the Certainty Factor (CF) method. The CF method is a method used to express belief in an event in the form of facts or hypotheses based on evidence or expertise. The results obtained is an expert system application consisting of 7 tomato diseases with 16 symptoms. In this system, the user can choose the symptoms on plant, each selected symptom has a weight that will be used to obtain the results of disease determination. Testing on 25 data shows the suitability between the application and the expert is 88%. This represents the system is able to diagnose tomato plant diseases.
Evaluating Information Technology at Libraries and Archives’ Department Using ITIL V3 Domain Service Transition

The use of information technology in the government sector is the main tool in providing services to the environment and especially to the community. The Dki Jakarta Provincial Library and Archives Office (DISPUSIP) is a government agency under the local government of DKI Jakarta Province. DISPUSIP has services in accordance with the organization and work procedures stipulated in the regulation of the Governor of DKI Jakarta Province. Information Technology Infrastructure Library (ITIL) version 3 domain service transition is used to determine the transition of service governance management. The use of questionnaires uses the Process Maturity Framework (PMF) as a framework to assess the maturity of each service management process. DISPUSIP has a maturity rate ranging from 41% to 60% in 7 (seven) subdomains, this value represents the maturity level at level 3, which is defined as a level.
Popular Machine Learning Methods Tend to Over-Select Features

Abstract Background : Machine learning methods have been a standard approach to select features that are associated with an outcome and build a prediction model when the number of candidate features is large. Lasso has been one of the most popular approaches to this end. LASSO approach selects features with large regression estimates, rather than based on statistical significance, associating the outcome, while imposing L1-norm penalty to overcome the high dimensionality of the candidate features. As a result, LASSO may select insignificant features while possibly missing significant ones. Furthermore, from our experience, LASSO has been found to select too many features. By selecting features that are not associated with the outcome, we may have to spend more cost to collect and manage them in the future use of a fitted prediction model. Using the combination of L1- and L2-norm penalties, elastic net tends to select more features than LASSO. Results : The overly selected features that are not associated with the outcome play the role of white noise, so that the fitted prediction model loses the prediction accuracy. In this paper, we propose to use the standard regression methods (without any penalizing approach) with stepwise variable selection procedure to overcome these issues. Unlike the machine learning methods, this method selects features based on statistical significance. Through extensive simulations, we show that this maximum likelihood estimation based method selects very small number of features while maintaining a high prediction power, while the machine learning methods make a large number of false selections to result in loss of prediction accuracy. Conclusions : Contrary to the machine learning methods, the regression methods combined with a stepwise variable selection method is a standard statistical method, so that any biostatistician can use it to analyze high dimensional data even without advanced bioinformatics knowledge.
Factorizing Knowledge in Neural Networks

In this paper, we explore a novel and ambitious knowledge-transfer task, termed Knowledge Factorization~(KF). The core idea of KF lies in the modularization and assemblability of knowledge: given a pretrained network model as input, KF aims to decompose it into several factor networks, each of which handles only a dedicated task and maintains task-specific knowledge factorized from the source network. Such factor networks are task-wise disentangled and can be directly assembled, without any fine-tuning, to produce the more competent combined-task networks. In other words, the factor networks serve as Lego-brick-like building blocks, allowing us to construct customized networks in a plug-and-play manner. Specifically, each factor network comprises two modules, a common-knowledge module that is task-agnostic and shared by all factor networks, alongside with a task-specific module dedicated to the factor network itself. We introduce an information-theoretic objective, InfoMax-Bottleneck~(IMB), to carry out KF by optimizing the mutual information between the learned representations and input. Experiments across various benchmarks demonstrate that, the derived factor networks yield gratifying performances on not only the dedicated tasks but also disentanglement, while enjoying much better interpretability and modularity. Moreover, the learned common-knowledge representations give rise to impressive results on transfer learning. Our code is available at https://github.com/Adamdad/KnowledgeFactor.
IMPLEMENTASI METODE RAPID APLICATION DEVELOPMENT (RAD) DALAM RANCANGAN SISTEM INFORMASI PELAYANAN ADMINISTRASI DI KELURAHAN TABAM KOTA TERNATE

