Reviewerly/exHarmony huggingface dataset:
An Investigation about Modern Deep Learning Strategies for Colon Carcinoma Grading

Developing computer-aided approaches for cancer diagnosis and grading is currently receiving an increasing demand: this could take over intra- and inter-observer inconsistency, speed up the screening process, increase early diagnosis, and improve the accuracy and consistency of the treatment-planning processes.The third most common cancer worldwide and the second most common in women is colorectal cancer (CRC). Grading CRC is a key task in planning appropriate treatments and estimating the response to them. Unfortunately, it has not yet been fully demonstrated how the most advanced models and methodologies of machine learning can impact this crucial task.This paper systematically investigates the use of advanced deep models (convolutional neural networks and transformer architectures) to improve colon carcinoma detection and grading from histological images. To the best of our knowledge, this is the first attempt at using transformer architectures and ensemble strategies for exploiting deep learning paradigms for automatic colon cancer diagnosis. Results on the largest publicly available dataset demonstrated a substantial improvement with respect to the leading state-of-the-art methods. In particular, by exploiting a transformer architecture, it was possible to observe a 3% increase in accuracy in the detection task (two-class problem) and up to a 4% improvement in the grading task (three-class problem) by also integrating an ensemble strategy.
The Effect of Augmented Reality Technology on Middle School Students’ Mathematic Academic Achievement, Self-Regulated Learning Skills, and Motivation

This study aimed to investigate the effects of augmented reality (AR) applications in geometry teaching on academic achievement, self-regulated learning skills (SRLS), and motivation of secondary school students. The research was carried out on students in the eighth grade at a state secondary school in Turkey. The research was carried out according to the quasi-experimental design with the pretest-posttest control group, including 20 students in the experimental group (EG) and 20 in the control group (CG). While geometry teaching supported by AR applications was carried out with the EG students, traditional education was carried out with the CG students. Within the scope of the mathematics course, the achievements of the three-dimensional geometric objects unit in the curriculum were discussed. As a result of the analysis, it was determined that the difference in achievement test, self-regulated learning scale, and motivation scale scores between the groups was statistically significant in favor of the EG students. The study’s qualitative findings show that the AR application is fun and interesting for the students and positively affects academic achievement and active participation in the lesson. The research results were discussed according to the results in the literature, and suggestions were made for new research and applications.
Unbalanced regression sample generation algorithm based on confrontation

Data imbalance is an issue because the number of samples in different categories or target value ranges varies significantly. Numerous studies have been developed to address the data imbalance problem in classification samples. However, the issue of data imbalance in regression samples has not been researched well. The distribution of the target value of regression samples with the unbalanced data problem is more complicated than classification samples with the unbalanced data problem due to the continuity of the target values of regression samples. To solve this problem, we defined three basic modes of the data imbalance problem of regression samples: PSIR-mode (Positive Skewed Imbalanced Regression-mode), UNIR-mode (Un-Normal Imbalanced Regression-mode) and NSIR-mode (Negative Skewed Imbalanced Regression-mode). Any regression samples having data imbalance problems with complex target value distributions can be split into these three modes. To solve the data imbalance problem in regression samples, we proposed the DIRVAE (Deep Imbalanced Regression Variational Autoencoder) algorithm to generate missing and minority samples. The model can learn the distribution information of the original sample and the sample information between adjacent samples in the target value distribution. Experiments in biology, medicine and aerospace have proved the superiority of the model.
Quantum Advantage from Any Non-local Game

We show a general method of compiling any k-prover non-local game into a single-prover (computationally sound) interactive game maintaining the same quantum completeness and classical soundness guarantees, up to a negligible additive factor in a security parameter. Our compiler uses any quantum homomorphic encryption scheme (Mahadev, FOCS 2018; Brakerski, CRYPTO 2018) satisfying a natural form of correctness with respect to auxiliary quantum input. The homomorphic encryption scheme is used as a cryptographic mechanism to simulate the effect of spatial separation, and is required to evaluate k−1 prover strategies out of k on encrypted queries.
Obfuscation of Pseudo-Deterministic Quantum Circuits

We show how to obfuscate pseudo-deterministic quantum circuits, assuming the quantum hardness of learning with errors (QLWE) and post-quantum virtual black-box (VBB) obfuscation for classical circuits. Given the classical description of a quantum circuit Q, our obfuscator outputs a quantum state Q that can be used to evaluate Q repeatedly on arbitrary inputs.
Medical students and residents appreciate ebooks’ convenience, but prefer the print book reading experience

