Reviewerly/exHarmony huggingface dataset:
Carmelo Bene e il «progetto-ricerca Achilleide»: dal pre-testo alla voce

Why did Carmelo Bene specifically choose Achilles as the last personality with which to confront himself? Is it possible to identify in the «progetto-ricerca Achilleide»? – a project that encompasses more than a decade of Bene’s opus – his testament? These are the questions that animate and direct the hereby study that attempts to carry out a global reconnaissance of the project, confronted by its intermedial nature. The primary subject of interest is Pentesilea. Ovvero della Vulnerabile invunerabilità e necrofilia in Achille, a text that represents Bene’s poetry debut. The object of analysis is not merely this specific literary work, but also the “pre-texts” which constitute its skeleton: Homer, Statius, Kleist. A rereading of these classics in the light of later Bene’s work, in an attempt to specify the reasons behind a choice. Therefore a reconstruction of the voicing of the text – that is already “aural poetry” by itself – is proposed, passing through various “moments” of the «progetto-ricerca», inspecting all the available reviews, critical texts, and testimonies. There is particular attention given to the «spettacolo-sconcerto» from the year 2000, last of Bene’s opus, a conclusive desertion from the scene in an encounter with the absolute musicality of silence.
Fake news detection using python

This Research Paper proposed COVID-19 pandemic, a few achievement and cash related impels come to ungracefully play with identification of given data. This has presented distortion and disarray from one side of the world to the other. The issues of phony news have achieved rising importance in the dispersing of trim reports. A broad bundle of them stop to rely on the papers, magazines, and so on and began to depend on internet based redirection completely. Online redirection changed into the focal news point of convergence for a tremendous number of individuals because of their clear access, unassuming, genuinely beguiling and quick spread. The phony substance began to spread at a gigantic speed to get inescapability over electronic redirection to divert individuals from the ceaseless major issues, in unambiguous events spreading more and quicker than the genuine data. Individuals spread counterfeit news through virtual redirection for cash-related and political augmentation. Counterfeit information in all plans should be perceived quickly to keep away from a threatening outcome on society. This try makes an assessment of the appraisal-related with counterfeit news divulgence, we organized and endeavored different AI calculations independently to show the productivity of the social event on the dataset.
Mobile App for Maize: State of the Art, Potential Areas, and Future Directions in Bangladesh

The mobile phone is no longer just a communication device, but also an essential part of people’s entertainment and daily life. Now android system in the electronics market is becoming more and more popular, especially in the smartphone market. The most popular smartphone application is games followed by listening to music, watching videos, communicating with social media, exploring photos, taking selfies, etc. Mobile apps also become more popular than desktop computer-based software. The mobile phone is used for different purposes activities from simple communication to video conferencing, from playing games to the utilization of apps for day to day life. Since Mobile networks reach every corner of Bangladesh, it is now a potential time to use the mobile phone for providing need-based information to the farming communities for their benefit. Since Bangladesh is an agriculture-based country and most of the farming communities subsist in the northern part of Bangladesh. It was found that Nilphamari (Northern district of Bangladesh) suitable area for providing benefits to the farming communities by developing a mobile app. The researchers chose the disease identification and their’ management for the Maize crop as Maize is the 3rd most common in that area. This paper mainly discussed two sections; 1. The application interface in the Bengali language with multimodal function; text, voice with the local language, and images; 2. Feedback from the users about the app. It was found that most of the end-users were able to properly identify the diseases and manage them well.
Graph Learning Model for Saliency Detection in Thermal Pedestrian Videos

Thermal imaging is becoming popular recently due to its all-weather and all-time capability. However, video saliency detection, an active topic in computer vision, has not been well studied for thermal videos. In this paper, we propose an effective saliency detection method for thermal pedestrian videos. Different from graph-based saliency detection methods with fixed graph structures, we introduce a saliency-guided graph learning model (SGL), which integrates a multi-view graph and saliency to achieve joint graph learning and saliency diffusion. We also propose an efficient video saliency detection approach for thermal pedestrian videos with SGL. Specifically, a proposal-based objectness descriptor is first integrated with motion saliency to obtain initial foreground and background probabilities. Then, a multi-view spatiotemporal graph is built to extract comprehensive inter-frame and intra-frame connections. Finally, optimal saliency could be obtained by an iterative optimization process. Besides, a challenging thermal pedestrian video dataset with 67 sequences and 1462 frames in total is built to compensate for the insufficient scale and complexity of existing datasets. Extensive experiments on a public dataset and our newly built dataset demonstrate the superior performance of our method compared to 11 state-of-the-art (SOTA) video saliency detection methods.
A Distributed Virtual-Machine Placement and Migration Approach Based on Modern Portfolio Theory

