Reviewerly/exHarmony huggingface dataset:
Signal Detection and Parameter Estimation of Low SNR Direct Sequence Spread Spectrum Signal

In order to realize the “ blind ” detection and parameter estimation of direct sequence spread spectrum signals under low signal-to-noise ratio (SNR) conditions, a direct sequence spread spectrum signal denoising algorithm based on wavelet packet-energy detection is proposed, which can significantly improve the SNR. Combining the wavelet packet denoising algorithm, the improved cyclic spectrum method, quadratic spectrum method and delay multiplication method, the accurate “ blind ” parameter estimation of binary phase shift keying / direct sequence spread spectrum (BPSK / DSSS) signal carrier frequency, spreading code period and spreading code rate can be realized under the condition of low SNR. The simulation results show that the proposed algorithm can realize the detection and parameter estimation of BPSKIDSSS signals more effectively.
Evaluating Rotation Invariant Strategies for Mitosis Detection Through YOLO Algorithms

Cancer diagnosis is of major importance in the field of human medical pathology, wherein a cell division process known as mitosis constitutes a relevant biological pattern analyzed by professional experts, who seek for such occurrence in presence and number through visual observation of microscopic imagery. This is a time-consuming and exhausting task that can benefit from modern artificial intelligence approaches, namely those handling object detection through deep learning, from which YOLO can be highlighted as one of the most successful, and, as such, a good candidate for performing automatic mitoses detection. Considering that low sensibility for rotation/flip variations is of high importance to ensure mitosis deep detection robustness, in this work, we propose an offline augmentation procedure focusing rotation operations, to address the impact of lost/clipped mitoses induced by online augmentation. YOLOv4 and YOLOv5 were compared, using an augmented test dataset with an exhaustive set of rotation angles, to investigate their performance. YOLOv5 with a mixture of offline and online rotation augmentation methods presented the best averaged F1-score results over three runs.
Financial innovation, corruption, and economic growth: analysis of Sub-Saharan African countries

Financial innovation allows the expansion of financing, facilitates domestic and international commercial transactions, and reduces bureaucracy for financial institutions in customer satisfaction. We aim to examine the determinants of financial innovation and to verify whether financial innovation positively impacts economic growth, despite the prevalence of corruption in the Sub-Saharan Africa region. Results show that the determinants of financial innovation in Sub-Saharan African countries are competitiveness and the crisis in the banking sector, the need to extend the portfolio of deposits and bank loans (financial inclusion), and increased internet access for households and investment. Similarly, corruption prevents the transmission of the positive effects of the financial system on economic growth, and reductions in corruption boost economic growth, so it will be important for policymakers to bear in mind that without the reduction and control of corruption in developing countries, financial innovation does not produce major advantages concerning the economic growth of African countries.
Energy saving slot allocation-based multicast routing in cloud wireless mesh network

Many techniques of cloud computing are affected by surge load and time issues. This proposed method for virtual cloud planning utilise simple protocols in a single or multiple data centres. A new load balanced amortised multi-scheduling algorithm (LBAM) is proposed for assigning the task to cloud based on active load in the cloud environment. The proposed system calculates the multiple attribute weight for each workplace and introduces an application source that changes in application virtualisation environment. The system calculates the cloud data weight based upon the allocation of data and its consequence based on the processing efficiency of the cloud machine. This approach works effectively in most configurations, also virtual machines hold the ability to process load with high pace and efficient memory management. A detailed comparative analysis is made with the existing methods namely SLA, FPGGA and DJS algorithms. The obtained results indicated that the LBAM model is superior to compared methods under several aspects.
A hybrid encryption for secure data deduplication the cloud

Cloud computing (CC) is a cost-effective platform for users to store their data on the internet rather than investing in additional devices for storage. Data deduplication (DD) defines a process of eliminating redundant data in the cloud by leaving a single copy. This paper provides a hybrid encryption scheme using genetic algorithm (GA) and attribute-based encryption (ABE) to ensure secure data transmission in the cloud and preventing the data deduplication by using message locked encryption (MLE). The hybrid encryption scheme provides a secure data transmission in the cloud. From the simulation output, the time requirement is reduced up to 94% and consumes 43% bandwidth compared with existing works. The proposed method tends to be quite effective in reducing storage and bandwidth costs.
Bipartite quantum state discrimination and decomposable entanglement witness

We consider bipartite quantum state discrimination using positive-partial-transpose measurements and show that minimum-error discrimination by positive-partial-transpose measurements is closely related to entanglement witness. By using the concept of decomposable entanglement witness, we establish conditions on minimum-error discrimination by positive-partial-transpose measurements. We also provide conditions on the upper bound of the maximum success probability over all possible positive-partial-transpose measurements. Finally, we illustrate our results using examples of multidimensional bipartite quantum states.
Workflow assessment of an augmented reality application for planning of perforator flaps in plastic reconstructive surgery: Game or game changer?

