Reviewerly/exHarmony huggingface dataset:
An interpretable evolving fuzzy neural network based on self-organized direction-aware data partitioning and fuzzy logic neurons

This paper proposes the definition of the architecture of an evolving fuzzy neural network based on self-organizing direction aware data partitioning through stochastic processes based on the dataset used in the model. The choice between four different types of logical neurons in the second layer and the six different types of neuron activation function that compose the artificial neural network of aggregation are defined in a procedure of generating random pairs of combinations between the two factors. The combination that obtains the maximization of training accuracy is chosen to compose the network and perform the composition of the model structure, whose components can be transferred to readable IF-THEN rules for interpretability purposes. Furthermore, the model is able to adapt its parameters and evolve its structure autonomously with new data samples through self-organized direction-aware data partitioning (SODA). In this context, we also propose a technique to measure the degree of changes of neurons (rules), which could be used for structural active learning purposes (e.g, to request user feedback in the case of significant changes). To compare the proposed approach, binary pattern classification tests were performed, and the results were compared with other models of fuzzy neural networks and neural networks, obtaining satisfactory results with the stochastic definition of elements that compose their architecture comparing the final accuracy of the model when classifying real datasets. The proposed model obtained the best result in 3 of the four synthetic bases evaluated, in addition to the best accuracy results in the classification of patterns in five of the nine evaluated real datasets. It highlights the accuracy in problems in patients who underwent breast cancer surgery (72.44%), diabetes evaluation (67.87%), Australian (72.27%) and German (80.51%) credit ratings evaluation, and finally in the classification of radar signals in the ionosphere (90.46%). It is also noteworthy to obtain fuzzy rules in evolution extracted from a real problem of the identification of respiratory diseases through the collection of saliva with 76.67% of accuracy. The results presented by the model were superior to traditional artificial intelligence models, while it was possible to extract knowledge in form of interpretable rules and to realize how these changed over time.
Secure Keyed Hashing on Programmable Switches

Cyclic Redundancy Check (CRC) is a computationally inexpensive function readily available in many high-speed networking devices, and thus it is used extensively as a hash function in many data-plane applications. However, CRC is not a true cryptographic hash function, and it leaves applications vulnerable to attack. While cryptographically secure hash functions exist, there is no fast and efficient implementation for such functions on high-speed programmable switches. In this paper, we introduce an implementation of a secure keyed hash function optimized for commodity programmable switches and capable of running entirely within the data plane. We implement HalfSipHash on the Barefoot Tofino switch by using dependency management schemes to conserve pipeline stages and slicing semantics for concise circular bit shift operations. We show that our efficient implementation performs 67 million, 90 million, 150 million, and 304 million hashes per second for 32-byte, 24-byte, 16-byte, and 8-byte input strings, respectively.
VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation

Sparsity of user-to-item rating data becomes one of challenging issues in the recommender systems, which severely deteriorates the recommendation performance. Fortunately, context-aware recommender systems can alleviate the sparsity problem by making use of some auxiliary information, such as the information of both the users and items. In particular, the visual information of items, such as the movie poster, can be considered as the supplement for item description documents, which helps to obtain more item features. In this paper, we focus on movie recommender system and propose a probabilistic matrix factorization based recommendation scheme called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visual recurrent convolutional matrix factorization</i> (VRConvMF), which utilizes the textual and multi-level visual features extracted from the descriptive texts and posters respectively. We implement the proposed VRConvMF and conduct extensive experiments on three commonly used real world datasets to validate its effectiveness. The experimental results illustrate that the proposed VRConvMF outperforms the existing schemes.
Virtual reality perspective-taking at scale: Effect of avatar representation, choice, and head movement on prosocial behaviors

The present investigation examined the effect of avatar representation, choice, and head movement on prosocial behaviors and measures of presence after a virtual reality perspective-taking (VRPT) task. Participants were either represented by a set of virtual hands or had no representation during a VRPT task. Of those with hands, only half were able to choose their skin tone. Results showed that there was no significant advantage to having an avatar representation. However, if participants had an avatar and were able to choose their own skin tone, a higher proportion of participants performed prosocial behaviors and reported higher social presence scores compared with participants who had no choice. Regardless of condition, head rotations significantly predicted petition signatures such that the more participants rotated their heads side to side, the more likely they were to sign the petition. Moreover, when participants do not consistently rotate their head side to side, the proportion of petitions signed is on par with individuals who do not complete a VRPT task at all.
HPRNet: Hierarchical point regression for whole-body human pose estimation

