Reviewerly/exHarmony huggingface dataset:
Simple scheme for implementing the Grover search algorithm with superconducting qubits

This work concerns a three-qubit processor implemented as three transmon-type superconducting qubits capacitively coupled to a superconducting resonator driven by a microwave field. Based on an effective Hamiltonian of this system, we propose a scheme to realize an X-rotation gate for three qubits requiring only a single-step operation. During the gate operation, the cavity photon-number is canceled due to the strong microwave field. It does not require the cavity to be initially in the vacuum state and the scheme is insensitive to cavity decay. We also present an effective scheme to implement Grover's search algorithm based on this quantum gate and a phase oracle. Using numerical simulation and analysis, we show that the proposed schemes allow for efficiently finding the target state with a high fidelity.
Using association rule mining to enrich user profiles with research paper recommendation

Association rules are used in recommender systems to develop a model that enhances the profiles of users, as well as to address the cold start problem.Our approach proposes a model which is implemented in a system for recommending scientific papers called Collaborative Topic Regression (CTR).Collaborative Topic Regression consists of two matrices, U and V, where U represents the relationship between users and paper topics, while V represents the relationship between papers and the paper topics.The CTR model is focused on matrix V, by adopting it, and as the result, it is influenced by the paper's textual content, leaving the content of matrix U essentially unchanged.The depicted model extracts association rules from matrix U, and then enriches it, with the information gleaned from the mining process.The outcomes are based on both, out-of-matrix prediction and in-matrix prediction.Our approach improved the quality of the results by up to 20% for out-of-matrix prediction in the best-case scenario.Unfortunately, the same cannot be said for in-matrix prediction, which will be further investigated in the future.
Domain-Aware Universal Style Transfer

Style transfer aims to reproduce content images with the styles from reference images. Existing universal style transfer methods successfully deliver arbitrary styles to original images either in an artistic or a photo-realistic way. However, the range of 'arbitrary style' defined by existing works is bounded in the particular domain due to their structural limitation. Specifically, the degrees of content preservation and stylization are established according to a predefined target domain. As a result, both photo-realistic and artistic models have difficulty in performing the desired style transfer for the other domain. To overcome this limitation, we propose a unified architecture, Domain-aware Style Transfer Networks (DSTN) that transfer not only the style but also the property of domain (i.e., domainness) from a given reference image. To this end, we design a novel domainness indicator that captures the domainness value from the texture and structural features of reference images. Moreover, we introduce a unified framework with domain-aware skip connection to adaptively transfer the stroke and palette to the input contents guided by the domainness indicator. Our extensive experiments validate that our model produces better qualitative results and outperforms previous methods in terms of proxy metrics on both artistic and photo-realistic stylizations.
Research on Agricultural Products Supply Chain Traceability System: Blockchain consensus algorithm optimization

In order to improve the authenticity and credibility of agricultural products traceability information, this paper proposes an improved blockchain consensus algorithm FPBFT for the whole process traceability system from planting to consumption of agricultural products supply chain based on blockchain, in view of the high communication overhead and low efficiency of the consensus algorithm PBFT of the coalition blockchain when the information is on the chain. The credit value of the nodes is calculated based on the behavior of each node in the consensus process, and the nodes are given different voice rights based on the credit value; then a simplified consistency protocol is proposed to reduce the time complexity by combining the node characteristics of the coalition chain. Finally, a comparison experiment is conducted to verify that the improved PBFT algorithm can reduce the communication volume and improve the efficiency.
Interactive and Explainable Advising Dashboard Opens the Black Box of Student Success Prediction

This paper presents exploratory research regarding the design and evaluation of a dashboard supporting the advising of aspiring university students incorporating a black-box predictive model for student success. While black-box predictive models can provide accurate predictions, incorporating them in dashboards is challenging as the black-box nature can threaten the interpretability and negatively impact trust of end-users. Explainable Learning Analytics aims to provide insights to black-box predictions by for instance explaining how the input features impact the prediction made. Two dashboards were designed to visualize the prediction and the outcome of the explainer. The dashboards supplemented the explainer with an interactive visualisation allowing to simulate how changes in the student’s features impact the prediction. Both dashboards were evaluated in user tests with 13 participants. The results show the potential of explainable AI techniques to bring predictive models to advising practice. We found that the combination of the explainer with the simulation helped users to compare the predictive model with their mental models of student success, challenging understanding of users and influencing trust in the predictive model.
Toward Findable, Accessible, Interoperable and Reusable (FAIR) Photovoltaic System Time Series Data

