Reviewerly/exHarmony huggingface dataset:
HybridBranchNet: A novel structure for branch hybrid convolutional neural networks architecture

ConvNet deep neural networks are developed with a consistent structure. The availability of abundant resources helps these structures to be scaled and redesigned in different sizes so that they can be optimized for different applications. By increasing one or more dimensions of the network, such as depth, resolution and width, the number of trainable network parameters will increase and, as a result, the accuracy and performance It should be noted that the backtracking of the convolutional neural network will improve. However, but increasing the number of network parameters increases the complexity of the network, which is not desirable. Therefore, adjusting the structure of the network, increasing the speed, and reducing the number of network parameters along with ensuring accuracy optimization will be important. This study aims to examine a branch network structure systematically, which can lead to better performance. In this study, in order to increase the speed, to reduce the size of the convolutional network model, and to increase the accuracy optimization, a new scaling method, which optimally designs all dimensions of depth, width, and resolution, is proposed based on a branch neural network. A family of HybridBranchNet networks, which is more accurate and efficient than ConvNets, has been created along with this design. HybridBranchNet3 has a classification accuracy of 83.1%. The proposed model was compared with a family of EfficientNet convolutional networks. The comparison results revealed that the proposed network exceeded the mentioned models in terms of accuracy and speed by 1.03% and 39%, respectively. They also showed that the number of trainable parameters is 13% less than that of the EfficientNet network. The proposed method has an accuracy of 92.3% in the CIFAR-100 dataset and 98.8% in the Flowers-102 dataset. Although the architectures such as CoAtNet have slightly higher classification accuracy than the proposed method, they have a greater number of parameters that cannot be used in a conventional system.
Quantum computing for near-term applications in generative chemistry and drug discovery

In recent years, drug discovery and life sciences have been revolutionized with machine learning and artificial intelligence (AI) methods. Quantum computing is touted to be the next most significant leap in technology; one of the main early practical applications for quantum computing solutions is predicted to be in quantum chemistry simulations. Here, we review the near-term applications of quantum computing and their advantages for generative chemistry and highlight the challenges that can be addressed with noisy intermediate-scale quantum (NISQ) devices. We also discuss the possible integration of generative systems running on quantum computers into established generative AI platforms.
Robotic Accident Prevention and Alert System for Visually Impaired

This paper proposes a walking aid cane robot for the elderly and visually impaired people, which is equipped with a walking stick and force sensors in the handlebar. The cane’s movement is controlled based on the force applied to the handlebar, and an ultrasonic sensor detects obstacles and alerts the user through a speaker. A driver circuit manages the cane’s movement, and a MEMS sensor detects falls, stopping the cane’s movement. The user’s location is updated via the cloud, and a message is sent to the caregiver. The system employs various sensors, including MEMS, EEG, force, and ultrasonic sensors, to enhance safety. The GSM module plays a crucial role in sending alerts and notifications to the user’s mobile phone in case of potential danger. The use of these sensors is intended to provide real-time information about the stick holder’s status and surroundings, detecting potential dangers and alerting the user in a timely manner to reduce accidents and injuries.
Approach to machine learning for extraction of real-world data variables from electronic health records

Background: As artificial intelligence (AI) continues to advance with breakthroughs in natural language processing (NLP) and machine learning (ML), such as the development of models like OpenAI’s ChatGPT, new opportunities are emerging for efficient curation of electronic health records (EHR) into real-world data (RWD) for evidence generation in oncology. Our objective is to describe the research and development of industry methods to promote transparency and explainability. Methods: We applied NLP with ML techniques to train, validate, and test the extraction of information from unstructured documents (e.g., clinician notes, radiology reports, lab reports, etc.) to output a set of structured variables required for RWD analysis. This research used a nationwide electronic health record (EHR)-derived database. Models were selected based on performance. Variables curated with an approach using ML extraction are those where the value is determined solely based on an ML model (i.e. not confirmed by abstraction), which identifies key information from visit notes and documents. These models do not predict future events or infer missing information. Results: We developed an approach using NLP and ML for extraction of clinically meaningful information from unstructured EHR documents and found high performance of output variables compared with variables curated by manually abstracted data. These extraction methods resulted in research-ready variables including initial cancer diagnosis with date, advanced/metastatic diagnosis with date, disease stage, histology, smoking status, surgery status with date, biomarker test results with dates, and oral treatments with dates. Conclusion: NLP and ML enable the extraction of retrospective clinical data in EHR with speed and scalability to help researchers learn from the experience of every person with cancer.
A comprehensive dataset of damaged banknotes in Indian currency (Rupees) for analysis and classification

