Reviewerly/exHarmony huggingface dataset:
Creation of two distant entangled qutrits via interference of polarized photons: With and without rotating wave approximation

A proposal has been given in Feng et al. (2003) for generating the entangled states of two distant λ-type qutrits (three-level atoms) utilizing the interference of polarized photons under rotating wave approximation (RWA). In the present work, we first motivated to investigate the same scenario, however, using V-type atoms. Interestingly, we observed that the maximum probability of achieving the purpose (of entangling atoms) increases from 0.2 for Λ- to 0.34 for V-type atoms. Meanwhile, our detailed results show that, in both of the above model interactions, the best results are obtained when the atom–field couplings are enough large, particularly in the symmetric couplings. In this way, one enters the strong coupling constant regime, wherein the RWA is no longer valid. Accordingly, in order to study the effect of counter-rotating terms on the maximum accessible entanglement via this method, we motivated to reconsider the approach either with Λ- or V-type atoms, however, now without RWA. In this respect, for the λ-type atoms, our results interestingly show that the maximum probability 0.43 may be achieved, which is more than two times of the maximum reported value in the above-mentioned Ref. wherein the RWA is considered. Moreover, for V-type atoms, beyond RWA, we obtained the probability 0.4, which is a little more than the case with RWA, but still twice the case with RWA for Λ-type atom. Therefore, generally counter rotating terms have constructive effect on the probability of detection of entangled states for Λ- and V-type atoms.
A Multimixed Strategy Improved Sparrow Search Algorithm and Its Application in TSP

Aiming at the shortcomings of the sparrow search algorithm (SSA), such as falling into local optimum and slow convergence speed, an improved sparrow search algorithm based on multimixed strategy (MISSA) is proposed in this paper. In the initial stage, the iterative chaotic mapping is used to initialize the population in order to improve the diversity of population. In the foraging stage, the golden sine algorithm and nonlinear convergence factor strategy are introduced to optimize the discoverer-follower model, which make search process more comprehensive and extensive for the discoverer. The elite opposition-based learning strategy is used to update the optimal solution and the population obtained in each iteration to improve the self-learning ability of the algorithm. To verify the rationality of the multimixed strategy selection and efficiency of the proposed algorithm, MISSA is compared with three derived single-strategy improved algorithms, other improved SSAs, and five typical swarm intelligence algorithms using ten basic benchmark functions and CEC 2014 function. The optimization results, diversity analysis, and Wilcoxon rank-sum test results certify that the proposed MISSA has better optimization accuracy, convergence speed, and robustness than other compared methods. Moreover, the practicability and feasibility of MISSA are veriﬁed by solving the traveling salesman problem (TSP).
Bit‐level image encryption algorithm based on fully‐connected‐like network and random modification of edge pixels

A bit-level image encryption algorithm based on Fully-Connected-Like network(FCLN) and random modification of edge pixels is proposed. In the paper, in order to enhance the security of the cryptographic system, random noise is first used to modify the least significant bits of the edge pixels of the image, and the modified image is used as the input image. Later,the chaotic sequence is used to perform cyclic shift transformation on the image. In the subsequent steps, the FCLN is generated based on a fully connected neural network, which can perform scrambling and diffusion operations on the input image. Finally, the bidirectional diffusion method is used to diffuse the image forward and backward. In addition, the image after the edge pixel modification is convolved with the chaotic sequence, and the initial value of the chaotic system is set by the result to establish the correlation between the plain image and the algorithm, which makes the algorithm resistant to known/chosen plaintext attack. Experimental results show that although the image is modified by random noise, the decrypted image is visually the same as the original image. At the same time, through the analysis of common attacks such as differential attacks, noise attacks, and data loss attacks, our algorithm shows high security.
Digital Hebrew Paleography: Script Types and Modes

Paleography is the study of ancient and medieval handwriting. It is essential for understanding, authenticating, and dating historical texts. Across many archives and libraries, many handwritten manuscripts are yet to be classified. Human experts can process a limited number of manuscripts; therefore, there is a need for an automatic tool for script type classification. In this study, we utilize a deep-learning methodology to classify medieval Hebrew manuscripts into 14 classes based on their script style and mode. Hebrew paleography recognizes six regional styles and three graphical modes of scripts. We experiment with several input image representations and network architectures to determine the appropriate ones and explore several approaches for script classification. We obtained the highest accuracy using hierarchical classification approach. At the first level, the regional style of the script is classified. Then, the patch is passed to the corresponding model at the second level to determine the graphical mode. In addition, we explore the use of soft labels to define a value we call squareness value that indicates the squareness/cursiveness of the script. We show how the graphical mode labels can be redefined using the squareness value. This redefinition increases the classification accuracy significantly. Finally, we show that the automatic classification is on-par with a human expert paleographer.
Teaching Morphophonetic Characters in Teaching Chinese as a Foreign Language Based on Internet of Things Assistance

