Reviewerly/exHarmony huggingface dataset:
Error-Robust Indoor Augmented Reality Navigation: Evaluation Criteria and a New Approach

Tracking errors severely impact the effectiveness of augmented reality display techniques for indoor navigation. In this work we take a look at the sources of error and accuracy of existing tracking technologies. We derive important design criteria for robust display techniques and present objective criteria. These serve evaluation of indoor navigation techniques without or in preparation of quantitative user studies. Based on these criteria we propose a new error tolerant display technique called Bending Words, where words move along the navigation path guiding the user. Bending Words outranks the other evaluated display techniques in many of the tested criteria and provides a robust, error-tolerant alternative to established augmented reality indoor navigation display techniques.
CDR-ULPT:Cross-domain recommendation for e-commerce using user-level preferences transfer network

Recommender systems have been widely used on e-commerce websites because they can assist users in finding products that fit their needs among the masses of products. However, recommender systems face the problem of data sparsity. Cross-domain recommender systems can alleviate this question and have been a hot research area. Adversarial domain adaptation has been a hot topic in cross-domain recommender systems due to its ability to mitigate the differences in feature distribution among different domains. Recent research has focused on learning users' domain sharing and private preferences. However, adversarial domain adaptation models ignore the individualized personal distinctions of users' domain-sharing preferences in the alignment process. In addition, these models missed indirect interaction. Graph convolutional model can mine the indirect interaction but ignore the different influences from neighboring users. Therefore, this paper proposes a Cross-domain Recommendation for e-commerce using the User-level Preferences Transfer Network (CDR-ULPT). The main work is as follows: (1) Adding an attention mechanism on adversarial domain adaptation to personalize the alignment of users' domain-sharing preferences. (2) Capturing users' indirect interactions using multiple graph convolution layers, adding node attention on the graph convolution layer to dynamically aggregate neighbor features. Experiments on five public datasets demonstrate that the method proposed in this work outperforms other cross-domain recommendation methods.
Analysis of Hotspots and Inverse Distance Weighting (IDW) of Polluted Habitats using ArcGIS Pro.: A Case Study in the Sea of Najaf and Surrounding Terrestrial Area

Abstract The Sea of Najaf, which is a natural groundwater. 60 samples were studied to locate the amount of 4 heavy metals (Cr, Mn, Co, and Cd) in each sample. With the analysis of three different types of pollution indicators (pollution factor, geographic accumulation index, and enrichment factor), the first heavy metal for which a significant difference can be seen is chromium. The high level of chromium was (101) μg/g, and the low was (50. 9) μg/g. This indicates that the soil was contaminated. Manganese is the second heavy metal with low to moderate contamination. It can be found in soil samples with a maximum concentration of (1022) μg/g and a minimum concentration of (254) μg/g. For cobalt and cadmium levels, the measured amounts do not differ much from the normal limit. This means that the water is not contaminated with them. Contamination indicators are placed in different areas to see what minerals could be in the sediments and where they came from. The results of the study showed that mineral levels were often higher near the edge of the lake than they were further afield. You can find the source of the mineral contamination in the sediments, some of the pollution markers were above the maximum on the beaches but were just below the maximum on the lake. The inverse distance weighted (IDW) in ArcGIS Pro approach is one of the most frequently employed interpolation techniques. It is used to estimate the value of a site that has not yet been assessed by comparing it to the values of nearby sites, the hot spot in ArcGIS Pro This program uses data queries to figure out which parameters for hot spot analysis give the best performance statistics may generate a map with event points or weighted features that show statistically significant hot and cold areas, the results show the distribution of minerals as a map Chromium and manganese are found in nearby farms in high concentrations, A pH test revealed between (7.9) to (10.5), The EC measurement showed between (2.3-4.3) ds/m for water, while the soil ranged between (6.3 – 10.1) ds/m, This indicates that the water and soil are salty. The turbidity ranged between( 13.1 - 8.5) NTU.
Multi‐strategy enhanced coot algorithm for coverage optimization in wireless sensor networks

Abstract An improved coot optimization algorithm (COOT) is proposed for the coverage optimization in wireless sensor networks (WSNs). To monitor areas of interest and obtain valid data, a WSNs coverage model is developed. The population is initialized with a Cubic map and an opposition‐based learning strategy. Carrying out dimension‐by‐dimensional opposition‐based learning on the leader population to strengthen the global search ability of the algorithm, the simplex method is introduced to optimize the local exploration of the population. The experimental results demonstrate that the enhanced COOT can effectively reduce energy consumption and improve network coverage. A COOT based on simplex method and dimension‐by‐dimension opposition‐based learning is proposed to optimize the coverage of WSNs. The experimental results as Figure 2a and 2c show that the improved algorithm has faster convergence speed and higher precision, which reduces the node redundancy in the coverage optimization process of wireless sensor network and improves the coverage.
The study of the hyper-parameter modelling the decision rule of the cautious classifiers based on the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg" display="inline" id="d1e858"><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mi>β</mml:mi></mml:mrow></mml:msub></mml:math> measure

