Reviewerly/exHarmony huggingface dataset:
VeDB: A Software and Hardware Enabled Trusted Relational Database

Blockchain-like ledger databases emerge in recent years as a more efficient alternative to permissioned blockchains. Conventional ledger databases mostly rely on authenticated structures such as the Merkle tree and transparency logs for supporting auditability, and hence they suffer from the performance problem. As opposed to conventional ledger DBMSes, we design VeDB - a high-performance verifiable software (Ve-S) and hardware (Ve-H) enabled DBMS with rigorous auditability for better user options and broad applications. In Ve-S, we devise a novel verifiable Shrubs array (VSA) with two-layer ordinals (serial numbers) which outperforms conventional Merkle tree-based models due to lower CPU and I/O cost. It enables rigorous auditability through its efficient credible timestamp range authentication method, and fine-grained data verification at the client side, which are lacking in state-of-the-art relational ledger databases. In Ve-H, we devise a non-intrusive trusted affiliation by TEE leveraging digest signing, monotonic counters, and trusted timestamps in VeDB, which supports both data notarization and lineage applications. The experimental results show that VeDB-VSA outperforms Merkle tree-based authenticated data structures (ADS) up to 70× and 3.7× for insertion and verification; and VeDB Ve-H data lineage verification is 8.5× faster than Ve-S.
Imbalanced Multiclass Medical Data Classification based on Learning Automata and Neural Network

Data classification in the real world is often faced with the challenge of data imbalance, where there is asignificant difference in the number of instances among different classes. Dealing with imbalanced data isrecognized as a challenging problem in data mining, as it involves identifying minority-class data with ahigh number of errors. Therefore, the selection of unique and appropriate features for classifying data withsmaller classes poses a fundamental challenge in this research. Nowadays, due to the widespread presenceof imbalanced medical data in many real-world problems, the processing of such data has gained attentionfrom researchers. The objective of this research is to propose a method for classifying imbalanced medicaldata. In this paper, the hypothyroidism dataset from the UCI repository is used. In the feature selection stage,a support vector machine algorithm is used as a cost function, and the wrapper algorithm is employed asa search strategy to achieve an optimal subset of features. The proposed method achieves high accuracy,reaching 99.6% accuracy for data classification through the optimization of a neural network using learningautomata.
Gender gap in the ownership and use of cryptocurrencies: Empirical evidence from Spain

Over the last few years, the holding and use of cryptocurrencies, as well as the entire ecosystem of decentralised finance (DeFi), has become popular worldwide. However, their acceptance and use are not equal for various reasons; and it is important to know these limiting reasons. There is a gender gap in the acceptance and use of so-called decentralised finance (DeFi), where females have much lower acceptance and usage rates than men. Our study has focused on analysing why this situation occurs. A structured questionnaire was used, including age, level of education and gender, as well as closed-ended questions and questions with respondent ratings (Likert scale). Replicating the Theory of Planned Behaviour (TPB) model, applied in this case to the study on the use and acceptance of cryptocurrencies by females. This has allowed us to generate a sample of 326 people living in Spain, 168 of whom are females. We obtained as a result that the barriers that limit the acceptance and use of cryptocurrencies by females are several: The lack of investment experience in traditional assets, the general lack of knowledge on the part of females about cryptocurrencies; as well as concepts such as blockchain, how to carry out transactions or what an exchange is and how it works. Also, the fact that cryptocurrencies do not inspire any security in females and risk aversion. In contrast, we found no evidence that females do not accept or use cryptocurrencies due to a lack of trust in traditional money, pressure from social media and influencers, fear of not doing the same as the next guy or speculation, which are mostly present in the motivation of males. Nor do we find that income or lack of digital skills among females are barriers to entry.
Siamese Image Modeling for Self-Supervised Vision Representation Learning

Self-supervised learning (SSL) has delivered superior performance on a variety of downstream vision tasks. Two main-stream SSL frameworks have been proposed, i.e., Instance Discrimination (ID) and Masked Image Modeling (MIM). ID pulls together representations from different views of the same image, while avoiding feature collapse. It lacks spatial sensitivity, which requires modeling the local structure within each image. On the other hand, MIM reconstructs the original content given a masked image. It instead does not have good semantic alignment, which requires projecting semantically similar views into nearby representations. To address this dilemma, we observe that (1) semantic alignment can be achieved by matching different image views with strong augmentations; (2) spatial sensitivity can benefit from predicting dense representations with masked images. Driven by these analysis, we propose Siamese Image Modeling (SiameseIM), which predicts the dense representations of an augmented view, based on another masked view from the same image but with different augmentations. SiameseIM uses a Siamese network with two branches. The online branch encodes the first view, and predicts the second view's representation according to the relative positions between these two views. The target branch produces the target by encoding the second view. SiameseIM can surpass both ID and MIM on a wide range of downstream tasks, including ImageNet finetuning and linear probing, COCO and LVIS detection, and ADE20k semantic segmentation. The improvement is more significant in few-shot, long-tail and robustness-concerned scenarios. Code shall be released.
Medical Diagnosis and Treatment Plans Derived from a Hybrid Expert System