Tabam Village is one of the villages in North Ternate City with a modern community background, where smartphones and laptops are no longer new items for the local community. However, the administrative management system in the sub-district is still far from expectations in the current era. The administrative management system starting from making Identity Cards, Family Cards, Birth Certificates to Death Certificates is still conventional. This kind of administrative management system is believed to have many weaknesses such as paper wastage, interference with computer media and requires a long time to process one letter. How is the implementation of the Rapid Application Development (RAD) method in the design of the administrative information system in Tabam village. The research objective is to determine the implementation of the Rapid Application Development (RAD) method in the design of administrative information systems in Tabam village. The research method carried out in the study there are several methods, namely, conducting observations to see directly the administrative services in the output office, as well as conducting library studies to review relevant previous research journals and conducting studies on books related to research. The research system or application design uses the Rapid Application Development (RAD) method in several phases or stages, namely the Requirement Planning stage, the Design stage, and the Implementation stage.
A Comparative Study on Intensifiers in Opinion Articles on Rudaw Portal

Semantically, intensifiers are concerned with the degree that scales the element to which the intensifiers applied to a point either higher than the norm or lower. Thus, the writers’ involvement with or detachment from a statement can be reflected through the use of intensifiers. This paper explores the use of intensifiers by Kurdish authors in comparison to non-Kurdish authors who write opinion articles on Rudaw portal. The aim of the study is to demonstrate how these two groups of authors differ in their preference for the use of intensifiers. To this end, two corpora were compiled: Kurdish authors (COKA) and non-Kurdish authors (CONKA). The online interface of UCREL Semantic Analysis System (USAS) in Wmatrix was employed to identify the semantic categories of intensifiers. The results showed that boosters had the highest frequency (140 in COKA and 280 in CONKA). However, the use of all categories was higher by the non-Kurdish authors and with wider range of lexicons than the Kurdish authors. This study contributes to literature of the use of automated semantic tagger (USAS) for identifying intensifiers; it is a mechanism that can be fruitful for investigating other lexicogrammatical items for a better understanding of the link between language and socio-cultural factors.
Face mask detection using machine learning algorithm

The COVID-19 pandemic is causing a worldwide They wellbeing emergency so the viable security techniques is They caring a facial covering in open regions as per the World Health Organization (WHO). The COVID-19 pandemic constrained state-run administrations across the world to force lockdowns to forestall infection transmissions. Reports show that They caring facial coverings while at work obviously lessens the gamble of transmission. The way of proficient and financial methodology involves AI to establish a protected climate in an assembling arrangement. A mixture model utilizing profound and traditional AI for facial covering identification will be introduced. A facial covering identification dataset comprises of with veil and without cover pictures, they will utilize OpenCV to do continuous face recognition from a live stream through our They became. They will utilize the dataset to construct a COVID-19 facial covering locator with PC vision utilizing Python, OpenCV, and Tensor Flow and Keras. They want to distinguish whether the individual on video transfer is They caring a facial covering or not with the assistance of PC vision and (RCNN) profound learning.&#x0D;
Analytical Solution of two Model Equations for the Variation of Capital Market Prices

This paper investigated different methods for estimation of parameters of Weibull distribution, using Mean Square Error (MSE) as a criterion for selecting the best model. The result showed that Method of Moments outstripped other methods. In the same vain, the estimated results were logically extended to form a matrix that would help in predicting different commodity price processes, and the result obtained by exploring the properties of the principal component analysis solution and results showed the level of proportion accounted by first Principal Component Analysis (PCA). However, the eigenvectors describe the direction of the stock market prices in terms of changes in short-run and long-run respectively.
Scholarly Data Share: A Model for Sharing Big Data in Academic Research