Virtual machine placement and migration (VMPM) are key operations for management of cloud resources. Considering the large scale of cloud infrastructures, several proposals lack in providing a comprehensive and scalable solution. A variety of approaches have been used to address this issue, such as the modern portfolio theory (MPT). Originally formulated for financial markets, it enables constructing a portfolio of financial assets to maximize profit and reduce risk. This paper presents a novel VMPM approach applying MPT and incremental statistics computation for VMPM decision making, in order to maximize resource usage, while minimizing under and overload. Simulation experiments were conducted using CloudSim Plus, relying on synthetic data, PlanetLab and Google Cluster traces. Results show the proposal is highly scalable and largely reduces computational complexity and memory footprint, being suitable for large-scale cloud service providers.
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Creating Python Algorithms to Mimic R Functions from the R Stats Package

While R and Python are the most used languages within Data Analytics, the differences between them are becoming less polarized as it is now possible to complete the same tasks using both. A Python library developed from analyzing original R source code would help users cross-reference between both platforms. To design the Python algorithms, pseudocode was produced from registering the source code of different R stats functions. A Python test case was generated that would input result textfiles from both scripts and strive to output 0 differences to check for compatibility. So far, several R functions were able to be mimicked and there is still continued work being done to expand the scripts. We hope to later integrate the R and Python scripts into a web application to better display the results.
ВЕСТНИК ЗКУ

The analysis of the modern socio-cultural educational environment of the Russian Federation as a basis for the individual functional literacy formation, culture of semantic reading and work with the text of the younger generation is presented. The authors focuses that the success of a child's personality development, the results of his education and upbringing is associated not only with didactic, methodological, technological (informational) and program innovations, but also with the experience gained by students in the process of educational or other activities at school. The main types of functional literacy of primary schoolchildren, formed during the development of the content of the main educational program, are characterized. The activity of a temporary creative team of teachers-researchers of the Slobodsky district of the Kirov region on the development of didactic materials for teaching schoolchildren in the techniques of semantic reading in the process of lesson and extracurricular activities, in the course of additional education is characterized. The key aspects of the theoretical and methodological training of members of the temporary creative team of teachers-researchers on the formation and development of students' textual competence are revealed.
An Ontology based Smart Management of Linguistic Knowledge

Natural language processing provides a very significant contribution to various application areas such as multilingual big data, information retrieval, data integration and multilingual web. However, handling linguistic knowledge to develop such lingware applications is a crucial issue, especially for linguistic novice users. To deal with this issue, a "smart" linguistic knowledge management may help the users to understand the meaning, scope and especially the use of related techniques and algorithms. In this paper, (1) we propose a semantic processing of linguistic knowledge based on a multilingual linguistic domain ontology, called LingOnto. Compared to related work, LingOnto does not only handles linguistic data, but also linguistic processing functionalities and linguistic processing features. Besides, it allows, via a reasoning engine, inferring new linguistic knowledge and assisting in the process of proposing lingware applications. This is particularly useful for novice users, but can also provide new perspectives for the expert ones. LingOnto covers the French, English and Arabic languages. (2) We propose also an assisted user friendly ontology visualization tool called LingGraph. It facilitates the interaction with LingOnto. It offers an easy to use interface for users not familiar with ontologies. It is based on a SPARQL pattern-based approach to allow a smart search interaction functionality to visualize only the ontological view corresponding to the user’s needs and preferences. In order to evaluate LingOnto, we apply it to a framework of identifying valid natural language processing pipelines. Finally, we give the results of the carried-out experiments.
Investigating non-visual eye movements non-intrusively: Comparing manual and automatic annotation styles

Non-visual eye-movements (NVEMs) are eye movements that do not serve the provision of visual information. As of yet, their cognitive origins and meaning remain under-explored in eye-movement research. The first problem presenting itself in pursuit of their study is one of annotation: in virtue of their being non-visual, they are not necessarily bound to a specific surface or object of interest, rendering conventional eye-trackers nonideal for their study. This, however, makes it potentially viable to investigate them without requiring high resolution data. In this report, we present two approaches to annotating NVEM data - one of them grid-based, involving manual annotation in ELAN (18), the other one Cartesian coordinate-based, derived algorithmically through OpenFace (1). We evaluated a) the two approaches in themselves, e.g. in terms of consistency, as well as b) their compatibility, i.e. the possibilities of mapping one to the other. In the case of a), we found good overall consistency in both approaches, in the case of b), there is evidence for the eventual possibility of mapping the OpenFace gaze estimations onto the manual coding grid.
A Face Recognition System's Worst Morph Nightmare, Theoretically

