Reviewerly/exHarmony huggingface dataset:
Giant atom-mediated single photon routing between two waveguides

In this work, the single photon scattering due to a giant atom coupled with a pair of waveguides is investigated theoretically. Using the real-space Hamiltonian, four scattering amplitudes are obtained, and the single photon routing properties are studied. Calculations reveal that the single photon routing properties are strongly dependent on the size of the giant atom. The possible physical mechanism is also discussed. To improve routing efficiency, the configuration where one waveguide is terminated is further studied. The calculated results indicate that an incident photon can be transferred to the other waveguide with unit efficiency by choosing the appropriate configuration for a fixed size of the giant atom. Our results may be used in quantum information processing and design quantum devices at single-photon level.
Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Neural IR models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their generalization capabilities. To address this, and to allow researchers to more broadly establish the effectiveness of their models, we introduce BEIR (Benchmarking IR), a heterogeneous benchmark for information retrieval. We leverage a careful selection of 17 datasets for evaluation spanning diverse retrieval tasks including open-domain datasets as well as narrow expert domains. We study the effectiveness of nine state-of-the-art retrieval models in a zero-shot evaluation setup on BEIR, finding that performing well consistently across all datasets is challenging. Our results show BM25 is a robust baseline and Reranking-based models overall achieve the best zero-shot performances, however, at high computational costs. In contrast, Dense-retrieval models are computationally more efficient but often underperform other approaches, highlighting the considerable room for improvement in their generalization capabilities. In this work, we extensively analyze different retrieval models and provide several suggestions that we believe may be useful for future work. BEIR datasets and code are available at this https URL.
Reconceptualising digital pedagogy during the COVID-19 pandemic: A qualitative inquiry into distance teaching in China

Digital pedagogy was used as a contingency plan to achieve teaching and learning during the COVID-19 pandemic. This study aimed to observe digital pedagogy in lecturers' online teaching practices and instructional supervisors' evaluations of teaching outcomes. A sample of 32 lecturers and instructional supervisors from three Chinese universities participated in the in-depth interviews. The thematic analysis approach of grounded theory was used in data analysis. The results show that pedagogical dimensions were extended. Besides technology, innovative pedagogy, and learning environments, this study added two constructs: sustained support and cyber safety. Collaboration is required at different levels; meanwhile, cyber safety is highlighted to ensure healthy digital learning environments.
Intra- and Inter-Pair Consistency for Semi-Supervised Gland Segmentation

Accurate gland segmentation in histology tissue images is a critical but challenging task. Although deep models have demonstrated superior performance in medical image segmentation, they commonly require a large amount of annotated data, which are hard to obtain due to the extensive labor costs and expertise required. In this paper, we propose an intra- and inter-pair consistency-based semi-supervised (I2CS) model that can be trained on both labeled and unlabeled histology images for gland segmentation. Considering that each image contains glands and hence different images could potentially share consistent semantics in the feature space, we introduce a novel intra- and inter-pair consistency module to explore such consistency for learning with unlabeled data. It first characterizes the pixel-level relation between a pair of images in the feature space to create an attention map that highlights the regions with the same semantics but on different images. Then, it imposes a consistency constraint on the attention maps obtained from multiple image pairs, and thus filters low-confidence attention regions to generate refined attention maps that are then merged with original features to improve their representation ability. In addition, we also design an object-level loss to address the issues caused by touching glands. We evaluated our model against several recent gland segmentation methods and three typical semi-supervised methods on the GlaS and CRAG datasets. Our results not only demonstrate the effectiveness of the proposed due consistency module and Obj-Dice loss, but also indicate that the proposed I2CS model achieves state-of-the-art gland segmentation performance on both benchmarks.
Level of knowledge, skills and attitude of trainee teachers on Web 2.0 applications in teaching geography in Malaysia schools