A number of approaches have been used in attempts to build useful computerized decision aids. This is especially true in the field of medicine where early techniques proved to be less than satisfactory. In this chapter, a system is described which utilizes a combination of rule-based methods encompassing approximate reasoning and neural network models. The method is illustrated in the development of treatment plans for patients with malignant melanoma. The rule-based approach allows expert input as well as provides explanation capabilities. Neural networks are used in two ways: first, as a device for extracting important decision making parameters from accumulated data, and secondly, to analyze laboratory tests directly to determine the likelihood of malignant disease. The resulting system has been tested and shows promising results for the establishment of treatment plans for this devastating disease.
A Compendium of Parameterized Problems at Higher Levels of the Polynomial Hierarchy

We present a list of parameterized problems together with a complexity classification of whether they allow a fixed-parameter tractable reduction to SAT or not. These problems are parameterized versions of problems whose complexity lies at the second level of the Polynomial Hierarchy or higher.
Better Orders for Saturated Cost Partitioning in Optimal Classical Planning

Cost partitioning is a general method for adding multiple heuristic values admissibly. In the setting of optimal classical planning, saturated cost partitioning has recently been shown to be the cost partitioning algorithm of choice for pattern database heuristics found by hill climbing, systematic pattern database heuristics and Cartesian abstraction heuristics. To evaluate the synergy of the three heuristic types, we compute the saturated cost partitioning over the combined sets of heuristics and observe that the resulting heuristic is outperformed by the heuristic that simply maximizes over the three saturated cost partitioning heuristics computed separately for each heuristic type. Our new algorithm for choosing the orders in which saturated cost partitioning considers the heuristics allows us to compute heuristics outperforming not only the maximizing heuristic but even state-of-the-art planners.
An Adaptive Quantizer for High Dynamic Range Content: Application to Video Coding

In this paper, we propose an adaptive perceptual quantization method to convert the representation of high dynamic range (HDR) content from the floating point data type to integer, which is compatible with the current image/video coding and display systems. The proposed method considers the luminance distribution of the HDR content, as well as the detectable contrast threshold of the human visual system, in order to preserve more contrast information than the perceptual quantizer (PQ) in integer representation. Aiming to demonstrate the effectiveness of this quantizer for HDR video compression, we implemented it in a mapping function on the top of the HDR video coding system based on high efficiency video coding standard. Moreover, a comparison function is also introduced to decrease the additional bit-rate of side information, generated by the mapping function. Objective quality measurements and subjective tests have been conducted in order to evaluate the quality of the reconstructed HDR videos. Subjective test results have shown that the proposed method can improve, in a significant manner, the perceived quality of some reconstructed HDR videos. In the objective assessment, the proposed method achieves improvements over PQ in terms of the average bit-rate gain for metrics used in the measurement.
Joint Autoencoders: A Flexible Meta-learning Framework

We develop a framework for learning multiple tasks simultaneously, based on sharing features that are common to all tasks, achieved through the use of a modular deep feedforward neural network consisting of shared branches, dealing with the common features of all tasks, and private branches, learning the specific unique aspects of each task. Once an appropriate weight sharing architecture has been established, learning takes place through standard algorithms for feedforward networks, e.g., stochastic gradient descent and its variations. The method deals with meta-learning (such as domain adaptation, transfer and multi-task learning) in a unified fashion, and can deal with data arising from different modalities. Numerical experiments demonstrate the effectiveness of learning in domain adaptation and transfer learning setups, and provide evidence for the flexible and task-oriented representations arising in the network. In particular, we handle transfer learning between multiple tasks in a straightforward manner, as opposed to many competing state-of-the-art methods, that are unable to handle more than two tasks. We also illustrate the network’s ability to distill task-specific and shared features.
Visualizing a Thinker's Life