Detecting authentic and quality banknotes presents a significant challenge, particularly for individuals with low vision or visual impairments. Extensive research has been dedicated to achieving accurate banknote detection. It is crucial for clean banknotes to be readily detectable and accepted in daily transactions. However, existing Indian currency datasets suffer from limitations, including insufficient size, a lack of datasets on damaged/spoiled banknotes, and the unavailability of publicly accessible datasets featuring spoiled, torn, or altered banknotes. Recognizing the vital importance of a spoiled banknote dataset for the benefit of low vision and visually impaired individuals, we introduce a comprehensive dataset of spoiled banknotes comprising 5125 Indian currency notes. This dataset encompasses both old and new denominations of 10, 20, 50, and 100 Rupees, aiming to significantly enhance the accessibility and accuracy of banknote detection systems. By making this dataset openly accessible to the researchers, we aim to promote research and development of solutions for detection of spoiled banknote.
ggdensity: Improved Bivariate Density Visualization in R

The [ggdensity](https://CRAN.R-project.org/package=ggdensity) R package extends the functionality of [ggplot2](https://CRAN.R-project.org/package=ggplot2) by providing more interpretable visualizations of bivariate density estimates using highest density regions (HDRs). The visualizations are created via drop-in replacements for the standard [ggplot2](https://CRAN.R-project.org/package=ggplot2) functions used for this purpose: geom_hdr() for geom_density_2d_filled() and geom_hdr_lines() for geom_density_2d(). These new geoms improve on those of [ggplot2](https://CRAN.R-project.org/package=ggplot2) by communicating the probabilities associated with the displayed regions. Various statistically rigorous estimators are available, as well as convenience functions geom_hdr_fun() and geom_hdr_fun_lines() for plotting HDRs of user-specified probability density functions. Associated geoms for rug plots and pointdensity scatterplots are also presented.
ID-Based Digital Signatures with Security Enhanced Approach

n the paper the ID-based digital signatures with signer’s protection in case of the private key compromising is investigated. The proposed protocols have two main ingredients. First is the application of the credential system for the suitable verification key approval. Second is the application of the subliminal channel together with the interactive generation of the secret key, to obtain the increased resistance of the system against the powerful adversary. The particular interest was turned towards the significance of the deniable encryption in creation of the corresponding protocols.
Autonomization of Monoidal Categories

We show that contrary to common belief in the DisCoCat community, a monoidal category is all that is needed to define a categorical compositional model of natural language. This relies on a construction which freely adds adjoints to a monoidal category. In the case of distributional semantics, this broadens the range of available models, to include non-linear maps and cartesian products for instance. We illustrate the applications of this principle to various distributional models of meaning.
Synergy: A Hypervisor Managed Holistic Caching System

Efficient system-wide memory management is an important challenge for over-commitment based hosting in virtualized systems. Due to the limitation of memory domains considered for sharing, current deduplication solutions simply cannot achieve system-wide deduplication. Popular memory management techniques like sharing and ballooning enable important memory usage optimizations individually. However, they do not complement each other and, in fact, may degrade individual benefits when combined. We propose $\mathsf{Synergy}$Synergy, a hypervisor managed caching system to improve memory efficiency in over-commitment scenarios. $\mathsf{Synergy}$Synergy builds on an exclusive caching framework to achieve, for the first time, system-wide memory deduplication. $\mathsf{Synergy}$Synergy also enables the co-existence of the mutually agnostic ballooning and sharing techniques within hypervisor managed systems. Finally, $\mathsf{Synergy}$Synergy implements a novel file-level eviction policy that prevents hypervisor caching benefits from being squandered away due to partial cache hits. $\mathsf{Synergy}$Synergy's cache is flexible with configuration knobs for cache sizing and data storage options, and a utility-based cache partitioning scheme. Our evaluation shows that $\mathsf{Synergy}$Synergy consistently uses 10 to 75 percent lesser memory by exploiting system-wide deduplication as compared to inclusive caching techniques and achieves application speedup of 2x to 23x. We also demonstrate the capabilities of $\mathsf{Synergy}$Synergy to increase VM packing density and support for dynamic reconfiguration of cache partitioning policies.
General 3-D Type-II Fuzzy Logic Systems in the Polar Frame: Concept and Practice

