Reviewerly/exHarmony huggingface dataset:
Probability-Possibility Transformation under Perspective of Random-fuzzy Dual Interpretation of Unknown Uncertainty

Information-preservation is recognized as the only principle for probability-possibility transformation in this work and the normalized transformation is the right method. This is based on the viewpoint that the reason we can transfer probability and possibility is that we believe the uncertainty being handled can be information-equivalently described by both probability and possibility. That viewpoint is endorsed by the random-fuzzy dual interpretation of unknown uncertainty, which says that unknown uncertainty being estimated could be interpreted as either randomness or fuzziness, depending on the available prior information and the perspective of cognition and modeling. Information of uncertain variable is defined in this work as its distribution. The suggested information-preservation principle is different from Klir’s principle, which is in fact an entropy-preservation principle. Then we investigated the problem of information preservation and propagation in parallel probability-possibility systems. By parallel, we mean the two uncertainty systems have the same priori information. After uncertainty propagation the two parallel systems will generally bifurcate, which means information preservation only holds locally between the two parallel systems. This observation accords with our intuition since probability and possibility use different normalizations as well as different disjunctive operators, which makes them two different uncertainty systems appropriate for randomness and fuzziness, respectively.
Unknown terrain imaging with adaptive spatial resolution using UAV

Recently, Unmanned Aerial Vehicles (UAVs), or drones equipped with advanced cameras, have introduced a major revolution in surveying wide landscapes to collect ground data very fast. Capturing images of an unknown area with the desired resolution is of utmost importance for many real-life applications. For a UAV fitted with a given camera, its altitude from the ground surface primarily determines the spatial resolution of mapping. Hence, for an uneven terrain, it is a great challenge to maneuver the UAV dynamically to capture snapshots maintaining the desired spatial resolution throughout the area. This paper proposes a novel fully-automated technique for simple nadir drones to capture images of an unknown 3D terrain satisfying a predefined spatial resolution criterion. Also, an adaptive real-time flight path planning scheme is presented to minimize the distance traversed by the drone. A detailed performance evaluation study on real terrain data shows that the proposed technique can cover almost 97−99% of the area, maintaining the specified resolution with an almost 46−59% reduction in the path length compared to the conventional techniques.
TWD-Net:Two-Way Detection Network for Active Stereo Matching

Abstract We propose a two-way detection network(TWD-Net) for active stereo matching. We design a depth reconstruction model of binocular speckle structured light based on self-supervised learning. First, the network achieves a dual model and obtains left and right disparity respective. Then, we propose an image-guided filtering method to optimize the edge of rough disparity. Finally, we detect the consistency based on the disparity images and establish the two-way detection loss function.
Reflections of secondary school teachers on the usefulness and viability of participatory techniques in the formal education system

In today's society, big changes happen in a fleeting way. Technological progress implies that teachers must constantly adapt and reinvent themselves and that their training process is developed throughout their lives. It is no longer enough to have a degree or qualification to achieve success or happiness. To professional performance we must add some key skills, such as communication skills, relationship skills, attitude, etc. together with the flexibility to accept the challenges that arise on a daily basis. Twelve activities were developed, based on Kolb's experiential learning cycle and Dewey's Experiential Learning Theory with the students of a Secondary Education Center with the aim of observing and verifying the degree of usefulness and feasibility of the project. The research was based on the qualitative paradigm through the content analysis of the semi-structured interviews that were carried out with four teachers from the Center, who participated in the experience. A table of categories was designed with a mixed process: deductive and inductive. Among the main conclusions obtained, it is highlighted that the activities carried out have encouraged the participation of the students, they have learned the contents treated, their curiosity and their capacity for organization and their autonomy. On the other hand, a very positive assessment has been obtained from the teaching staff and they emphasize that for the experience to be viable in other Educational Centers, a good organization of all the agents involved and the educational community itself is necessary, at the same time that teacher involvement is necessary. The use of a qualitative methodology in this research has made it possible to deepen the opinion of the agents involved, to improve and disseminate the experience.
Batch Evaluation Metrics in Information Retrieval: Measures, Scales, and Meaning