Objective In contrast to the rising amount of financial investments for research and development in medical technology worldwide is the lack of usability and clinical readiness of the produced systems. We evaluated an augmented reality (AR) setup under development for preoperative perforator vessel mapping for elective autologous breast reconstruction. Methods In this grant-supported research pilot, we used magnetic resonance angiography data (MR-A) of the trunk to superimpose the scans on the corresponding patients with hands-free AR goggles to identify regions-of-interest for surgical planning. Perforator location was assessed using MR-A imaging (MR-A projection) and Doppler ultrasound data (3D distance) and confirmed intraoperatively in all cases. We evaluated usability (System Usability Scale, SUS), data transfer load and documented personnel hours for software development, correlation of image data, as well as processing duration to clinical readiness (time from MR-A to AR projections per scan). Results All perforator locations were confirmed intraoperatively, and we found a strong correlation between MR-A projection and 3D distance measurements (Spearman r = 0.894). The overall usability (SUS) was 67 ± 10 (=moderate to good). The presented setup for AR projections took 173 min to clinical readiness (=availability on AR device per patient). Conclusion In this pilot, we calculated development investments based on project-approved grant-funded personnel hours with a moderate to good usability outcome resulting from some limitations: assessment was based on one-time testing with no previous training, a time lag of AR visualizations on the body and difficulties in spatial AR orientation. The use of AR systems can provide new opportunities for future surgical planning, but has more potential for educational (e.g., patient information) or training purposes of medical under- and postgraduates (spatial recognition of imaging data associated with anatomical structures and operative planning). We expect future usability improvements with refined user interfaces, faster AR hardware and artificial intelligence-enhanced visualization techniques.
Educational Issues Raised by the Availability of the Quantum Computer

The development of quantum computers is bringing major changes to the IT sector. These computers, based on completely new principles, can provide effective solutions to previously unsolvable problems. Regulations have already been introduced in the European Union and Hungarian law to confirm that we are getting closer to the era of quantum computers. Therefore, we believe that education and teachers should follow the development of these machines so that future students in the field of information technology, whether they are IT teachers, physicists, or programmers, are not caught unawares. In this article, we present some examples from abroad where quantum computing topics are already included at certain levels of education.
Digital competence and its components in basic education

Digital competence development in basic education is essential. The main purpose of this study was to measure the levels of digital competencies among students in four areas using a digital competency questionnaire to improve the effectiveness of digital management in basic education. The authors used the digital competence questionnaire for students as a measurement tool, which was subjected to validation. The respondents were 323 students aged 8–14 from three schools in the People’s Republic of China. The PLS-SEM-based CFA approach revealed the validity of the questionnaire, which resulted in high data reliability and consistency. Statistical analysis revealed no significant differences with gender and significant correlations (p < 0.05) with age. The students aged 13–14 scored the highest on the domains, while the youngest team aged 8–10 scored the lowest. This article is of practical value as it provides a verified tool for checking the digital competence levels of Chinese students. According to the results, it is crucial to incorporate the development of digital competencies into the elementary school settings to facilitate learning and provide children with an opportunity to achieve a higher digital competence in the future.
Using ChatGPT as a Digital/Smart Reference Robot: How May ChatGPT Impact Digital Reference Services?

The emergence of OpenAI Chat technology can replace the human expert because this technology, using machine learning and intelligent algorithms, is able to automatically provide the best answers, which leads to increased productivity and reduced human error. In this study, we will explain to what extent ChatGPT can be used instead of digital services. We answer this question by chatting with ChatGPT.
SN-YOLO: Improved YOLOv5 with Softer-NMS and SIOU for Object Detection

As a lightweight target detection network, YOLOv5 is popular in the industry for its advantages of fast speed and small model, but the detection accuracy is not very high. In response to this problem, we propose an improved model SN-YOLO based on YOLOv5. First, we introduce Softer-NMS as the post-processing method of the model, which will make the prediction box more accurate. Secondly, we improved the loss function of the original algorithm and introduced the SIOU loss function to optimize the model and improve the accuracy of the algorithm. Finally, in order to improve the feature extraction ability of the backbone, we implanted the CBAM (Convolutional block attention module) module into the algorithm. We validate the model using the 2007 and 2012 datasets of PASCAL VOC. The experimental results show that SN-YOLO has a great improvement over the original model in all aspects. The effectiveness of the algorithm is verified.
The Multi-Level Cryptodata Analytical Intelligence System (MCAI-System)

