Reviewerly/exHarmony huggingface dataset:
An Efficient PSO-Based Algorithm for Finding Maximal Exact Match in Large DNA Sequences

With the appearance of complete mammalian genomes, comparative approaches have experienced a recent upsurge. Searching maximal exact match is among the most used tasks in sequence searching within a larger DNA sequence or database. Many exact algorithms have been designed to deal with this problem. The best improvements made by these algorithms have led to a time and space complexity of O(n) and they remain practically less effective for large sequences. Heuristic methods will therefore be a good alternative to implement. In this work, we present an efficient heuristic algorithm based on PSO metaheuristic to deal with the problem of searching the maximal exact match of small searched sequences in large ones. The time and space complexity of the designed algorithm is of O(1). The experimental results showed the efficiency of the proposed search algorithm for finding maximal exact match in large sequences when compared with two other sequence searching algorithms.
Research on the influences of information presentation on information capture and performance in digital control systems

This study investigates the effects of information presentation on operator performance in digital control systems at different levels of task difficulty in order to resolve the conflict between ‘large amounts of information’ and ‘limited display’. By using software to present figures of varying difficulty as target information, the operator’s behaviour such as page switching and target clicking is simulated to obtain data. Simultaneously, eye-tracking technique was used to analyse how the way information was presented at different levels of difficulty influenced the subjects’ search performance. The results show that subjects’ performance gradually decreases as the difficulty of the numbers grows for both presentations, and it is found that the complexity of the content of the navigation bar may have influenced the subjects to some extent. In addition, the different position of the navigation bar where the target is located will result in a change in the search pattern. It is noticeable that dividing the single page into double pages can effectively alleviate the contradiction of ‘large amount of information’ and ‘limited display’ in designing the system interface. Therefore, this study provides certain guideline value for the interface design of digital control systems.
Automated translation from domain knowledge to software model: EXCEL2UML in the tunneling domain

The development of software tools is a collaborative process involving both the domain experts and the software engineers. This requires efficient communication considering different expertise and perspectives. Additionally, the two groups utilize language and communication tools in disparate ways. This, in turn, may lead to hidden misunderstandings in the requirement analysis phase and potentially result in implementation problems later on, that is difficult and costly to correct. In this paper, we demonstrate the above mentioned challenge via a use case from the tunneling domain. In particular, during the requirement analysis phase for a software capable of handling the data model of the subsoil. The domain experts in the field can best express the complexity of their domain by describing its artifacts, which in most cases are incomprehensible to the software engineers. We outline a method that interleaves requirement analysis and software modeling to enable an iterative increase of the accuracy and completeness of the information extracted from those artifacts and integrated into a flexible software model, which can produce testable software code automatically. Furthermore, we present a prototypical implementation of our method and a preliminary evaluation of the approach.
An Interactive Image Segmentation Method Based on Multi-Level Semantic Fusion

Understanding and analyzing 2D/3D sensor data is crucial for a wide range of machine learning-based applications, including object detection, scene segmentation, and salient object detection. In this context, interactive object segmentation is a vital task in image editing and medical diagnosis, involving the accurate separation of the target object from its background based on user annotation information. However, existing interactive object segmentation methods struggle to effectively leverage such information to guide object-segmentation models. To address these challenges, this paper proposes an interactive image-segmentation technique for static images based on multi-level semantic fusion. Our method utilizes user-guidance information both inside and outside the target object to segment it from the static image, making it applicable to both 2D and 3D sensor data. The proposed method introduces a cross-stage feature aggregation module, enabling the effective propagation of multi-scale features from previous stages to the current stage. This mechanism prevents the loss of semantic information caused by multiple upsampling and downsampling of the network, allowing the current stage to make better use of semantic information from the previous stage. Additionally, we incorporate a feature channel attention mechanism to address the issue of rough network segmentation edges. This mechanism captures richer feature details from the feature channel level, leading to finer segmentation edges. In the experimental evaluation conducted on the PASCAL Visual Object Classes (VOC) 2012 dataset, our proposed interactive image segmentation method based on multi-level semantic fusion demonstrates an intersection over union (IOU) accuracy approximately 2.1% higher than the currently popular interactive image segmentation method in static images. The comparative analysis highlights the improved performance and effectiveness of our method. Furthermore, our method exhibits potential applications in various fields, including medical imaging and robotics. Its compatibility with other machine learning methods for visual semantic analysis allows for integration into existing workflows. These aspects emphasize the significance of our contributions in advancing interactive image-segmentation techniques and their practical utility in real-world applications.
Discrete Listwise Content-aware Recommendation