Increasingly, libraries buy medical monographs as ebooks, but that may not be what medical students and residents want. Some studies have shown that they prefer print books for some types of reading. On the other hand, for participants in distributed medical programs, ebooks are more accessible.To determine whether medical students and residents at an institution with a distributed medical education program prefer medical ebooks or print books.In February 2019, 844 medical students and residents were invited to complete an online questionnaire on their format preferences.Two hundred thirty-two students and residents responded. Most preferred electronic format for reading a few pages, but print for entire books. Respondents preferred ebooks because they were immediately available, searchable and could be used on the go, and print books because they strained users' eyes less, facilitated absorption of the text and could be held in users' hands. The location of respondents and year of study had little effect on responses.Libraries should consider buying quick reference and large, heavy textbooks as ebooks and pocket-sized or shorter, single-topic titles, in print format.Libraries have a responsibility to make both print and ebooks available to their users.
A novel approach for software defect prediction using CNN and GRU based on SMOTE Tomek method

Abstract Software defect prediction (SDP) plays a vital role in enhancing the quality of software projects and reducing maintenance-based risks through the ability to detect defective software components. SDP refers to using historical defect data to construct a relationship between software metrics and defects via diverse methodologies. Several prediction models, such as machine learning (ML) and deep learning (DL), have been developed and adopted to recognize software module defects, and many methodologies and frameworks have been presented. Class imbalance is one of the most challenging problems these models face in binary classification. However, When the distribution of classes is imbalanced, the accuracy may be high, but the models cannot recognize data instances in the minority class, leading to weak classifications. So far, little research has been done in the previous studies that address the problem of class imbalance in SDP. In this study, the data sampling method is introduced to address the class imbalance problem and improve the performance of ML models in SDP. The proposed approach is based on a convolutional neural network (CNN) and gated recurrent unit (GRU) combined with a synthetic minority oversampling technique plus the Tomek link (SMOTE Tomek) to predict software defects. To establish the efficiency of the proposed models, the experiments have been conducted on benchmark datasets obtained from the PROMISE repository. The experimental results have been compared and evaluated in terms of accuracy, precision, recall, F-measure, Matthew’s correlation coefficient (MCC), the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR), and mean square error (MSE). The experimental results showed that the proposed models predict the software defects more effectively on the balanced datasets than the original datasets, with an improvement of up to 19% for the CNN model and 24% for the GRU model in terms of AUC. We compared our proposed approach with existing SDP approaches based on several standard performance measures. The comparison results demonstrated that the proposed approach significantly outperforms existing state-of-the-art SDP approaches on most datasets.
Learning global image representation with generalized‐mean pooling and smoothed average precision for large‐scale CBIR

Abstract Content‐based image retrieval (CBIR) is the problem of searching for items in an image database that are similar to the query image. Most of the existing image retrieval methods are trained based on metric learning loss functions (e.g. contrastive loss or triplet loss), however, which require the use of hard sample mining strategies (HMS) to better train the model. The HMS implies that picking out hard positive or negative samples increases the complexity of model training and requires a large amount of additional training time. To address this issue, lessons from recent work are leveraged on representation learning and a model called GS is proposed that combines the state‐of‐the‐art Generalized‐Mean (GeM) pooling and the smoothed average precision (AP). The entire network can be learned end‐to‐end by approximating the non‐differentiable AP function to a differentiable one‐without mining hard samples, only image‐level annotations. A model named GSA is also presented which achieves excellent retrieval performance jointly trained by two various loss functions. Experimental results validate the effectiveness of the proposed approach and demonstrate the competitive performance on a common standard image retrieval dataset (Revisited Oxford and Paris).
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In the present paper, we define a quantum analog of Lotka-Volterra algebras using a coalgebra scheme. This new framework provides a fresh perspective for the treatment of generic algebras. Additionally, a flow of quantum analogs of Lotka-Volterra genetic algebras is investigated. It's worth mentioning that such types of algebras are first introduced in this work. We observe that a flow of algebras is a particular type of continuous-time dynamical system, with states that are algebras and a structural constant matrix that depends on time and satisfies certain analogs of the Kolmogorov-Chapman equations. Using quantum quadratic operators, it is constructed a flow of quantum Lotka-Volterra algebras for the given multiplication. Furthermore, time-dependent behavior properties of these flow algebras are examined. The algebraic properties of the introduced flows are also studied, such as finding idempotents and examining an algebra generated by a pair of idempotents. It is shown that the later one is associative, while the flow is not associative. Additionally, derivations of the flow of the algebras are described.
Generic Reed-Solomon Codes Achieve List-Decoding Capacity