It has been shown that Face Recognition Systems (FRSs) are vulnerable to morphing attacks, but most research focusses on landmark-based morphs. A second method for generating morphs uses Generative Adversarial Networks, which results in convincingly real facial images that can be almost as challenging for FRSs as landmark-based attacks. We propose a method to create a third, different type of morph, that has the advantage of being easier to train. We introduce the theoretical concept of \textit{worst-case morphs}, which are those morphs that are most challenging for a fixed FRS. For a set of images and corresponding embeddings in an FRS's latent space, we generate images that approximate these worst-case morphs using a mapping from embedding space back to image space. While the resulting images are not yet as challenging as other morphs, they can provide valuable information in future research on Morphing Attack Detection (MAD) methods and on weaknesses of FRSs. Methods for MAD need to be validated on more varied morph databases. Our proposed method contributes to achieving such variation.
Rethinking Supervised Pre-training for Better Downstream Transferring

The pretrain-finetune paradigm has shown outstanding performance on many applications of deep learning, where a model is pre-trained on a upstream large dataset (e.g. ImageNet), and is then fine-tuned to different downstream tasks. Though for most cases, the pre-training stage is conducted based on supervised methods, recent works on self-supervised pre-training have shown powerful transferability and even outperform supervised pre-training on multiple downstream tasks. It thus remains an open question how to better generalize supervised pre-training model to downstream tasks. In this paper, we argue that the worse transferability of existing supervised pre-training methods arise from the negligence of valuable intra-class semantic difference. This is because these methods tend to push images from the same class close to each other despite of the large diversity in their visual contents, a problem to which referred as "overfit of upstream tasks". To alleviate this problem, we propose a new supervised pre-training method based on Leave-One-Out K-Nearest-Neighbor, or LOOK for short. It relieves the problem of overfitting upstream tasks by only requiring each image to share its class label with most of its k nearest neighbors, thus allowing each class to exhibit a multi-mode distribution and consequentially preserving part of intra-class difference for better transferring to downstream tasks. We developed efficient implementation of the proposed method that scales well to large datasets. Experimental studies on multiple downstream tasks show that LOOK outperforms other state-of-the-art methods for supervised and self-supervised pre-training.
Supervision Exists Everywhere: A Data Efficient Contrastive  Language-Image Pre-training Paradigm

Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks. However, CLIP is quite data-hungry and requires 400M image-text pairs for pre-training, thereby restricting its adoption. This work proposes a novel training paradigm, Data efficient CLIP (DeCLIP), to alleviate this limitation. We demonstrate that by carefully utilizing the widespread supervision among the image-text pairs, our De-CLIP can learn generic visual features more efficiently. Instead of using the single image-text contrastive supervision, we fully exploit data potential through the use of (1) self-supervision within each modality; (2) multi-view supervision across modalities; (3) nearest-neighbor supervision from other similar pairs. Benefiting from intrinsic supervision, our DeCLIP-ResNet50 can achieve 60.4% zero-shot top1 accuracy on ImageNet, which is 0.8% above the CLIP-ResNet50 while using 7.1 x fewer data. Our DeCLIP-ResNet50 outperforms its counterpart in 8 out of 11 visual datasets when transferred to downstream tasks. Moreover, Scaling up the model and computing also works well in our framework.Our code, dataset and models are released at: https://github.com/Sense-GVT/DeCLIP
Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained  EfficientNet-Based Convolutional Network

Some recent studies have described deep convolutional neural networks to diagnose breast cancer in mammograms with similar or even superior performance to that of human experts. Shen et al. (2019) present one of the best techniques that consists of two transfer learnings. The first uses a model trained on natural images to create a "patch classifier" that categorizes small subimages. The second uses the patch classifier to scan the whole mammogram and create the "single-view whole-image classifier". We propose to make a third transfer learning to obtain a "two-view classifier" to use the two mammographic views: bilateral craniocaudal and mediolateral oblique. We use modern EfficientNet as the basis of our model. We "end-to-end" train the entire system using CBIS-DDSM dataset. To ensure statistical robustness, we test our system twice using: (a) 5-fold cross validation; and (b) the original training/test division of the dataset. Our technique reached an AUC of 0.934 using 5-fold cross validation (sensitivity and specificity are 85.13% at the equal error rate of ROC). Using the original dataset division, our technique achieved an AUC of 0.8483, the largest AUC reported for this problem, as far as we know.
Application of the Waterfall Method in Software Design on Android-Based Programming Language Course Applications