This paper deals with the concept and application of general three-dimensional (3-D) type-2 fuzzy logic systems in the polar frame (G3DT2FLS). <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> We focused on the automatic membership function (MF) Generator, general type-2 polar fuzzy membership, new geometric operators in polar frame, inference consisting of fuzzy 3-D polar rules and antecedents/consequents θ-slice and α-planes. The cubic smoothing spline is introduced to generate the upper and lower MFs according to the information theory. Three indices of compactness, smoothness, and entropy are employed for tuning of MFs. A measure of ultrafuzziness, min, max, equal, and reduced ultrafuzziness are suggested for the polar method. Additionally, the set theoretic operations of 3-D type-2 fuzzy sets in the polar frame are discussed. We also prove the join, meet, centroid, and type-reduction operations in the polar frame. Several rule sets are given to show the usefulness and complexity of the proposed method. The performance of the partial general 3D polar type-2 fuzzy logic system showed linear growth as the number of rules was increased. Computation time tests showed that the algorithm reduces the computation time by a maximum of 67% compared with discrete MF and is shown by an extreme 98% for the geometric procedure. These results indicate significant improvements in computation time for the spline interpolation over the existing methods.
A Software Product Line Design Based Approach for Real-time Scheduling of Reconfigurable Embedded Systems

In this paper, we deal with the development of dynamically reconfigurable embedded systems in terms of the production of execution schedules of system tasks (feasible configuration) under hard real-time constraints. Indeed, several real-time embedded systems must be dynamically reconfigured to account for hardware/software faults and/or maintain acceptable performances. Depending on the run-time environment, some reconfigurations might be unfeasible, i.e., they violate some real-time constraints of the system. More specifically, we propose an approach that starts from a set of reconfigurations to construct a Software Product Line (SPL) that can be reused in a predictive and organized way to derive real-time embedded systems. To make sure that the SPL offers various feasible reconfigurations, we define an intelligent agent (IA) that automatically checks the system's feasibility after a reconfiguration scenario is applied on a multiprocessor embedded system. This agent dynamically determines precious technical solutions to define a new product whenever a reconfiguration is unfeasible. The set of products thus defined by the agent can then be unified into an SPL. The originality of our approach is its capacity to extract, from the unfeasible configurations of an embedded system, an SPL design enriched with real-time constraints and modeled with a UML Marte profile. The SPL design can assist in the comprehension, reconfiguration as well as evolution of the SPL in order to satisfy real-time requirements and to obtain a feasible system under normal and overload conditions.
Improved average complexity for comparison-based sorting

This paper studies the average complexity on the number of comparisons for sorting algorithms. Its information-theoretic lower bound is nlg⁡n−1.4427n+O(log⁡n). For many efficient algorithms, the first nlg⁡n term is easy to achieve and our focus is on the (negative) constant factor of the linear term. The current best value is −1.3999 for the MergeInsertion sort. Our new value is −1.4106, narrowing the gap by some 25%. An important building block of our algorithm is "two-element insertion," which inserts two elements A and B, A<B, into a sorted sequence T. This insertion algorithm is still sufficiently simple for rigorous mathematical analysis and works well for a certain range of the length of T for which the simple binary insertion does not, thus allowing us to take a complementary approach together with the binary insertion.
Tortoise and Hares Consensus: The Meshcash Framework for Incentive-Compatible, Scalable Cryptocurrencies

We propose Meshcash, a protocol for implementing a permissionless ledger (blockchain) via proofs of work, suitable for use as the underlying consensus mechanism of a cryptocurrency. Unlike most existing proof-of-work based consensus protocols, Meshcash does not rely on leader-election (e.g., the single miner who managed to extend the longest chain). Rather, we use ideas from traditional (permissioned) Byzantine agreement protocols in a novel way to guarantee convergence to a consensus from any starting state. Our construction combines a local "hare" protocol that guarantees fast consensus on recent blocks (but doesn't, by itself, imply irreversibility) with a global "tortoise" protocol that guarantees irreversibility. Our global protocol also allows the ledger to "self-heal" from arbitrary violations of the security assumptions, reconverging to consensus after the assumptions hold again. Meshcash is designed to be race-free: there is no "race" to generate the next block and honestly-generated blocks are always rewarded. This property, which we define formally as a game-theoretic notion, turns out to be useful in analyzing rational miners' behavior: we prove (using a generalization of the blockchain mining games of Kiayias et al.) that race-free blockchain protocols are incentive-compatible and satisfy linearity of rewards (i.e., a party receives rewards proportional to its computational power). Because Meshcash can tolerate a high block rate regardless of network propagation delays (which will only affect latency), it allows us to lower both the variance and the expected time between blocks for honest miners; together with linearity of rewards, this makes pooled mining far less attractive. Moreover, race-free protocols scale more easily (in terms of transaction rate). This is because the race-free property implies that the network propagation delays are not a factor in terms of rewards, which removes the main impediment to accommodating a larger volume of transactions. We formally prove that all of our guarantees hold in the bounded-delay communication model of Pass, Seeman and shelat, and against a constant fraction of Byzantine (malicious) miners; not just rational ones.
Algebraic XOR-RKA-Secure Pseudorandom Functions from Post-Zeroizing Multilinear Maps

