Reviewerly/exHarmony huggingface dataset:
Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm

According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women's death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach is necessary for early cancer identification. This research proposes a novel framework integrating metaheuristic optimization with deep learning and feature selection for robustly classifying breast cancer from ultrasound images. The structure of the proposed methodology consists of five stages, namely, data augmentation to improve the learning of convolutional neural network (CNN) models, transfer learning using GoogleNet deep network for feature extraction, selection of the best set of features using a novel optimization algorithm based on a hybrid of dipper throated and particle swarm optimization algorithms, and classification of the selected features using CNN optimized using the proposed optimization algorithm. To prove the effectiveness of the proposed approach, a set of experiments were conducted on a breast cancer dataset, freely available on Kaggle, to evaluate the performance of the proposed feature selection method and the performance of the optimized CNN. In addition, statistical tests were established to study the stability and difference of the proposed approach compared to state-of-the-art approaches. The achieved results confirmed the superiority of the proposed approach with a classification accuracy of 98.1%, which is better than the other approaches considered in the conducted experiments.
A Non-Convex Hybrid Overlapping Group Sparsity Model with Hyper-Laplacian Prior for Multiplicative Noise

Multiplicative noise removal is a quite challenging problem in image denoising. In recent years, hyper-Laplacian prior information has been successfully introduced in the image denoising problem and significant denoising effects have been achieved. In this paper, we propose a new hybrid regularizer model for removing multiplicative noise. The proposed model consists of the non-convex higher-order total variation and overlapping group sparsity on a hyper-Laplacian prior regularizer. It combines the advantages of the non-convex regularization and the hybrid regularization, which may simultaneously preserve the fine-edge information and reduce the staircase effect at the same time. We develop an effective alternating minimization method for the proposed nonconvex model via an alternating direction method of multipliers framework, where the majorization–minimization algorithm and the iteratively reweighted algorithm are adopted to solve the corresponding subproblems. Numerical experiments show that the proposed model outperforms the most advanced model in terms of visual quality and certain image quality measurements.
On one type of pedagogical situations: classification and practical aspects

On the basis of the previous theoretical research conducted by the author, in this paper the concept of pedagogical situation in described in connection with the concept of pedagogical event. The classification of pedagogical situations presented in the paper is based on the possibility and ability of a pedagogical situation to reach a pedagogical problem, to achieve a pedagogical effect and thus to become a pedagogical event. On the basis of these grounds as well as the logic and practice of a teacher's everyday work, the three types of situations were identified. The first of them, that is a pedagogical situation in which the goal is achievable, which means that the situation can change from a potential situation to an actual situation, is described in the paper. The goal-oriented principle of analysis opens up the prospect of establishing relationships between all the parameters of the situation and the personal characteristics of its participants. The scheme for describing pedagogical situations logically includes the following components: participants (teachers and students a potential pedagogical agent), goal/task, conditions (locus).
Anonymous Methods Based on Multi-Attribute Clustering and Generalization Constraints

The dissemination and sharing of data sheets in IoT applications presents privacy and security challenges that can be addressed using the k-anonymization algorithm. However, this method needs improvement, for example, in areas related to its overgeneralization and its insufficient attribute diversity constraints during the anonymization process. To address these issues, this study proposes a multi-attribute clustering and generalization constraints (k,l)-anonymization method that can be applied to multidimensional data tables. The algorithm first used a greedy strategy to rank the attributes by width first, derived the division into dimensions to construct a multidimensional generalization hierarchy, and then selected the attributes with the most significant width values as the priority generalization attributes. Next, the k-nearest neighbor (KNN) clustering method was introduced to determine the initial clustering center by the width-first results, divide the quasi-identifier attributes into KNN clusters according to a distance metric, and generalize the quasi-identifier attributes in the equivalence class using a hierarchical generalization structure. Then, the proposed method re-evaluated the attributes to be generalized before each generalization operation. Finally, the algorithm employed an improved frequency–diversity constraint to generalize sensitive attributes in order to ensure that there were at least l records that were mutually dissimilar and closest in the equivalence class. While limiting the frequency threshold for the occurrence of sensitive attributes, the sensitive attribute values remained similar within the group, thus achieving protection of anonymity for all the attributes.
The Well-Balanced DDA: Access, Ownership, and the Impact of Campus Closure on a Multidisciplinary Demand-Driven Acquisition Program

