Reviewerly/exHarmony huggingface dataset:
One Concept, One Term, Good Practice but How to Achieve? —Improving Facet Values Quality for Samuel Proctor Oral History Collection, Hosted by the University of Florida Digital Collections

Faceted search, also known as dynamic taxonomies, is a popular feature applied to digital collection sites. Appearing as clickable labels, facets facilitate search result refinement and content browsing. To achieve the utmost efficiency, faceted search requires each facet value to represent a single concept–that is, one controlled vocabulary term represents one concept. However, in reality, this status is hard to achieve. An example of this can be seen in the digital collection of Samuel Proctor Oral History Program, a high-profile international resource hosted by the University of Florida Digital Collections. The Topical Subject and Genre terms appear with many vocabulary control issues: the same concept is often expressed with different terms; the same term appears with different spelling variations; and/or outdated terms mingle with more up-to-date ones. Additionally, compound terms that represent multiple concepts prohibit the grouping of content that share individual concepts. In short, a great deal of improvement will be needed to optimize the faceted search. To address these issues, the Digital Support Services department of the George A. Smathers Libraries at the University of Florida launched a pilot metadata remediation project using an out-of-box product – Oxygen XML Editor. This article, in addition to providing one more metadata remediation case study, traces the discussions around metadata quality and analyses the general metadata remediation process. Moreover, this article enriches the discussion of vocabulary control in relation to a core function of digital collection sites–faceted search.
Bacteria Associated with Students’ Mobile Phones-Caritas University, Enugu State

Microorganisms are ubiquitous, they are found everywhere. Mobile phones are not an exception. Mobile phones, also referred to as palmtops act as fomites, a source of infection because the palms used to handle mobile phones are often times in contact with surfaces which may have been pre-infected; nevertheless, their potential role in transmission of infections is of great concern. A cross-sectional study (male and female) was done from June to August 2018 at Caritas University, Amorji-Nike, Enugu State, in order to investigate the prevalence of bacterial contamination of mobile phones of students. Swab samples were collected from 50 mobile phones of Caritas University students. These were tested for bacterial contamination in the Microbiology laboratory. Quantification of bacteria was performed using standard streak plate technique. Isolated bacteria were identified using standard microbiological methods which include: cultural and morphological characterization and biochemical test. Antimicrobial susceptibility was also done. The microorganism isolated from students’ mobile phones and their percentage frequency of occurrence were: E. coli (31.9%), S. aureus (40.4%), P. aeruginosa (8.5%), K. pneumonia (4.3%) and Streptococcus sp. (14.9%). The result showed that Staphylococcus sp. has the highest percentage of occurrence. The antibiotic sensitivity test indicated the varied resistance of isolated bacteria to antibiotics used in this study, although most isolated bacteria were sensitive to erythromycin and chloramphenicol except the isolates of E. coli which were the most resistant to the antibiotics used. The high prevalence of bacterial agents isolated from students’ mobile phones was attributed to poor hygiene and sanitary practices. It is recommended that students should wash their hands after using mobile phones, before eating or undertaking any venture requiring sepsis.
Insider Threats Among Dutch SMEs: Nature and Extent of Incidents, and Cyber Security Measures

Insider threats represent a latent risk to all organizations, whether they are large companies or SMEs. Insiders, the individuals with privileged access to the assets of organizations, can compromise their proper functioning and cause serious consequences that can be direct—such as financial—or indirect—such as reputational. Insider incidents can have a negative impact on SMEs, as their resources are often limited, making it paramount to implement adequate cyber security measures. Despite its indisputable relevance, the empirical study of insider incidents from a criminological point of view has received little attention. This paper presents the results of an exploratory study that aims to understand the nature and extent of three type of insider incidents—malicious, negligent, and well-meaning—and how they are related to the adoption of cyber security measures. To that end, we administered a questionnaire among a panel of 496 Dutch SME entrepreneur and managers and analysed the results quantitatively and qualitatively. The results show that although the prevalence of insider incidents is relatively low among Dutch SMEs, few organizations report a disproportionate number of incidents that often entail serious consequences. A regression model shows that there are cyber security measures related to both higher and lower incident likelihood. The implications of these findings for the cyber security policies of SMEs are discussed.
Meta-Analysis of early childhood learning during the Covid-19 Pandemic using the prism method