To perform online inference efficiently, hashing techniques, devoted to encoding model parameters as binary codes, play a key role in reducing the computational cost of content-aware recommendation (CAR), particularly on devices with limited computation resource. However, current hashing methods for CAR fail to align their learning objectives (e.g., squared loss) with the ranking-based metrics (e.g., Normalized Discounted Cumulative Gain (NDCG)), resulting in suboptimal recommendation accuracy. In this article, we propose a novel ranking-based CAR hashing method based on Factorization Machine (FM), called Discrete Listwise FM (DLFM), for fast and accurate recommendation. Concretely, our DLFM is to optimize NDCG in the Hamming space for preserving the listwise user-item relationships. We devise an efficient algorithm to resolve the challenging DLFM problem, which can directly learn binary parameters in a relaxed continuous solution space, without additional quantization. Particularly, our theoretical analysis shows that the optimal solution to the relaxed continuous optimization problem is approximately the same as that of the original discrete optimization problem. Through extensive experiments on two real-world datasets, we show that DLFM consistently outperforms state-of-the-art hashing-based recommendation techniques.
Electronic Booking and Payment Platforms for Inter-Campus E-Bikes

In South Africa, traditional transportation has become more expensive and not easily accessible to everyone. The current use of internal combustion engine (ICE) vehicles in the transport sector has been contributing about 14% to the country’s greenhouse gas emissions. In booking any e-hauling service, most of the current transportation requires pre-booking or standing in long queues to make a booking. These transactions require usually physical cash or insertion of card details online to process a booking payment. The outcome is prone to hackers and theft. Therefore, this paper focuses on the design and building of a safe electronic booking and payment application software for inter-campus e-bikes through a Quick Response (QR) code and Near-Field Communication (NFC). The application also encourages the use and integration of clean energy solutions in the transport sector and the saving of waiting time for inter-campus ICE buses as students would rather use e-bikes for their inter-campus mobility. Also, they can transact safely, affordably, and securely for their mobility costs using the developed application.
A statistical approach to aid examiners in the forensic analysis of handwriting

We develop a statistical approach to model handwriting that accommodates all styles of writing (cursive, print, connected print). The goal is to compute a posterior probability of writership of a questioned document given a closed set of candidate writers. Such probabilistic statements can support examiner conclusions and enable a quantitative forensic evaluation of handwritten documents. Writing is treated as a sequence of disjoint graphical structures, which are extracted using an automated and open-source process. The graphs are grouped based on the similarity of their shapes through a K-means clustering template. A person's writing pattern can be characterized by the rate at which graphs are emitted to each cluster. The cluster memberships serve as data for a Bayesian hierarchical model with a mixture component. The rate of mixing between two parameters in the hierarchy indicates writing style.
Library Catalogue on the Cloud: The BIBFRAME Approach

The purpose of this paper is to suggest that cataloguing community could benefit by publishing the catalogue records on the cloud using BIBFRAME model with its vocabularies to increase access of the library collection beyond the library. A desk research based on literature review extracted from different information sources. Literature was interpreted based on the search of the researcher on key concepts of the topic. Literature indicates that the establishments that publishes their catalogue on the cloud receive more exposure. Adoption of BIBFRAME could benefit the library users as genuine information published by the libraries will be available on the web.
Enhancing sparrow search algorithm with hybrid multi-strategy and its engineering applications

An enhancing sparrow optimization algorithm with hybrid multi-strategy (EGLTA-SSA) is proposed, to improve the defects of the sparrow search algorithm (SSA), which is easy to fall into local optimum. Firstly, the elite backward learning strategy is introduced to initialize the sparrow population, to generate high-quality initial solutions. Secondly, the leader position is updated by fusing multi-strategy mechanisms. On one hand, the high distributivity of arithmetic optimization algorithm operators are used to deflate the target position, and enhance the ability of SSA to jump out of the local optimum. On the other hand, the leader position is perturbed by adopting the golden levy flight method and the t-distribution perturbation strategy to improve the shortcoming of SSA in the late iteration when the population diversity decreases. Further, a probability factor is added for random selection to achieve more effective communication among leaders. Finally, to verify the effectiveness of EGLTA-SSA, CEC2005 and CEC2019 functions are tested and compared with state-of-the-art algorithms, and the experimental results show that EGLTA-SSA has a better performance in terms of convergence rate and stability. EGLTA-SSA is also successfully applied to three practical engineering problems, and the results demonstrate the superior performance of EGLTA-SSA in solving project optimization problems.
Disparity refinement framework for learning-based stereo matching methods in cross-domain setting for laparoscopic images