A sequence of recent papers has considered the role of measurement scales in information retrieval (IR) experimentation, and presented the argument that (only) uniform-step interval scales should be used, and hence that well-known metrics such as reciprocal rank, expected reciprocal rank, normalized discounted cumulative gain, and average precision, should be either discarded as measurement tools, or adapted so that their metric values lie at uniformly-spaced points on the number line. These papers paint a rather bleak picture of past decades of IR evaluation, at odds with the community's overall emphasis on practical experimentation and measurable improvement. Our purpose in this work is to challenge that position. In particular, we argue that mappings from categorical and ordinal data to sets of points on the number line are valid provided there is an external reason for each target point to have been selected. We first consider the general role of measurement scales, and of categorical, ordinal, interval, ratio, and absolute data collections. In connection with the first two of those categories we also provide examples of the knowledge that is captured and represented by numeric mappings to the real number line. Focusing then on information retrieval, we argue that document rankings are categorical data, and that the role of an effectiveness metric is to provide a single value that represents the usefulness to a user or population of users of any given ranking, with usefulness able to be represented as a continuous variable on a ratio scale. That is, we argue that current IR metrics are well-founded, and, moreover, that those metrics are more meaningful in their current form than in the proposed "intervalized" versions.
Secured Cloud Data Storage Encryption Using Post-Quantum Cryptography

Cloud computing is evolving daily for securing large data in diverse industries and organizations.The two major challenges about cloud storage are reliability and security.Clients cannot entrust their data to another company without any assurance that they can access their information anytime and no third party will be granted access to it.Data can also be requested from the cloud by the users.However, uploading data in the cloud faces some security issues due to cyber threats and prevalent fraudulent activities.With advancement in technology and research, different solutions have evolved to protect cloud data.Cryptographic techniques utilizing different encryption mechanisms are used to protect the data and ensure its integrity.The existing cryptographic techniques are classical and vulnerable to attacks by quantum computers.In addition, the classical computers utilizes multiple algorithms for encryption and decryption which takes time to encrypt and decrypt a file.In this paper, we presented an optimized new security mechanism using postquantum cryptography to provide block-wise security to cloud data storage irrespective of the size of the file uploaded or downloaded.
The Union of Manifolds Hypothesis and its Implications for Deep  Generative Modelling

Deep learning has had tremendous success at learning low-dimensional representations of high-dimensional data. This success would be impossible if there was no hidden low-dimensional structure in data of interest; this existence is posited by the manifold hypothesis, which states that the data lies on an unknown manifold of low intrinsic dimension. In this paper, we argue that this hypothesis does not properly capture the low-dimensional structure typically present in data. Assuming the data lies on a single manifold implies intrinsic dimension is identical across the entire data space, and does not allow for subregions of this space to have a different number of factors of variation. To address this deficiency, we put forth the union of manifolds hypothesis, which accommodates the existence of non-constant intrinsic dimensions. We empirically verify this hypothesis on commonly-used image datasets, finding that indeed, intrinsic dimension should be allowed to vary. We also show that classes with higher intrinsic dimensions are harder to classify, and how this insight can be used to improve classification accuracy. We then turn our attention to the impact of this hypothesis in the context of deep generative models (DGMs). Most current DGMs struggle to model datasets with several connected components and/or varying intrinsic dimensions. To tackle these shortcomings, we propose clustered DGMs, where we first cluster the data and then train a DGM on each cluster. We show that clustered DGMs can model multiple connected components with different intrinsic dimensions, and empirically outperform their non-clustered counterparts without increasing computational requirements.
Extensive characterization of a family of efficient three-qubit gates at the coherence limit

While all quantum algorithms can be expressed in terms of single-qubit and two-qubit gates, more expressive gate sets can help reduce the algorithmic depth. This is important in the presence of gate errors, especially those due to decoherence. Using superconducting qubits, we have implemented a three-qubit gate by simultaneously applying two-qubit operations, thereby realizing a three-body interaction. This method straightforwardly extends to other quantum hardware architectures, requires only a "firmware" upgrade to implement, and is faster than its constituent two-qubit gates. The three-qubit gate represents an entire family of operations, creating flexibility in quantum-circuit compilation. We demonstrate a gate fidelity of $97.90\%$, which is near the coherence limit of our device. We then generate two classes of entangled states, the GHZ and W states, by applying the new gate only once; in comparison, decompositions into the standard gate set would have a two-qubit gate depth of two and three, respectively. Finally, we combine characterization methods and analyze the experimental and statistical errors on the fidelity of the gates and of the target states.
A reassessment of public awareness and legislative framework on cybersecurity in South Africa

