Reviewerly/exHarmony huggingface dataset:
Energy-Saving Multi-version Timestamp Ordering Algorithm for Virtual Machine Environments

In order to realize distributed applications, conflicting transactions are required to be serializable. The Multi-Version Concurrency Control (MVCC) is widespread to not only improve the concurrency of transactions but also serialize transactions. The Multi-Version Timestamp Ordering (MVTO) algorithm is one of algorithms to realize the MVCC. However, the MVTO algorithm does not consider the electric energy consumption of servers to perform transactions. In this paper, the Energy-Saving Multi-Version Timestamp Ordering with Virtual Machines (ESMVTO-VM) algorithm is newly proposed to not only improve the concurrency of transactions but also decrease the electric energy consumption of a cluster of servers which are equipped with virtual machines without performing meaningless write methods. In evaluation, we show the ESMVTO-VM algorithm can decrease the electric energy consumption of a cluster of servers than the MVTO algorithm.
CPU Usage Prediction Model: A Simplified VM Clustering Approach

Machine learning algorithms play an important role in resource management, allowing the improvement of the efficiency of resource usage in data centers (DCs) by predicting workload trends. In this paper, we propose a simplified system to predict the CPU usage of virtual machines (VMs) in a DC using Linear Regression Models while performing VM clustering based on common statistical characteristics of VM time series, which facilitates grouping VMs with similar behaviors and establishing clusters based on these characteristics. For each cluster, three representative VMs are established based on the time series of the closest VM to the cluster centroid, averaged time series for the cluster, and concatenated time series. Then, training of representative VMs is performed to finally choose the one with the lowest mean error per cluster. Simulation results show that, by performing clustering and training the model with representative time series, it is indeed possible to obtain a low mean error while reducing the local training time per VM.
Artificial intelligence-based diagnosis of breast cancer by mammography microcalcification

Mammography is the mainstream imaging modality used for breast cancer screening. Identification of microcalcifications associated with malignancy may result in early diagnosis of breast cancer and aid in reducing the morbidity and mortality associated with the disease. Computer-aided diagnosis (CAD) is a promising technique due to its efficiency and accuracy. Here, we demonstrated that an automated deep-learning pipeline for microcalcification detection and classification on mammography can facilitate early diagnosis of breast cancer. This technique can not only provide the classification results of mammography, but also annotate specific calcification regions. A large mammography dataset was collected, including 4,810 mammograms with 6,663 microcalcification lesions based on biopsy results, of which 3,301 were malignant and 3,362 were benign. The system was developed and tested using images from multiple centers. The overall classification accuracy values for discriminating between benign and malignant breasts were 0.8124 for the training set and 0.7237 for the test set. The sensitivity values of malignant breast cancer prediction were 0.8891 for the training set and 0.7778 for the test set. In addition, we collected information regarding pathological sub-type (pathotype) and estrogen receptor (ER) status, and we subsequently explored the effectiveness of deep learning-based pathotype and ER classification. Automated artificial intelligence (AI) systems may assist clinicians in making judgments and improve their efficiency in breast cancer screening, diagnosis, and treatment.
On Enhanced Intelligent Water Drops Algorithm for Travelling Salesman Problem under Uncertain Paradigm

Abstract Travelling salesman problem (TSP) is a well known combinatorial optimization problem which has drawn colossal attention due to its eclectic range of applications. In this article, we have proposed two modified versions of intelligent water drops (IWD) algorithm. The first one is the enhanced IWD (e-IWD) algorithm to solve single objective TSP. In the second modification, e-IWD algorithm has been extended to enhanced multi-objective IWD(e-MIWD) algorithm for solving multi-objective TSP. In order to achieve a better exploration capability in both of the proposed algorithms, the soil and velocity parameters of a randomly selected water drop are updated after every iteration of the algorithm when it traverses all the intermediate vertices for a tour. The proposed algorithms have been compared with some other existing similar algorithms on different benchmark instances of TSPs. Furthermore, we have addressed the TSP for both single and multiple objectives under uncertain environment.
Fast but approximate homomorphic k-means based on masking technique