In this paper, we present a new bottom-up one-stage method for whole-body pose estimation, which we call “hierarchical point regression,” or HPRNet for short. In standard body pose estimation, the locations of ~17 major joints on the human body are estimated. Differently, in whole-body pose estimation, the locations of fine-grained keypoints (68 on face, 21 on each hand and 3 on each foot) are estimated as well, which creates a scale variance problem that needs to be addressed. To handle the scale variance among different body parts, we build a hierarchical point representation of body parts and jointly regress them. The relative locations of fine-grained keypoints in each part (e.g. face) are regressed in reference to the center of that part, whose location itself is estimated relative to the person center. In addition, unlike the existing two-stage methods, our method predicts whole-body pose in a constant time independent of the number of people in an image. On the COCO WholeBody dataset, HPRNet significantly outperforms all previous bottom-up methods on the keypoint detection of all whole-body parts (i.e. body, foot, face and hand); it also achieves state-of-the-art results on face (75.4 AP) and hand (50.4 AP) keypoint detection. Code and models are available at https://github.com/nerminsamet/HPRNet.git.
The Str(AI)ght Scoop: Artificial Intelligence Cues Reduce Perceptions of Hostile Media Bias

In the face of increasing public distrust for journalistic institutions, stories sourced from artificially intelligent (AI) journalists have the potential to lower hostile media bias by activating the machine heuristic—a mental shortcut assuming machines are more unbiased, systematic, and accurate than are humans. An online experiment targeting issue partisans found support for the prediction: a story presented as sourced from an AI journalist activated the machine heuristic that, in turn, mitigated hostile media bias. This mediation effect was moderated: perceived bias was more strongly reduced as partisan-attitude extremity increased.
Quantum private set intersection cardinality based on bloom filter

Abstract Private Set Intersection Cardinality that enable Multi-party to privately compute the cardinality of the set intersection without disclosing their own information. It is equivalent to a secure, distributed database query and has many practical applications in privacy preserving and data sharing. In this paper, we propose a novel quantum private set intersection cardinality based on Bloom filter, which can resist the quantum attack. It is a completely novel constructive protocol for computing the intersection cardinality by using Bloom filter. The protocol uses single photons, so it only need to do some simple single-photon operations and tests. Thus it is more likely to realize through the present technologies. The validity of the protocol is verified by comparing with other protocols. The protocol implements privacy protection without increasing the computational complexity and communication complexity, which are independent with data scale. Therefore, the protocol has a good prospects in dealing with big data, privacy-protection and information-sharing, such as the patient contact for COVID-19.
Robust and reversible image watermarking in homomorphic encrypted domain

This paper proposes a robust and reversible watermarking scheme for the encrypted image by using Paillier cryptosystem. In the proposed method, the original image is divided into a number of non-overlapping blocks sized by 8 × 8 and Paillier cryptosystem is applied to encrypt the pixels in each block. Firstly, a data hider can calculate the statistical values of encrypted blocks by employing modular multiplicative inverse (MMI) method and looking for a mapping table. Then a watermark sequence can be embedded into the encrypted image by shifting the histogram of the statistical values. On the receiver side, the shifted histogram can be obtained from both the encrypted image and the decrypted image. Furthermore, the embedded watermark can be extracted from the shifted histogram. The encrypted original image can be restored by employing inverse operations of histogram shifting. This is followed by a decryption operation to restore the original image. In the proposed method, the hidden bits can still be extracted correctly under some typical content-preserving operations, such as JPEG/JPEG2000 compression and additive Gaussian noise. Compared with the previous reversible watermarking methods in plaintext domain, the proposed method has satisfactory performance in image quality and robustness. Experimental results have shown the validity of the proposed method. • Propose a robust reversible image watermarking scheme in encrypted domain. • Propose a method to compute the statistical values of blocks in encrypted domain. • Propose an integer transform to shift the statistical values in encrypted domain.
Machine learning based early mortality prediction in the emergency department