Stereo matching methods that enable depth estimation are crucial for visualization enhancement applications in computer-assisted surgery. Learning-based stereo matching methods have shown great promise in making accurate predictions on laparoscopic images. However, they require a large amount of training data, and their performance may be degraded due to domain shifts.Maintaining robustness and improving the accuracy of learning-based methods are still open problems. To overcome the limitations of learning-based methods, we propose a disparity refinement framework consisting of a local disparity refinement method and a global disparity refinement method to improve the results of learning-based stereo matching methods in a cross-domain setting. Those learning-based stereo matching methods are pre-trained on a large public dataset of natural images and are tested on two datasets of laparoscopic images.Qualitative and quantitative results suggest that our proposed disparity framework can effectively refine disparity maps when they are noise-corrupted on an unseen dataset, without compromising prediction accuracy when the network can generalize well on an unseen dataset.Our proposed disparity refinement framework could work with learning-based methods to achieve robust and accurate disparity prediction. Yet, as a large laparoscopic dataset for training learning-based methods does not exist and the generalization ability of networks remains to be improved, the incorporation of the proposed disparity refinement framework into existing networks will contribute to improving their overall accuracy and robustness associated with depth estimation.
Digital Transformation for Education Forced by COVID-19 Case Study – a Rural School from Romania

Abstract The historical events of COVID-19 pandemic reshaped society forcing an advancement in digital transformation. All the domains had to change and introduce innovation to be able to continue activities in a social distancing context. This was also the case for education which proposed the movement to online education as a solution for reducing the spread of the virus. This paper seeks to present the journey of a school in a rural area of Romania through the pandemic period. The changes they had to make and the lessons they learned are also highlighted. In the end, a glimpse into how things are in the present and how the pieces of digital methods used in the pandemic period bleed through to the present day-to-day activities in a face-to-face context is shown to underline the advancement in digital transformation that the COVID-19 pandemic imposed.
A Study of the Requirements for Online Teaching and Learning in Romanian Schools

Abstract The COVID-19 pandemic has compelled educational institutions to adopt online teaching to continue activity even when distancing was necessary. While the pandemic has had a detrimental impact on education, it has been demonstrated that online learning is superior to learning alone. This paradigm change in the delivery of education has made it possible for online programs to be considered outside of the pandemic. For countries with weak infrastructure in certain locations, which makes it difficult for all children to attend school every day, especially during harsh weather conditions, online learning might be utilized to provide continuous learning for all pupils. It can also be employed in circumstances where illness prevents students from attending class if online teaching is also available. Prerequisites for online teaching can be assessed based on the challenges that schools and pupils encountered during the pandemic. The present study was based on a survey, in which 522 teachers from different categories of Romanian schools participated. Differences in the digital means and attendance to online courses were analyzed based on the school's funding (public vs. private) and the school's environment (rural vs urban). The outcomes have revealed variations based on each of the analyzed categories, which can lead to a better understanding of how schools and students can be supported regarding the integration of technology in the teaching and learning process.
Improving Internet of Things Vulnerability Disclosure and Coordination

Abstract Internet of Things (“IoT”), specifically in the consumer space, describes an environment where consumer devices, connected to the Internet in a smart home, communicate to each other directly or through the cloud. Cheap manufacturing and a fast-growing market brought billions such devices in everyday homes, and consequently new concerns emerged about their security. The complexity added by these new systems, with fragmented in-house hardware and software platforms, have been recently the target of both scrutiny and controversy. When IoT devices get hacked it’s no longer just “script kiddies” and part-time hackers, it’s state actors and national security on the line. Where for PCs there is a robust cybersecurity product market (eg. “antivirus”), the majority of IoT devices in households are designed with little or no regard towards cybersecurity and the typical consumer’s understanding of how to secure these is lacking. Effective ways to safeguard IoT products are bug bounties, programs that offer a financial reward to anyone discovering vulnerabilities, but they are costly and hard to manage, thus usually adopted by more mature companies. All manufacturers can additionally benefit from responsible vulnerability disclosure, or ethical hacking, where researchers attempt to find vulnerabilities for recognition or as a public service. Unfortunately disclosing and coordinating vulnerability research challenges are downplayed. This paper proposes to investigate impact, discuss time considerations, and suggest potential solutions for consumers, companies, and regulators to mitigate and improve IoT vulnerability reporting, fixing and disclosure.
The Role of Attachment in Improving Consumer-AI Interactions