AbstractAbstractIn 2019, Pollak Library at California State University, Fullerton (CSUF) reviewed their long-standing Ebook Central demand-driven acquisition (DDA) program, focusing on manipulating administrative settings to balance access and ownership. Shortly after the review, the COVID pandemic led to a seventeen-month campus closure, expected to result in increased DDA expenditures. In 2022, the review was readministered to determine the impact that settings and campus closure had on DDA with regard to ownership and expenditure. The reviews explored changes in ebook costs and eligibility, the impact of preferred access model and automatic upgrades (auto-upgrades), and a scenario analysis of short-term loans (STLs).Keywords: Academic librariesdemand-driven acquisition (DDA)short-term loan (STL)EbooksCOVID-19 pandemicautomatic upgrades (auto-upgrades) Disclosure statementThe author reports there are no competing interests to declare.
Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets

The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the disease-related datasets of a Canadian government data portal, Nova Scotia Open Data. We leveraged the Semantic Web technologies to transform the disease-related datasets into Resource Description Framework (RDF) and enriched them with semantic rules. An RDF data model using the RDF Cube vocabulary was designed in this work to develop a graph that adheres to best practices and standards, allowing for expansion, modification and flexible re-use. The study also discusses the lessons learned during the cross-dimensional knowledge graph construction and integration of open statistical datasets from multiple sources.
The Compression Optimality of Asymmetric Numeral Systems

Source coding has a rich and long history. However, a recent explosion of multimedia Internet applications (such as teleconferencing and video streaming, for instance) renews interest in fast compression that also squeezes out as much redundancy as possible. In 2009 Jarek Duda invented his asymmetric numeral system (ANS). Apart from having a beautiful mathematical structure, it is very efficient and offers compression with a very low coding redundancy. ANS works well for any symbol source statistics, and it has become a preferred compression algorithm in the IT industry. However, designing an ANS instance requires a random selection of its symbol spread function. Consequently, each ANS instance offers compression with a slightly different compression ratio. The paper investigates the compression optimality of ANS. It shows that ANS is optimal for any symbol sources whose probability distribution is described by natural powers of 1/2. We use Markov chains to calculate ANS state probabilities. This allows us to precisely determine the ANS compression rate. We present two algorithms for finding ANS instances with a high compression ratio. The first explores state probability approximations in order to choose ANS instances with better compression ratios. The second algorithm is a probabilistic one. It finds ANS instances whose compression ratios can be made as close to the best ratio as required. This is done at the expense of the number θ of internal random "coin" tosses. The algorithm complexity is O(θL3), where L is the number of ANS states. The complexity can be reduced to O(θLlog2L) if we use a fast matrix inversion. If the algorithm is implemented on a quantum computer, its complexity becomes O(θ(log2L)3).
Legends of the dashboard: an empirical evaluation of split and joint layout designs for geovisual analytics interfaces

Geodashboards are often designed with explanatory elements guiding users. These elements (e.g. legends or annotations) need to be carefully designed to mitigate split attention or information integration issues. In this paper, we report expert interviews followed by a controlled experiment where we compare two interface designs with a focus on the split attention effect: (1) a multiple-legend layout with explanatory elements located next to each view, and (2) a single-legend layout with all explanatory elements gathered in one place. Different legend layouts did not affect the performance, but affected user satisfaction. 75% of the participants preferred the multiple-legend layout, and rated it with a higher usability score, mainly attributing this preference to the proximity of legend elements to the view of interest. Eye tracking data strongly and clearly verifies that participants indeed make use of the proximity: With the single-legend, the majority of eye-movement transitions were between the single-legend and the closest view to the legend, whereas with multiple-legend participants have shorter and more frequent legend visits, as well as more transitions between legends and views. Taken together, the design lesson we learned from this experiment can be summarized as ‘split the legend elements, but make it close to the explained elements’.
Energy-aware intelligent scheduling for deadline-constrained workflows in sustainable cloud computing

It is challenging to handle the non-linear power consumption model, complex workflow structures, and diverse user-defined deadlines for energy-efficient workflow scheduling in sustainable cloud computing. Although metaheuristics are very attractive to solve this problem, most of the existing work regards the problem as a black-box and ignores the use of domain knowledge. To make up for their shortcomings, this paper tailors an energy-aware intelligent scheduling algorithm (EIS) with three new mechanisms. First, we derive the optimal execution time that minimizes energy consumption for each task on a given resource. Second, based on the optimal execution time of each workflow task, the EIS distributes the workflow slack time (difference between its completion time and deadline) to reduce the voltages and frequencies of task executions for energy saving. Third, the EIS mines the idle time gaps caused by task precedence constraints to further reduce dynamic energy consumption whilst satisfying workflows’ deadline constraints. To measure the performance of the EIS, we conduct extensive comparison experiments based on actual workflow applications. The results demonstrate that the energy consumption of the EIS is much lower than that of the competitors under different deadlines, and has a faster descend rate with the evolution process.
Investigating the Effect of Immersive VR on Conceptual Knowledge and Procedural Knowledge Transfer