It is a great challenge for emergency physicians to early detect the patient's deterioration and prevent unexpected death through a large amount of clinical data, which requires sufficient experience and keen insight.To evaluate the performance of machine learning models in quantifying the severity of emergency department (ED) patients and identifying high-risk patients.Using routinely-available demographics, vital signs and laboratory tests extracted from electronic health records (EHRs), a framework based on machine learning and feature engineering was proposed for mortality prediction. Patients who had one complete record of vital signs and laboratory tests in ED were included. The following patients were excluded: pediatric patients aged < 18 years, pregnant woman, and patients died or were discharged or hospitalized within 12 h after admission. Based on 76 original features extracted, 9 machine learning models were adopted to validate our proposed framework. Their optimal hyper-parameters were fine-tuned using the grid search method. The prediction results were evaluated on performance metrics (i.e., accuracy, area under the curve (AUC), recall and precision) with repeated 5-fold cross-validation (CV). The time window from patient admission to the prediction was analyzed at 12 h, 24 h, 48 h, and entire stay.We studied a total of 1114 ED patients with 71.54% (797/1114) survival and 28.46% (317/1114) death in the hospital. The results revealed a more complete time window leads to better prediction performance. Using the entire stay records, the LightGBM model with refined feature engineering demonstrated high discrimination and achieved 93.6% (±0.008) accuracy, 97.6% (±0.003) AUC, 97.1% (±0.008) recall, and 94.2% (±0.006) precision, even if no diagnostic information was utilized.This study quantifies the criticality of ED patients and appears to have significant potential as a clinical decision support tool in assisting physicians in their clinical routine. While the model requires validation before use elsewhere, the same methodology could be used to create a strong model for the new hospital.
A reliable chaos-based cryptography using Galois field

Chaos-based image encryption schemes have been extensively employed over the past few years. Many issues such as the dynamical degradation of digital chaotic systems and information security have been explored, and plenty of successful solutions have also been proposed. However, the impact of finite precision in different hardware and software setups has received little attention. In this work, we have shown that the finite precision error may produce distinct cipher-images on different devices. In order to overcome this problem, we introduce an efficient cryptosystem, in which the chaotic logistic map and the Galois field theory are applied. Our approach passes in the ENT test suite and in several cyberattacks. It also presents an astonishing key space of up to 24096. Benchmark images have been effectively encrypted and decrypted using dissimilar digital devices.
Implied volatility estimation of bitcoin options and the stylized facts of option pricing

The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging. The need for these tools dates back to the market crash of 1987, when investors needed better ways to protect their portfolios through option insurance. These tools provide greater flexibility to trade and hedge volatile swings in Bitcoin prices effectively. The violation of constant volatility and the log-normality assumption of the Black-Scholes option pricing model led to the discovery of the volatility smile, smirk, or skew in options markets. These stylized facts; that is, the volatility smile and implied volatilities implied by the option prices, are well documented in the option literature for almost all financial markets. These are expected to be true for Bitcoin options as well. The data sets for the study are based on short-dated Bitcoin options (14-day maturity) of two time periods traded on Deribit Bitcoin Futures and Options Exchange, a Netherlands-based cryptocurrency derivative exchange. The estimated results are compared with benchmark Black-Scholes implied volatility values for accuracy and efficiency analysis. This study has two aims: (1) to provide insights into the volatility smile in Bitcoin options and (2) to estimate the implied volatility of Bitcoin options through numerical approximation techniques, specifically the Newton Raphson and Bisection methods. The experimental results show that Bitcoin options belong to the commodity class of assets based on the presence of a volatility forward skew in Bitcoin option data. Moreover, the Newton Raphson and Bisection methods are effective in estimating the implied volatility of Bitcoin options. However, the Newton Raphson forecasting technique converges faster than does the Bisection method.
Solving Schrödinger Bridges via Maximum Likelihood

The Schrödinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution. As well as applications in the natural sciences, problems of this kind have important applications in machine learning such as dataset alignment and hypothesis testing. Whilst the theory behind this problem is relatively mature, scalable numerical recipes to estimate the Schrödinger bridge remain an active area of research. Our main contribution is the proof of equivalence between solving the SBP and an autoregressive maximum likelihood estimation objective. This formulation circumvents many of the challenges of density estimation and enables direct application of successful machine learning techniques. We propose a numerical procedure to estimate SBPs using Gaussian process and demonstrate the practical usage of our approach in numerical simulations and experiments.
Entering the world behind the clothes that we wear: practical applications of blockchain technology

