Reviewerly/exHarmony huggingface dataset:
ENIAD: A Reconfigurable Near-data Processing Architecture for Web-Scale AI-enriched Big Data Service

To meet the surging demands required by AI-enriched Big Data services, cloud vendors are turning toward domain specific accelerators for improved efficiency, scalability and performance. ENIAD, the first end-to-end infrastructure for AI-enriched Big Data serving in real time, accelerates both deep neural network inferencing and billion-scale indexing at the data-center scale. Exploiting near- data computation, reconfigurable computing and rapid/agile hardware deployment flow, ENIAD serves state-of-the-art, online built indexing service with high efficiency at low batch sizes. A high-performance, index (data)-adaptable FPGA soft processor is at the heart of the system and able to serve 10x larger index size with 14x lower latency compared to state-of-the-art CPU and GPU architectures.
ANALYSIS OF ENCODING PLAINTEXT DATA: USING ENHANCED HAMMING CODE TECHNIQUES

Most often than none, data (information) obtained as results, which are to be used, saved or transmitted across channels from a source to a specific destination are in form of plaintext. The efficiency and reliability of any result obtained is of great importance to any data (information) analyst (user) and this can only be guaranteed by encoding such results. It is against this background therefore, that this study on Analysis of Encoding Plaintext Data: Using Enhanced Hamming Code Techniques was carried out. More so, the results obtained as shown in this paper shall be useful specifically in plaintext and generally in coding theory.
Foreword to David Goss' JNT memorial volume

We investigate interconnected aspects of hyperderivatives of polynomials over finite fields, q-th powers of polynomials, and specializations of Vandermonde matrices. We construct formulas for Carlitz multiplication coefficients using hyperderivatives and symmetric polynomials, and we prove identities for hyperderivative power sums in terms of specializations of the inverse of the Vandermonde matrix. As an application of these results we give a new proof of a theorem of Thakur on explicit formulas for Anderson's special polynomials for log-algebraicity on the Carlitz module. Furthermore, by combining results of Pellarin and Perkins with these techniques, we obtain a new proof of Anderson's theorem in the general case.
Análisis y reflexión sobre la fragmentación del sistema educativo en tiempo de pandemia

The suspension of face-to-face activities became an unprecedented experience in the school that recovers its central and irreplaceable place for education. With this phenomenon, inequalities have deepened and with it the core idea of the fragmentation of the educational system takes center stage, such as the monopolization of knowledge, resources and opportunities by the best positioned sectors, while in the most vulnerable sectors are put at risk their access to the right to education. This paper will reflect on the concept of fragmentation of the educational system, widely studied in Argentina, which is resignified, in the scenario derived from the pandemic, marking different ways of facing the suspension of face-to-face classes and the intermittent returns of face-to-face classes in secondary schools in the province of Catamarca. The empirical material comes from interviews with officials of the province, through which we survey, from the perspective of the informants, the pre-existing inequalities, the manifestations of the dimensions that constitute the notion of fragmentation of the educational system and some strategies carried out by the actors of the educational communities in general, which made it possible to sustain the transition between 2020 and 2021 given the unequal distribution of resources between the different sectors.
The Development of Indonesian Language Teaching Materials for Class VII Semester 1 Based on North Sumatran Local Wisdom and the Digitization of Learning

This study aims to develop teaching materials for Indonesian class VII SMP in semester 1 based on local wisdom of North Sumatra and the digitization of learning. The teaching materials that will be developed are descriptive text, narrative story text, and procedure text. This study uses the Research & Development method referring to the research design with the stages of analyzing the needs of teaching materials based on local wisdom, data collection, product design, validation and testing. Product assessment is based on material quality, language quality, and media quality, as well as testing as student responses. The results of the research show that: (1) the results of the validation of the material experts indicate that the learning materials are declared feasible and valid with an average percentage gain of 87.8% with the criteria of good. Furthermore, the results of the validation of linguists showed that the language used in the learning materials was declared feasible and valid with an average percentage gain of 87.5% with the criteria of good. Then the results of the media validation showed that the media was deemed feasible and valid with the acquisition of a percentage of 92.2% with the criteria of good. (2) the results of the trial on junior high school students obtained an average percentage of 90.17% with very good criteria. The results of the percentage of trials carried out show that the Indonesian language learning materials for class VII SMP Semester 1 based on local wisdom of North Sumatra and their digitalization are appropriate and valid to be used as learning materials, especially in learning description texts and narrative texts for class VII semester 1 students.
How Does ARBook Promotional Media For MSME Crafts Using Augmented Reality Marker Tracking Works