Promoting knowledge transfer and developing students' complex problem-solving skills are important goals in today's education. The use of immersive virtual reality in teaching and learning has shown great potential to achieve this goal. This study used a quasi-experimental research method, with a video group as the control group, to investigate the effect of the immersive virtual reality learning environment on learners' conceptual and procedural knowledge transfer. The results found that learners in the immersive virtual reality learning environment scored significantly higher than those in the video group on the conceptual knowledge transfer and procedural knowledge transfer test. These results suggest that immersive virtual reality is more advantageous than video learning in facilitating knowledge transfer.
AEDAMIDL: An Enhanced and Discriminant Analysis of Medical Images using Deep Learning

In the field of healthcare, computer vision plays a very crucial role which is expected to expand exponentially in the coming decades. Computer vision solely focuses on understanding images and videos. This consists of tasks such as object detection, image classification, and segmentation. The process of visualizing the internal organs of the human body for clinical investigation and medical intrusion is called Medical imaging. Medical imaging has become a tool of paramount importance in clinical trials as it provides a swift diagnosis with visualization and quantitative assessment. Recent breakthroughs have significantly improved image classification and object detection models which are very useful for medical imaging. Medical Imaging technology can assist in detecting even the tiniest aberrations in the human body and for the last decades, computer-supported medical imaging applications have become a trustworthy help for physicians. It not only creates and analyzes images, but also helps doctors with their interpretation. However, a brain-related diagnosis necessitates extreme caution, and even the smallest error in judgment can be disastrous. This research engages with improving the diagnosis accuracy in identifying tumors in the brain. Several imaging technologies like CT scans and X-rays are available for investigating the brain but the MRI (Magnetic Resonance Imaging) has perpetually been the most reliable and safe for such diagnosis. Moreover, it provides a more desirable contrast in information about brain tissues. This further helps in building the model architecture which applies a comprehensive deep learning algorithm and Convolution Neural Networks (CNN) for classifying the tumor's possibility.
One Round Threshold ECDSA Without Roll Call

With the growing popularity of cryptocurrencies, interest in digital signatures is also on the rise. Researchers have been attempting since the 90s to enhance known signature schemes with new properties useful in specific cases. One such property of threshold schemes is non-interactivity: allowing a subset of a group of people to generate a signature without having to interact. A solution to the quest for non-interactivity is to divide the signature into two steps: the presigning (or offline) phase condenses most communication rounds and can be performed long before the signature is needed, while the signing (or online) phase takes only a single round and happens after the message is chosen. Most protocols however require that the subset of the signers be fixed before presigning, since they are the only ones who participate in it. In this paper, we present a non-interactive threshold ECDSA protocol that removes the need for this assumption entirely and works for any number of participants and threshold value. The security of this scheme is proven in a simulation-based definition. To evaluate the performance of the protocol, it has been implemented in Rust and benchmarked.
Multi-user CDH Problems and the Concrete Security of NAXOS and HMQV

We introduce $$\textsf{Corr}\textsf{GapCDH}$$ , the Gap Computational Diffie-Hellman problem in the multi-user setting with Corruptions. In the random oracle model, our assumption tightly implies the security of the authenticated key exchange protocols $$\textsf{NAXOS}$$ in the eCK model and (a simplified version of) $$\textsf{X3DH}$$ without ephemeral key reveal. We prove hardness of $$\textsf{Corr}\textsf{GapCDH}$$ in the generic group model, with optimal bounds matching the one of the discrete logarithm problem. We also introduce $$\textsf{CorrCR}\textsf{GapCDH}$$ , a stronger Challenge-Response variant of our assumption. Unlike standard $$\textsf{GapCDH}$$ , $$\textsf{CorrCR}\textsf{GapCDH}$$ implies the security of the popular AKE protocol HMQV in the eCK model, tightly and without rewinding. Again, we prove hardness of $$\textsf{CorrCR}\textsf{GapCDH}$$ in the generic group model, with (almost) optimal bounds. Our new results allow implementations of $$\textsf{NAXOS}$$ , $$\textsf{X3DH}$$ , and $$\textsf{HMQV}$$ without having to adapt the group sizes to account for the tightness loss of previous reductions. As a side result of independent interest, we also obtain modular and simple security proofs from standard $$\textsf{GapCDH}$$ with tightness loss, improving previously known bounds.
A Vulnerability in Implementations of SHA-3, SHAKE, EdDSA, and Other NIST-Approved Algorithms

