Reviewerly/exHarmony huggingface dataset:
Investigation alteration poly-metal Pasghaleh deposit (sheet 1:100000 Tehran) using remote sensing evidence

The study area (Pasghaleh) is located North of Tehran and is part of the Central Alborz Mountain Range. Pasghaleh deposit between coordinates 51o 25´15 up to 51o 25´ 54 Eastern longitude and 35o 49´42 up to 35o 50´15 Northern latitude in North of Tehran, in the geological sheet with a scale of 1:100000 Tehran and is located East of the Pasghaleh village. Intrusive bodies in this area with age after the Eocene in penetrating Eocene volcanic and pyroclastic units and have altered these units. For mapping minerals and explore the alteration patterns together with Polymetal mineralization in the metallogenic zone Pasghaleh, for sine satellite ASTER and ETM spectral processing and interpretation. According to remote sensing data, it leads to the appearance of Kaolinite and Montmorillonite minerals (Argillic alteration index), Chlorite, Epidote and Calcite (Propylitic alteration index), Muscovite (Sericite alteration index) and Gossan in the range studied. Major mineralization in the region in acid Tuffs altered and scattered. The lithology of this region including pyroclastic rocks of Karaj Formation with Eocene age. Sulfuric minerals observed Pyrite, Chalcopyrite, Sphalerite and Galena. In the whole region Pyrite expands but Lead mineralization, Zinc, Copper, Gold and Silver which process is parallel to the general layering of classes Tuff Silica altered- Feldspathic- Pyrite slightly observed. Pyrite form syngenetic among the Tuffs.Tuffs in formation stage and in the stage of diagenesis, the alteration process Sericitic, Kaolinite and or Chlorite have tolerated. In terms of construction, altered zone mineralize Pasghaleh as a lens and layers plate form between rock units Dacite, Rhyodacite are formed. Based on the study of remote sensing data, presence of alterations Sericite, Argillic and Propylitic (the presence of Chlorite and Epidote) proven in the region, consequently by putting together these alterations to one another, it can be attributed to the alteration pattern of Kuroko Massive sulfide deposits.
Identification of Fraudulent Scientific Journals

There is a growing need to produce scientific publications in universities, with the resulting increase in manuscripts submitted for review to scientific journals. Open access, a model designed to increase the dissemination of knowledge, has become a business model for certain fraudulent or predatory journals, which see the payment of publication rates by the authors as a money-making venture. These journals take advantage of some researchersâ lack of knowledge about the types of journals and how the normal publishing process of a serious journal works, characterized by a peer review of the scientific work, a process that usually takes several months. Predatory journals are a real threat, for both the quality of scientific articles published and for the authors, who pay high prices to have their work published in journals of low or no quality. The aim of this article is to describe the characteristics of the existing scientific journals a nd highlight aspects that authors should consider to identify and avoid fraudulent journals.
Task Allocation and Coordinated Motion Planning for Autonomous Multi-Robot Optical Inspection Systems

Autonomous multi-robot optical inspection systems are increasingly applied for obtaining inline measurements in process monitoring and quality control. Numerous methods for path planning and robotic coordination have been developed for static and dynamic environments and applied to different fields. However, these approaches may not work for the autonomous multi-robot optical inspection system due to fast computation requirements of inline optimization, unique characteristics on robotic end-effector orientations, and complex large-scale free-form product surfaces. This paper proposes a novel task allocation methodology for coordinated motion planning of multi-robot inspection. Specifically, (1) a local robust inspection task allocation is proposed to achieve efficient and well-balanced measurement assignment among robots; (2) collision-free path planning and coordinated motion planning are developed via dynamic searching in robotic coordinate space and perturbation of probe poses or local paths in the conflicting robots. A case study shows that the proposed approach can mitigate the risk of collisions between robots and environments, resolve conflicts among robots, and reduce the inspection cycle time significantly and consistently.
How to Factor a Number

The security of the RSA cryptosystem is based on the difficulty of factoring integers N that are the products of two large primes p and q. If p and q are chosen well, then factoring N is indeed hard, but there are also factoring methods that work very quickly on certain kinds of integers. In order to ensure security of an RSA system, one must be careful to choose an N that does not succumb to one of the faster methods. We will discuss the Pollard rho and Pollard $$p-1$$ methods first. These are not only used in general for factoring, but have been generalized to be applicable in other attacks against cryptographic systems. We then move on to CFRAC, a precursor to the state-of-the-art factoring method that is the primary topic of Chap. 12 .
Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification

