Reviewerly/exHarmony huggingface dataset:
Energy Complexity of Regular Languages

Each step that results in a bit of information being ``forgotten'' by a computing device has an intrinsic energy cost. Although any Turing machine can be rewritten to be thermodynamically reversible without changing the recognized language, finite automata that are restricted to scan their input once in ``real-time'' fashion can only recognize the members of a proper subset of the class of regular languages in this reversible manner. We study the energy expenditure associated with the computations of deterministic and quantum finite automata. We prove that zero-error quantum finite automata have no advantage over their classical deterministic counterparts in terms of the maximum obligatory thermodynamic cost associated by any step during the recognition of different regular languages. We also demonstrate languages for which ``error can be traded for energy'', i.e. whose zero-error recognition is associated with computation steps having provably bigger obligatory energy cost when compared to their bounded-error recognition by real-time finite-memory quantum devices. We show that regular languages can be classified according to the intrinsic energy requirements on the recognizing automaton as a function of input length, and prove upper and lower bounds.
Probabilistic Compositional Embeddings for Multimodal Image Retrieval

Existing works in image retrieval often consider retrieving images with one or two query inputs, which do not generalize to multiple queries. In this work, we investigate a more challenging scenario for composing multiple multimodal queries in image retrieval. Given an arbitrary number of query images and (or) texts, our goal is to retrieve target images containing the semantic concepts specified in multiple multimodal queries. To learn an informative embedding that can flexibly encode the semantics of various queries, we propose a novel multimodal probabilistic composer (MPC). Specifically, we model input images and texts as probabilistic embeddings, which can be further composed by a probabilistic composition rule to facilitate image retrieval with multiple multimodal queries. We propose a new benchmark based on the MS-COCO dataset and evaluate our model on various setups that compose multiple images and (or) text queries for multimodal image retrieval. Without bells and whistles, we show that our probabilistic model formulation significantly outperforms existing related methods on multimodal image retrieval while generalizing well to query with different amounts of inputs given in arbitrary visual and (or) textual modalities. Code is available here: https://github.com/andreineculai/MPC.
SWOT ANALYSIS OF THE COVID-19 PANDEMIC IN BRAZIL: A SYSTEMATIC REVIEW

Coronavirus is a family of viruses that has resulted in a worldwide pandemic. Covid-19 appeared in the city of Wuhan in China and reached Brazil. In European territory, Spain and Italy were the first countries affected, before the entire continent. The progression of the COVID-19 circulation continues in several areas. The COVID-19 pandemic has had an unprecedented impact on the lives of many people. This is a scenario study of the COVID-19 epidemic in Brazil using the SWOT matrix analysis instruments. A systematic literature review was carried out, using the Web of Science, Science Direct and Scopus databases, which resulted in the complete analysis of 17 articles. The results point out the strengths, weaknesses, opportunities and threats considering the particular context. This analysis allows a holistic assessment of the problem and can contribute to the development and direction of strategies for the prevention and control of the disease.
A Three-phase Augmented Classifiers Chain Approach Based on Co-occurrence Analysis for Multi-Label Classification

As a very popular multi-label classification method, Classifiers Chain has recently been widely applied to many multi-label classification tasks. However, existing Classifier Chains methods are difficult to model and exploit the underlying dependency in the label space, and often suffer from the problems of poorly ordered chain and error propagation. In this paper, we present a three-phase augmented Classifier Chains approach based on co-occurrence analysis for multi-label classification. First, we propose a co-occurrence matrix method to model the underlying correlations between a label and its precedents and further determine the head labels of a chain. Second, we propose two augmented strategies of optimizing the order of labels of a chain to approximate the underlying label correlations in label space, including Greedy Order Classifier Chain and Trigram Order Classifier Chain. Extensive experiments were made over six benchmark datasets, and the experimental results show that the proposed augmented CC approaches can significantly improve the performance of multi-label classification in comparison with CC and its popular variants of Classifier Chains, in particular maintaining lower computational costs while achieving superior performance.
Recognition of Freely Selected Keypoints on Human Limbs