Cybersecurity has become a growing concern globally, following this era of unparalleled resources, power relations and technological evolution. Technological vulnerabilities have led to massive data breaches in recent years and research has highlighted potential uses of artificial intelligence to engineer more powerful cyber-attacks thus revealing new hardware weaknesses. Cyber-attacks pose a threat to critical infrastructure thereby compelling countries to intensify their national security testing for cross-border partnerships. South Africa, however, is lagging in terms of readiness and capacity building against various forms of cyber-attack. Currently, the country has the world’s third-highest number of cybercrime victims. By the same token, there is evidence that the South African Banking Risk Information Centre (SABRIC) loses over R2.2bn annually to cyber-attacks. In October 2019, two seemingly separate syndicates of hackers threatened to close the finance and local government sectors at a time when South Africa was struggling to recover from its previous economic recession. Taking insights from the existing literature, the study demonstrates that the level of awareness of cybersecurity among the South African populace is very poor, while the legislations put in place have done little to alleviate the trends and shield the general population from cyberwarfare. With growing legislation focussing on cybersecurity, the study contends that there is also the need for diverse professional and academic institutes to deliver hands-on educative services to the society through research-led activities to ensure South Africa is resilient to the growing threats. Given the borderless nature of cybercrime, South Africa should start intensifying efforts to incorporate the Joint Cybercrime Action Taskforce (J-CAT) alongside the local laws and necessitate the establishment of police-oriented learning centres, where citizens can be educated on the dangers of cybercrime and its devastated impacts on national development. With the drive towards digital economy and advanced global technologies, there is the need for further consolidation of South Africa’s cybersecurity system with a view to ensuring the safety and security of national infrastructure, society, and global economic relations from malicious online crime spree. Until these are done, the storm is not yet over
Using a neural network in the second stage of the ensemble classifier to improve the quality of classification of objects in images

Object recognition in images is used in many areas of practical use. Very often, progress in its application largely depends on the ratio of the quality of object recognition and the required amount of calculations. Recent advances in recognition are related to the development of neural network architectures with a very significant amount of computing that are trained on large data sets over a very long time on state-of-the-art computers. For many practical applications, it is not possible to collect such large datasets for training and only computing machines with limited computing power can be used. Therefore, the search for solutions that meet these practical restrictions is relevant. This paper reports an ensemble classifier, which uses stacking in the second stage. The use of significantly different classifiers in the first stage and the multilayer perceptron in the second stage has made it possible to significantly improve the ratio of the quality of classification and the required volume of calculations when training on small data sets. The current study showed that the use of a multilayer perceptron in the second stage makes it possible to reduce the error compared to the use of the second stage of majority voting. On the MNIST dataset, the error reduction was 29‒39 %. On the CIFAR-10 dataset, the error reduction was 13‒17 %. A comparison of the proposed architecture of the ensemble classifier with the architecture of the transformer-type classifier demonstrated a decrease in the volume of calculations while reducing the error. For the CIFAR-10 dataset, an error reduction of 8 % was achieved with a calculation volume of less than 22 times. For the MNIST dataset, the error reduction was 62 % when winning by the volume of calculations by 50 times
POINT OF SALES AND INVENTORY MANAGEMENT SYSTEM

The whole E-Commerce branch shops a plentiful quantity of facts ordinary which occasionally consequences in lacking gadgets, mistaken stock manipulates and for this reason free the music in their database. This trouble isn’t most effective confined to them; However, customers also play a significant role in the development of this situation by doing things like updating the items that are in their shopping carts, leaving the cart with items at any point, which results in difficulties at checkout, and frequently cancelling the orders that they have placed. There is an urgent need for a device that now not most effective shops these varying facts but moreover holds it in a powerful approach. This gadget maintains a terrific music of all the statistics approximately the dealer, supplier, synthetic items and uncooked substances because it makes use of MongoDB to shop the facts at the backend and the frontend is advanced the use of Java on NetBeans to offer a terrific Graphical User Interface (GUI) in order that any individual with nontechnical history can get entry to the stock. The gift paintings might also additionally assist in excessive and agreed stage of purchaser service. It might also additionally cause choose bendy ability and allow us to address perks and troughs in demand.
Towards the Practical Utility of Federated Learning in the Medical Domain