This paper presents a visualization framework that aids readers in understanding and analyzing the contents of medium-sized text collections that are typical for the opus of a single or few authors. We contribute several document-based visualization techniques to facilitate the exploration of the work of the German author Bazon Brock by depicting various aspects of its texts, such as the TextGenetics that shows the structure of the collection along with its chronology. The ConceptCircuit augments the TextGenetics with entities - persons and locations that were crucial to his work. All visualizations are sensitive to a wildcard-based phrase search that allows complex requests towards the author's work. Further development, as well as expert reviews and discussions with the author Bazon Brock, focused on the assessment and comparison of visualizations based on automatic topic extraction against ones that are based on expert knowledge.
A Variable Sample-Size Stochastic Quasi-Newton Method for Smooth and Nonsmooth Stochastic Convex Optimization

Classical theory for quasi-Newton schemes has focused on smooth, deterministic, unconstrained optimization, whereas recent forays into stochastic convex optimization have largely resided in smooth, unconstrained, and strongly convex regimes. Naturally, there is a compelling need to address nonsmoothness, the lack of strong convexity, and the presence of constraints. Accordingly, this paper presents a quasi-Newton framework that can process merely convex and possibly nonsmooth (but smoothable) stochastic convex problems. We propose a framework that combines iterative smoothing and regularization with a variance-reduced scheme reliant on using an increasing sample size of gradients. We make the following contributions. (i) We develop a regularized and smoothed variable sample-size BFGS update (rsL-BFGS) that generates a sequence of Hessian approximations and can accommodate nonsmooth convex objectives by utilizing iterative regularization and smoothing. (ii) In strongly convex regimes with state-dependent noise, the proposed variable sample-size stochastic quasi-Newton (VS-SQN) scheme admits a nonasymptotic linear rate of convergence, whereas the oracle complexity of computing an [Formula: see text]-solution is [Formula: see text], where [Formula: see text] denotes the condition number and [Formula: see text]. In nonsmooth (but smoothable) regimes, using Moreau smoothing retains the linear convergence rate for the resulting smoothed VS-SQN (or sVS-SQN) scheme. Notably, the nonsmooth regime allows for accommodating convex constraints. To contend with the possible unavailability of Lipschitzian and strong convexity parameters, we also provide sublinear rates for diminishing step-length variants that do not rely on the knowledge of such parameters. (iii) In merely convex but smooth settings, the regularized VS-SQN scheme rVS-SQN displays a rate of [Formula: see text] with an oracle complexity of [Formula: see text]. When the smoothness requirements are weakened, the rate for the regularized and smoothed VS-SQN scheme rsVS-SQN worsens to [Formula: see text]. Such statements allow for a state-dependent noise assumption under a quadratic growth property on the objective. To the best of our knowledge, the rate results are among the first available rates for QN methods in nonsmooth regimes. Preliminary numerical evidence suggests that the schemes compare well with accelerated gradient counterparts on selected problems in stochastic optimization and machine learning with significant benefits in ill-conditioned regimes.
Modular Dimensionality Reduction

We introduce an approach to modular dimensionality reduction, allowing efficient learning of multiple complementary representations of the same object. Modules are trained by optimising an unsupervised cost function which balances two competing goals: Maintaining the inner product structure within the original space, and encouraging structural diversity between complementary representations. We derive an efficient learning algorithm which outperforms gradient based approaches without the need to choose a learning rate. We also demonstrate an intriguing connection with Dropout. Empirical results demonstrate the efficacy of the method for image retrieval and classification.
An agile approach for managing requirements change to improve learning and adaptability

Nowadays agile development has become a mainstream development methodology. Yet agile is still lacking in how in-depth it deals with Requirement Engineering (RE) compared to the other development stages. There have been attempts to apply agile RE techniques to traditional development and conversely to apply traditional RE techniques to agile development. But the biggest problem is that it is difficult to maintain the essence of agile which is to “collaborate”, “(iterative and frequent) inspect and adapt”, and “deliver customer value”. In this paper, we define six criteria for an agile requirement management approach and propose a lightweight agile approach that maintains the characteristics of agile while combining the RE techniques of academia, business analyst, and agile & lean community. Furthermore, we show an industrial case study which we've applied our approach.
Anticipating movie success through crowdsourced social media videos

Business houses and marketers have been relying on social media to affect consumer opinions and purchasing behavior. In this paper a framework has been proposed to identify and quantify the emotive value of any movie trailer. The proposed framework made use of Dlib-ml (a machine learning toolkit) and a Genetic Algorithm inspired Support Vector Machine algorithm (GAiSVM) for parameter tuning and classification and emotive analysis of movie trailers. A case study comprising of 141 movies trailers released from Jan 1, 2017 till April 31, 2018 was done to investigate the relationship between emotive score of a movie trailer and financial returns associated with it. Results revealed a direct correlation between emotive score of a movie trailer and financial returns. Further, it was concluded that an emotionally intense movie trailer could result high financial returns in comparison to non-much emotionally intense trailers.
OAuth 2.0 Security Best Current Practice