Due to the vast number of successful related-key attacks against existing block-ciphers, related-key security has become a common design goal for such primitives. In these attacks, the adversary is not only capable of seeing the output of a function on inputs of its choice, but also on related keys. At Crypto 2010, Bellare and Cash proposed the first construction of a pseudorandom function that could provably withstand such attacks based on standard assumptions. Their construction, as well as several others that appeared more recently, have in common the fact that they only consider linear or polynomial functions of the secret key over complex groups. In reality, however, most related-key attacks have a simpler form, such as the XOR of the key with a known value. To address this problem, we propose the first construction of RKA-secure pseudorandom function for XOR relations. Our construction relies on multilinear maps and, hence, can only be seen as a feasibility result. Nevertheless, we remark that it can be instantiated under two of the existing multilinear-map candidates since it does not reveal any encodings of zero. To achieve this goal, we rely on several techniques that were used in the context of program obfuscation, but we also introduce new ones to address challenges that are specific to the related-key-security setting.
Analysis of the Use of Background Distribution for Naive Bayes Classifiers

Abstract The naive Bayes classifier is a popular classifier, as it is easy to train, requires no cross-validation for parameter tuning, and can be easily extended due to its generative model. Moreover, recently it was shown that the word probabilities (background distribution) estimated from large unlabeled corpora could be used to improve the parameter estimation of naive Bayes. However, previous methods do not explicitly allow to control how much the background distribution can influence the estimation of naive Bayes parameters. In contrast, we investigate an extension of the graphical model of naive Bayes such that a word is either generated from a background distribution or from a class-specific word distribution. We theoretically analyze this model and show the connection to Jelinek-Mercer smoothing. Experiments using four standard text classification data sets show that the proposed method can statistically significantly outperform previous methods that use the same background distribution.
When are epsilon-nets small?

In many interesting situations the size of epsilon-nets depends only on $\epsilon$ together with different complexity measures. The aim of this paper is to give a systematic treatment of such complexity measures arising in Discrete and Computational Geometry and Statistical Learning, and to bridge the gap between the results appearing in these two fields. As a byproduct, we obtain several new upper bounds on the sizes of epsilon-nets that generalize/improve the best known general guarantees. In particular, our results work with regimes when small epsilon-nets of size $o(\frac{1}{\epsilon})$ exist, which are not usually covered by standard upper bounds. Inspired by results in Statistical Learning we also give a short proof of the Haussler's upper bound on packing numbers.
Strong$$^*$$ convergence of quantum channels

Recently, the existence of a strongly converging sequence of quantum channels that cannot be represented a reduction of a sequence of unitary channels strongly converging to a unitary channel was shown. In this work, we obtain several characterizations of sequences of quantum channels that have the above representation. The corresponding convergence is called the strong $$^*$$ convergence, since we have found that it is related to the convergence of selective Stinespring isometries in the strong $$^*$$ operator topology. We present the arguments showing that the strong $$^*$$ convergence is a proper, physically motivated type of convergence of infinite-dimensional quantum channels. Some properties of the strong $$^*$$ convergence of quantum channels are considered. It is shown that for Bosonic Gaussian channels the strong $$^*$$ convergence coincides with the strong convergence.
Entropy-based Consensus for Distributed Data Clustering

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in the consensus process, hence no private data are transferred. With the proposed use of entropy as an internal measure of consensus clustering validation at each machine, the cluster centers of the local machines with higher expected clustering validity have more influence in the final consensus centers. We also employ relative cost function of the local Fuzzy C-Means (FCM) and the number of data points in each machine as measures of relative machine validity as compared to other machines and its reliability, respectively. The utility of the proposed consensus strategy is examined on 18 datasets from the UCI repository in terms of clustering accuracy and speed up against the centralized version of FCM. Several experiments confirm that the proposed approach yields to higher speed up and accuracy while maintaining data security due to its protected and distributed processing approach.
Fast simulation approach dedicated to infrared thermographic inspection of delaminated planar pieces

In the present work, we propose a semi-analytical model based on the so-called truncated region eigenfunction expansion method, for the simulation of thermographic inspection. The problem is solved in the Laplace domain with respect to time, and the temperature distribution is approximated by its expansion on a tensor product basis. Configurations addressed by this model are stratified planar pieces affected by thin delamination flaws. Considered sources are lamps providing a thermal excitation at the surface of the inspected piece. The description of the delamination defects as thin air gaps between the piece layers proves to be equivalent with the introduction of a surface resistance to the heat flow, thus allowing their treatment via the applied modal approach without additional discretisation.