Classifying the sub-categories of an object from the same super-category (e.g., bird) in a fine-grained visual classification (FGVC) task highly relies on mining multiple discriminative features. Existing approaches mainly tackle this problem by introducing attention mechanisms to locate the discriminative parts or feature encoding approaches to extract the highly parameterized features in a weakly-supervised fashion. In this work, we propose a lightweight yet effective regularization method named Channel DropBlock (CDB), in combination with two alternative correlation metrics, to address this problem. The key idea is to randomly mask out a group of correlated channels during training to destruct features from co-adaptations and thus enhance feature representations. Extensive experiments on three benchmark FGVC datasets show that CDB effectively improves the performance.
Development of students' ability for collective learning activities

espanolEn el contexto del fortalecimiento del rol consultor del docente y el incremento en la proporcion de trabajo autonomo, se hace necesario desarrollar la capacidad de los estudiantes para trabajar colectivamente como una de las competencias clave formadas en los estudiantes de las instituciones de educacion superior. El proposito del articulo es analizar la experiencia de desarrollar la capacidad de un estudiante para la actividad de aprendizaje colectivo en condiciones modernas. Las actividades de aprendizaje colectivo implican la adhesion al principio de cooperacion que tiene como objetivo la consecucion conjunta de resultados. El articulo revela las caracteristicas de la formacion del equipo educativo, los metodos para influir en la efectividad del trabajo colectivo y la formacion de la estructura de las relaciones interpersonales entre los estudiantes. EnglishIn the context of the strengthening of the consulting role of the teacher and the increase in the proportion of independent work, it becomes necessary to develop the ability of students to work collectively as one of the key competencies formed among students of higher educational institutions The purpose of the article is to analyze the experience of developing a student's ability to collective learning activity in modern conditions. Collective learning activities involve adherence to the principle of cooperation, which is aimed at the joint achievement of results. The article reveals the features of the formation of the educational team, the ways of influencing the effectiveness of collective work and the formation of the structure of interpersonal relations between students
Pembuatan Bahan Ajar Digital Untuk SMK Kartini Batam

The design of digital teaching materials is carried out to solve the problems faced by SMK Kartini Batam regarding the teaching materials needed during distance learning since the COVID-19 pandemic. This change in the learning system causes many students to experience difficulties in the process of understanding the material. So that to simplify the learning process for both teachers and students, the authors of the preparation of digital teaching materials which contain accounting computerized material. The methods used in the implementation of community service activities (PkM) are interviews and observations. The author designed teaching materials with outputs in the form of powerpoints and videos to facilitate the online learning process at SMK Kartini Batam. The author recommends providing teaching materials in the form of modules, powerpoints, and instructional videos for other subjects in PkM further efforts to facilitate students' understanding and also the completeness of the material.
Compression Implies Generalization

Explaining the surprising generalization performance of deep neural networks is an active and important line of research in theoretical machine learning. Influential work by Arora et al. (ICML'18) showed that, noise stability properties of deep nets occurring in practice can be used to provably compress model representations. They then argued that the small representations of compressed networks imply good generalization performance albeit only of the compressed nets. Extending their compression framework to yield generalization bounds for the original uncompressed networks remains elusive. Our main contribution is the establishment of a compression-based framework for proving generalization bounds. The framework is simple and powerful enough to extend the generalization bounds by Arora et al. to also hold for the original network. To demonstrate the flexibility of the framework, we also show that it allows us to give simple proofs of the strongest known generalization bounds for other popular machine learning models, namely Support Vector Machines and Boosting.
The Mediating Effect of Student Engagement on the Relationship Between Student Aspiration and Organizational Citizenship Behavior

Educational institutions have strived for excellence (Nadaf and Siddiqui, 2016) and the labor has not always been productive and satisfying (Brusoni et al., 2014). Hitherto, much has been researched and said about aspirations, student engagement and Organizational Citizenship Behavior (OCB). But a plausible model that explains the right relationship of these vital factors has not been attempted so far. This study puts forward a model that primarily proves that effective student engagement mediates the relationship between students’ aspirations and OCB. It also discusses in detail the dimensions of aspirations, student engagement and OCB that could matter in the provision of high quality education.
An Epidemiological Model for contact tracing with the Dutch CoronaMelder App

We present an epidemiological model for the effectiveness of CoronaMelder, the Dutch digital contact tracing app developed on top of the Google/Apple Exposure Notification framework. We compare the effectiveness of CoronaMelder with manual contract tracing on a number of metrics. CoronaMelder turns out to have a small but noticeable positive influence in slowing down the COVID-19 pandemic, an effect that will become more pronounced in an opened-up society where adoption of CoronaMelder is increased.
Delving Deep into the Generalization of Vision Transformers under Distribution Shifts