This document describes best current security practice for OAuth 2.0.It updates and extends the OAuth 2.0 Security Threat Model toincorporate practical experiences gathered since OAuth 2.0 waspublished and covers new threats relevant due to the broaderapplication of OAuth 2.0.
The Effect of Diet-Exercise Trainings Provided to Overweight and Obese Teenagers through Creative Drama on Their Knowledge, Attitude, and Behaviors

Background: This study was conducted in Turkey as an empirical study in randomized, controlled, and time series design for the purpose of evaluating the effects of the Diet-Exercise trainings with creative drama method provided to overweight/obese teenagers. Methods: This study was designed to highlight the effect of interactive education provided to slightly overweight/obese teenagers through creative drama on modifiable factors that caused obesity. Seventy-six overweight/obese teenagers studying at sixth and seventh grades of upper primary education were included in the study (38 participants in the Study Group and 38 participants in the Control Group). Training courses that were designed based on creative drama method were implemented in five sessions with teenager groups (n = 12–13); and the mothers were also trained in a session in this study (for the Study Group). Results: At the end of the study, it was determined that the knowledge, attitude, the order of the meals, and healthy diet-exercise behavior points of the teenagers in the Study Group increased; and the difference between the Study Group and the Control Group was significant (p < 0.05). The BMI, body weight, and waist/height rate of the teenagers in the Study Group decreased at a significant level after the study was completed (p < 0.05); and significant increases were determined in the waist circumference and body weight measurements of the Control Group (p < 0.05). Conclusions: As a conclusion, overweight/obese teenagers may be reached with school-based educational programs, and they may be made to acquire positive attitudes and behaviors through diet and exercises designed with interactive methods like creative drama.
The foundation of information science: one world or three? A discussion of Gnoli (2018)

Purpose The purpose of this paper is to make a critical analysis of the views put forward by Claudio Gnoli (2018) in this paper concerning philosophical problems in library and information science (LIS). Design/methodology/approach The paper presents the basic ideas in Gnoli (2018) and discusses the set of basic assumptions, concepts and conclusions put forward. Findings It is argued that the idea of the theory of levels is basically sound, but we do not need to consider the material world, the mental world (minds) and the world of mentefacts as three different worlds. They represent different levels with different kinds of emergent properties in the world. Further, although the concepts of artifacts and mentefacts are useful, there are other terms within LIS, such as document, work and object that have been influential and should be discussed in this context. It is also argued that subjective vs objective knowledge is often confused with private vs public knowledge, which is problematic. Finally, it is claimed that the cognitive view and the “sociological view” are not about two different levels of reality but are competing views about the same reality. Originality/value The paper clarifies some aspects of the analytical framework of domain analysis and adds to the developments of the philosophical dimensions of information within LIS.
Fusion of CNN- and COSFIRE-Based Features with Application to Gender Recognition from Face Images

Convolution neural networks (CNNs) have been demonstrated to be very effective in various computer vision tasks. The main strength of such networks is that features are learned from some training data. In cases where training data is not abundant, transfer learning can be used in order to adapt features that are pre-trained from other tasks. Similarly, the COSFIRE approach is also trainable as it configures filters to be selective for features selected from training data. In this study we propose a fusion method of these two approaches and evaluate their performance on the application of gender recognition from face images. In particular, we use the pre-trained VGGFace CNN, which when used as standalone, it achieved 97.45% on the GENDER-FERET data set. With one of the proposed fusion approaches the recognition rate on the same task is improved to 98.9%, that is reducing the error rate by more than 50%. Our experiments demonstrate that COSFIRE filters can provide complementary features to CNNs, which contribute to a better performance.
Finding Similar Documents Using Frequent Pattern Mining Methods

Various problems are just rising with regard to mining in massive datasets, among which finding similar documents can be pinpointed. The Shingling method converts this problem to a set-based problem. Some of existing methods have used min-hashing to compress the results already driven from the shingling method and then have exploited LSH method to find candidate pairs for similarity search from all pairs of documents. In this paper, an apriori-based method is proposed for finding similar documents based on frequent itemset mining approach. To this end, the apriori algorithm is modified and is customized for similarity search problem. Modeling the similarity search problem as a frequent pattern mining problem, using a modified version of apriori, and dynamic selection the minimum support threshold are the most important advantages of the proposed method, which lead to its appropriate execution time and high quality results. The proposed method finds similar documents in less time than the combined method and MCVM method because it generates fewer candidate pairs for finding similar documents. Furthermore, experimental results show the high quality of the answers of the proposed methods.