The rationale of the Multi-Level Cryptodata Analytical Intelligence System (MCAI-System) revolves around the efficacy of multi-level mega-data analysis frameworks together with the uses of multidimensional graphs as the most effective analytical artificial intelligence learning machine tool to understand and solve any economic, financial, or business problems from a holistic approach. The main motivation behind the creation of the MCAI-System is to offer a new artificial intelligence learning machine tool to evaluate dynamic and complex economic, finance, or business problems to facilitate a large list of possible solutions efficiently. Thereby, the mission of the MCAI-System is to offer academics, researchers, and policy maker’s an alternative artificial intelligence approach. Hence, the MCAI-System is offering a set of different types of multidimensional artificial intelligence learning machines tools are presented: the mega-dynamic disks coordinate space (vertical position and horizontal position) learning machine tool and the mega-disks networks mapping (MDN-Mapping) learning machine tool.
Translation Quality Assessment on Medan City Tourism Official Web Pages

This research was conducted to analyze the quality of the translation on the Medan City Tourism web pages. In this research, the researcher used the translation quality assessment model proposed by Nababan et al. The TQA model by Nababan et al. focuses on three aspects, namely accuracy, acceptability, and readability. The final result of measuring these three aspects is the total value for the quality of the translation on a scale of 1-3. This research has the objectives to: 1) analyze the accuracy of the Indonesian- English translation of the Medan Tourism web page 2) to analyze the acceptability of the Indonesian-English translation of the Medan Tourism web page 3) to analyze the readability of the Indonesian-English translation of the Medan Tourism web page 4) to analyze the total quality of the Indonesian-English translation of the Medan Tourism web pages. There were three professional translators as the raters who helped in assessing the text of the Medan City Tourism web pages. In general, the results of this research indicate that the translation quality of the Medan City Tourism web page is accurate, quite acceptable, and quite readable. The score for the accuracy aspect is 2.7, for the acceptability aspect is 2.2, for the readability aspect is 2.2 and the total quality of the translation is 2.4.
Virtual Reality Training for Post-Earthquake Rescue Operators

Urban Search And Rescue (USAR) personnel need to be appropriately trained to effectively operate in post-disaster conditions, as they need to quickly identify locations where the presence of survivors is more likely. Currently, the training procedure for this kind of triage is based on showing static pictures presenting different types of building collapses accompanied by cards providing additional environmental information. This article presents VRescue, a simulator for training USAR operators using the paradigm of immersive virtual reality (VR). VRescue recreates a range of operation conditions (day/night, presence of people, dangerous locations, etc.) experienced by real rescuers, and enables learning the use of the equipment commonly used in such scenarios.
Small Files Problem Resolution via Hierarchical Clustering Algorithm

The Small Files Problem in Hadoop Distributed File System (HDFS) is an ongoing challenge that has not yet been solved. However, various approaches have been developed to tackle the obstacles this problem creates. Properly managing the size of blocks in a file system is essential as it saves memory and computing time and may reduce bottlenecks. In this article, a new approach using a Hierarchical Clustering Algorithm is suggested for dealing with small files. The proposed method identifies the files by their structure and via a special Dendrogram analysis, and then recommends which files can be merged. As a simulation, the proposed algorithm was applied via 100 CSV files with different structures, containing 2–4 columns with different data types (integer, decimal and text). Also, 20 files that were not CSV files were created to demonstrate that the algorithm only works on CSV files. All data were analyzed via a machine learning hierarchical clustering method, and a Dendrogram was created. According to the merge process that was performed, seven files from the Dendrogram analysis were chosen as appropriate files to be merged. This reduced the memory space in the HDFS. Furthermore, the results showed that using the suggested algorithm led to efficient file management.
Wartime Cyber Lessons for Everyday Businesses

Abstract As the war in Ukraine passes its first anniversary, full-stack software engineer Timothy Clark MBCS reflects on what the conflict teaches us about modern information security and how these lessons could be applied.
Application of ontology-based engineering and stem approach in learning