This study aims to discuss the level of knowledge, skills and attitude of trainee teachers from the Geography and Environment Education Program in Universiti Pendidikan Sultan Idris on the use and effectiveness of Web 2.0 applications in the teaching and learning process of the Geography subject in Malaysia schools. This research applied purposive sampling by using a questionnaire as a primary data collection and distributed to the respondent by Google Form. 100 trainee teachers undergoing teaching training sessions in various schools in Malaysia were selected as the study sample and the data collection was carried out from April to June 2020. By using SPSS, descriptive analyses such as frequency, percentage, mean and standard deviation were used to analyse and present the data. The findings of the study showed that the mean values for the level of knowledge (mean = 4.09), skills (mean = 3.69) and attitude (mean = 3.98) are high. We have proposed some recommendations to the Universiti Pendidikan Sultan Idris (UPSI) and Trainee Teachers of the Education Program to further reinforce the implementation of integrating Web 2.0 applications into the teaching and learning of geography. In implications, this study can guide trainee teachers and other stakeholders to consider the teaching strategies that are appropriate for the use of Web 2.0 applications in the classroom. The selection of a Web 2.0 application design that fits to the learning objectives can encourage the teaching and learning (T&L) process and subsequently helps in the improvement of students' academic achievement in the future.
Globalisation and Digitisation - The Exponential Spread of Infectious Information and its Possible Containment

There is a fundamental difference between the human migration movements of the past and those of the beginning of the 21st century. The latter impose the need for a cultural assimilation of the migrants which they cannot master within one generation. This cultural transformation includes the necessity to adapt to the compression of humans into a new living space, into technology-based megapolises, which altogether represent the equivalent of an artificial planet. This new planet does not provide new resources nor additional free spaces for an overall growth of material wealth. On the contrary, it asks for a drastic reduction of individual freedoms. The stability, even the survival of these mega centres, is at stake without consistent subdivisions of an overall shrinking of spaces needed for all kinds of movements and of a consistent restriction of the exploding communicative interference within and between these mega centres. This essay aims at a first-hand analysis of a possible introduction of digital borders without which adequate legal spaces appear infeasible as the constituting framework of “our“ artificial new planet.
AuSR1: Authentication and self-recovery using a new image inpainting technique with LSB shifting in fragile image watermarking

With the rapid development of multimedia technology, editing and manipulating digital images have become more accessible than ever. This paper proposed color image authentication based on blind fragile image watermarking for tamper detection and self-recovery named AuSR1. The AuSR1 divides each channel of the cover image into non-overlapping blocks with the size of 2 × 2 pixels. The authentication data is embedded into the original block location, while the recovery data is embedded into the distant location from the original location based on the block mapping algorithm. The watermark data is then embedded into the 2 LSB to achieve high quality of the recovered image under tampering attacks. In addition, the permutation algorithm is applied to ensure the security of the watermark data. The AuSR1 utilizes a three-layer authentication algorithm to achieve a high detection rate. The experimental results show that the scheme produced a PSNR value of 45.57 dB and an SSIM value of 0.9972 of the watermarked images. Furthermore, the AuSR1 detected the tampered area of the images with a high precision value of 0.9943. In addition, the recovered image achieved a PSNR value of 27.64 dB and an SSIM value of 0.9339 on a 50% tampering rate.
Impact of quality, type and volume of data used by deep learning models in the analysis of medical images

The need for time and attention given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the development of the option to support, constructively and effectively, deep learning (DL) models for applications in the interpretation of medical images. Imaging physicians combine data from different stages and medical experiences, as opposed to DL models that incorporate the same types and modes of artisanal features. Access to big-databased medical imaging can be considered a benefit to the performance of DL models in interpreting medical imaging but similar or superior performance has been achieved with small, multi-feature and well-categorized databases that have improved annotation and labelling. The major contribution of this paper is primarily to highlight the impact of data quality, type and volume used by deep learning models in medical image analysis accompanied by updated characterization of the components of the deep learning process from data to medical applications. Second, it describes the specific correlations between the components of the deep learning process. Finally, it presents problems and directions for future research.
A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems

In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO's performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO's exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions.
Proxemics for Human-Agent Interaction in Augmented Reality

Augmented Reality (AR) embeds virtual content in physical spaces, including virtual agents that are known to exert a social presence on users. Existing design guidelines for AR rarely consider the social implications of an agent’s personal space (PS) and that it can impact user behavior and arousal. We report an experiment (N=54) where participants interacted with agents in an AR art gallery scenario. When participants approached six virtual agents (i.e., two males, two females, a humanoid robot, and a pillar) to ask for directions, we found that participants respected the agents’ PS and modulated interpersonal distances according to the human-like agents’ perceived gender. When participants were instructed to walk through the agents, we observed heightened skin-conductance levels that indicate physiological arousal. These results are discussed in terms of proxemic theory that result in design recommendations for implementing pervasive AR experiences with virtual agents.
Autonomous Racing with Multiple Vehicles using a Parallelized Optimization with Safety Guarantee using Control Barrier Functions