In some sensitive domains where data imperfections are present, standard classification techniques reach their limits. To avoid misclassifications that have serious consequences, recent works propose cautious classification algorithms to handle this problem. Despite of the presence of uncertainty and/or imprecision, a point prediction classifier is forced to bet on a single class. While a cautious classifier proposes the appropriate subset of candidate classes that can be assigned to the sample in the presence of imperfect information. On the other hand, cautiousness should not be at the expense of precision and a trade-off has to be made between these two criteria. Among the existing cautious classifiers, two classifiers propose to manage this trade-off in the decision step by the mean of a parametrized objective function. The first one is the non-deterministic classifier (ndc) proposed within the framework of probability theory and the second one is “evidential classifier based on imprecise relabelling” (eclair) proposed within the framework of belief functions. The theoretical aim of the mentioned hyper-parameters is to control the size of predictions for both classifiers. This paper proposes to study this hyper-parameter in order to select the “best” value in a classification task. First the utility for each candidate subset is studied related to the values of the hyper-parameter and some thresholds are proposed to control the size of the predictions. Then two illustrations are proposed where a method to choose this hyper-parameters based on the calibration data is proposed. The first illustration concerns randomly generated data and the second one concerns the images data of fashion mnist. These illustrations show how to control the size of the predictions and give a comparison between the performances of the two classifiers for a tuning based on our proposition and the one based on grid search method.
The impact of digitalisation on the development of e-learning

This paper presents an overview of the latest developments in electronic learning and teaching methods and tools brought about by digital transformations aimed at making education more efficient and effective. A set of general and specific research methods was used to conduct the study, in particular bibliographical and descriptive, chronological, specification, analysis, systematisation and classification, observation, survey and expert interviews, abstraction, analogy and comparison, comparative, modelling, forecasting and argumentation, and logical generalisation. The authors describe their approach to structural organisation of the novel e-learning ecosystem and single out its key components, including technology, learning contexts, and learning outcomes, as well as a set of secondary external factors that determine the quality and effectiveness of education when digitalised. This paper explores characteristics and features of e-learning such as customisation, autonomy, and interactivity with a focus on micro-learning, and presents the findings of an analysis of the most popular open educational resources (OERs), massive open online courses (MOOCs), and online learning platforms, highlighting their major advantages and disadvantages.
Decentralized access control for secure microservices cooperation with blockchain

With the rapid advancement of cloud-native computing, the microservice with high concurrency and low coupling has ushered in an unprecedented period of vigorous development. However, due to the mutability and complexity of cooperation procedures, it is difficult to realize high-efficient security management on these microservices. Traditional centralized access control has the defects of relying on a centralized cloud manager and a single point of failure. Meanwhile, decentralized mechanisms are defective by inconsistent policies defined by different participants. This paper first proposes a blockchain-based distributed access control policies and scheme, especially for microservices cooperation with dynamic access policies. We store the authorized security policies on the blockchain to solve the inconsistent policy problem while enabling individual management of personalized access policies by the providers rather than a central authority. Then we propose a graph-based decision-making scheme to achieve an efficient access control for microservices cooperation. Through the evaluations and experiments, it shows that our solution can realize effective distributed access control at an affordable cost.
IRIS: An embedded secure boot for IoT devices

This study proposes a hardware secure boot solution, an instant retrieval information system (IRIS) that is suitable for integrating Internet of Things (IoT) devices. IRIS can boot a Linux kernel image pre-stores in removable media and comprises a data verifier securing the authenticity, integrity, and confidentiality of the boot process. IRIS is fully developed as a hardware module and the results reveal short boot-up times and a small hardware footprint when implemented on field programmable gate array chips. In addition, IRIS is an open-source generic solution that can be adapted to multiple architectures and includes a crypto-core called E-LUKS that can be used outside the boot-loading process to add confidentiality, integrity, and authenticity to data stored on off-chip storage like a flash device. IRIS shows a reduction in lookup tables footprint when compared to other IoT solutions consuming from 90% to 750% less resources and only slightly greater, around 30%, with solutions that only cover authentication and integrity.
Boosting separated softmax with discrimination for class incremental learning

