Reviewerly/exHarmony huggingface dataset:
Multi-party Secure Comparison of Strings Based on Outsourced Computation

Data is an important production factor in the era of digital economy. Privacy computing can ensure that data providers do not disclose sensitive data, carry out multi-party joint analysis and computation, securely and privately complete the full excavation of data value in the process of circulation, sharing, fusion, and calculation, which has become a popular research topic. String comparison is one of the common operations in data processing. To address the string comparison problem in multi-party scenarios, we propose an algorithm for secure string comparison based on outsourced computation. The algorithm encodes the strings with one hot encoding scheme and encrypts the encoded strings using an XOR homomorphic encryption scheme. The proposed algorithm achieves efficient and secure string comparison and counts the number of different characters with the help of a cloud-assisted server. The proposed scheme is implemented and verified using the new coronavirus gene sequence as the comparison string, and the performance is compared with that of a state-of-the-art security framework. Experiments show that the proposed algorithm can effectively improve the string comparison speed and obtain correct comparison results without compromising data privacy.
Parameters for designing media facades: A research toward different cases from Turkey and other countries

The media facades that provide an interactive communication structure for urban users have emerged in the modern period, and the use of these facades has become common. This paper aims to determine the parameters that will affect the process of designing media facades and discover how design-related parameters are distributed in the practices of designing these media facades. For the evaluation process, 14 sample facades that are universally representative and serve different functions in buildings located in Turkey and other countries (control group) were selected. Analyzing these facades is important to reveal the digitalization level and detailed know-how in this regard. Data were compared using the charts for the media facade formations reviewed through observation method. Examining the parameters regarding the designs of media facades through the comparative analysis method plays a key role in predicting the development of media facades in Turkey. According to the comparison results, the media facades in Turkey were not able to catch up with the recent developments. It was observed that the diversification of the use purposes of media facades and the differentiation of the parameters for design were insufficient. As a result, it is thought that the designers and practitioners will be guided by the results obtained through the examples of the media facades.
Adaptive pseudo-Siamese policy network for temporal knowledge prediction

Temporal knowledge prediction is a crucial task for early event warning, which has gained increasing attention recently. It aims to predict future facts based on relevant historical facts using temporal knowledge graphs. There are two main difficulties associated with the prediction task: from the perspective of historical facts, modeling the evolutionary patterns of facts to accurately predict the query and from the query perspective, handling the two cases where the query contains seen and unseen entities in a unified framework. Driven by these two problems, we propose a novel adaptive pseudo-Siamese policy network for temporal knowledge prediction based on reinforcement learning. Specifically, we design the policy network in our model as a pseudo-Siamese network consisting of two sub-policy networks. In the sub-policy network I, the agent searches for the answer to the query along the entity-relation paths to capture static evolutionary patterns. In sub-policy network II, the agent searches for the answer to the query along relation-time paths to deal with unseen entities. Moreover, we develop a temporal relation encoder to capture the temporal evolutionary patterns. Finally, we design a gating mechanism to adaptively integrate the results of the two sub-policy networks to help the agent focus on the destination answer. To assess the performance of our model, we conduct link prediction on four benchmark datasets, and extensive experimental results demonstrate that our method achieves considerable performance compared with existing methods.
A Low-Resolution Used Electronic Parts Image Dataset for Sorting Application

The accumulation of electronic waste (e-waste) is becoming a problem in society. Old parts and components are conveniently discarded instead of being recycled. Economic and environmental measures should be taken by individuals and organizations to enhance sustainability. This could include desoldering and reusing parts from electronic circuit boards. Hence, the purpose of the dataset presented in this paper is for the classification of used electronic parts in linear voltage regulator power supply circuits. The dataset presented in this paper comprises low-resolution (30 × 30 pixels) grayscale images of major reusable electronic parts from a typical adjustable regulated linear voltage power supply kitset. The three major reusable parts are capacitors, potentiometers, and voltage regulator ICs. These are typically the most relatively expensive components. Data representing the parts are extracted from 960 × 720 pixel workspace images containing multiple parts. This permits the dataset to be used with multiple types of classifiers, such as lightweight shallow neural networks (SNNs), support vector machines (SVMs), or convolutional neural networks (CNNs). Classification accuracies of 93.5%, 94.9%, and 98.4% were achieved with SNNs, SVMs, and CNNs, respectively. Successful detection and classification of parts will permit a Niryo Ned robotic arm to pick and place parts in the desired locations. The dataset can be used by other academics and researchers working with the Niryo Ned robot and Matlab to handle electronic parts. It can be expanded to include relatively expensive components from other types of electronic circuit boards.
FedLive: A Federated Transmission Framework for Panoramic Livecast with Reinforced Variational Inference