Abstract A successful implementation of artificial intelligence and robots in the service industry requires the acceptance and trust of consumers. In order to be able to provide similar services as human employees, AI devices and robots have to have the ability to interact and relate to the human consumer and, at the same time, to provide the expected information. Researches in the field of consumer services have shown that empathy and emotions can improve the relation between service employees and consumers, by providing more enjoyable interactions and a better exchange of information. In our research we investigate if the attachment between the consumer and AI devices can facilitate a higher quality of information exchange, a better and more pleasant interaction and if it determines a higher self-disclosing behavior from the consumer. Our results confirm that attachment has a positive impact on consumer-AI interactions, improving the perceived enjoyment of the conversation, a better quality of the exchanged information and determining a higher self-disclosing behavior. These results have important implications on the design of the future consumer-AI-interactions.
Unified chip hardware architecture of KD-tree mean-based trainer and speeding-up classifier with repeat-point searching for various applications

In this paper, we propose a total ASIC solution of unified low-latency K-dimensional tree (KD-tree) chip hardware architecture, including online mean-based trainer and speeding-up classifier. As compared with the other works in the current literature, we also develop a first KD-tree chip implementation to simultaneously perform training and classification tasks. Instead of supporting only one specific application, our work can be widely applied for various applications and well-verified with different datasets. Meanwhile, for different purposes, we also develop three useful design techniques, such as efficient trainer with mean-based splitting (ET-MBS), speeding-up classifier with repeat-point searching (SC-RPS), and double-leaf tree structure (DLTS). With TSMC 40-nm CMOS technology, the post-APR ASIC chip layout is well-verified with 7 representative datasets for various applications. The total core area only occupies 0.357 mm2, operating at a maximum frequency of 474 MHz and consuming power of 71.6 mW. Regarding online training, the worst-case training latency is only 1.9 μs for dealing with 1 K data. As for classification performance, the decision throughput varies from 0.51 GBps to 3.56 GBps.
Design of tables for the presentation and communication of data in ecological and evolutionary biology

Tables and charts have long been seen as effective ways to convey data. Much attention has been focused on improving charts, following ideas of human perception and brain function. Tables can also be viewed as two-dimensional representations of data; yet, it is only fairly recently that we have begun to apply principles of design that aid the communication of information between the author and reader. In this study, we collated guidelines for the design of data and statistical tables. These guidelines fall under three principles: aiding comparisons, reducing visual clutter, and increasing readability. We surveyed tables published in recent issues of 43 journals in the fields of ecology and evolutionary biology for their adherence to these three principles, as well as author guidelines on journal publisher websites. We found that most of the over 1000 tables we sampled had no heavy grid lines and little visual clutter. They were also easy to read, with clear headers and horizontal orientation. However, most tables did not aid the vertical comparison of numeric data. We suggest that authors could improve their tables by the right-flush alignment of numeric columns typeset with a tabular font, clearly identify statistical significance, and use clear titles and captions. Journal publishers could easily implement these formatting guidelines when typesetting manuscripts.
HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES

The main aim of predictive maintenance is to minimize downtime, failure risks and maintenance costs in manufacturing systems. Over the past few years, machine learning methods gained ground with diverse and successful applications in the area of predictive maintenance. This study shows that performing preprocessing techniques such as oversampling and features selection for failure prediction, is promising. For instance, to handle imbalanced data, the SMOTE-Tomek method is used. For features selection, three different methods can be applied: Recursive Feature Elimination, Random Forest and Variance Threshold. The data considered in this paper for simulation is used in literature; it is applied to aircraft engine sensors measurements to predict engines failure, while the predicting algorithm used is a Support Vector Machine. The results show that classification accuracy can be significantly boosted by using the preprocessing techniques.