Abstract Nowadays, computing on encrypted data seems to be more practical than a few years ago, thanks to the emergence of new Homomorphic Encryption schemes. In this paper, an algorithm based on Homomorphic Encryption for Arithmetic of Approximate Numbers (Cheon et al., in: Takagi, Peyrin (eds) Advances in cryptology—ASIACRYPT 2017, Springer, Cham, pp 409–437, 2017) (HEAAN, or also CKKS) scheme, that is able to perform a secure k -means algorithm which processes encrypted data, has been studied and presented. The performance of the classifier running on encrypted data has been evaluated using a standard k -means algorithm that works on plain data as a supervised structure, since the results are obtained by approximated computations. The main point of this paper is to take existent theoretical techniques (for example approximations of $$\text {sgn}(x)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mtext>sgn</mml:mtext> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> ), to use them and to observe if they are valid in practical applications. The output of the algorithm is a set of k encrypted masks that can be applied to the original dataset in order to obtain different clusters. The setting is a standard client–server one. The workload is heavily server-centric, as the client only has to execute a light masking algorithm at the end of each iteration, which, excluding the decryption, is faster than a plain k -means iteration; the main disadvantage concerns the accuracy of the results. Experiments show that the algorithm can be executed fairly quickly: the execution time of the training phase is on the order of seconds, while classification is on the order of tenths of a second.
Credit Card Fraud Detection

Nowadays, detecting credit card fraud is a major social issue. Credit card usage on e-commerce and banking websites has quickly expanded in recent years. The usage of credit cards in online transactions has made it simple, but it has also increased the frequency of fraud transactions. Modernization will have both beneficial and negative effects. It is always encouraged for banks and e-commerce sites to have automatic fraud detection systems as part of the operations taking place. Huge financial losses could be the outcome of credit card theft. Machine learning approaches offer good answers when searching for ways to stop credit card fraud from happening. When compared to other algorithms currently being used, the proposed system achieves greater accuracy by using a random forest application to solve the issue. All of the fundamental classifiers have the same weight, but the random forest algorithm has a relatively high weight while the others have relatively low weights due to the fact that the bootstrap sampling of decision-making and attribute selection cannot be guaranteed to be equally stable across all classifiers.
Determination of Maximum Accuracy of Concrete Textures as Natural Targets for Movement Tracking Through DIC

Abstract The use of natural targets is one of the obstacles to the extensive use of digital image cross-correlation for measuring movements in civil structures. Long distance measurement through image and without access to the structure itself, brings results in an improvement in accessibility, being the procedure cheaper and safer than common methods that require direct access to the measuring point. One of the most used materials in construction is concrete. Therefore, it is important to analyze its performance when using image cross-correlation. In this work, we have made a series of concrete probes with different production characteristics to have a representative variety of concrete surfaces. With them, we have studied their floor error in a cross-correlation procedure using different illumination and blur conditions, to evaluate the influence of those parameters on the results. All results are compared to those obtained using the conventional texture for image cross-correlation techniques, that is a pseudo-speckle target. All experiments are done in laboratory conditions to control all variables involved and to avoid the influence of other variables linked to open air conditions, such as atmospheric disturbances. As a result, we have determined the best conditions to use the natural concrete texture and we have quantified that using this texture leads to a decrease in the accuracy of the results from two to three times the one obtained with a typical pseudo-speckle texture.
Conversational chat system using attention mechanism for COVID-19 inquiries

Conversational artificial intelligence (AI) is a type of artificial intelligence that uses machine learning techniques to understand and respond to user inputs. This paper presents a conversational chat system that uses an attention mechanism to respond to COVID-19 inquiries. The model is based on the Luong Attention Mechanism’s three scoring methodologies the Dot Attention Mechanism, the General Attention Mechanism, and the Concat Attention Mechanism. The results show that the accuracy of the dot attention mechanism is highest and is 87% when the test questions were obtained directly from the database, as determined by an examination of the results, compared to 38% when the attention mechanism is not used. Furthermore, when the questions are asked with natural variations, human verification accuracy is 63% compared to 16% when the attention mechanism is not used. The research suggests that chatbots can be used everywhere due to their accuracy and accessibility around the clock.
Solidarity as an Empirical-Ethical Framework for the Analysis of Contact Tracing Apps — a Novel Approach

Abstract Digital contact tracing is used in different countries to help contain the COVID-19 pandemic. It raises challenging empirical and ethical questions due to its complexity and widespread effects calling for a broader approach in ethical evaluation. However, existing approaches often fail to include all relevant value perspectives or lack reference to empirical data on the artifact in question. In this paper, we describe the development of an interdisciplinary framework to analyze digital contact tracing from an empirical and ethical perspective. Starting with an analysis of methodological tensions in the attempt to analyze digital contact tracing, we, firstly, set out three methodological premises regarding (a) a specific view of technology, (b) a fruitful normative perspective, and (c) ways to gather empirical knowledge about the object under investigation. Secondly, we inspect consequences of these premises to develop our research methodology. In doing so, we argue for a postphenomenological perspective on technology, solidarity as an important concept to guide the ethical perspective, and an empirical approach based on qualitative social science research and the concept of affordances. We illustrate these considerations by referring to our analysis of the German Corona-Warn-App as an instance of contact tracing based on the Exposure Notification Framework by Google and Apple. We then discuss the implications of using such a framework, including considerations concerning future developments of technologies similar to or inspired by the current concepts of contact tracing.
More human than human: LLM-generated narratives outperform human-LLM interleaved narratives