Teaching Chinese as a foreign language is a very important part of teaching Chinese as a foreign language, and morphemes account for a large proportion of Chinese characters. In this paper, a questionnaire survey on the cognition of morphemes was conducted for each grade of students in the School of International Cultural Education of Heilongjiang University. Through the survey, it was concluded that international students in the Chinese culture circle and non-Chinese culture circle affect the cognition of morphemes at the primary stage, and a study on the teaching of morphemes in teaching Chinese as a foreign language based on the assistance of Internet of Things was proposed to analyze that students learning Chinese no longer affect the cognition of morphemes. In teaching Chinese as a foreign language, the use of the Internet of Things system and the introduction of video sensors (mainly for the access and computer analysis of spoken Chinese as a foreign language learner model) and voice sensor (mainly for the Chinese as a foreign language learner voice access and computer analysis), with the standard spoken language pattern in the software and the comparison of standard pronunciation, the oral mode and pronunciation of TCSL learners can be corrected. The experiment knows the biases that international students are prone to when learning Chinese characters and summarizes the implications for teaching Chinese characters to foreigners; in addition, the higher the level of learning, the higher is the degree of cognition of morphophonetic characters.
Workload-optimized sensor data store for industrial IoT gateways

In industrial Internet of Things (IoT) environments, sensor devices continue to generate a stream of sensor data, and the management of an ever-growing amount of data is a vital feature for IoT gateways. However, our preliminary analysis of some popular key–value stores used in IoT gateways revealed that none of the systems exploits the distinctive characteristics of IoT workloads – append-only and immutable – thus the systems show limitations in managing sensor data. To address this issue, in this study, we propose Indexing-of-Indexes (IoI) and LogFlush-and-Append (LFA) to exploit such characteristics. IoI is an indexing and data organization scheme designed to eliminate the notorious compaction-induced write amplification observed in legacy key–value stores, and LFA is a data ingestion scheme intended to remove double data write issues in legacy write-ahead logging implementations. We implement a prototype key–value store, Sen-Store, which incorporates our proposals, and we evaluate its performance using synthetic workloads and the TPCx-IoT benchmark. The evaluation results show that Sen-Store achieves up to 17.6× and 2.1× higher IoTps than industry-leading RocksDB and state-of-the-art IoTDB, respectively.
AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System

Industrial search and recommendation systems mostly follow the classic multi-stage information retrieval paradigm: matching, pre-ranking, ranking, and re-ranking stages. To account for system efficiency, simple vector-product based models are commonly deployed in the pre-ranking stage. Recent works consider distilling the high knowledge of large ranking models to small pre-ranking models for better effectiveness. However, two major challenges in pre-ranking system still exist: (i) without explicitly modeling the performance gain versus computation cost, the predefined latency constraint in the pre-ranking stage inevitably leads to suboptimal solutions; (ii) transferring the ranking teacher's knowledge to a pre-ranking student with a predetermined handcrafted architecture still suffers from the loss of model performance. In this work, a novel framework AutoFAS is proposed which jointly optimizes the efficiency and effectiveness of the pre-ranking model: (i) AutoFAS for the first time simultaneously selects the most valuable features and network architectures using Neural Architecture Search (NAS) technique; (ii) equipped with ranking model guided reward during NAS procedure, AutoFAS can select the best pre-ranking architecture for a given ranking teacher without any computation overhead. Experimental results in our real world search system show AutoFAS consistently outperforms the previous state-of-the-art (SOTA) approaches at a lower computing cost. Notably, our model has been adopted in the pre-ranking module in the search system of Meituan, bringing significant improvements.
Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