Nearly all Human Pose Estimation (HPE) datasets consist of a fixed set of keypoints. Standard HPE models trained on such datasets can only detect these keypoints. If more points are desired, they have to be manually annotated and the model needs to be retrained. Our approach leverages the Vision Transformer architecture to extend the capability of the model to detect arbitrary keypoints on the limbs of persons. We propose two different approaches to encode the desired keypoints. (1) Each keypoint is defined by its position along the line between the two enclosing keypoints from the fixed set and its relative distance between this line and the edge of the limb. (2) Keypoints are defined as coordinates on a norm pose. Both approaches are based on the TokenPose architecture, while the keypoint tokens that correspond to the fixed keypoints are replaced with our novel module. Experiments show that our approaches achieve similar results to TokenPose on the fixed keypoints and are capable of detecting arbitrary keypoints on the limbs.
Local available quantum correlations of X states: The asymmetric case

Local available quantum correlations (LAQC), as defined by Mundarain et al., are analyzed for non-symmetric 2-qubit X states, that is, X-states that are not invariant under the exchange of subsystems and therefore have local Bloch vectors whose norms are different. A simple analytic expression for their LAQC quantifier is obtained. As an example, we analyze the local application of the amplitude damping channel for Werner states and general canonical X states. Although this local quantum channel can create quantum discord in some cases, no such outcome is possible for LAQC, which hints toward their monotonicity under LOCC operations. This work, along with our previous result for so-called symmetric and anti-symmetric X states, gives a complete characterization and analytical expressions for the LAQC quantifier for the whole family of canonical 2-qubit X states.
Developing Learn English Better (LEB) For Grammatical Skills

The purpose of this research is to improve student's English skills in grammar skills through android-based learning media. This research uses R&amp;D research method. The data collection in this study was obtained by sharing questioner, to know the needs and obstacles experienced by students in learning grammar. To meet validity and reliability, the researchers used two validators tasked with testing the feasibility of learning media for students to use. Testers consist of material experts and media design experts. The results of this study show that application-based learning media is feasible to use. Improvements can be seen from the use of data analysis techniques to calculate scores from questionnaires using Linkert scale techniques with 5 answer options. According to the assessment by 15 students, the total score obtained was 593, the total score was then calculated using the formula described earlier to determine the feasibility of learning media. The average score obtained from the respondence is 39.5, the data then re-calculates. So, the assessment results by respondents were 3.95 which fall into the category of good and worth using. The benefits of research conducted to facilitate students in learning grammar using android applications. Based on the results of the study, students can conclude that learning grammar is easier using the Grammar Android Application.
Innovation of Information and Communication Technology in Teacher Education

In the realm of pedagogy the manner in which technology is used implies integration.ICT ascertain the efficiency and effectiveness for communication and the devices assist in handling information.The impact of ICT is felt by those teachers who are in use but in the long run it is going to be used by all the teachers for effective teaching learning process.To ensure the fulfilment of integration of ICT in TTIs there is a need for critical analysis in relation to Infrastructure, Administration work, Curriculum transaction, Teachers competency, Capacity-building, Maintenance facility, Evaluation and Research activity.ICTs have an important role to play in changing and modernizing educational systems and ways of innovation in teaching learning process.The application of ICT in the education setting has to be cultivated, promoted and nurtured as new interactive relationships among teachers, learners and technologies are in high demand.It is high time for the teachers to acquire mastery over the technical know how or else the teachers have to face unwanted problems.
Clustering Tingkat Penjualan Menu (Food and Beverage) Menggunakan Algoritma K-Means

Menu planning in a restaurant is part of the sales strategy. Each menu has a different level of sales. To determine the effectiveness of sales and raw materials, restaurants need knowledge of what menus need to be maintained and vice versa. An analysis that can determine the sales level menu is the analysis of the k-means algorithm data mining clustering method. The source of research data is from the history of menu sales transactions for 1 year, then analyzed by the k-means algorithm. The information found is in the form of popular F&amp;B menus and sales level menus. The purpose of this study is to group the data menu on the level of sales (Food and Beverage). The method used is the Clustering method with the performance of the K-Means algorithm. The results showed that the clustering method with the K-Means algorithm gave a significant output in grouping sales data. The research contribution provides knowledge in the form of information in conducting sales data management
Alumni Tracking System

The Alumni Tracking System is an online tool that assists in the tracking of college graduates. The project's goal is to 3develop a system for tracking college graduates and giving alumni data to colleges. Its goal is to create a web portal that will allow the college to keep track of alumni while also allowing alumni to update their current status on campus activities. This system's goal is to provide an optimal solution for gathering and maintaining alumni data. The system will be a one-stop shop for managing alumni data, facilitating contact between alumni and the university, and providing a wealth of configurable capabilities for data management.
Egocentric Human-Object Interaction Detection Exploiting Synthetic Data