The present study aimed to develop a virtual book displaying craftwork using Augmented Reality (AR) technology as an interactive promotional media to advertise handicraft products originated from the Special Region of Yogyakarta. Kerajinan Jogja (Jogja Handicraft Book) application under this research was developed through Research and Development (R&D). The first stage of its Development was needs analysis and literature study, followed by designing the application using Use Case driven object modeling. The next was the development stage of the application and its functional testing by two media experts. In the fourth stage, two-steps software testing was carried out, namely (1) verification and validation test, and (2) quality and feasibility testing to examine its functionality, usability, portability,  efficiency,  maintainability,  and reliability  (ISO  9126-1).  The findings suggest that (1) the Development of the product, Buku Kerajinan Jogja,  might have gone through a  series of proper software engineering processes from design to testing consistent with the desired specifications. Secondly, (2) the overall performance of Buku Kerajinan seems to be Good with test results of 82.98%, 82.5%, 83.3%, 85.0%, 75.0%, 75.0% consecutively for usability, functionality, reliability, portability, maintainability, and efficiency (ISO 9126-1).
The Bombieri-Vinogradov theorem for nilsequences

We establish results of Bombieri-Vinogradov type for the von Mangoldt function $\Lambda(n)$ twisted by a nilsequence. In particular, we obtain Bombieri-Vinogradov type results for the von Mangoldt function twisted by any polynomial phase $e(P(n))$; the results obtained are as strong as the ones previously known in the case of linear exponential twists. We derive a number of applications of these results. Firstly, we show that the primes $p$ obeying a set condition, such as $\|\alpha p^k\|<\varepsilon$, exhibit bounded gaps. Secondly, we show that the Chen primes are well-distributed in nil-Bohr sets, generalizing a result of Matomaki. Thirdly, we generalize the Green-Tao result on linear equations in the primes to primes belonging to an arithmetic progression to large modulus $q\leq x^{\theta}$, for almost all $q$.
Evidential Softmax for Sparse Multimodal Distributions in Deep  Generative Models

Many applications of generative models rely on the marginalization of their high-dimensional output probability distributions. Normalization functions that yield sparse probability distributions can make exact marginalization more computationally tractable. However, sparse normalization functions usually require alternative loss functions for training since the log-likelihood is undefined for sparse probability distributions. Furthermore, many sparse normalization functions often collapse the multimodality of distributions. In this work, we present $\textit{ev-softmax}$, a sparse normalization function that preserves the multimodality of probability distributions. We derive its properties, including its gradient in closed-form, and introduce a continuous family of approximations to $\textit{ev-softmax}$ that have full support and can be trained with probabilistic loss functions such as negative log-likelihood and Kullback-Leibler divergence. We evaluate our method on a variety of generative models, including variational autoencoders and auto-regressive architectures. Our method outperforms existing dense and sparse normalization techniques in distributional accuracy. We demonstrate that $\textit{ev-softmax}$ successfully reduces the dimensionality of probability distributions while maintaining multimodality.
The Effect of Using E-Books in Teaching at High Schools in Hanoi, Vietnam

An electronic book (e-book) is another version of the traditional print book combined with technology that has benefited to educators worldwide. This chapter is a part of a research effort of using e-book in science teaching at high school with the aim of investigating e-book effectiveness. One investigation is administered to biological teachers at more than 20 high schools in Hanoi, Vietnam. Results indicated that the IT literacy rate among investigators is high; the e-book is mostly used in high school but not fully utilized and adopted. The main reason for using an e-book is academic related. The use of e-book makes teaching methods more interesting and makes students take interest in the lesson and stimulate their thinking. The use of an e-book has also created a connection between the student−teacher and student−student interaction. Besides, feedback of respondents indicated positive awareness on using an e-book and ready to change of traditional teaching method. This study has given new insights into the effect of an e-book to science teachers, thus building a solid foundation for future research in e-book projects in Vietnam. At the same time, this research proposed a process and illustrated an example of designing e-book in teaching biology, physics, and chemistry subjects.
Prospective Cryptocurrency effectiveness over the basis for monetary policy with the central bank