In the current Covid-19 transition period, innovations in early childhood learning models are needed that are effectively used. The many models of early childhood learning during this pandemic need to be studied further which are appropriate for implementation. This study aims to empirically verify the early childhood learning model during the COVID-19 pandemic and analyze the factors that influence early childhood learning during the Covid-19 period. The method used in writing this article is a systematic literature review, which is a literature search from national article sources conducted using indexed searches on Google Scholar and Microsoft Academic. This literature review was conducted using the prism method technique. The articles reviewed are the most recent 50 articles from 2020 to 2021, and the results were sorted into 26 articles. All articles are classified by year of publication, type of indexing journal, method, and early childhood learning model. The results of the study are presented as follows. (1) The search results prove an increase in the number of articles published from 2020-2021. (2) The results of the study show that the most widely used learning model in early childhood learning involves the participation and assistance of parents in children's learning at home through learning media created by teachers and distributed through social media. (3) The obstacles that can be identified are: (a) the role of parents in children's learning during the pandemic is still less than optimal, (b) the lack of mastery of technology skills of students and parents during online learning. So it is necessary to develop an early childhood learning model that can be applied in online implementation, able to facilitate collaboration between parents, teachers, and students.
Artificial intelligence as the cognitive value of heuristic models

This article is devoted to heuristic modeling of the structure of interconnections in nano-, bio- and information technologies and in cyber-physical systems, graphic images of which allow studying the fine structure of information sources of various natures. It is shown here that the information content of models of the structure of interconnections of nano-, bio-, information technologies and components of cyber-physical systems is due to the use of a fractal triangle and logic. The current work harmonizes human-computer interaction through digital complementary methods for studying the structure of information sources of various natures, as well as using the circle of natural colors by I. Goethe and a new interpretation of the Star of David. Here we show that balanced conjugate triangles in heuristic models allow inversion of transition states of information sources of different nature. Static, dynamic and statistical generalized heuristic models are based on the balance of resources and the asymmetry of feedback between the elements of a complex dynamic system. Their relationship determines the variability of cyclic processes, and the complementarity of dynamic and statistical heuristic models is associated with the dualism of nature. A systematic analysis of explicit and hidden relationships in information flows of various natures opens up qualitatively new opportunities for a cognitive dialogue with nature and an understanding of reality. The complementarity of heuristic and cognitive models in the transdisciplinary cognitive space provides innovative potential for solving urgent problems of education, science and new technologies. This is especially important for the further development of artificial intelligence and the harmonization of human-computer interaction. In particular, online forecasting of the transient functional states of information sources of various nature under unforeseen conditions simplifies the interdisciplinary exchange of ideas, methods and technologies. Since the number of honeycomb structures in nature is two to three orders of magnitude greater than that of artificial models, their connection with the harmony of colors in nature contributes to the development of emotional intelligence, creating new opportunities for solving relevant security problems.
An Improved Target Tracking Scheme based on MC-MPMC Method for Mobile Wireless Sensor Networks

Abstract Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (Measurement Compensation based Mixture Population Monte Carlo) method which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee the sample diversity. And also, extra measurements or observations generating method is introduced to compensate missing predict locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, the HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.
eCom'22: The SIGIR 2022 Workshop on eCommerce

eCommerce Information Retrieval (IR) is receiving increasing attention in the academic literature and is an essential component of some of the world's largest web sites (e.g. Airbnb, Alibaba, Amazon, eBay, Facebook, Flipkart, Lowe's, Taobao, and Target). SIGIR has for several years seen sponsorship from eCommerce organisations, reflecting the importance of IR research to them. The purpose of this workshop is (1) to bring together researchers and practitioners of eCommerce IR to discuss topics unique to it, (2) to determine how to use eCommerce's unique combination of free text, structured data, and customer behavioral data to improve search relevance, and (3) to examine how to build datasets and evaluate algorithms in this domain. Since eCommerce customers often do not know exactly what they want to buy (i.e. navigational and spearfishing queries are rare), recommendations are valuable for inspiration and serendipitous discovery as well as basket building.
Analisa UI/UX Sistem Informasi Penjualan Berbasis Mobile Menggunakan Metode Prototype