We present the application of FAIR principles to photovoltaic time series data to increase their reusability within the photovoltaic research community. The main requirements for a "FAIRified" dataset is to have a clearly defined data format, and to make accessible all metadata for this dataset to humans and machines. To achieve FAIRification, we implement a data model that separates the photovoltaic data and its metadata. The metadata and their descriptions are registered on a data repository in a human and machine readable format, using JSON-LD. Also, secure APIs are developed to access photovoltaic data. This approach has long term scalability and maintainability.
Human Computer Interaction – Hand Gesture Recognition

The creation of intelligent and natural interfaces between users and computer systems has received a lot of attention. Several modes of knowledge like visual, audio, and pen can be used individually or in combination have been proposed in support of this endeavour. Human communication relies heavily on the use of gestures to communicate information. Gesture recognition is a subject of science and language innovation that focuses on numerically quantifying human gestures. It is possible for people to communicate properly with machines using gesture recognition without the use of any mechanical devices. Hand gestures are a form of nonverbal communication that can be applied to several fields, including deaf-mute communication, robot control, human–computer interaction (HCI), home automation, and medical applications. Many different methods have been used in hand gesture research papers, including those focused on instrumented sensor technology and computer vision. To put it another way, the hand sign may be categorized under a variety of headings, including stance and motion, dynamic and static, or a combination of the two. This paper provides an extensive study on hand gesture methods and explores their applications.
APPLICATION OF NEURAL NETWORKS IN MEDICAL DIAGNOSTICS

This article discusses the possibilities of application of artificial neural networks to solve problems of increasing the diagnostic outcomes in clinical laboratory examination. High diagnostic sensitivity (96 %) and diagnostic accuracy (89.5 %) of the results were shown on a large amount of cellular material digitized by artificial intelligence microscopy automation system like the Vision Cyto Pap. The high resolution and sharpness of digital slides, the mode of viewing objects (cells) in the gallery, quick access to the results of preclassification, all of these factors together allow to reduce turnearound time in more than 2.5 times reducing disadvantages of the microscopy.&lt;br&gt;Application of artificial neural networks does not substitute a doctor’s skills. The role in validation of reports eligible only for cytopathologist. This concept indicates a carefully approach for staff working with a microscope, respectful attitude to them professional skills, and highlights a personalized approach to patients.
survidm: An R package for Inference and Prediction in an Illness-Death Model

Multi-state models are a useful way of describing a process in which an individual moves through a number of finite states in continuous time.The illness-death model plays a central role in the theory and practice of these models, describing the dynamics of healthy subjects who may move to an intermediate "diseased" state before entering into a terminal absorbing state.In these models, one important goal is the modeling of transition rates which is usually done by studying the relationship between covariates and disease evolution.However, biomedical researchers are also interested in reporting other interpretable results in a simple and summarized manner.These include estimates of predictive probabilities, such as the transition probabilities, occupation probabilities, cumulative incidence functions, and the sojourn time distributions.The development of survidm package has been motivated by recent contribution that provides answers to all these topics.An illustration of the software usage is included using real data.
Sketch2Bento: Sketch-based Arrangement Guidance for Lunch Boxes

In recent years, sharing the images of the meals has been popular on social media as an artwork. In terms of the food culture in Japan, the lunch box is especially prevalent because of the lovely looking, such as character-bento. However, it is challenging and difficult to arrange a lunch box for common users without experiences and delicate skills. In this work, we propose Sketch2Bento, a sketch-based arrangement guidance system of food ingredients using spatial augmented reality technology. The proposed sketch interface allows users to design the blueprint of a desired lunch box. We then utilize the projection mapping to guide users with the projected arrangement hints in the boxes accordingly. Finally, we conduct a user study to verify the system usage and user experiences.
CONCEPTUAL MODEL DEPICTING RISK FACTORS INFLUENZING CLOUD DATA SECURITY

Cloud Platform has the data stored in a remote server and accessed with the help of services provided by cloud service providers. The primary objective is to provide data security due to the confidentiality of data transmitted to the remote server, over an unmonitored and multi-tenancy channel (internet). The major components of cloud platform are application, service, runtime cloud, storage and infrastructure. This is a conceptual paper trying to explain the risk factors to be deemed in a cloud environment. The benefits include scalability, availability, reliability, flexibility, increased collaboration, competitive edge, sustainability, reduced proportional cost. Risk factors influencing the data security and Integrity are identified up to three level of sub-constructs.. All the risk factors including hackers, mishandling of passwords, risk of data accessed from public /unmonitored sources, security in communication channels, intentional and unintentional data security threats created due to human interference are included in the scope of this paper. In this paper, the identification of sub factors of cloud data security risk components is achieved. The futuristics scope of this paper is to identify early risk prediction and mitigation strategies to all the identified risk factors related to data security by implementing SEM methodology.
Performance of Coherent Frequency-Shift Keying for Classical Communication on Quantum Channels