The purpose of this paper is to look at the rationale for the unconventional monetary policies not adopted by central banks before Covid-19; however, crypto effectiveness impacts in response to the Global Financial Crisis. Most digital currencies have a limited and controlled supply, is on it, Quantitative easing in the time of Coronavirus Is the revitalization of the national economy? Is there recasting compulsory monetarism? The Bank of China indicated that China's digital currency would be a digital form of the yuan with no room to speculate on its value. In contrast, the Bank of Lebanon, the currency to be issued, is a digital currency subject to its controls and will help create new business and commercial activity types. Some aspects of monetarism renounced at this time, at the entry of central banks into the digital age, can see as the first sign of the era of global chaos. An understanding of the industry and, thus, the channels of monetary transmission remains a chunk ongoing.
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through its continuous time version, namely stochastic gradient flow. We explicitly characterise the solution chosen by the stochastic flow and prove that it always enjoys better generalisation properties than that of gradient flow. Quite surprisingly, we show that the convergence speed of the training loss controls the magnitude of the biasing effect: the slower the convergence, the better the bias. To fully complete our analysis, we provide convergence guarantees for the dynamics. We also give experimental results which support our theoretical claims. Our findings highlight the fact that structured noise can induce better generalisation and they help explain the greater performances observed in practice of stochastic gradient descent over gradient descent.
PENGGUNAAN MODEL PEMBELAJARAN COOPERATIVE GROUP INVESTIGATION DALAM PENINGKATAN KEMAMPUAN MENULIS TEKS PIDATO

The aim of this research is to increase student ability in writing of speech text. It was conducted by using Classroom Action reseach through two cycles. This method consists of 4 stages, planning, action, observation and reflection. Based on the analysis, it can be seen that the application of cooperative group investigation can increase student ability in English writing. Therefore, this model can be recommended to help students in exploring their idea in writing. Keyword: Writing, English Speech text, Cooperative group Investigation.
Residual Relaxation for Multi-view Representation Learning

Multi-view methods learn representations by aligning multiple views of the same image and their performance largely depends on the choice of data augmentation. In this paper, we notice that some other useful augmentations, such as image rotation, are harmful for multi-view methods because they cause a semantic shift that is too large to be aligned well. This observation motivates us to relax the exact alignment objective to better cultivate stronger augmentations. Taking image rotation as a case study, we develop a generic approach, Pretext-aware Residual Relaxation (Prelax), that relaxes the exact alignment by allowing an adaptive residual vector between different views and encoding the semantic shift through pretext-aware learning. Extensive experiments on different backbones show that our method can not only improve multi-view methods with existing augmentations, but also benefit from stronger image augmentations like rotation.
Alignment Attention by Matching Key and Query Distributions

The neural attention mechanism has been incorporated into deep neural networks to achieve state-of-the-art performance in various domains. Most such models use multi-head self-attention which is appealing for the ability to attend to information from different perspectives. This paper introduces alignment attention that explicitly encourages self-attention to match the distributions of the key and query within each head. The resulting alignment attention networks can be optimized as an unsupervised regularization in the existing attention framework. It is simple to convert any models with self-attention, including pre-trained ones, to the proposed alignment attention. On a variety of language understanding tasks, we show the effectiveness of our method in accuracy, uncertainty estimation, generalization across domains, and robustness to adversarial attacks. We further demonstrate the general applicability of our approach on graph attention and visual question answering, showing the great potential of incorporating our alignment method into various attention-related tasks.
Chapter 2. Getting Started with R

Chapter 2 of Library Technology Reports (vol. 57, no. 1) introduces the R programming language and RStudio’s integrated development environment (IDE). It guides a reader through installation steps for R and RStudio and recommends resources for learning R. The chapter also provides a general workflow for basic visualization in RStudio, presents R packages essential for creating plots, and discusses strategies for choosing graphic representations of data that serve specific analytical tasks. The last section considers what can be visualized in a digital collection.
A Web Based Document Encryption Application Software for Information Security in Tertiary Institutions