In general, companies have problems that make a job more difficult. Apart from taking a long time, ineffective processing can also have a negative impact on a company. PT. Anugerah Mitra Semesta is a company engaged in PVC trading. The problem that exists in PT. Anugerah Mitra Semesta is the Sales Information System which is still done manually. Thus making the company slower in pursuing sales targets. Therefore, the solution that can help these problems is to conduct an analysis in order to create a good sales program, easy to use and become an illustration for making a sales program for the company. This analysis is used with UI/UX design using the prototype method. The Prototype method is a design display system design process that is created as an example to develop a product as an illustration for the user directly, and the types presented are Wireframe, Mockup and prototypes
Measure the Performance by Analysis of Different Boosting algorithms on Various Patterns of Phishing datasets

Abstract The internet has become an important component of our daily lives. The most common internet service is web surfing. Many individuals use their browser to do things like online shopping, bill payment, cell phone recharge, and banking transactions. Customers confront different security dangers, such as cyber crime, as a result of the widespread usage of this service. Cyber phishing is a type of web threat that entices users to connect with a false website. The main goal of this research paper is to prevent the user's sensitive information. The proposed model is developed in three steps in step1 we choose a dataset to train on, and then test classifiers on the dataset. In step2 we have applied the three classifiers finding phishing detection accuracy and finally step3 after completing all of the predictions, we discovered that XGBoost outperformed AdaBoost and Gradient boosting machine learning algorithms.
Supervised Visual Attention for Simultaneous Multimodal Machine Translation

There has been a surge in research in multimodal machine translation (MMT), where additional modalities such as images are used to improve translation quality of textual systems. A particular use for such multimodal systems is the task of simultaneous machine translation, where visual context has been shown to complement the partial information provided by the source sentence, especially in the early phases of translation. In this paper, we propose the first Transformer-based simultaneous MMT architecture, which has not been previously explored in simultaneous translation. Additionally, we extend this model with an auxiliary supervision signal that guides the visual attention mechanism using labelled phrase-region alignments. We perform comprehensive experiments on three language directions and conduct thorough quantitative and qualitative analyses using both automatic metrics and manual inspection. Our results show that (i) supervised visual attention consistently improves the translation quality of the simultaneous MMT models, and (ii) fine-tuning the MMT with supervision loss enabled leads to better performance than training the MMT from scratch. Compared to the state-of-the-art, our proposed model achieves improvements of up to 2.3 BLEU and 3.5 METEOR points.
Credit Card Fraud Detection Using Machine Learning Techniques

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today's banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary losses, not just for financial institutions but also for individuals. as technology and usage patterns evolve, making credit card fraud detection a particularly difficult task. Traditional statistical approaches for identifying credit card fraud take much more time, and the result accuracy cannot be guaranteed. Machine learning algorithms have been widely employed in the detection of credit card fraud. The main goal of this review intends to present the previous research studies accomplished on Credit Card Fraud Detection (CCFD), and how they dealt with this problem by using different machine learning techniques.
An Adaptive Moving Mesh Method for the Five-Equation Model

The five-equation model of multi-component flows has been attracting much attention among researchers during the past twenty years for its potential in the study of the multi-component flows.In this paper, we employ a second order finite volume method with minmod limiter in spatial discretization, which preserves local extrema of certain physical quantities and is thus capable of simulating challenging test problems without introducing non-physical oscillations.Moreover, to improve the numerical resolution of the solutions, the adaptive moving mesh strategy proposed in [Huazhong Tang, Tao Tang, Adaptive mesh methods for one-and two-dimensional hyperbolic conservation laws, SINUM, 41: 487-515, 2003] is applied.Furthermore, the proposed method can be proved to be capable of preserving the velocity and pressure when they are initially constant, which is essential in material interface capturing.Finally, several classical numerical examples demonstrate the effectiveness and robustness of the proposed method.
Towards a General Purpose CNN for Long Range Dependencies in $N$D