Currently, there is a need to improve the educational system and develop interdisciplinary studies at all stages of education, from school to postgraduate education. The implementation of cross-curriculum connections promotes a holistic view of natural phenomena and the relationship between them, that is, this knowledge becomes more meaningful and applicable in practice. The article proposes an approach to building a conceptual model of the content of education in the form of a thesaurus and ontology, the use of which will ensure that the educational information is adaptively selected and put straight. The article discusses the possibility of and experience in using ontological modeling and engineering for the conceptual description of school and higher education. The article discusses the development of the ontological science, technology, engineering, and mathematics (STEM) Education at School model. The article builds an ontological model, which is an integration of the ontology of school and the ontology of university. When filling in the knowledge base, it becomes possible to identify interdisciplinary relationships. The use of ontological engineering methods will improve the quality of education for schoolchildren and students through the semantic description of knowledge of the subject area by using interdisciplinary and STEM approaches in the process of education.
Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment

Abstract Cloud computing adoption has been increasing rapidly amid COVID-19 as organisations accelerate the implementation of their digital strategies. Most models adopt traditional dynamic risk assessment, which does not adequately quantify or monetise risks to enable business-appropriate decision-making. In view of this challenge, a new model is proposed in this paper for assignment of monetary losses terms to the consequences nodes, thereby enabling experts to understand better the financial risks of any consequence. The proposed model is named Cloud Enterprise Dynamic Risk Assessment (CEDRA) model that uses CVSS, threat intelligence feeds and information about exploitation availability in the wild using dynamic Bayesian networks to predict vulnerability exploitations and financial losses. A case study of a scenario based on the Capital One breach attack was conducted to demonstrate experimentally the applicability of the model proposed in this paper. The methods presented in this study has improved vulnerability and financial losses prediction.
Cross Domain Lifelong Learning Based on Task Similarity

Humans gradually learn a sequence of cross-domain tasks and seldom experience catastrophic forgetting. In contrast, deep neural networks achieve good performance only in specific tasks within a single domain. To equip the network with lifelong learning capabilities, we propose a Cross-Domain Lifelong Learning (CDLL) framework that fully explores task similarities. Specifically, we employ a Dual Siamese Network (DSN) to learn the essential similarity features of tasks across different domains. To further understand similarity information across domains, we introduce a Domain-Invariant Feature Enhancement Module (DFEM) to better extract domain-invariant features. Moreover, we propose a Spatial Attention Network (SAN) that assigns different weights to various tasks based on the learned similarity features. Ultimately, to maximize the use of model parameters for learning new tasks, we propose a Structural Sparsity Loss (SSL) that can make the SAN as sparse as possible while ensuring accuracy. Experimental results show that our method effectively reduces catastrophic forgetting compared with state-of-the-art methods when continuously learning multiple tasks across different domains. It is worth noting that the proposed method scarcely forgets old knowledge while consistently enhancing the performance of learned tasks, more closely aligning with human learning.
Design of Anonymous Endorsers in Hyperledger Fabric with Linkable Threshold Ring Signature

Blockchain technology has gained its attention from its application in bitcoin which circumventedthe problem of double spending that existed in the prior digital currencies, through validation. Particularlypermissioned blockchain framework became popular with organizations forming consortiumthat allowed only authorized entities to participate in the network. Hyperledger Fabric, a popular distributedledger technology hosted by Linux Foundation has remarkable features because of the factthat it is open source. It stands out from other frameworks as it focuses on the privacy-preservingrequirements of the enterprises. Apart from only allowing authenticated organizations to participatein the network it implements channels that allows a subset of organizations to communicate concealingthe existence of such a channel to other members. Optionally fabric also provides anonymity andunlinkability of the participating clients through a cryptographic protocol suite called Idemix thatoperates based on zero-knowledge proofs. Fabric follows the execute-order-validate transaction flowas opposed to order-execute flow that had certain limitations in other platforms. For executing thetransactions submitted by the clients, fabric has designated endorsing peers which holds the smartcontract - programmable business logic. Endorsing peers or endorsers execute the transactions andattach their signatures to the results for validation purpose. But revealing the endorser identities maybe a problem when there is conflict of interest among the enterprises. Hence to have an unbiased flowof work it is important to conceal the endorser identity. According to [1] anonymization of endorsingpeers is still a open problem in fabric community. We propose a solution to this problem which useslinkable threshold ring signatures that conceals the identity of endorsers. Ring signatures are knownfor preserving the privacy of the signer in a group. Threshold ring signature allows t-out-of-n signersto collaborate on the signing procedure. Employing threshold ring signature implicitly addressesone more problem stated in [10] where the verifiers need to manually count the valid ring signaturethat increases the verification time. This process of separately verifying each of the signatures andchecking if the number of signatures is more than the threshold value is replaced by having just onethreshold signature collaboratively signed by the required endorsers.