Providing premium panoramic livecast services to worldwide viewers considering their ultra-high data rate and delay-sensitivity is a significant challenge in the current network delivery environment. Therefore, it is important to design an efficient way of improving viewer quality of experience while conserving bandwidth resources. In this context, this paper introduces a novel cost-efficient federated transmission framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedLive</i> and a set of algorithms to support it. First a gradient-based clustering method is proposed to group the geo-distributed viewers with similar viewing behavior into content delivery alliances by exploiting the geometric properties of the gradient loss. Next, a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> einforced <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</u> ariational <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> nference (RVI) structure-based approach is proposed to assist with the collaborative training of the viewer field of view (FoV) prediction model while also accelerating the tile delivery process. A novel prediction-based asynchronous delivery algorithm is designed in which both the high accuracy FoV prediction and efficient live 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> video transmission are achieved in a decentralized manner. FedLive was implemented for testing and an open source code is made available. Finally, the proposed solution was evaluated against a benchmark and three alternative state-of-the-art solutions using a real-world dataset. The experimental results show that our approach provides the highest prediction accuracy, better service performance, and saves bandwidth when compared with the other solutions.
Toward Smart and Efficient Service Systems: Computational Layered Federated Learning Framework

As increasing concerns have arisen on privacy leakage in data-driven smart services, federated learning (FL) has been introduced to collaboratively learn an efficient model across multiple participants without sharing raw data. However, existing FL schemes hinge on participating nodes to perform intensive ondevice training and network communication, which is a significant burden for energy-constrained mobile devices. In this work, we present the computational layered FL (CLFL) framework to enable resource-constrained devices to perform computationefficient on-device training and lightweight message transmitting. We first introduce the network structure and key aspects of the entities in the framework. Then, we give the implementation principles of CLFL, and present two instance schemes that allow devices to participate in joint training without the need for direct gradient computation or continuous data transmission. In order to more intuitively reflect the performance and efficiency of the proposed methods, we carry out a preliminary implementation and give the comparison with traditional FL. Finally, for the future exploration, we present four related research challenges of CLFL and offer possible solutions.
Las emociones percibidas por el profesorado en activo en el uso de metodologías activas en el aula

In the teaching-learning processes that take place in the classroom, as in any human activity, aspects referring to emotions are of great importance. In this context, the focus of attention has generally been directed towards students, and not so much towards teachers. The role of teachers is determined by the methodology used, but this role is established not only for pedagogical but also for emotional reasons. In the case of active methodologies, the transformation of the teacher’s traditional role from instructional to guiding entails a certain emotional framework that influences and is influenced by all of this. Aware of this reality, the aim of this study is to gain a deeper understanding of the emotions perceived by in-service teachers when using an active methodology in the classroom. To this end, a qualitative approach has been followed, using an open questionnaire developed ad hoc as the main tool for data collection. This questionnaire was completed virtually by the participating teachers. The results reveal that the most significant emotions for teachers were the positive ones, such as enthusiasm, satisfaction and fun. In conclusion, it highlights the importance of enhancing the emotional dimension of teachers in their training, especially in relation to the performance of their role as a guide and advisor in the learning process of students.
Building an automated, machine learning-enabled platform for predicting post-operative complications

Abstract Objective . In 2019, the University of Florida College of Medicine launched the MySurgeryRisk algorithm to predict eight major post-operative complications using automatically extracted data from the electronic health record. Approach . This project was developed in parallel with our Intelligent Critical Care Center and represents a culmination of efforts to build an efficient and accurate model for data processing and predictive analytics. Main Results and Significance . This paper discusses how our model was constructed and improved upon. We highlight the consolidation of the database, processing of fixed and time-series physiologic measurements, development and training of predictive models, and expansion of those models into different aspects of patient assessment and treatment. We end by discussing future directions of the model.
Generating High-Resolution 3D Faces and Bodies Using VQ-VAE-2 with PixelSNAIL Networks on 2D Representations