The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learning framework. However, performant CNN architectures must be tailored to specific tasks in order to incorporate considerations such as the input length, resolution, and dimentionality. In this work, we overcome the need for problem-specific CNN architectures with our Continuous Convolutional Neural Network (CCNN): a single CNN architecture equipped with continuous convolutional kernels that can be used for tasks on data of arbitrary resolution, dimensionality and length without structural changes. Continuous convolutional kernels model long range dependencies at every layer, and remove the need for downsampling layers and task-dependent depths needed in current CNN architectures. We show the generality of our approach by applying the same CCNN to a wide set of tasks on sequential (1$\mathrm{D}$) and visual data (2$\mathrm{D}$). Our CCNN performs competitively and often outperforms the current state-of-the-art across all tasks considered.
GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval

This paper is interested in investigating whether human gaze signals can be leveraged to improve state-of-the-art search engine performance and how to incorporate this new input signal marked by human attention into existing neural retrieval models. In this paper, we propose GazBy (Gaze-based Bert model for document relevancy), a lightweight joint model that integrates human gaze fixation estimation into transformer models to predict document relevance, incorporating more nuanced information about cognitive processing into information retrieval (IR). We evaluate our model on the Text Retrieval Conference (TREC) Deep Learning (DL) 2019 and 2020 Tracks. Our experiments show encouraging results and illustrate the effective and ineffective entry points for using human gaze to help with transformer-based neural retrievers. With the rise of virtual reality (VR) and augmented reality (AR), human gaze data will become more available. We hope this work serves as a first step exploring using gaze signals in modern neural search engines.
Key Generation for Vigenere Ciphering Based on Genetic Algorithm

Cryptography is a science securing of information. Encryption requires impregnable keys to encrypt or decrypt data these keys should be unpredictable and not easily to break. In this research we use genetic algorithm to generate keys for vigenere cipher. The best key is used to perform encryption. The keys created by genetic algorithm are tested for randomness by using the entropy test. The entropy calculation shows that randomness of key generated based on genetic processing is better than chosen key in the classical vigenere cipher.
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation

The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by occlusions, signal missing, or manual annotation errors, can confuse deep 3D object detectors during training, thus deteriorating detection accuracy. However, existing methods overlook such issues to some extent and treat the labels as deterministic. In this paper, we formulate the label uncertainty problem as the diversity of potentially plausible bounding boxes of objects. Then, we propose GLENet, a generative framework adapted from conditional variational autoencoders, to model the one-to-many relationship between a typical 3D object and its potential ground-truth bounding boxes with latent variables. The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors to build probabilistic detectors and supervise the learning of the localization uncertainty. Besides, we propose an uncertainty-aware quality estimator architecture in probabilistic detectors to guide the training of the IoU-branch with predicted localization uncertainty. We incorporate the proposed methods into various popular base 3D detectors and demonstrate significant and consistent performance gains on both KITTI and Waymo benchmark datasets. Especially, the proposed GLENet-VR outperforms all published LiDAR-based approaches by a large margin and achieves the top rank among single-modal methods on the challenging KITTI test set. The source code and pre-trained models are publicly available at \url{https://github.com/Eaphan/GLENet}.
Optimized Load Balancing Using Adaptive Algorithm in Cloud Computing with Round Robin Technique

Abstract: Developments in the field of computer networks have been carried out by several groups. However, there are still a lot of wrong problems one is the server load. For this reason, a system will be implemented Load Balancing with the aim of overcoming the server load which is not in accordance with its capacity and to optimize server load before and after the implementation of the Round robin Algorithm Load Balancing system on the cloud servers. The method used is the comparative method, namely researches that compares and analyze two or more symptoms, compare least connection algorithm as the previous algorithm with Round robin algorithm. Load Balancing Testing with both algorithms using a software called Httperf. Httperf displays the value according to parameters. The parameters used are Throughput, Response Time, Error and CPU Utilization. The test results show that load balancing with the algorithm round robin is more effective to handle server load than algorithm.