In this era of technology and information proliferating, programming skills are needed. Technology affects every area of life from industry, business, communication, transportation, health, and others. Everyone has the same opportunity to learn and master technology thus programming language courses are needed to provide education, innovation, and improvement of skills and abilities in the field of programming and data science to the public. Until now, programming language courses still use the conventional system, where everything is done manually, from class registration, class scheduling, teaching and learning process, and payment processing which results in many archives that must be stored for administrative purposes and require a relatively large amount of time for customers to come to the course location. Therefore, an information technology-based system is needed to fix the weaknesses of the old system. In this study, an Android-based programming language course application is designed to facilitate customers and course owners in teaching and learning activities and transactions. The design in this study uses the waterfall method, which consists of five stages, needs analysis, design, code, testing, and maintenance. The results obtained from testing applications using questionnaires on programming language course applications are 80% stated by course customers, where the application is easy to use, faster, and more practical in registering. In conclusion, this designed application can make it easier for customers to carry out teaching and learning activities and transact quickly and practically.
2D Game â€œOmarâ€™s Adventureâ€ design using the Finite State Machine Method

A video game is a game that has rules in it that must be obeyed when the game takes place so that the video game ends either in a win or a loss, played with a device / console. Video games themselves consist of various kinds, one of which is 2D video games. A 2D game is a game that only has 2 spaces or 2 axes, namely the X and Y axes. This research was conducted with the aim of incorporating Islamic elements into a video game so that players can recognize and learn Islamic elements through more interesting media. The result of the research is a 2D game called "Omar's Adventure" which was designed using the Unity game engine, and applied the Finite State Machine (FSM) method in making the game. The FSM given in this game will make several NPCs move / react according to the player's movements to add interactivity in playing it. The resulting game contains the adventure of Omar, where he needs to pass the levels or obstacles that prevent him from his goal in the form of the path of goodness
Evaluasi Progam Literasi Informasi pada Kegiatan Hari Kunjung Perpustakaan

Information literacy program that can be categorized as a training program requires evaluation to show whether the program has been achieved as expected. This study aims to develop a literacy program and determine the effectiveness of the information literacy program on Library Visit Day. The study used a descriptive research method with the research population being all participants of the information literacy program on the 2019 Library Visit Day at the Ministry of Agriculture's PUSTAKA totaling 69 people. Samples were taken from 27 participants in the literacy class for researchers and students, and 42 people in the literacy class for extension workers/farmers/general public. The research method refers to the evaluation of the effectiveness of the training program/training Kirkpatrick (2004). The results of the study show that all of the indicators assessed (level 1-level 4) in the evaluation of the information literacy program carried out by PUSTAKA on Library Visit Day 2019 were considered very satisfactory even though they had not been able to meet the 100% expectations. The improvement strategies needed to improve the information literacy program in the future are: socialization through various media is needed with sufficient time; development of training materials; additional time for the implementation of the literacy program; literacy development programs for students; making a guide for program literacy participants; develop literacy programs directly to extension workers and farmers in the field.
Analisis Sistem Katalogisasi di Pepustakaan SMA N 1 Payakumbuh

The school library is a facility and pre-facility to support teaching and learning activities at the school level because the school library is an integral part of the education program at the school level. The school library is designed to meet the information needs of the community in every school environment, especially teachers and students. In the school library there are various collections that must be processed so that they are easy to use by library visitors or users. The things that can be done by librarian is cataloging library materials or collections to facilitate the process of retrieval of information needed by users. Therefore, this study uses descriptive qualitative research methods, in order to be able to describe how the cataloging in the library of SMA N 1 Payakumbuh. From the data and research results, it is known that the activity of cataloging or cataloging has followed and is in accordance with the cataloging system guided by the International Standard Bibliographic Description (ISBD). In the process of cataloging collections or library materials using SLiMS is an convenience for librarians in data management so that it affects the services provided to library visitors or users.