Modeling and representing 3D shapes of the human body and face is a prominent field due to its applications in the healthcare, clothes, and movie industry. In our work, we tackled the problem of 3D face and body synthesis by reducing 3D meshes to 2D image representations. We show that the face can naturally be modeled on a 2D grid. At the same time, for more challenging 3D body geometries, we proposed a novel non-bijective 3D–2D conversion method representing the 3D body mesh as a plurality of rendered projections on the 2D grid. Then, we trained a state-of-the-art vector-quantized variational autoencoder (VQ-VAE-2) to learn a latent representation of 2D images and fit a PixelSNAIL autoregressive model to sample novel synthetic meshes. We evaluated our method versus a classical one based on principal component analysis (PCA) by sampling from the empirical cumulative distribution of the PCA scores. We used the empirical distributions of two commonly used metrics, specificity and diversity, to quantitatively demonstrate that the synthetic faces generated with our method are statistically closer to real faces when compared with the PCA ones. Our experiment on the 3D body geometry requires further research to match the test set statistics but shows promising results.
Technological project in the development of computational thinking and problem-solving

In the article, the authors propose technological projects to develop computational thinking following the four problem-solving phases: understanding the problem, making the plan, executing the plan, and reviewing the solution. This study was based on a quantitative approach; the participants were 37 engineering students who had just entered university; also, the Pearson correlation statistical test was used to assess the relationship between computational thinking and problem- resolve. The activities of the technological projects have been developed using technological resources, such as the Arduino board, the mBlock software, and electronic sensors. It has been shown that following the problem-solving method based on the four phases contributes to the development of computational thinking skills in engineering students who have recently entered university.
Center transfer for supervised domain adaptation

Domain adaptation (DA) is a popular strategy for pattern recognition and classification tasks. It leverages a large amount of data from the source domain to help train the model applied in the target domain. Supervised domain adaptation (SDA) approaches are desirable when only few labeled samples from the target domain are available. They can be easily adopted in many real-world applications where data collection is expensive. In this study, we propose a new supervision signal, namely center transfer loss (CTL), to efficiently align features under the SDA setting in the deep learning (DL) field. Unlike most previous SDA methods that rely on pairing up training samples, the proposed loss is trainable only using one-stream input based on the mini-batch strategy. The CTL exhibits two main functionalities in training to increase the performance of DL models, i.e., domain alignment and increasing the feature's discriminative power. The hyper-parameter to balance these two functionalities is waived in CTL, which is the second improvement from the previous approaches. Extensive experiments completed on well-known public datasets show that the proposed method performs better than recent state-of-the-art approaches.
Exact entanglement cost of quantum states and channels under positive-partial-transpose-preserving operations

This paper establishes single-letter formulas for the exact entanglement cost of simulating quantum channels under free quantum operations that completely preserve positivity of the partial transpose (PPT). First, we introduce the $\ensuremath{\kappa}$-entanglement measure for point-to-point quantum channels, based on the idea of the $\ensuremath{\kappa}$ entanglement of bipartite states, and we establish several fundamental properties for it, including amortization collapse, monotonicity under PPT superchannels, additivity, normalization, faithfulness, and nonconvexity. Second, we introduce and solve the exact entanglement cost for simulating quantum channels in both the parallel and sequential settings, along with the assistance of free PPT-preserving operations. In particular, we establish that the entanglement cost in both cases is given by the same single-letter formula, the $\ensuremath{\kappa}$-entanglement measure of a quantum channel. We further show that this cost is equal to the largest $\ensuremath{\kappa}$ entanglement that can be shared or generated by the sender and receiver of the channel. This formula is calculable by a semidefinite program, thus allowing for an efficiently computable solution for general quantum channels. Noting that the sequential regime is more powerful than the parallel regime, another notable implication of our result is that both regimes have the same power for exact quantum channel simulation, when PPT superchannels are free. For several basic Gaussian quantum channels, we show that the exact entanglement cost is given by the Holevo-Werner formula [Holevo and Werner, Phys. Rev. A 63, 032312 (2001)], giving an operational meaning of the Holevo-Werner quantity for these channels.
Benchmark of Data Preprocessing Methods for Imbalanced Classification