In a recent paper, Brakensiek, Gopi and Makam introduced higher order MDS codes as a generalization of MDS codes. An order-ℓ MDS code, denoted by MDS(ℓ), has the property that any ℓ subspaces formed from columns of its generator matrix intersect as minimally as possible. An independent work by Roth defined a different notion of higher order MDS codes as those achieving a generalized singleton bound for list-decoding. In this work, we show that these two notions of higher order MDS codes are (nearly) equivalent.
A framework for population inference: Combining machine learning, network analysis, and non-probability road sensor data

An increasing number of technologies is recording information about human traces, offering enormous potential for analyzing human behavior. Sensor data is typically non-probability-based because not every element in the target population has a positive and known probability of being recorded. Accordingly, using such data as a primary data source for population inference is currently an active field of research. In this paper, an algorithmic population inference framework using network analysis and non-probability data is developed. This approach is demonstrated using road sensor data as the primary source to infer the Dutch freight traffic across the state road network. We interpret the Dutch state road network as a graph, with traffic junctions as vertices and state roads as edges. Road sensors are installed on a non-probability sample of edges detecting passing transport vehicles. Photographs of the license plates allow for the rare opportunity of linking sensor data with population registers. Extreme gradient boosting is applied to learn the probability of vehicle detection by a sensor from features about time, edge, vehicle and vehicle owner. Population inference is made using the learned relationship to predict the probability of detection on each day of the year, along each edge in the network for each vehicle in the population. Different data scenarios were designed to simulate the effects of the non-probability nature of the data and the extreme class imbalance. Furthermore, several performance metrics were applied. With about 27 million records and over 100 features trained and tested on an imbalanced non-probability sample, substantial variation in model performance across test sets was found. Promising results were achieved using a balanced probability sample as a control: the model performed about halfway between random guessing and perfect prediction. These results are of high practical importance because combining non-probability with administrative data is currently considered one of the most promising across several disciplines.
Collaborative filtering recommendations based on multi-factor random walks

Using trust relationships can improve the accuracy of recommendation systems; however, it is affected by data sparsity. Random walks can harvest the behavioral relationships between users and compensate for data sparsity. However, existing random walk methods may generate information of little value or even interference, which affects recommendation accuracy. A collaborative filter recommendation based on multi-factor random walk was proposed to address these problems. In this method, the comprehensive trust values of the current user over other users based on the rating time, user attribute preference, and number of mutual friends were computed more accurately to determine the trust neighborhood of the current user. Thus, trustworthy users with preferences similar to those of the current user were accurately and conveniently obtained to act as neighboring users, and the sparsity of the trust relationship could be alleviated. The final predicted rating of the target item was obtained using the ratings of multiple neighboring users for the target item or similar items to improve recommendation accuracy. Moreover, to avoid generating ratings that affect the prediction accuracy during the walk, a decision to stop the walk was made based on the comprehensive trust value, item similarity, and depth of the current walk, further improving the recommendation accuracy. An evaluation was performed on two datasets, and the proposed method achieved superior prediction accuracy and coverage rate even with sparse data and exhibited a high recommendation accuracy even with cold-start users.
Digital Twin Enabled Remote Data Sharing for Internet of Vehicles: System and Incentive Design

With the boom in advanced intelligent vehicles, the amount of data associated with the Internet of Vehicles (IoVs) grows exponentially. Sharing road data among vehicles effectively can greatly improve their driving efficiency and road experience. This, however, is hindered by security concerns, bandwidth limitations, and mobility and communication distance between vehicles. In this article, we propose a citywide data sharing platform among vehicles based on the digital twin. Specifically, we propose a digital twin based vehicular platform on the cloud to facilitate the effective data sharing of vehicles. A digital twin is a digital representation of a vehicle on the cloud that synchronises data with the vehicle in real time. Therefore, data sharing among vehicles can be accomplished by their digital twins on the cloud without any physical limitations. Considering the data privacy of vehicles, a framework that combines federated learning and transfer learning is applied which can realise personalised data sharing among vehicles without disclosing their data privacy. An incentive mechanism based on the game-theoretic approach is devised to combat the mutual distrust between vehicles and encourage their contribution to data sharing. By sewing the above mechanisms, the proposed digital twin enabled data sharing platform can effectively address privacy, bandwidth limitation and incentive issues. Using extensive trace-driven simulations, we demonstrate the effectiveness and efficiency of the proposed system.
Can Edu-Metaverse Reshape Virtual Teaching Community (VTC) to Promote Educational Equity? An Exploratory Study