Speech recognition is the most important research direction in human-computer interaction. It is the key to the connection between human beings and machines and the expression of intelligence and automation in the information society. Taking English as the research object, using the related knowledge of speech recognition, it is based on the hidden Markov model technology of deep learning and clustering analysis algorithm and evaluated according to the cross-language English phonemic recognition system of sparse autoencoder (SA) method. By studying the speech recognition algorithm of the English translation, the influence of the speech recognition environment on the accuracy of speech recognition is confirmed. This provides a direction for humans to study speech recognition at a deeper level. Based on the language model of Transformer and the language model based on Seq2Seq, it sets different vocabularies, and the data are collected in the laboratory and outdoors, respectively, and the posttest template library is formed after collection. In the task of restoring phonetic symbols to English characters when phonemes are modeling units, the error rate is the lowest. The error rate on the test set reached 9.54%, which was 6.97 percentage points higher than that of the syllable modeling unit.
A Short Text Similarity Calculation Method Combining Semantic and Headword Attention Mechanism

Short text similarity computation plays an important role in various natural language processing tasks. Siamese neural networks are widely used in short text similarity calculation. However, due to the complexity of syntax and the correlation between words, siamese networks alone cannot achieve satisfactory results. Many studies show that the use of an attention mechanism will improve the impact of key features that can be utilized to measure sentence similarity. In this paper, a similarity calculation method is proposed which combines semantics and a headword attention mechanism. First, a BiGRU model is utilized to extract contextual information. After obtaining the headword set, the semantically enhanced representations of the two sentences are obtained through an attention mechanism and character splicing. Finally, we use a one-dimensional convolutional neural network to fuse the word embedding information with the contextual information. The experimental results on the ATEC and MSRP datasets show that the recall and F1 values of the proposed model are significantly improved through the introduction of the headword attention mechanism.
Development of a protected birds identification system using a convolutional neural network

Abstract. Hermadi I, Wulandari, Dhira D. 2022. Development of a protected birds identification system using a convolutional neural network. Biodiversitas 23: 2561-2569. The protected animals are the animals having small populations, a sharp decline in the number of individuals in the wild, or endemic. The government has banned owning, keeping, or trading these animals. The first step in conserving these animals is identification. The government of the Republic of Indonesia has defined 564 species of bird as protected. This issue becomes a challenge to bird species identification. This study aims to develop a web application that implements a convolutional neural network (CNN) model for image-based protected bird species identification. This study uses the images of ten protected bird species in Indonesia as the research subject. This study consists of the stages viz. data collection, data preprocessing, data splitting, CNN model development, model evaluation, and web development using the Prototyping method. This study has successfully developed a model that gained 97% accuracy, 98% precision, and 97% recall on testing data. The study utilized HTML, CSS, Javascript, and Tensorflow.js for Web development. The black-box testing result shows that the prototype is acceptable.
The Impact of Ensemble Techniques on Software Maintenance Change Prediction: An Empirical Study

Various prediction models have been proposed by researchers to predict the change-proneness of classes based on source code metrics. However, some of these models suffer from low prediction accuracy because datasets exhibit high dimensionality or imbalanced classes. Recent studies suggest that using ensembles to integrate several models, select features, or perform sampling has the potential to resolve issues in the datasets and improve the prediction accuracy. This study aims to empirically evaluate the effectiveness of the ensemble models, feature selection, and sampling techniques on predicting change-proneness using different metrics. We conduct an empirical study to compare the performance of four machine learning models (naive Bayes, support vector machines, k-nearest neighbors, and random forests) on seven datasets for predicting change-proneness. We use two types of feature selection (relief and Pearson’s correlation coefficient) and three types of ensemble sampling techniques, which integrate different types of sampling techniques (SMOTE, spread sub-sample, and randomize). The results of this study reveal that the ensemble feature selection and sampling techniques yield improved prediction accuracy over most of the investigated models, and using sampling techniques increased the prediction accuracy of all models. Random forests provide a significant improvement over other prediction models and obtained the highest value of the average of the area under curve in all scenarios. The proposed ensemble feature selection and sampling techniques, along with the ensemble model (random forests), were found beneficial in improving the prediction accuracy of change-proneness.
All-cause mortality prediction in T2D patients with iTirps