Severe class imbalance is one of the main conditions that make machine learning in cybersecurity difficult. A variety of dataset preprocessing methods have been introduced over the years. These methods modify the training dataset by oversampling, undersampling or a combination of both to improve the predictive performance of classifiers trained on this dataset. Although these methods are used in cybersecurity occasionally, a comprehensive, unbiased benchmark comparing their performance over a variety of cybersecurity problems is missing. This paper presents a benchmark of 16 preprocessing methods on six cybersecurity datasets together with 17 public imbalanced datasets from other domains. We test the methods under multiple hyperparameter configurations and use an AutoML system to train classifiers on the preprocessed datasets, which reduces potential bias from specific hyperparameter or classifier choices. Special consideration is also given to evaluating the methods using appropriate performance measures that are good proxies for practical performance in real-world cybersecurity systems. The main findings of our study are: 1) Most of the time, a data preprocessing method that improves classification performance exists. 2) Baseline approach of doing nothing outperformed a large portion of methods in the benchmark. 3) Oversampling methods generally outperform undersampling methods. 4) The most significant performance gains are brought by the standard SMOTE algorithm and more complicated methods provide mainly incremental improvements at the cost of often worse computational performance.
Enabling Scalable Analytics of Physiological Sensor and Derived Feature Multi-Modal Time-Series with Big Data Management

With the increasing interconnection of smart sensors in long-term care facilities, the amount of data available for multi-modal Big Data analytics is advantageous. Using raw smart sensor data, researchers can derive and extract physiological features useful for health monitoring. Nonetheless, with the immense amount of smart sensor data, the ability to utilize these data for multi-modal analytics as the data grows presents a great challenge for researchers and long-term care facilities. This paper proposes a database design system for multi-modal derived time-series featured data (respiration and restlessness) by using techniques such as hierarchical time-indexed databases and dense numerical array storage. We present evaluations and findings for our proposed database system design for multi-modal time-series feature data to assess the various performance characteristics in data access time, storage, and usability; demonstrating an extremely scalable design and simple integration with existing analytic tools via SQL interfaces. Furthermore, we introduce a data-processing pipeline enabling Big Data analytics for multi-modal time-series feature data.
Systematic Literature Review: Virtual Tour 360 Degree Panorama

The application of VR as a medium to promote a place, be it tourist attractions, education, tourism or health, has become a common thing in the current era. 360-degree panoramic virtual tour itself is one method in making Virtual Reality. A 360-degree panoramic virtual tour is a collection of 360-degree images which are then processed so that they can be enjoyed virtually as if they were real. The goal to be achieved in this paper is to analyze a 360-degree panoramic virtual tour as a medium to promote a place, by conducting in-depth reviews and evaluating searches through selected literature based on certain criteria and the selected studies will be processed to answer research questions. Systematic Literature Review (SLR) is a research method that aims to identify and evaluate research results with the best technique based on specific procedures from comparison results. The results of the research on the selection of journal topics, Tourism, Education, and Health can be the main reference regarding the 360-degree panoramic virtual tour and there are various methods that can be used starting from MDLC, Luther Sutopo, Image Method, IMSDD, Qualitative.
Phishing Website Detection with and Without Proper Feature Selection Techniques: Machine Learning Approach

Indeed, successful phishing website attempts could result in catastrophic data loss, login credential compromise, ransomware infection, and financial loss. It also significantly hampers the competitiveness and productivity of online users and internet-dependent organizations unless an intelligent anti-phishing solution is devised. Due to detecting fresh phishing website attacks with maximum accuracy by discovering hidden patterns from complex datasets is shown to be an intrinsic property of M- Learning approaches, the study conducted rigorous experiments on four purposely selected efficient supervised M-Learning algorithms before and after applying five widely used proper feature selection techniques such as Recursive Feature Elimination, Pierson Correlation Coefficient, Principal Component Analysis, Uni-variate Feature Selection, and Mutual Information. The proposed study was conducted to balance the research gaps and scientific disputes in the rigorously reviewed studies. The study’s final outcome is a proposal for an intelligent phishing website model that yields higher accuracy, faster response times, and fewer average misclassification rates. The study also explored the feature selection techniques that had more, less, and no contributions to enhancing each classifier's accuracy. As compared to the remaining classifiers, the Cat-Boost Classifier attained superior phishing website detection accuracy (97.46%), F1-score (97.49%), a lower average misclassification rate (2.54%), and acceptable train-test computational time (7 s) after using the UFS technique. On the other hand, the PCA technique failed to enhance the accuracy of the Cat-Boost, Gradient-Boost, and Random Forest Classifiers due to scoring less accuracy than the accuracy reached before using proper feature selection techniques. To obtain more promising results, in future work, phishing website detection is expected to be carried out using a Hybrid proper feature selection technique, huge datasets, proper deep learning algorithms, and proper model hyper-parameters.