Reviewerly/exHarmony huggingface dataset:
Academic and research librarians engaging a global mindset: From awareness to action

Five years ago, the first C&amp;RL New s International Insights column was published in response to the question raised during the Research Forum at ACRL 2015, What can ACRL do to highlight research and practice that provides an international and/or comparative understanding of issues that affect academic and research libraries? As the current column coeditors, who end our stewardship of the quarterly column this issue, our goal has been to provide a global perspective on issues relevant to academic and research libraries, and offer ideas and opportunities for action. Thus, we wish to conclude our editorship with a focus on action by the academic and research library community that engages a global mindset.
APPLICATION OF THE BAYESIAN MATHEMATICAL STATISTICS LIBRARY IN THE INFOINTEGRATOR SOFTWARE PACKAGE

The article presents a module for generating large samples with a given distribution law based on small samples with unknown distribution types using Bayesian mathematical statistics. For samples obtained from an external medium or from another program, the coefficients of asymmetry and kurtosis projected onto the plane of Pearson moments are considered. Classes of distributions with corresponding dividing boundaries are defined on the plane of moments. When a point with coordinates equal to the values of the coefficients of asymmetry and kurtosis is hit, the most probable distribution class is determined, as well as the nearest – least probable classes. For the selected class, a large sample is generated by the volume defined by the user. The main characteristics of the distribution are determined by the sample. Graphs are plotted for the most and least probable distributions, and the data obtained (the sample, its main characteristics) are output to a file that is transmitted for further processing to an external program. The software module can be used both when working with technical systems and when modeling socioeconomic processes in order to obtain correct results.
Iteration Causes, Impact, and Timing in Software Development Lifecycle: An SLR

Context: Iteration-performing an activity once it has already been done-is unavoidable and omnipresent during software development.Management of iteration is a challenging task due to the lack of detailed analysis and use of different terms for the iteration at different places in software engineering.Objective: In order to manage iteration in a better way during software development processes, we investigate different iterative situations, the causes, the stages at which it can occur during the development, and its impacts.Method: We use the systematic literature review (SLR) method to search using six bibliographic databases.The SLR includes 153 primary studies published from 2007 to February 2017.Results: We identify twenty-two leading causes, five stages, and positive and negative impacts of iteration.Then, we develop a lucid taxonomy of iteration perspectives based on the causes and timing at which it occurs during the software development lifecycle.Conclusions: The frequently reported causes of iteration are defects, code smell, and conflicts, whereas the least referenced causes are poor management and different methods followed by teams.The most cited phase at which iteration occurs during the software development is maintenance.The most cited positive consequence of iteration is quality improvement, whereas the negative impacts of iteration are increasing time, effort, and cost.Our study provides a framework to understand the nature of iteration, what sources can lead to which iterative perspective, a particular iterative situation can have what kind of impacts on project milestones, and also provide research directions.
Real-Time Hand Gesture Identification in Thermal Images

Hand gesture-based human-computer interaction is an important problem that is well explored using color camera data. In this work we proposed a hand gesture detection system using thermal images. Our system is capable of handling multiple hand regions in a frame and process it fast for real-time applications. Our system performs a series of steps including background subtraction-based hand mask generation, k-means based hand region identification, hand segmentation to remove the forearm region, and a Convolutional Neural Network (CNN) based gesture classification. Our work introduces two novel algorithms, bubble growth and bubble search, for faster hand segmentation. We collected a new thermal image data set with 10 gestures and reported an end-to-end hand gesture recognition accuracy of $${\sim }97\%$$ .
Visual Saliency Detection in High-Resolution Remote Sensing Images Using Object-Oriented Random Walk Model

As high-resolution remote sensing images begin to integrate new characteristics such as a great volume of data, a wide variety of ground objects and a high structural complexity, traditional methods previously use d for feature extraction in lowresolution remote sensing images are inefficient and inadequate for the accurate feature des cription of various objects.Thus, object feature extraction from high-resolution remote sensing image remains to be a challenge.To address this issue, we introduced the visual attention mechanism into high-resolution remote sensing image analysis in this study by proposing a novel object-oriented random walk model for visual saliency (ORWVS) detection from high-resolution remote sensing images.In the proposed model, an object-oriented random walk strategy is designed to simulate the transfer path of visual focus on the images, and to extract the local salient regions in an efficient and accurate manner, laying a foundation for accurate feature descriptors.The ORWVS model is compared to eight visual attention models , and the experiments prove its superiority.
Human Activity Recognition in Car Workshop