This paper describes a vulnerability in several implementations of the Secure Hash Algorithm 3 (SHA-3) that have been released by its designers. The vulnerability has been present since the final-round update of Keccak was submitted to the National Institute of Standards and Technology (NIST) SHA-3 hash function competition in January 2011, and is present in the eXtended Keccak Code Package (XKCP) of the Keccak team. It affects all software projects that have integrated this code, such as the scripting languages Python and PHP Hypertext Preprocessor (PHP). The vulnerability is a buffer overflow that allows attacker-controlled values to be eXclusive-ORed (XORed) into memory (without any restrictions on values to be XORed and even far beyond the location of the original buffer), thereby making many standard protection measures against buffer overflows (e.g., canary values) completely ineffective. First, we provide Python and PHP scripts that cause segmentation faults when vulnerable versions of the interpreters are used. Then, we show how this vulnerability can be used to construct second preimages and preimages for the implementation, and we provide a specially constructed file that, when hashed, allows the attacker to execute arbitrary code on the victim's device. The vulnerability applies to all hash value sizes, and all 64-bit Windows, Linux, and macOS operating systems, and may also impact cryptographic algorithms that require SHA-3 or its variants, such as the Edwards-curve Digital Signature Algorithm (EdDSA) when the Edwards448 curve is used. We introduce the Init-Update-Final Test (IUFT) to detect this vulnerability in implementations.
SiamORPN: Enabling Orthogonality between Object and Background in Siamese Object Tracking

Siamese-based trackers currently are the dominant tracking paradigm due to the balance between speed and performance. However, it is prone to drift and tracking failure when the environment is complex and similar objects interfere. While the Siamese-based trackers perform the correlation operation, the responses of the target object and background appear in different channels, i.e., the feature spaces of the target object and background have some orthogonality. However, when meeting background clutters and similar objects interfere, this orthogonality becomes weaker and the wrong classification contribution of the object and the background reduces the stability of the learned similarity function, leading to many misclassified pixels in the heatmaps. In this work, we proposed a SiamORPN to solve the above issues. It is incorporated at two levels: an Orthogonal Region Proposal Network (ORPN) and an Adaptive Pixel-wise Aggregation (APA) module. Specifically, for ORPN, the orthogonality between the object and the background maximizes the inter-class inertia. Moreover, the ORPN introduces the orthogonal module to enhance this orthogonality. For APA, it introduces two lightweight networks to predict the weights of all pixels in different heatmaps and the weights of all pixels in different regression offsets. Experiments on challenging benchmarks, including OTB2015, VOT2016, VOT2018, GOT-10k test set, UAV123, LaSOT, and TrackingNet, demonstrate the proposed SiamORPN outperforms many SOTA trackers and achieves leading performance. The inference speed at GTX1080Ti can reach about 32 FPS, meeting the real-time requirements.
Unlinkable Policy-Based Sanitizable Signatures

In CT-RSA 2020, P3S was proposed as the first policy-based sanitizable signature scheme, allowing the signer to designate future message sanitizers by defining an access policy relative to their attributes rather than their keys. However, since P3S utilizes a policy-based chameleon hash (PCH), it does not achieve unlinkability, a required notion in privacy-preserving applications. Moreover, P3S requires running a procedure to share the secret trapdoor information for PCH with each new sanitizer before sanitizing a new message. We further observe that to maintain transparency in P3S’s multiple sanitizers setting, the signature size should grow linearly with the number of sanitizers. In this work, we propose an unlinkable policy-based sanitizable signature scheme (UP3S) where we employ a rerandomizable digital signature scheme and a traceable attribute-based signature scheme as its building blocks. Compared to P3S, UP3S achieves unlinkability, does not require new secrets to be shared with future sanitizers prior to sanitizing each message, and has a fixed signature size for a given sanitization policy. We define and formally prove the security notions of the generic scheme, propose an instantiation of UP3S utilizing the Pointcheval-Sanders rerandomizable signature scheme and DTABS traceable attribute-based signature scheme, and analyze its efficiency. Finally, we compare UP3S with P3S in terms of the features of the procedures, scalability, and security models.
Stream Reasoning approach for Anticipating Human Activities in Ambient Intelligence environments

The advent of the internet of things (IoT) and artificial intelligence (AI) technologies enable continuous data generation, also known as data streams. Considering data streams is required for ambient intelligence (AmI) systems for context-aware assistance services. This paper proposes a hybrid approach combining deep learning models and probabilistic commonsense reasoning over data streams to anticipate human activities. The reasoning is performed using the event calculus formulated in answer set programming (ECASP); the latter allows for abductive and temporal reasoning, which enables an eXplainable AI (XAI) approach. An activity context ontology is exploited for the reasoning axiomatisation. Several experiments were conducted to demonstrate the effectiveness of the proposed approach in terms of accuracy and computation time.