The Scholarly Data Share (SDS) is a lightweight web interface that facilitates access to large, curated research datasets stored in a tape archive. SDS addresses the common needs of research teams working with and managing large and complex datasets, and the associated storage. The service adds several key features to the standard tape storage offerings that are of particular value to the research community: (1) the ability to capture and manage metadata, (2) metadata-driven browsing and retrieval over a web interface, (3) reliable and scalable asynchronous data transfers, and (4) an interface that hides the complexity of the underlying storage and access infrastructure. SDS is designed to be easy to implement and sustain over time by building on existing tool chains and proven open-source software and by minimizing bespoke code and domain-specific customization. In this paper, we describe the development of the SDS and the implementation of an instance to provide access to a large collection of geospatial datasets.
Towards a Scalable and Secure Blockchain Based on Post-Quantum Cryptography.

Blockchain systems rely on classical cryptography of public key encryption and hash functions for its security.These security mechanisms of the distributed technology is made possible by complex mathematical computations, integer factorization and discrete logarithm problems.The emergence of quantum technology is expected to reduce the security of current cryptographic systems.As a result, companies adopting blockchain-based solutions, becomes prone to quantum attack in long-term strategic planning.Grover's and Shor's quantum algorithms, which attack the cryptographic principles on which the blockchain is built, currently pose the biggest threat to the blockchain.To counter these threats, the post-quantum cryptography was proposed.The main aim of this study is to critically analyzed the security of classical blockchain and present some post-quantum implementation algorithms in developing a quantumbased blockchain system that would be able to withstand any attacks using quantum technology.
Network Binarization via Contrastive Learning

Neural network binarization accelerates deep models by quantizing their weights and activations into 1-bit. However, there is still a huge performance gap between Binary Neural Networks (BNNs) and their full-precision (FP) counterparts. As the quantization error caused by weights binarization has been reduced in earlier works, the activations binarization becomes the major obstacle for further improvement of the accuracy. BNN characterises a unique and interesting structure, where the binary and latent FP activations exist in the same forward pass (i.e., $\text{Binarize}(\mathbf{a}_F) = \mathbf{a}_B$). To mitigate the information degradation caused by the binarization operation from FP to binary activations, we establish a novel contrastive learning framework while training BNNs through the lens of Mutual Information (MI) maximization. MI is introduced as the metric to measure the information shared between binary and FP activations, which assists binarization with contrastive learning. Specifically, the representation ability of the BNNs is greatly strengthened via pulling the positive pairs with binary and FP activations from the same input samples, as well as pushing negative pairs from different samples (the number of negative pairs can be exponentially large). This benefits the downstream tasks, not only classification but also segmentation and depth estimation, etc. The experimental results show that our method can be implemented as a pile-up module on existing state-of-the-art binarization methods and can remarkably improve the performance over them on CIFAR-10/100 and ImageNet, in addition to the great generalization ability on NYUD-v2.
Local Inversion of maps: Black box Cryptanalysis

This paper is a short summery of results announced in a previous paper on a new universal method for Cryptanalysis which uses a Black Box linear algebra approach to computation of local inversion of nonlinear maps in finite fields. It is shown that one local inverse $x$ of the map equation $y=F(x)$ can be computed by using the minimal polynomial of the sequence $y(k)$ defined by iterates (or recursion) $y(k+1)=F(y(k))$ with $y(0)=y$ when the sequence is periodic. This is the only solution in the periodic orbit of the map $F$. Further, when the degree of the minimal polynomial is of polynomial order in number of bits of the input of $F$ (called low complexity case), the solution can be computed in polynomial time. The method of computation only uses the forward computations $F(y)$ for given $y$ which is why this is called a Black Box approach. Application of this approach is then shown for cryptanalysis of several maps arising in cryptographic primitives. It is shown how in the low complexity cases maps defined by block and stream ciphers can be inverted to find the symmetric key under known plaintext attack. Then it is shown how RSA map can be inverted to find the plaintext as well as an equivalent private key to break the RSA algorithm without factoring the modulus. Finally it is shown that the discrete log computation in finite field and elliptic curves can be formulated as a local inversion problem and the low complexity cases can be solved in polynomial time.