Deep neural networks (DNNs) suffer from catastrophic forgetting when learning new classes continually and tend to classify old samples to new classes. Existing methods like exemplar-memory and knowledge distillation alleviate forgetting, but face prediction bias due to data imbalance. Separated softmax (Ahn et al., 2021) was proposed to solve this problem. However, they ignore the discrimination between old classes and new classes, which greatly limits the performance of DNNs. To enhance model’s discrimination, we propose discriminative separated softmax. We divide new samples into two parts: one combined with old samples to build a balanced dataset of all seen classes, and the other combined with old samples as an imbalanced dataset. Furthermore, we apply mixup for these two datasets respectively to enhance discrimination. We evaluate our method on CIFAR100 and ImageNet100. Experimental results show that our method can effectively improve the discrimination and achieves superior performance compared with separated softmax, while outperforming state-of-the-art methods.
An incremental random walk algorithm for sampling continuous fitness landscapes

Fitness landscape analysis (FLA) is used to mathematically characterize optimization problems. It is critical to get appropriate sampling points using random walk (RW) algorithms to perform FLA on continuous optimization problems. However, most of the existing approaches face the problems of poor robustness of the RW algorithm and inadequate coverage of the fitness landscape. In this study, an incremental random walk (IRW) algorithm is proposed to sample the fitness landscape for continuous optimization problems. IRW includes two major improvements: (i) An incremental perturbation mechanism is proposed to generate perturbation variables to enhance the diverse distribution of sampling points. (ii) The problem of getting trapped in the local region is alleviated by using the mirrored boundary handling method. The experimental results tested on various cases demonstrate the excellence of IRW in terms of coverage. Moreover, IRW has shown superior reliability at different problem dimensions on other different benchmark functions. Consequently, IRW is well-suited as an alternative for sampling continuous fitness landscapes under a variety of problem dimensions.
Data strategies in forensic automatic speaker comparison

Automatic speaker recognition (ASR) is a method used in forensic speaker comparison (FSC) casework. It needs collections of audio data that are representative of the case audio in order to perform reference normalization and to train a score-to-LR function. Audio from a certain minimum number of speakers is needed for each of those purposes to obtain relatively stable performance of ASR. Although it is not possible to set a hard cut-off, for the purpose of this work this number was chosen to be 30 for each, and 60 for both. Lack of representative data from that many speakers and uncertainty about what exactly constitutes representative data are major reasons for not employing ASR in FSC. An experiment was carried out in which a situation was simulated where a practitioner has only 30 speakers available. Several data strategies are tried out to handle the lack of data: leaving out reference normalization, splitting the 30 speakers into two groups of 15 (ignoring the minimum of 30) and a leave 1 or 2 out strategy where all 30 speakers are used for both reference normalization and calibration. They are compared to the baseline situation where the practitioner does have the required 60 speakers. The leave 1 or 2 out strategy with 30 speakers performs on par with baseline, and extension of that strategy to the full 60 speakers even outperforms baseline. This shows that a strategy that halves the data need is viable, lessening the data requirements for ASR in FSC and making the use of ASR possible in more cases.
Collusion-resistant dynamic privacy-preserving attribute-access control scheme based on blockchain

Ciphertext-policy attribute based encryption(CP-ABE) is widely used to protect data and privacy security in data sharing because of its fine-grained access control. However, due to the rapid development of the Internet and the explosive growth of all kinds of data and users, the traditional CP-ABE will make privacy and information face the risk of tampering and leakage. To solve the above problems, this paper proposes an attribute access control model based on blockchain technology for collusion-resistant dynamic privacy protection. The scheme effectively protects the integrity and confidentiality of encrypted data. In addition, resistant-regeneration key technology and chameleon hash function are used to generate collusion-resistant decryption key in this paper. Meanwhile, we verify the access right before decryption, and deploy fog node for outsourcing decryption. Finally, we provide the security of this scheme and make a comprehensive comparison with other schemes. The results show that this method is reliable and effective.
Bugs4Q: A benchmark of existing bugs to enable controlled testing and debugging studies for quantum programs