Human activity recognition has become so widespread in recent times.Due to the modern advancements of technology, it has become an important solution to many problems in various fields such as medicine, industry, and sports.And this subject got the attention of a lot of researchers.Along with problems like wasted time in maintenance centers, we proposed a system that extracts worker poses from videos by using pose classification.In this paper, we have tested two algorithms to detect worker activity.This system aims to detect and classify positive and negative worker's activities in car maintenance centers such as (changing the tire, changing oil, using the phone, standing without work).We have conducted two experiments, the first experiment was for comparison between algorithms to determine the most accurate algorithm in recognizing the activities performed.The experiment was done using two different algorithms (1 dollar recognizer and Fast Dynamic time warping) on 3 participants in a controlled area.The one-dollar recognizer has achieved a 97% accuracy compared to the fastDTW with 86%.The second experiment was conducted to measure the performance of a one-dollar algorithm with different participants.The results show that a 1 dollar recognizer achieved an accuracy of 94.2% when tested on 420 different videos.
Embedding the UC Model into the IITM Model

Universal Composability is a widely used concept for the design and analysis of protocols. Since Canetti's original UC model and the model by Pfitzmann and Waidner several different models for universal composability have been proposed, including, for example, the IITM model, GNUC, CC, but also extensions and restrictions of the UC model, such as JUC, GUC, and SUC. These were motivated by the lack of expressivity of existing models, ease of use, or flaws in previous models. Cryptographers choose between these models based on their needs at hand (e.g., support for joint state and global state) or simply their familiarity with a specific model. While all models follow the same basic idea, there are huge conceptually differences, which raises fundamental and practical questions: (How) do the concepts and results proven in one model relate to those in another model? Do the different models and the security notions formulated therein capture the same classes of attacks? Most importantly, can cryptographers re-use results proven in one model in another model, and if so, how? In this paper, we initiate a line of research with the aim to address this lack of understanding, consolidate the space of models, and enable cryptographers to re-use results proven in other models. As a start, here we focus on Canetti's prominent UC model and the IITM model proposed by Küsters et al. The latter is an interesting candidate for comparison with the UC model since it has been used to analyze a wide variety of protocols, supports a very general protocol class and provides, among others, seamless treatment of protocols with shared state, including joint and global state. Our main technical contribution is an embedding of the UC model into the IITM model showing that all UC protocols, security and composition results carry over to the IITM model. Hence, protocol designers can profit from the features of the IITM model while being able to use all their results proven in the UC model. We also show that, in general, one cannot embed the full IITM model into the UC model.
Solar Resource: Assessment and Utilization

Basics in assessment of solar resources on earth and their utilization are described here. Sunlight is everywhere. Sun falls on every part of earth at least for a few hours in a year. The resource received on earth varies with the geographic location, time, season, weather, and landscape. Assessing solar resources is vital for knowing the potential for their utilization. Utilization involves capturing the radiation, its conversion, and storage in usable forms of energy by application of technology.
From Hyperbolic Geometry Back to Word Embeddings

We choose random points in the hyperbolic disc and claim that these points are already word representations.However, it is yet to be uncovered which point corresponds to which word of the human language of interest.This correspondence can be approximately established using a pointwise mutual information between words and recent alignment techniques.
Beyond the Entanglement of Qubit Pairs in Multipartite Systems

What can we learn about the entanglement of a multipartite system and residual correlations beyond two-tangle if we have access only to two-qubits at a time? Algebraic analysis of two-qubit marginal states of a multiqubit system in a pure state yields monogamy constraints at different levels on distribution of entanglement between sub-systems. Monogamy constraints, reported in this letter, are relations between well known entanglement measures that is one-tangle, two-tangles and three-tangles of an N-qubit pure state. Criterion to detect multipartite entanglement of an N-qubit pure state is also reported.
Village Status Classification Based Tree Algorithm

Knowing the status of the village is very important to how developed the village is.The Central Statistics Agency (BPS) has carried out a classification process using the traditional scoring method, making it difficult to provide timely data.In this study, decision tree algorithm will be applied.The algorithm will test on the Podes2016 dataset focused on the Daerah Istimewa Yogyakarta (DIY) region, where the accuracy matrix is used to measure the algorithm's performance.The results showed that the proposed algorithm highest accuracy (87,40%) than the two comparison algorithms, such as the ID3 (83,81%) and C4.5 (80,62%).It can be concluded that the proposed algorithm can score very well and has good accuracy.
What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge

There are limitations in learning language from text alone.Therefore, recent focus has been on developing multimodal models.However, few benchmarks exist that can measure what language models learn about language from multimodal training.We hypothesize that training on a visual modality should improve on the visual commonsense knowledge in language models.Therefore, we introduce two evaluation tasks for measuring visual commonsense knowledge in language models 1 and use them to evaluate different multimodal models and unimodal baselines.Primarily, we find that the visual commonsense knowledge is not significantly different between the multimodal models and unimodal baseline models trained on visual text data.
Application Status of Artificial Neural Network Technology in Clinical Pharmacy

With the development of society and the progress of science and technology, human beings pay more and more attention to life and health issues, and the safety of medication is no exception. Clinical medicine is a basic subject in the medical field. At the same time, in order to better meet clinical needs, artificial neural network technology is also attracting attention in the medical field. Artificial neural network is a product of highly integrated and intelligent information in the new era. It is the most widely used in many fields and has great potential, especially in the biological field. In recent years, neural networks have been widely used in the field of pharmacy, providing effective data methods for clinical pharmacy data analysis, model construction, and real-time control. This article uses experimental analysis and data analysis to better understand the predictive performance of artificial neural network technology in drug analysis, so as to explore its application in clinical pharmacy. According to the experimental results, the correlation coefficients of the experimental samples calculated by the artificial neural network are higher than those obtained by the binary regression, and the prediction results of the drug analysis by the artificial neural network are significantly better than the results of the binary regression.
ProZe: Explainable and Prompt-Guided Zero-Shot Text Classification

As technology accelerates the generation and communication of textual data, the need to automatically understand this content becomes a necessity. In order to classify text, being it for tagging, indexing, or curating documents, one often relies on large, opaque models that are trained on preannotated datasets, making the process unexplainable, difficult to scale, and ill-adapted for niche domains with scarce data. To tackle these challenges, we propose ProZe, a text classification approach that leverages knowledge from two sources: prompting pretrained language models, as well as querying ConceptNet, a common-sense knowledge base which can be used to add a layer of explainability to the results. We evaluate our approach empirically and we show how this combination not only performs on par with state-of-the-art zero shot classification on several domains, but also offers explainable predictions that can be visualized.
Application of RESNET and Combined RESNET+LSTM Network for Retina Inspired Emotional Face Recognition System

Various Facial Expression Recognition (FER) systems have been studied in the field of computer vision and machine learning to encode expression information from facial representations. In this research paper, a facial emotion recognition system is proposed, addressing automatic face detection and facial expression recognition using 1) Residual Neural Network (RESNET) and 2) Combined Residual Neural Network + Long Short-Term Memory (Combined RESNET+LSTM). The architectures of RESNET and Combined RESNET+LSTM are inspired by the human retina structure and human primary visual cortex structure. The proposed architectures are compared with each other. They are tested using a challenging public database.
Distributed Quantum Machine Learning

Quantum computers can solve specific complex tasks for which no reasonable-time classical algorithm is known. Quantum computers do however also offer inherent security of data, as measurements destroy quantum states. Using shared entangled states, multiple parties can collaborate and securely compute quantum algorithms. In this paper we propose an approach for distributed quantum machine learning, which allows multiple parties to securely perform computations, without having to reveal their data. We will consider a distributed adder and a distributed distance-based classifier.
Efficient Segment-based Image Ciphering using Discretized Chaotic Standard Map with ECB, OFB and CBC

This paper presents a block-based ciphering scheme that employs the 2D discretized chaotic Standard map (CSM) in three different operation modes.The employed operation modes include the electronic codebook (ECB), the output feedback (OFB) and the cipher block chaining (CBC) modes.In the presented 2D discretized CSM with the OFB and CBC, the initiation vector (IV) is employed as the primary secret key.The presented 2D discretized CSM with the CBC has two merits.The first merit is the ability of the presented 2D discretized CSM with the ECB, OFB and CBC to encipher images of any dimensions in a comparatively short time.The second merit is the high level of security of the presented 2D discretized CSM with the OFB and CBC through the integration of both diffusion and confusion operations.Different security key metrics like histogram deviation, irregular, and coefficient of correlation, are examined to assess the functionality of the presented 2D discretized CSM with the OFB and CBC.The resistance to noise, uniformity of histogram, and encryption speed are also investigated.The suggested 2D discretized CSM with the OFB and CBC is compared with the 2D discretized CSM in ECB.The achieved outcomes demonstrate that the proposed 2D discretized CSM with the OFB and CBC has a high security than in ECB from cryptographic viewpoint.Also, achieved outcomes demonstrate that the proposed 2D discretized CSM has better noise immunity in OFB compared with ECB and OFB.
Aanisha@TamilNLP-ACL2022:Abusive Detection in Tamil