Narrative story generation has gained emerging interest in the field of large language models. The present paper aims to compare stories generated by an LLM only (non-interleaved) with those generated by interleaving human-generated and LLM-generated text (interleaved). The study’s hypothesis is that interleaved stories would perform better than non-interleaved stories. To verify this hypothesis, we conducted two tests with roughly 500 participants each. Participants were asked to rate stories of each type, including an overall score or preference and four facets—logical soundness, plausibility, understandability, and novelty. Our findings indicate that interleaved stories were in fact less preferred than non-interleaved stories. The result has implications for the design and implementation of our story generators. This study contributes new insights into the potential uses and restrictions of interleaved and non-interleaved systems regarding generating narrative stories, which may help to improve the performance of such story generators.
A Study of Creative Development with an IoT-based Audiovisual System: Creative Strategies and Impacts for System Design

In this paper we describe a qualitative study investigating how artists work with a scalable and distributed audio-visual installation system that utilises IoT technology. With no prior experience of the system invited artists incorporated the new technology according to a creative brief for a public performance. We examined how they (i) built an understanding of the technology’s affordances, (ii) refined their creative goals, and (iii) deployed collaborative strategies to achieve creative outcomes. We examine how the artists worked from the examples we provided to integrate our audio-visual system and develop their creative work. We identify three distinct creative strategies and use these to suggest ways that the design of examples, presets and readymade configurations can be successfully integrated into interfaces for new creative technologies.
Tangible, Public, and Miniature Creative Exchanges: What HCI and Design Researchers Can Learn From the Free Little Art Gallery Movement

HCI researchers are continually exploring new ways of engaging the public in participatory design and bringing creative making research activities to new audiences. In this paper, we interviewed individuals who independently began public and DIY installations for sharing miniature art among their neighbours. During the COVID-19 pandemic, participatory miniature art exchanges, commonly known as Free Little Art Galleries (FLAGs), organically spread in response to lockdowns and institutional constraints. In this qualitative study, we interviewed 20 FLAG ‘curators’ to understand the implications involved in setting up and maintaining these long-term deployments. From the analysis of these interviews, we provide 5 practical recommendations on supporting these types of deployments, and discuss how HCI researchers can expand upon these DIY participatory practices to bring creative ideation activities on the future of technology to broader audiences.
Embodying an Interactive AI for Dance Through Movement Ideation

What expectations exist in the minds of dancers when interacting with a generative machine learning model? During two workshop events, experienced dancers explore these expectations through improvisation and role-play, embodying an imagined AI-dancer. The dancers explored how intuited flow, shared images, and the concept of a human replica might work in their imagined AI-human interaction. Our findings challenge existing assumptions about what is desired from generative models of dance, such as expectations of realism, and how such systems should be evaluated. We further advocate that such models should celebrate non-human artefacts, focus on the potential for serendipitous moments of discovery, and that dance practitioners should be included in their development. Our concrete suggestions show how our findings can be adapted into the development of improved generative and interactive machine learning models for dancers’ creative practice.
Youth digital writing on WhatsApp and the teaching of spelling