This paper investigates blockchain technology from a Triple Bottom Line (TBL) perspective. Transparency is vital for achieving accountability, improving the environmental footprint and ensuring that workers’ rights are respected. Blockchain-enabled capabilities provide opportunities to support improved information transparency to sub-suppliers and customers. Case study research is used within the complex multi-tier global fashion industry to explore the application of blockchain technology in enhancing TBL performance. This paper responds to calls from the literature to provide practice-based research on blockchain and supply chain management challenges. The research is based on three technology-based start-ups (at different Technology Readiness Levels (TRL)) and investigates their practical experiences to date, in transforming and advancing supply chain TBL. As this research focuses on the early adoption of blockchain technology within the fashion industry, it demonstrates the practicalities of implementation including its capabilities, operational improvements, challenges and limitations.
Visual assessment of mitotic figures in breast cancer: a comparative study between light microscopy and whole slide images

Background and aims Visual assessment of mitotic figures in breast cancer (BC) remains a challenge. This is expected to be more pronounced in the digital pathology era. This study aims to refine the criteria of mitotic figure recognition, particularly in whole slide images (WSI). Method and results Haematoxylin and eosin (H&amp;E)‐stained BC sections ( n = 506) were examined using light microscopy (LM) and WSI. A set of features for identifying mitosis in WSI and to distinguish true figures from mimickers was developed. Changes in the mitotic count between the two platforms was explored. Morphological features of mitoses were recorded separately, including absence of nuclear membrane, chromatin hairy‐like projections, shape, cytoplasmic features, mitotic cell size and relationship to surrounding cells. Each mitotic phase has its own mimickers. Fifty‐eight per cent of mitoses showed absent hairy‐like projection in WSI; however, 89% retained their ragged nuclear border, which distinguished them from mimickers including apoptotic cells, lymphocytes and dark elongated hyperchromatic structures. Mitosis in WSI showed loss of fine details, and there was a 20% average reduction rate of mitotic counts when compared to the same area on LM. Using refined mitosis recognition criteria in WSI resulted in a twofold improvement of interobserver concordance. However, when compared to LM, 19% of cases were underscored in WSIs. Conclusions All morphological features of mitosis should be considered to enable recognition and differentiation from their mimickers, particularly in WSI, to ensure reliable BC grading. Refining mitotic cut‐offs per specific area when using WSI, based on the degree of reduction and association with outcome, is warranted.
Unsupervised Deep Cross-Modal Hashing by Knowledge Distillation for Large-scale Cross-modal Retrieval

Cross-modal hashing (CMH) maps heterogeneous multiple modality data into compact binary code to achieve fast and flexible retrieval across different modalities, especially in large-scale retrieval. As the data don't need a lot of manual annotation, unsupervised cross-modal hashing has a wider application prospect than supervised method. However, the existing unsupervised methods are difficult to achieve satisfactory performance due to the lack of credible supervisory information. To solve this problem, inspired by knowledge distillation, we propose a novel unsupervised Knowledge Distillation Cross-Modal Hashing method (KDCMH), which can use similarity information distilled from unsupervised method to guide supervised method. Specifically, firstly, the teacher model adopted an unsupervised distribution-based similarity hashing method, which can construct a modal fusion similarity matrix.Secondly, under the supervision of teacher model distillation information, student model can generate more discriminative hash codes. In two public datasets NUS-WIDE and MIRFLICKR-25K, extensive experiments have proved the significant improvement of KDCMH on several representative unsupervised cross-modal hashing methods.
The Academic Reading Format International Study (ARFIS): final results of a comparative survey analysis of 21,265 students in 33 countries

Purpose This paper presents the complete findings from the Academic Reading Format International Study (ARFIS), the world's largest study of tertiary students' format preferences and behaviors. The analysis of ARFIS proceeded in two stages. This paper reveals results from the second stage for the first time and compares them with the earlier results. The authors then present and discuss the results from the combined datasets of 21,266 students in 33 countries. Design/methodology/approach A total of 44 members in the ARFIS research team distributed an online survey composed of 22 Likert-style, multiple choice and open-ended questions to tertiary students on their format preferences and behaviors. Inferential statistical analysis was used on participant responses, and descriptive statistics analysis was used on the combined amalgamated dataset. Findings Majorities of students in all countries consistently show preference for reading their academic texts in print. However, variations of preferences do occur between countries. Overall, the language of a reading does not affect reading format preferences, but national scores on this question are greatly diverse. Originality/value ARFIS is the largest study of its kind created through a collaboration of researchers in countries on six continents, collecting data in more than 20 languages. The sample size, rigorous statistical analysis and consistency of results strengthen the reliability of the findings. Analysis of the first dataset has received widespread recognition, but this paper is the first to publish the second dataset and the complete amalgamated results.