We consider the problem of detecting Egocentric HumanObject Interactions (EHOIs) in industrial contexts. Since collecting and labeling large amounts of real images is challenging, we propose a pipeline and a tool to generate photo-realistic synthetic First Person Vision (FPV) images automatically labeled for EHOI detection in a specific industrial scenario. To tackle the problem of EHOI detection, we propose a method that detects the hands, the objects in the scene, and determines which objects are currently involved in an interaction. We compare the performance of our method with a set of state-of-the-art baselines. Results show that using a synthetic dataset improves the performance of an EHOI detection system, especially when few real data are available. To encourage research on this topic, we publicly release the proposed dataset at the following url: https://iplab.dmi.unict.it/EHOI_SYNTH/.
RL-CoSeg : A Novel Image Co-Segmentation Algorithm with Deep Reinforcement Learning

This paper proposes an automatic image co-segmentation algorithm based on deep reinforcement learning (RL). Existing co-segmentation tasks mainly rely on deep learning methods, and the obtained foreground edges are often rough. In order to obtain more precise foreground edges, we use deep RL to solve this problem and achieve the finer segmentation. To our best knowledge, this is the first work to apply RL methods to co-segmentation. We define the problem as a Markov Decision Process (MDP) and optimize it by RL with asynchronous advantage actor-critic (A3C). The RL image co-segmentation network uses the correlation between images to segment common and salient objects from a set of related images. In order to achieve automatic segmentation, our RL-CoSeg method eliminates user's hints. For the image co-segmentation problem, we propose a collaborative RL algorithm based on the A3C model. We propose a Siamese RL co-segmentation network structure to obtain the co-attention of images for co-segmentation. We improve the self-attention for automatic RL algorithm to obtain long-distance dependence and enlarge the receptive field. The image feature information obtained by self-attention can be used to supplement the deleted user's hints and help to obtain more accurate actions. Experimental results have shown that our method can improve the performance effectively on both coarse and fine initial segmentations, and it achieves the state-of-the-art performance on Internet dataset, iCoseg dataset and MLMR-COS dataset.
Application of modern information systems and technologies in scientific activity

The development of a single information space with the intensive introduction of information systems has led to the rapid accumulation of information resources, the bulk of which are publications of research results.In modern society, the most important result is the result of scientific research, not just the process of activity or thought.As the number of scientists increases, the question of the scientific contribution of each of them arises.The weight of a scientist in the scientific community, his influence on events in his chosen field of science, is now largely determined by how fully, constructively and organically presented the results of his research on the Internet.It is difficult to evaluate scientific activity with only one parameter, especially since there is a need to evaluate using quantitative and qualitative indicators.This is due to many problems, the main of which is to take into account the quality of scientific work and interpretation of indicators in the numerical dimension, which allows to obtain important information about the relevance of a particular topic and vice versa.It is important to remember that international open access scientometric platforms are a powerful non-profit tool for disseminating, publishing and analyzing the use of research results.They provide an opportunity not only for quality evaluation of scientific information, but also for close cooperation between scientists around the world.Scientometric calculations in such systems allow to determine important citation rates and statistics of scientific activity of researchers.The article highlights the problem of using international scientometric databases in research activities as web-oriented resources and services that are a means of publishing and disseminating research results.The basic concepts of the problems of using open digital systems for the evaluation of scientific research are systematized.Functionalities and tools of scientometric databases are investigated.An overview of the most popular open access scientometric databases.The advantages of using international scientometric databases and social scientific profiles in conducting research and their prospects, which are the allocation of cloud information and analytical services of this system.
Sinhala Sign Language Recognition using Leap Motion and Deep Learning

A sign language recognition system for low-resource Sinhala Sign Language using Leap Motion (LM) and Deep Neural Networks (DNN) has been presented in this paper. The study extracts static and dynamic features of hand movements of Sinhala Sign Language (SSL) using a LM controller which acquires the position of the palm, radius of hand sphere and positions of five fingers, and the proposed system is tested with the selected 24 letters and 6 words. The experimental results prove that the proposed DNN model with an average testing accuracy of 89.2% outperforms a Naïve Bayes model with 73.3% testing accuracy and a Support Vector Machine (SVM) based model with 81.2% testing accuracy. Therefore, the proposed system which uses 3D non-contact LM Controller and machine learning model has a great potential to be an affordable solution for people with hearing impairment when they communicate with normal people in their day-to-day life in all service sectors.
Tennis Serve Trajectory Capture Algorithm Based on Wavelet Multiscale Decomposition