We evaluate the performance of coherent frequency-shift keying (CFSK) alphabets for communication on quantum channels. We show that, contrarily to what previously thought, the square-root-measurement (SRM) is sub-optimal for discriminating CFSK states. Furthermore, we compute the maximum information transmission rate of the CFSK alphabet, observing that it employs at least as many frequency modes as the signal states, and compare it with standard phase-shift-keying. Finally, we introduce a discretized CFSK alphabet with higher mode-efficiency, exhibiting comparable error-probability performance with respect to CFSK and better rate performance. Our results suggest the existence of a tradeoff between the CFSK reduced error-probability and its mode efficiency.
Channel Exchanging for RGB-T Tracking

It is difficult to achieve all-weather visual object tracking in an open environment only utilizing single modality data input. Due to the complementarity of RGB and thermal infrared (TIR) data in various complex environments, a more robust object tracking framework can be obtained using video data of these two modalities. The fusion methods of RGB and TIR data are the core elements to determine the performance of the RGB-T object tracking method, and the existing RGB-T trackers have not solved this problem well. In order to solve the current low utilization of information intra single modality in aggregation-based methods and between two modalities in alignment-based methods, we used DiMP as the baseline tracker to design an RGB-T object tracking framework channel exchanging DiMP (CEDiMP) based on channel exchanging. CEDiMP achieves dynamic channel exchanging between sub-networks of different modes hardly adding any parameters during the feature fusion process. The expression ability of the deep features generated by our data fusion method based on channel exchanging is stronger. At the same time, in order to solve the poor generalization ability of the existing RGB-T object tracking methods and the poor ability in the long-term object tracking, more training of CEDiMP on the synthetic dataset LaSOT-RGBT is added. A large number of experiments demonstrate the effectiveness of the proposed model. CEDiMP achieves the best performance on two RGB-T object tracking benchmark datasets, GTOT and RGBT234, and performs outstandingly in the generalization testing.
Network Communication Data Encryption Method Based on Wireless Channel Characteristics

In order to improve the secure transmission capability of wireless transmission network communication data, a network communication data encryption design is required. An encryption and secure transmission method of wireless transmission network communication data based on wireless channel feature detection is proposed. The ECC (Elliptic Curves Cryptography) algorithm analyzes by monitoring single-channel and multi-channel samples separately. The ciphertext protocol for data secure transmission is constructed, the Hash dynamic transmission protocol is used for data access control, the data dynamic symmetric key, the key construction and arithmetic coding design are constructed. Combined with the ellipse linear mapping method, the data is encrypted safely. According to the intensity of the wireless channel feature distribution, the scrambling degree is rearranged for the random scrambling and encryption of the data, and the random linear coding method is used to realize the random scrambling and encryption of the data, so as to realize the secure transmission of network communication data and the secure storage of information. The simulation test results show that using this method to encrypt and transmit wireless transmission network communication data has better security and stronger anti-attack ability, thereby improving the secure transmission performance of network communication data.
Leveraging Speaker Attribute Information Using Multi Task Learning for Speaker Verification and Diarization

Deep speaker embeddings have become the leading method for encoding speaker identity in speaker recognition tasks. The embedding space should ideally capture the variations between all possible speakers, encoding the multiple acoustic aspects that make up a speaker's identity, whilst being robust to non-speaker acoustic variation. Deep speaker embeddings are normally trained discriminatively, predicting speaker identity labels on the training data. We hypothesise that additionally predicting speaker-related auxiliary variables -- such as age and nationality -- may yield representations that are better able to generalise to unseen speakers. We propose a framework for making use of auxiliary label information, even when it is only available for speech corpora mismatched to the target application. On a test set of US Supreme Court recordings, we show that by leveraging two additional forms of speaker attribute information derived respectively from the matched training data, and VoxCeleb corpus, we improve the performance of our deep speaker embeddings for both verification and diarization tasks, achieving a relative improvement of 26.2% in DER and 6.7% in EER compared to baselines using speaker labels only. This improvement is obtained despite the auxiliary labels having been scraped from the web and being potentially noisy.
A multi-sample particle swarm optimization algorithm based on electric field force