Vision Transformers (ViTs) have achieved impressive performance on various vision tasks, yet their generalization under distribution shifts (DS) is rarely understood. In this work, we comprehensively study the out-of-distribution (OOD) generalization of ViTs. For systematic investigation, we first present a taxonomy of DS. We then perform extensive evaluations of ViT variants under different DS and compare their generalization with Convolutional Neural Network (CNN) models. Important observations are obtained: 1) ViTs learn weaker biases on backgrounds and textures, while they are equipped with stronger inductive biases towards shapes and structures, which is more consistent with human cognitive traits. Therefore, ViTs generalize better than CNNs under DS. With the same or less amount of parameters, ViTs are ahead of corresponding CNNs by more than 5% in top-1 accuracy under most types of DS. 2) As the model scale increases, ViTs strengthen these biases and thus gradually narrow the in-distribution and OOD performance gap. To further improve the generalization of ViTs, we design the Generalization-Enhanced ViTs (GE-ViTs) from the perspectives of adversarial learning, information theory, and self-supervised learning. By comprehensively investigating these GE-ViTs and comparing with their corresponding CNN models, we observe: 1) For the enhanced model, larger ViTs still benefit more for the OOD generalization. 2) GE-ViTs are more sensitive to the hyper-parameters than their corresponding CNN models. We design a smoother learning strategy to achieve a stable training process and obtain performance improvements on OOD data by 4% from vanilla ViTs. We hope our comprehensive study could shed light on the design of more generalizable learning architectures.
Layered gradient accumulation and modular pipeline parallelism: fast and efficient training of large language models

The advent of the transformer has sparked a quick growth in the size of language models, far outpacing hardware improvements. (Dense) transformers are expected to reach the trillion-parameter scale in the near future, for which training requires thousands or even tens of thousands of GPUs. We investigate the challenges of training at this scale and beyond on commercially available hardware. In particular, we analyse the shortest possible training time for different configurations of distributed training, leveraging empirical scaling laws for language models to estimate the optimal (critical) batch size. Contrary to popular belief, we find no evidence for a memory wall, and instead argue that the real limitation -- other than the cost -- lies in the training duration. In addition to this analysis, we introduce two new methods, \textit{layered gradient accumulation} and \textit{modular pipeline parallelism}, which together cut the shortest training time by half. The methods also reduce data movement, lowering the network requirement to a point where a fast InfiniBand connection is not necessary. This increased network efficiency also improve on the methods introduced with the ZeRO optimizer, reducing the memory usage to a tiny fraction of the available GPU memory.
Wifi Printer Driver For Chromebooks

Printing from a Chromebook hasn’t always been easy. In fact, not that long ago, it wasn’t even possible! Then Google Cloud Print was introduced ...
Universal resource-efficient topological measurement-based quantum computation via color-code-based cluster states

Topological measurement-based quantum computation (MBQC) enables one to carry out universal fault-tolerant quantum computation via single-qubit Pauli measurements with a family of large entangled states called cluster states as resources. Raussendorf's three-dimensional cluster states (RTCSs) based on the surface codes are mainly concerned for topological MBQC, but costly state distillation is required to fault-tolerantly implement the logical Hadamard, phase, and $\pi/8$ ($T$) gates which are essential for building up arbitrary logical gates. We suggest a family of cluster states called color-code-based cluster states (CCCSs) based on the two-dimensional color codes instead of the surface codes. We describe the fault-tolerant MBQC scheme via CCCSs by defining logical qubits, constructing elementary logical gates, and suggesting error correction schemes. We show that not only the logical controlled-not gate but also the logical Hadamard and phase gates can be implemented fault-tolerantly without state distillation, although the fault-tolerant logical $T$ gate still requires it. We further prove that the minimal number of physical qubits per logical qubit in a CCCS is at most approximately 1.8 times smaller than the case of a RTCS. We lastly show that the error threshold of MBQC via CCCSs for logical-$Z$ errors is approximately 3.0%, which is comparable to the value for RTCSs, assuming a simple error model where physical qubits have $Z$ or $X$-measurement errors independently with the same probability.
Prediksi Klasifikasi Royalti Batubara Menggunakan Algoritma Fuzzy K-Nearest Neighbor

As one of energy commodity, coal has been explored and exploited in order to fulfil the energy needs of world community. The calculation of coal royalties in Indonesia is based on the value of the quality of exploited or sold coal and the benchmark coal price determined by the government. CV Fazar Utama Kota Samarinda East Kalimantan Province classifies coal royalties based on calculations that have been made on coal sample inspection such as low, medium, and high. The Fuzzy K-Nearest Neighbor (FK-NN) method is one of the lazy learner algorithm which is used to determine the classification prediction. The K-Fold Cross Validation technique is used to obtain the optimal K value on the Fuzzy K-Nearest Neighbor (FK-NN) algorithm in order to get more accurate prediction. In this research, the first step is to find the optimal K value by experimenting 1-Fold Cross Validation, 3-Fold Cross Validation and 5-Fold Cross Validation using the 80:20 proportion of training and testing data. The next step is find the percentage accuracy of coal Royalty classification prediction in CV Fazar Utama in 2017 using Fuzzy K-Nearest Neighbor (FK-NN) Algorithm with optimal K value. Based on this research, the optimal value of K used in FK-NN Algorithm is F3-NN obtained in 1-Fold Cross Validation experiment. Furthermore, the result shows that the percentage accuracy of coal Royalty classification prediction at CV Fazar Utama in 2017 using F3-NN with 1-Fold Cross Validation is 100%.
Sistem Informasi Penjualan Berbasis Website Aplikasi Citi Chicken House