Human motion analysis is one of the important research directions in computer vision technology. Aiming at the problems of poor effect, accuracy, and efficiency in the current tennis serve trajectory capture process, a tennis serve trajectory capture algorithm based on wavelet multiscale decomposition is proposed. This article analyzes the related concepts and principles of wavelet transform and multiresolution analysis, uses the computer three-dimensional vision acquisition method to collect the tennis serve trajectory image, and uses the mean filter to smooth and denoise the collected tennis serve trajectory image. The wavelet multiscale decomposition algorithm is used to process the tennis serve trajectory image after smooth denoising, find the local maximum point through the modulus value and phase angle value, and obtain the initial tennis serve trajectory image. Using the local layer threshold method, the adaptive threshold is set, and the trajectory line under each scale is obtained to capture the trajectory line of tennis serve. It can be observed from the experimental and simulation results that the trajectory capture effect of the proposed algorithm is good, and it can effectively improve the accuracy and efficiency of trajectory capture.
Gamma-Minimax Wavelet Shrinkage with Three-Point Priors

In this paper we propose a method for wavelet denoising of signals contaminated with Gaussian noise when prior information about the $L^2$-energy of the signal is available. Assuming the independence model, according to which the wavelet coefficients are treated individually, we propose a simple, level dependent shrinkage rules that turn out to be $\Gamma$-minimax for a suitable class of priors. The proposed methodology is particularly well suited in denoising tasks when the signal-to-noise ratio is low, which is illustrated by simulations on the battery of standard test functions. Comparison to some standardly used wavelet shrinkage methods is provided.
Beam Decoding with Controlled Patience

Text generation with beam search has proven successful in a wide range of applications. We point out that, though largely overlooked in the literature, the commonly-used implementation of beam decoding (e.g., Hugging Face Transformers and fairseq) uses a first come, first served heuristic: it keeps a set of already completed sequences over time steps and stops when the size of this set reaches the beam size. Based on this finding, we introduce a patience factor, a simple modification to this beam decoding implementation, that generalizes the stopping criterion and provides flexibility to the depth of search. Empirical results demonstrate that adjusting this patience factor improves decoding performance of strong pretrained models on news text summarization and machine translation over diverse language pairs, with a negligible inference slowdown. Our approach only modifies one line of code and can be thus readily incorporated in any implementation. Further, we find that different versions of beam decoding result in large performance differences in summarization, demonstrating the need for clarity in specifying the beam search implementation in research work. Our code will be available upon publication.
PEMANFAATAN LIMBAH BATOK KELAPA DAN KAYU ULIN UNTUK PRODUK PERHIASAN WANITA

The development of a modern, dynamic, and fashionable lifestyle, Jewelry was born in the midst of human life, especially for women. Jewelry is a trinket or decoration that has a function as a tool to beautify or beautify oneself to make it look more attractive. The problem at this time is the lack of utilization of coconut shell and ironwood waste which is only burned or thrown away. The purpose of making this product is to utilize coconut shell and ironwood waste material into a jewelry product that has selling value and aesthetic value. This product design method starts from alternative sketches, developing selected designs to become final designs or final designs, with research methods through data obtained from questionnaires, until the final results of jewelry designs are obtained from coconut shell waste and wood with a wood connection system with glue, with finishing using varnish that shows the natural color of coconut shell and ironwood, which is brown. So the conclusion of this design is the design of jewelry from coconut shell waste and ironwood that can be used and has aesthetic value and selling value.
Construction and Visualization of the Ontology of the Educational Course for the Digital Information Space of the University

Despite the significant volume of educational courses published on the university's information portals, there is no full or partial mechanism for obtaining the necessary information in one place and an accessible form. Each course is a separate resource, without the ability to build logical connections (navigational transitions) between adjacent or sequential courses and courses of the same thematic cluster or subject area. The lack of systematization of e-courses does not allow realizing a full-fledged contextual search and navigation between related resources.&#x0D; Within the framework of this study, the problem of constructing and presenting an ontology of an electronic course is considered. Applied solutions to this problem will significantly increase the constructiveness of existing services for working with training courses and provide a new set of opportunities based on the systematization of knowledge. As a structure of knowledge representation, it is proposed to use a structured set of concepts (terms) that describe the theoretical and practical aspects of the electronic course. Such a domain ontology is represented as a semantic network, which can be visualized by a specialized software tool or using a graphical library, for example, d3 for the JavaScript language.