In social media, there are instances where people present their opinions in strong language, resorting to abusive/toxic comments.There are instances of communal hatred, hate-speech, toxicity and bullying.And, in this age of social media, it's very important to find means to keep check on these toxic comments, as to preserve the mental peace of people in social media.While there are tools, models to detect and potentially filter these kind of content, developing these kinds of models for the low resource language space is an issue of research.In this paper, the task of abusive comment identification in Tamil language, is seen upon as a multiclass classification problem.There are different pre-processing as well as modelling approaches discussed in this paper.The different approaches are compared on the basis of weighted average accuracy.1
“Does it come in black?” CLIP-like models are zero-shot recommenders

Product discovery is a crucial component for online shopping.However, item-to-item recommendations today do not allow users to explore changes along selected dimensions: given a query item, can a model suggest something similar but in a different color?We consider item recommendations of the comparative nature (e.g."something darker") and show how CLIP-based models can support this use case in a zero-shot manner.Leveraging a large model built for fashion, we introduce GradREC and its industry potential, and offer a first rounded assessment of its strength and weaknesses.* * GradRECS started as a (failed) experiment by JT; PC actually made it work, and he is the lead researcher on the project.FB, CG and DC all contributed to the paper, providing support for modelling, industry context and domain knowledge.PC and JT are the corresponding authors.
Influence of self-care using complementary and integrative therapies during the COVID-19 pandemic in children and adolescents aged 4 to 13 years with bruxism and temporomandibular disorders

Bruxism is a behavior of the mandibular muscles, characterized by clenching and/or dental grinding, both in wakefulness and in sleep, which can lead to Temporomandibular Disorders (TMD). Highly prevalent in children and possessing a multifactorial etiology, psychosocial aspects stand out, especially stress and anxiety. The need for social distancing during the COVID-19 pandemic has had consequences for children and has given rise to new formats of actions in Dentistry. This study evaluated the influence of self-care on the manifestations of Bruxism/TMD in children who were undergoing treatment, before the pandemic, Care Center for children with Bruxism and TMD, whose acronyms in Portuguese is SABDI, located in Ribeirão Preto School of Dentistry (FORP-USP), through assisted interventions, implemented remotely, of Complementary and Integrative Therapies (CIT). Prior to the interventions (T0), the parents answered the questionnaire (Q1) about the child (in Google Forms, sent by WhatsApp and e-mail). The proposed practices were explained in virtual meetings (Google Meet) and performed for 21 days and recorded in a “Control Diary”. A new questionnaire (Q2) was completed for comparative analysis after the interventions (T1). Of the 37 children who were undergoing treatment, the final sample of 18 who completed all the proposed steps showed a quantitative difference in the items: “pain in the mouth/face when chewing”; “pain or difficulty opening and closing the mouth”; “waking up with pain in the face or headache” and “headache during the day or night”; “grinding teeth while awake”; “frequency grinding teeth while awake”; “grinding teeth while sleeping”; “frequency grinding teeth while sleeping”; “restless sleep”; “nightmare”; and “insomnia”. Evidence of statistical difference was observed with a P value &lt;0.05, in the item “clenches teeth while awake” (p=0.0057). It is concluded that the proposed interventions influenced the manifestations of Bruxism, favoring its control.
Hybrid Semantics for Goal-Directed Natural Language Generation

We consider the problem of generating natural language given a communicative goal and a world description.We ask the question: is it possible to combine complementary meaning representations to scale a goal-directed NLG system without losing expressiveness?In particular, we consider using two meaning representations, one based on logical semantics and the other based on distributional semantics.We build upon an existing goal-directed generation system, S-STRUCT, which models sentence generation as planning in a Markov decision process.We develop a hybrid approach, which uses distributional semantics to quickly and imprecisely add the main elements of the sentence and then uses first-order logic based semantics to more slowly add the precise details.We find that our hybrid method allows S-STRUCT's generation to scale significantly better in early phases of generation and that the hybrid can often generate sentences with the same quality as S-STRUCT in substantially less time.However, we also observe and give insight into cases where the imprecision in distributional semantics leads to generation that is not as good as using pure logical semantics.