Citi Cicken House is a trading business that provides / sells products related to superior native chickens. Products sold include free-range chicken eggs, native chicken chicks and various other native chicken preparations. During the establishment of this Citi Chicken House, sales still used a manual buying and selling system so that even though the sales continued little by little, the results were not maximized due to a lack of information and marketing so that only local people knew about this Citi Chicken House. This research was conducted to create an automatic system of buying and selling that is carried out online which aims to further reach buyers outside the region and of course be able to expand the wings of Citi Chicken House in the trading business sector. The method used in this research is the System Development Life Cyle (SDLC) method with the waterfall model. The purpose and results of making this website-based sales system are to facilitate the process of buying and selling transactions between sellers and buyers and can support government programs related to preventing the spread of Covid-19 that have not been completed.
Does the Adam Optimizer Exacerbate Catastrophic Forgetting?

Catastrophic forgetting remains a severe hindrance to the broad application of artificial neural networks (ANNs), however, it continues to be a poorly understood phenomenon. Despite the extensive amount of work on catastrophic forgetting, we argue that it is still unclear how exactly the phenomenon should be quantified, and, moreover, to what degree all of the choices we make when designing learning systems affect the amount of catastrophic forgetting. We use various testbeds from the reinforcement learning and supervised learning literature to (1) provide evidence that the choice of which modern gradient-based optimization algorithm is used to train an ANN has a significant impact on the amount of catastrophic forgetting and show that-surprisingly-in many instances classical algorithms such as vanilla SGD experience less catastrophic forgetting than the more modern algorithms such as Adam. We empirically compare four different existing metrics for quantifying catastrophic forgetting and (2) show that the degree to which the learning systems experience catastrophic forgetting is sufficiently sensitive to the metric used that a change from one principled metric to another is enough to change the conclusions of a study dramatically. Our results suggest that a much more rigorous experimental methodology is required when looking at catastrophic forgetting. Based on our results, we recommend inter-task forgetting in supervised learning must be measured with both retention and relearning metrics concurrently, and intra-task forgetting in reinforcement learning must-at the very least-be measured with pairwise interference.
Ensemble Sentiment Model: Bagging with Linear Discriminant Analysis (BLDA)

Rapid development in information technology and social media platforms increased the demand for sentiment analysis and modelling. Online reviews contain information about customer choice towards a particular product or a service. Existing approaches of sentiment analysis worked on a single learner or predefined ensemble learner to classify the sentiments. This work proposed a bagging ensemble approach with Linear Discriminant Analysis (BLDA) model for analysing the restaurant reviews. The Linear Discriminant Analysis (LDA) was chosen as a base classifier for each random subset in the bagging meta-estimator model. Our solution mainly focuses on enhancement, standardisation, preprocessing, and performance of sentiment analysis with topic-modelling and ensemble learning. Topic-Modelling is used to find the context of a customer regarding a service. The BLDA model's effectiveness has tested on restaurant reviews with various measurement units as Recall, Precision, F1-Score and ROC-AUC curve. The comparative study with Gaussian Naive Bayes (GaussianNB) and K-Nearest Neighbor (KNN) present the proposed model's best performance.
Programming the full stack of an open-access quantum computer

We present a new quantum programming language called "Quala" that enables true full-stack programming of quantum hardware. Quala allows seamless integration of abstraction layers such as the digital circuit layer and the analog control pulse waveform layer. Additionally, the language supports user-issued low-level hardware instructions like FPGA actions. Mid-circuit measurements and branching decision logic support real-time, adaptive programs. This flexibility allows users to write code for everything from quantum error correction to analog quantum simulation. The combination of a user-facing calibration database and a powerful symbolic algebra framework provides users with an unprecedented level of expressiveness and transparency. We display the salient characteristics of the language structure and describe how the accompanying compiler can translate programs written in any abstraction layer into precisely timed hardware commands. We intend for this language to bridge the gap between circuit-level programming and physical operations on real hardware while maintaining full transparency in each level of the stack. This eliminates the need for "behind-the-scenes" compilation and provides users with insights into the day-to-day calibration routines.