A web based user friendly document encryption application software was developed as a better cryptographic online system used in securing important tertiary institutions’ documents such as students' Official Grade Report (OGR) sheets, computed and approved results, transcripts, examination and test question papers, Senate/Council documents and any other sensitive or important document needed to be secured from unauthorized users. It is a known fact that problems like unsecured information, information misuse, tampering of sensitive documents by unauthorized persons, stress of hiding sensitive documents from unauthorized persons and locating hidden sensitive documents exist in any system/unit where proper data security mechanism is not in place. In this research work, an online cryptographic system was designed, using Advanced Encryption Standard (AES) algorithm, to secure important documents. The Analysis and Design of this system followed the Structured System Analysis and Design Methodology (SSADM) using web tools. The output of the software shows that the application can encrypt files and save in the database and can only be decrypted using a cipher key automatically generated by the system. With this software, sensitive information can be easily accessed without stress or fear and it has created a more reliable and safer platform to secure such sensitive documents other than the primitive method of using username and password.
Upaya Pencegahan Pencemaran Air Sungai Mahakam melalui Pemodelan Geographically Weighted Logistic Regression pada Data BOD

Since the early years, Mahakam River has important roles in supporting human needs in East Kalimantan province. Activities around Mahakam watershed such as restaurants, fishery, and industries were in the potential of generating waste around the flow area. The waste consisted of domestic and nondomestic waste. The waste was a threat to the Mahakam River water quality. Water pollution around the Mahakam River was a threat to public health, and therefore, there’s a need for precaution. One of the precautions is to give the public information regarding the factors that influence the chances of polluted water in the Mahakam River increased through logistic regression modeling. One way to detect water pollution is to indicate by using Biochemical Oxygen Demand (BOD). BOD data was suspected spatial, therefore the appropriate statistical modeling is Geographically Weighted Logistic Regression (GWLR). GWLR is a regression model that developed from a logistic regression in which parameter estimation is done locally at every observation location. The purpose of the research is to determine the GWLR model on the BOD data of Mahakam River and to find out the factors that influence water pollution at 27 observation points along with the Mahakam River flow. The parameter estimation method is the Maximum Likelihood Estimation (MLE). The spatial weighting is calculated by using the Adaptive Bisquare weighting function and the optimum bandwidth is determined by using Generalized Cross-Validation (GCV) criteria. Research shows that the closed-form of the Maximum Likelihood estimator can’t be obtained analytically and the approximation is obtained by using Newton-Raphson (N-R) iterative method. Based on parameter testing of the GWLR model result, it was concluded that the factors were influences the probability of Mahakam River water were polluted based on the BOD indicator was locally and different in each 27 observation locations. The factors that influence locally were water temperature, acidity, Total Dissolved Solids (TDS), ammonia concentration, and water debit, meanwhile, the factors which influence globally were acidity and TDS.
DEVELOPING LEARNING AND TEACHING: TRAINING TEACHERS IN RURAL NIGERIA USING MOBILE TECHNOLOGY

The study investigated developing learning and teaching, training teachers in rural Nigeria using mobile technology. The sample comprised of 1,200 in-service training teachers purposively drawn from the National Teacher Institute (NTI) Kaduna offering teacher education programme in Kaduna state respectively. Data were collected using a questionnaire whereas mean and t-test statistics were used to analyse the data. The result of the study showed that the mobile technology can be an effective mode for teacher training as it has great impact on the classroom improvement and enhances the self-image, confidence, motivation, and professionalism of teachers. Based on the findings, it was recommended among others that government should procure ICT facilities to aid teaching and government should improve the orientation programme by providing more effective training on the use of phone features.
Twitter Profile Information based on Feature Levels for the Identification of Individuals

The objective of a biometric method is to make a human-like decision about identifying an individual based on physiological or potentially social characteristics. Due to the low quality of the data, biometric-based recognition schemes can be exceptionally confounded. In order for a computerized biometric framework to make decisions consistently and dynamically, Social Behavioral (SB) prompts are essential. A system that identifies an individual person on the social network (SN) is proposed in order to improve the efficiency of the traditional biometric frameworks. The social-behavioural cues of individual people were extracted from an online tweeter network. A study shows that social biometric features are more effective than biometric features based on rank. A person's score is determined by the Product Moment Correlation Coefficient (PMCC) based on the SB attributes. Furthermore, the correlation coefficient is used to calculate four different SBB features. The impact of absolute scores with their SBB model is tested directly from the OSN datasets for various kinds of subjects. Through the use of specific datasets and by comparing registered features with different types of test features, the cross-validation functionality of the system is demonstrated. The proposed system performs better than the conventional biometric framework for identifying individuals. The experimental results using SB fusion techniques demonstrated a recognition rate of 95% and were faster than most other biometric systems. Based on the SB information available on the network, the feature-based fusion technique is the best compromise, and it is commonly applied in multimodal biometric frameworks to improve precision.