Aiming at the premature convergence problem of particle swarm optimization algorithm, a multi-sample particle swarm optimization (MSPSO) algorithm based on electric field force is proposed. Firstly, we introduce the concept of the electric field into the particle swarm optimization algorithm. The particles are affected by the electric field force, which makes the particles exhibit diverse behaviors. Secondly, MSPSO constructs multiple samples through two new strategies to guide particle learning. An electric field force-based comprehensive learning strategy (EFCLS) is proposed to build attractive samples and repulsive samples, thus improving search efficiency. To further enhance the convergence accuracy of the algorithm, a segment-based weighted learning strategy (SWLS) is employed to construct a global learning sample so that the particles learn more comprehensive information. In addition, the parameters of the model are adjusted adaptively to adapt to the population status in different periods. We have verified the effectiveness of these newly proposed strategies through experiments. Sixteen benchmark functions and eight well-known particle swarm optimization algorithm variants are employed to prove the superiority of MSPSO. The comparison results show that MSPSO has better performance in terms of accuracy, especially for high-dimensional spaces, while maintaining a faster convergence rate. Besides, a real-world problem also verified that MSPSO has practical application value.
How to Use Research Activity Tools in Bachelor Students Research Competence Formation

This article analyzing the application of research activities as a tool in bachelor's research competence formation.Research background is a determination of bachelors' research competence resulting in objective education systematic phenomena development.The research results in this work are objective knowledge reflected in the system of concepts in mathematical knowledge.To interpret the research competence definition, we consider bachelor students' research skills competence formation during mathematics courses by performing various types of research activities.Based on the analysis and generalization, a connection between research competencies formations was revealed through the bachelor students' research activities.In the higher education process, the scientific activity of bachelors planned and research competence is formed by effective problem teaching methods.
Designing and Implementing e-School Systems: An Information Systems Approach to School Management of a Community College in Northern Mindanao, Philippines

Colleges and Universities have been established to provide educational services to the people. Like any other organization, the school has processes and procedures similar to business or industry that involve admissions, processing of data, and generation of reports. Those processes are made possible through a centralized system in storing, processing, and retrieval of data and information. The absence of a computer system and the complexity of the transactions of the college which makes the personnel be loaded with paper works in storing and keeping student records and information is the motivating factor why the School Management Information System has been designed and developed for a community college in the northern part of Mindanao. This paper discusses the Major Functionalities and Modules of the system through its implementation methodology which is the Agile Model and its impact on the delivery of services and procedures in the overall operation of the college. The project has been evaluated based on ISO 25010, a quality model used for product/software quality evaluation systems. Based on the results of the evaluation, SMIS has been Functional, Usable, and Reliable with an average for every criterion above 4.04 indicating very good performance based on a Likert scale descriptive interpretation. Based on the preceding findings of the study, the respondents agreed that the developed e-school system was functional and lifted the transaction process of the school. The overall quality and performance of the system was very good in terms of functionality, usability, and reliability. It is recommended that future development such as the smartphone and tablet-based attendance monitoring should be integrated, a kiosk for grades and schedule viewing should also be placed inside the campus that is connected to the database server.
Quantum-accessible reinforcement learning beyond strictly epochal environments

Abstract In recent years, quantum-enhanced machine learning has emerged as a particularly fruitful application of quantum algorithms, covering aspects of supervised, unsupervised and reinforcement learning. Reinforcement learning offers numerous options of how quantum theory can be applied, and is arguably the least explored, from a quantum perspective. Here, an agent explores an environment and tries to find a behavior optimizing some figure of merit. Some of the first approaches investigated settings where this exploration can be sped-up, by considering quantum analogs of classical environments, which can then be queried in superposition. If the environments have a strict periodic structure in time (i.e. are strictly episodic), such environments can be effectively converted to conventional oracles encountered in quantum information. However, in general environments, we obtain scenarios that generalize standard oracle tasks. In this work, we consider one such generalization, where the environment is not strictly episodic, which is mapped to an oracle identification setting with a changing oracle. We analyze this case and show that standard amplitude-amplification techniques can, with minor modifications, still be applied to achieve quadratic speed-ups. In addition, we prove that an algorithm based on Grover iterations is optimal for oracle identification even if the oracle changes over time in a way that the “rewarded space” is monotonically increasing. This result constitutes one of the first generalizations of quantum-accessible reinforcement learning.