Education equity has become a critical sustainable development goal worldwide, and it has aroused significant concern among educators. While virtual teaching communities have emerged as an approach to minimizing educational inequity through resource sharing and information exchange, the achievement of deep collaboration among individuals remains an area that requires further study. Metaverse, with its advanced behavior interaction capabilities, has shown potential in promoting deep collaboration and communication among individuals. However, the pathway towards leveraging Metaverse for enhancing educational equity remains unclear. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">To address this gap, this study aims to review the concept and educational applications of Metaverse, as well as its evolution to pedagogical interactions within virtual teaching communities. Specifically, this paper proposes the possibility of achieving educational equity by establishing a Metaverse-supported virtual community that leverages immersive environments for behavior interaction, employs diversified avatars for affective communication, and implements equitable mechanisms for knowledge creation. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Overall, the present study underscores the importance of constructing a warmer virtual teaching community that promotes deep collaboration and equitable knowledge creation. By leveraging the capabilities of Metaverse, educators and learners can work together to create a more inclusive and equitable learning environment.
Acceleration-aware, Retraining-free Evolutionary Pruning for Automated Fitment of Deep Learning Models on Edge Devices

Deep Learning architectures used in computer vision, natural language and speech processing, unsupervised clustering, etc. have become highly complex and application-specific in recent times. Despite existing automated feature engineering techniques, building such complex models still requires extensive domain knowledge or a huge infrastructure for employing techniques such as Neural Architecture Search (NAS). Further, many industrial applications need in-premises decision-making close to sensors, thus making deployment of deep learning models on edge devices a desirable and often necessary option. Instead of freshly designing application-specific Deep Learning models, the transformation of already built models can achieve faster time to market and cost reduction. In this work, we present an efficient re-training-free model compression method that searches for the best hyper-parameters to reduce the model size and latency without losing any accuracy. Moreover, our proposed method takes into account any drop in accuracy due to hardware acceleration, when a Deep Neural Network is executed on accelerator hardware.
Automated Deep Learning Model Partitioning for Heterogeneous Edge Devices

Deep Neural Networks (DNN) have made machine learning accessible to a wide set of practitioners working with field deployment of analytics algorithms over sensor data. Along with it, focus on data privacy, low latency inference, and sustainability has highlighted the need for efficient in-situ analytics close to sensors, at the edge of the network, which is challenging given the constrained nature of the edge platforms, including Common Off-the-Shelf (COTS) AI accelerators. Efficient DNN model partitioning across multiple edge nodes is a well-studied approach, but no definitive characterization exists as to why there is a performance improvement due to DNN model partitioning, and whether the benefits hold for currently used edge hardware & state-of-the-art DNN models. In this paper, we present a detailed study and analyses to address the above-mentioned shortcomings and propose a framework that automatically determines the best partitioning scheme and enhances system efficiency.
Towards Systematically Engineering Autonomous Systems Using Reinforcement Learning and Planning

Autonomous systems need to be able dynamically adapt to changing requirements and environmental conditions without redeployment and without interruption of the systems functionality. The EU project ASCENS has developed a comprehensive suite of foundational theories and methods for building autonomic systems. In this paper we specialise the EDLC process model of ASCENS to deal with planning and reinforcement learning techniques. We present the “AIDL” life cycle and illustrate it with two case studies: simulation-based online planning and the PSyCo reinforcement learning approach for synthesizing agent policies from hard and soft requirements. Related work and potential avenues for future research are discussed.
A mentor initiated bi-directional controlled remote state preparation protocol for non-maximally entangled bell and GHZ states

In this paper, we present a quantum communication protocol for remote bi-directional preparation of two and three-particle entangled states. The state to be prepared at one end is known to the party at the other end and vice versa. This information is utilized by the respective parties in the process of execution of the protocol. A Mentor and a Controller act at the beginning and towards the end of the process, respectively. The Mentor initiates the process through a measurement by which he creates entanglement between the two primary parties who are otherwise not connected through any quantum resource. The Controller signals the final step of the protocol after being satisfied with the performances of the other parties. The protocol has the advantage of avoiding the requirement of quantum resources with a relatively large number of qubits.