Realistic benchmarks of reproducible bugs and fixes are vital to good experimental evaluation of debugging and testing approaches. However, there is no suitable bug benchmark suite that can systematically evaluate the debugging and testing methods of quantum programs until now. This paper proposes Bugs4Q, a benchmark of forty-two real, manually validated Qiskit bugs from three popular platforms (GitHub, StackOverflow, and Stack Exchange) in programming, supplemented with test cases to reproduce buggy behaviors. Bugs4Q also provides interfaces for accessing the buggy and fixed versions of the Qiskit programs and executing the corresponding source code and unit tests, facilitating the reproducible empirical studies and comparisons of Qiskit program debugging and testing tools. Bugs4Q is publicly available at https://github.com/Z-928/Bugs4Q-Framework. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e356" altimg="si181.svg"><mml:mi>β</mml:mi></mml:math>-SGP: Scaled Gradient Projection with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e361" altimg="si181.svg"><mml:mi>β</mml:mi></mml:math>-divergence for astronomical image restoration

Image restoration in astronomy has been considered a vital step in many ground-based observational programs that often suffer from sub-optimal seeing due to atmospheric turbulence, distortion of stellar shapes due to instrumental aberrations, trailing, and other issues. It holds importance for various tasks: improved astrometry, deblending of overlapping sources, faint source detection, and identification of point sources near bright extended objects, such as galaxies, to name a few. We conduct an empirical study by applying the Scaled Gradient Projection (SGP) iterative image deconvolution algorithm to restore distorted stellar shapes in our observed data. We investigate using a more flexible divergence measure, the β-divergence, which contains the commonly-used Kullback–Leibler (KL) divergence as a special case and allows automatic adaptation of the parameter β to the data. An extensive set of experiments comparing the performance of SGP and its β-divergence variant (β-SGP) is carried out on extracted star stamps and on images containing multiple stars (both crowded and relatively sparser fields). We show a consistent enhancement in the flux conservation across all considered scenarios using β-SGP compared to SGP. Using a few quantifiable metrics such as the Full-Width-at-Half-Maximum (FWHM) and ellipticity of stars, we observe that β-SGP improves restoration quality, compared to the SGP, in many cases and still preserves restoration quality in others. We conclude that generalized versions of image restoration algorithms are more robust due to their enhanced flexibility and could be a promising modification for astronomical image restoration.
Twenty-Five Years of Evolution in Speech and Language Processing

n this article, we summarize the evolution of speech and language processing (SLP) in the past 25 years.We first provide a snapshot of popular research topics and the associated state of the art (SOTA) in various subfields of SLP 25 years ago, and then highlight the shift in research topics over the years.We describe the major breakthroughs in each of the subfields and the main driving forces that led us to the SOTA today.Societal impacts and potential future directions are also discussed.
Predicting Cryptocurrency Prices Model Using a Stacked Sparse Autoencoder and Bayesian Optimization

In recent years, digital currencies, also known as cybercash, digital money, and electronic money, have gained significant attention from researchers and investors alike. Cryptocurrency has emerged as a result of advancements in financial technology and has presented a unique opening for research in the field. However, predicting the prices of cryptocurrencies is a challenging task due to their dynamic and volatile nature. This study aims to address this challenge by introducing a new prediction model called Bayesian optimization with stacked sparse autoencoder-based cryptocurrency price prediction (BOSSAE-CPP). The main objective of this model is to effectively predict the prices of cryptocurrencies. To achieve this goal, the BOSSAE-CPP model employs a stacked sparse autoencoder (SSAE) for the prediction process and resulting in improved predictive outcomes. The results were compared to other models, and it was found that the BOSSAE-CPP model performed significantly better.
The Green Revolution of Smart Contracts

The focus of this study is to examine the challenges associated with smart contracts and their overall concept. The research proposes a solution that can effectively address all the current issues related to smart contracts. Furthermore, the study recommends a new architecture for smart contracts and blockchain. The author identifies the problems, conducts an analysis, and presents a solution that will enhance the performance of smart contracts, increase their extensibility, and promote an eco-friendly environment by reducing pollution and conserving energy.
Convention of Blockchain in Financial Services

Literature reviews are used by practitioners and researchers to combine numerous bodies of knowledge. There are various SotA papers on blockchain applications. The authors looked at 100 articles about the use of blockchain in financial services that were published between 2013 and 2023. The data sources used for the same are Science Direct and Google Scholar. The three phases for conducting SotA reviews are provided in this chapter. In the 100 articles looked at for SotA in title, 69 were tagged as SotA reviews, and the rest were either tagged as ‘literature review' or ‘systematic literature review'. The summary of 69 articles is presented in the literature review section of the study. An interpretive synthesis of SotA reviews in the current study describes where we are presently in the field of application of blockchain in finance. The chapter also presents the changes brought by application of blockchain in the value chain of the banking sector and future scope of study in the field.