Federated learning (FL) is an active area of research. One of the most suitable areas for adopting FL is the medical domain, where patient privacy must be respected. Previous research, however, does not provide a practical guide to applying FL in the medical domain. We propose empirical benchmarks and experimental settings for three representative medical datasets with different modalities: longitudinal electronic health records, skin cancer images, and electrocardiogram signals. The likely users of FL such as medical institutions and IT companies can take these benchmarks as guides for adopting FL and minimize their trial and error. For each dataset, each client data is from a different source to preserve real-world heterogeneity. We evaluate six FL algorithms designed for addressing data heterogeneity among clients, and a hybrid algorithm combining the strengths of two representative FL algorithms. Based on experiment results from three modalities, we discover that simple FL algorithms tend to outperform more sophisticated ones, while the hybrid algorithm consistently shows good, if not the best performance. We also find that a frequent global model update leads to better performance under a fixed training iteration budget. As the number of participating clients increases, higher cost is incurred due to increased IT administrators and GPUs, but the performance consistently increases. We expect future users will refer to these empirical benchmarks to design the FL experiments in the medical domain considering their clinical tasks and obtain stronger performance with lower costs.
Semi-supervised Object Detection via Virtual Category Learning

Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing. However, handling confusing samples is nontrivial: discarding valuable confusing samples would compromise the model generalisation while using them for training would exacerbate the confirmation bias issue caused by inevitable mislabelling. To solve this problem, this paper proposes to use confusing samples proactively without label correction. Specifically, a virtual category (VC) is assigned to each confusing sample such that they can safely contribute to the model optimisation even without a concrete label. It is attributed to specifying the embedding distance between the training sample and the virtual category as the lower bound of the inter-class distance. Moreover, we also modify the localisation loss to allow high-quality boundaries for location regression. Extensive experiments demonstrate that the proposed VC learning significantly surpasses the state-of-the-art, especially with small amounts of available labels.
Application of Gaussian Filter and Histogram Equalization for Repair x-ray Image

The X-ray image is a medical examination procedure that uses electromagnetic wave radiation to get a picture of the inside of the body. However, in the process, there is noise that appears due to the exposure factor. This research builds a system to improve the X-ray image with noise by using Gaussian Filter and Histogram Equalization. In this study, in order to see the optimization of image enhancement, the two methods were combined. The data used are 60 x-ray images that have noise and each has an original image without noise as a comparison image to get system accuracy using PSNR and SSIM. Gaussian Filter method is used to reduce noise by determining the size of the kernel matrix and the standard deviation used. Histogram Equalization method is used to even out the value of the gray level of the image. Based on the test results from the combination of the two methods, the larger the size of the kernel matrix used, the faster the duration of time needed to repair the image. The PSNR value and accuracy obtained in the X-ray image repair are 31 dB and 71% on a 3x3 kernel matrix with an average time duration of 9 seconds, 32 dB and 77% on a 5x5 kernel matrix with an average duration of 9 seconds, 32 dB and 78% on a 7x7 kernel matrix with an average time duration of 8 seconds
Implementation of Naïve Bayes for Classification of Learning Types

Learning is a process that is carried out by each individual from not knowing to knowing, or from bad behavior to being good, so that it has a good change for the individual, Each individual has a learning type in receiving the material presented by the teacher, but not all individuals understand what type of learning they need, The purpose of the research is to determine the type of learning of the students of the Faculty of Computer Science. The method used is nave Bayes for the accuracy of its calculations. The results of this study are the classification of visual learning types as many as 50 people, for audio as many as 24 people, while kinesthetic as many as 25 people, for the Informatics Engineering Study Program as many as 61, consists of 37 visual learning types, Auditory 14 people, Kinesthetic 10 people, While the Information Systems Study Program is 37 people, where is Visual 14 people, Auditory 9 people and Kinesthetic 14 people. With this classification, it can help lecturers apply learning methods that are suitable for their students. The best Naïve Bayes accuracy rate is 88.89%
An Overview on Designs and Applications of Context-Aware Automation Systems

Automation systems are increasingly being used in dynamic and various operating conditions. With higher flexibility demands, they need to promptly respond to surrounding dynamic changes by adapting their operation. Context information collected during runtime can be useful to enhance the system's adaptability. Context-aware systems represent a design paradigm for modeling and applying context in various applications such as decision-making. In order to address context for automation systems, a state of the art assessment of existing approaches is necessary. Thus, the objective of this work is to provide an overview on the design and applications of context and context models for automation systems. A systematic literature review has been conducted, the results of which are represented as a knowledge graph.