Instant messaging applications integrated in smartphones have favored the emergence of new digital writing systems, which are characterized by the use of specific spellings called textisms. This study analyses the relationship between the use of textisms and spelling mistakes in academic texts. The methodology applied was descriptive, based on a directed content analysis. The sample consisted of 270 students in the third and fourth years of compulsory secondary education enrolled in twelve public high schools in the autonomous community of Andalusia. Data were obtained from real texts provided by the students participating in the study, extracted, on the one hand, from WhatsApp messages from their smartphones, and, on the other, from academic texts produced as an activity in the secondary school classroom. The results obtained from the descriptive analyses and the bivariate correlation analyses (Pearson coefficient) allow us to affirm that the average number of textisms in WhatsApp of the sample is much higher than the number of misspellings in school texts, which confirms that textisms are intentional discrepancies with the academic norm in the digital context. Consequently, they cannot be considered as misspellings that occur due to a lack of knowledge of the Spanish language, but rather as new forms of language generated by the use of digital technologies. Las aplicaciones de mensajería instantánea integradas en los teléfonos inteligentes han favorecido el surgimiento de nuevos sistemas de escritura digital, que se caracterizan por emplear grafías específicas denominadas textismos. Este estudio analiza la relación entre el uso de textismos y las faltas de ortografía en textos académicos. La metodología aplicada ha sido de carácter descriptivo, basado en el análisis de contenido dirigido. La muestra la integran 270 estudiantes de tercero y cuarto de Educación Secundaria Obligatoria matriculados en doce institutos públicos de la Comunidad Autónoma Andaluza. Los datos se obtuvieron a partir de textos reales aportados por el alumnado participante en el estudio, extraídos por un lado de mensajes de WhatsApp de sus teléfonos inteligentes y por otro de textos académicos elaborados como actividad del aula de Secundaria. Los resultados obtenidos a partir de los análisis descriptivos y los análisis de correlación bivariada (coeficiente de Pearson) permiten afirmar que el promedio de textismos en WhatsApp de la muestra es muy superior al de faltas de ortografía en los textos escolares; lo que confirma que los textismos son discrepancias intencionadas con la norma académica en el contexto digital. En consecuencia, no pueden ser considerados como faltas de ortografía que se producen por desconocimiento de la lengua española, sino nuevas formas de lenguaje generadas por el uso de las tecnologías digitales.
BDSL 49: A comprehensive dataset of Bangla sign language

Language is a method by which individuals express their thoughts. Each language has its own alphabet and numbers. Oral and written communication are both effective means of human interaction. However, each language has a sign language equivalent. Hearing-impaired and/or nonverbal individuals communicate through sign language. BDSL is the abbreviation for the Bangla sign language. The dataset contains images of hand signs in Bangla. The collection comprises 49 individual sign language images of the Bengali alphabet. BDSL49 is a set of 29,490 images with 49 labels. During data collection, images of fourteen distinct adults, each with a unique appearance and context, were captured. During data preparation, numerous strategies have been utilized to reduce noise. This dataset is available for free to researchers. Using techniques such as machine learning, computer vision, and deep learning, they are able to develop automated systems. Moreover, two models were applied to this dataset. The first is for detection, and the second is for identification.
Prevention of E-Commerce Fraud in Bangladesh: A Critical Study on Legal and Institutional Framework

Bangladesh is a developing country that is starting to embark on its journey into the realm of e-commerce and online shopping. With the increasing number of online shops, there is potential for Bangladesh to take the lead in the e-commerce sector. However, there is a gap in e-commerce law in Bangladesh. Currently, there is no specific statutory law in Bangladesh governing the e-commerce industry, which means that various laws are applied depending on the situation and type of dispute. The legislation related to e-commerce scams is outdated and not suitable for the current era. This gap and lack of law and regulation hinder our growth in the e-commerce industry. So, it is essential to address the existing gaps and weaknesses in the relevant laws that encourage e-commerce fraud and this is the aim of this paper.
A Bayesian Convolutional Neural Network Model with Uncertainty for Multi-label Text Classification on Mechanisms of Action (MoA) Prediction

With the development of scientific research techniques, drug discovery has shifted from the serendipitous approach of the past to more targeted models based on an understanding of the underlying biological mechanisms of disease. However, there are hundreds or more of mechanism of action (MoA) data in the known drugs, which makes this process faced with complicated multi-label classification of text data. Traditional multi-label text classification algorithms will increase the complexity of the model and reduce the accuracy as the number of labels increases. Although deep learning algorithms can solve the problem of model complexity, they are currently only suitable for processing image format data. To overcome these problems, this study proposes a multi-label classification method based on Bayesian deep learning, which can convert non-image data format into image data, making it suitable for Convolutional neural network algorithm requirements. Then in the PyTorch environment, the Bayesian deep learning algorithm and the EfficientNet convolutional neural network are perfectly combined using the BLiTZ library to construct the Bayesian convolutional neural network model which named BCNNM. Not only improves the classification efficiency, this method also solves the problem of imbalanced classification of multi-label data, and fully considers the uncertainty in the neural network. In the process of drug development, this method has important practical significance for processing the multi-label classification of MoA data.
Semantic Wrap and Personalized Recommendations for Digital Archives

Many efforts have been made in the Cultural Heritage field to build digital repositories based on standards for technological interoperability and for enriching the semantic content and the interconnection among the archived objects and with other repositories. The availability of standard APIs for linked information allows for the design and implementation of personalized recommendation systems, which are a relevant service for digital archives that aims at promoting the valorization of their content to visitors. In this paper, a prototype development of such a mechanism that is based on IIIF standards and IIIF-compliant open-source technologies is described.