Mortality in the type II diabetic elderly population can sometimes be prevented through intervention, for which risk assessment through predictive modeling is required. Since Electronic Health Records data are typically heterogeneous and sparse, the use of Temporal Abstraction and time intervals mining to discover frequent Time Intervals Related Patterns (TIRPs) is employed. While TIRPs are used as features for a predictive model, the temporal relations between them in general, and among each TIRP's instances are not represented. We introduce a novel TIRP based representation called integer-TIRP (iTirp) in which the TIRPs become channels containing values that represent the TIRP instances that were detected at each time point. Then the iTirp representation is fed into a Deep Learning architecture, that learns this kind of temporal relations, using a Recurrent Neural Network or a Convolutional Neural Network. Additionally, a predictive committee is introduced in which raw data and iTirp data are concatenated as inputs. Our results show that iTirps based models outperform the use of deep learning with raw data, resulting in 82% AUC.
Intent-Controllable Citation Text Generation

We study the problem of controllable citation text generation by introducing a new concept to generate citation texts. Citation text generation, as an assistive writing approach, has drawn a number of researchers’ attention. However, current research related to citation text generation rarely addresses how to generate the citation texts that satisfy the specified citation intents by the paper’s authors, especially at the beginning of paper writing. We propose a controllable citation text generation model that extends a pre-trained sequence to sequence models, namely, BART and T5, by using the citation intent as the control code to generate the citation text, meeting the paper authors’ citation intent. Experimental results demonstrate that our model can generate citation texts semantically similar to the reference citation texts and satisfy the given citation intent. Additionally, the results from human evaluation also indicate that incorporating the citation intent may enable the models to generate relevant citation texts almost as scientific paper authors do, even when only a little information from the citing paper is available.
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles

While privacy concerns entice connected and automated vehicles to incorporate on-board federated learning (FL) solutions, an integrated vehicle-to-everything communication with heterogeneous computation power aware learning platform is urgently necessary to make it a reality. Motivated by this, we propose a novel mobility, communication and computation aware online FL platform that uses on-road vehicles as learning agents. Thanks to the advanced features of modern vehicles, the on-board sensors can collect data as vehicles travel along their trajectories, while the on-board processors can train machine learning models using the collected data. To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold. To satisfy this threshold, the central server accepts partially trained models, the distributed roadside units (a) perform downlink multicast beamforming to minimize global model distribution delay and (b) allocate optimal uplink radio resources to minimize local model offloading delay, and the vehicle agents conduct heterogeneous local model training. Using real-world vehicle trace datasets, we validate our FL solutions. Simulation shows that the proposed integrated FL platform is robust and outperforms baseline models. With reasonable local training episodes, it can effectively satisfy all constraints and deliver near ground truth multi-horizon velocity and vehicle-specific power predictions.
Enabling spatial thinking through an augmented reality for teaching crystal structure

The crystal structure is an important topic in the STEM field and the basic knowledge to determine the material structure and its behaviour. There is a wealth of literature on three-dimensional (3D) and augmented reality (AR) technology to represent 3D crystal environments to provide a new way of visualisation and to foster spatial skills. This article aimed to develop and evaluate an AR system for teaching Bravais Crystals in Natural Science, especially the potential of AR as an innovative teaching tool to encourage spatial skills. The users were satisfied with the AR system (Mean = 4.3, SD = 0.49) to learn spatial representation techniques in a cooperative learning environment. The AR system provided excitement and motivation and was attractive and flexible while allowing the students to engage in independent learning.
Blockchain for AI: A Disruptive Integration

Artificial intelligence (AI) and blockchain are two of the most disruptive technologies in recent years. Blockchain is widely regarded as a trust machine because of its decentralization, non-tampering, anonymity and traceability. AI provides machines with cognitive functions, including learning, reasoning, and adaptation based on the collected data, which enables human-like machines possess intelligence and decision-making capabilities. Also, both technologies are data-driven, and thus there are rapidly growing interests in integrating them for trustworthy artificial intelligence and intelligent blockchain. In this paper, we review the related research on the integration of AI and blockchain, mainly analyzing how blockchain technology can improve AI from five aspects and pointing out the future research direction of these two technologies. And our research shows that blockchain can drive various components of AI including data, algorithms, and computational power to higher levels.
Interpolant-based demosaicing routines for dual-mode visible/near-infrared imaging systems

Dual-mode visible/near-infrared imaging systems, including a bioinspired six-channel design and more conventional four-channel implementations, have transitioned from a niche in surveillance to general use in machine vision. However, the demosaicing routines that transform the raw images from these sensors into processed images that can be consumed by humans or computers rely on assumptions that may not be appropriate when the two portions of the spectrum contribute different information about a scene. A solution can be found in a family of demosaicing routines that utilize interpolating polynomials and splines of different dimensionalities and orders to process images with minimal assumptions.