This paper presents a novel planning and control strategy for competing with multiple vehicles in a car racing scenario. The proposed racing strategy switches between two modes. When there are no surrounding vehicles, a learning-based model predictive control (MPC) trajectory planner is used to guarantee that the ego vehicle achieves better lap timing performance. When the ego vehicle is competing with other surrounding vehicles to overtake, an optimization-based planner generates multiple dynamically-feasible trajectories through parallel computation. Each trajectory is optimized under a MPC formulation with different homotopic Bezier-curve reference paths lying laterally between surrounding vehicles. The time-optimal trajectory among these different homotopic trajectories is selected and a low-level MPC controller with control barrier function constraints for obstacle avoidance is used to guarantee the system's safety-critical performance. The proposed algorithm has the capability to generate collision-free trajectories and track them while enhancing the lap timing performance with steady low computational complexity, outper-forming existing approaches in both timing and performance for an autonomous racing environment. To demonstrate the performance of our racing strategy, we simulate with multiple randomly generated moving vehicles on the track and test the ego vehicle's overtaking maneuvers.
Robust Object Detection via Adversarial Novel Style Exploration

Deep object detection models trained on clean images may not generalize well on degraded images due to the well-known domain shift issue. This hinders their application in real-life scenarios such as video surveillance and autonomous driving. Though domain adaptation methods can adapt the detection model from a labeled source domain to an unlabeled target domain, they struggle in dealing with open and compound degradation types. In this paper, we attempt to address this problem in the context of object detection by proposing a robust object Detector via Adversarial Novel Style Exploration (DANSE). Technically, DANSE first disentangles images into domain-irrelevant content representation and domain-specific style representation under an adversarial learning framework. Then, it explores the style space to discover diverse novel degradation styles that are complementary to those of the target domain images by leveraging a novelty regularizer and a diversity regularizer. The clean source domain images are transferred into these discovered styles by using a content-preserving regularizer to ensure realism. These transferred source domain images are combined with the target domain images and used to train a robust degradation-agnostic object detection model via adversarial domain adaptation. Experiments on both synthetic and real benchmark scenarios confirm the superiority of DANSE over state-of-the-art methods.
Close Proximity Time-to-collision Prediction for Autonomous Robot Navigation: An Exponential GPR Approach

Fusion of X-band Doppler radar and infrared sensors can offer a great advantage for close proximity time-to-collision (TTC) prediction in the field of autonomous robot navigation due to precise obstacle speed detection and direction sensing. Nevertheless, poor ranging performance from the infrared sensors may result owing to fluctuating reflectivity of the moving obstacle. The TTC prediction accuracy may also be further degraded when the obstacle’s trajectory is non-parallel to the robot’s heading direction due to uncertainty in the radar’s radiation pattern which typically increases at the side lobes of the antenna. Thus, to enhance the performance, we propose an exponential Gaussian Process Regression (E-GPR)-based prediction model which is able to approximate an unknown function in a probabilistic manner. The proposed method is validated via a series of experiments with an obstacle approaching the robot from different viewing angles with a speed ranging between 30 and 63 cm/s. Results demonstrate that the average TTC error based on the sensor fusion is 0.31s; but with the E-GPR method, the error is successfully reduced to 0.0937s, which is the largest error reduction when compared against other competing machine-learning models such as multilayer perceptron neural network, support vector machine and boosted tree.
Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning

This paper presents LSHAD, an anomaly detection (AD) method based on Locality Sensitive Hashing (LSH), capable of dealing with large-scale datasets. The resulting algorithm is highly parallelizable and its implementation in Apache Spark further increases its ability to handle very large datasets. Moreover, the algorithm incorporates an automatic hyperparameter tuning mechanism so that users do not have to implement costly manual tuning. Our LSHAD method is novel as both hyperparameter automation and distributed properties are not usual in AD techniques. Our results for experiments with LSHAD across a variety of datasets point to state-of-the-art AD performance while handling much larger datasets than state-of-the-art alternatives. In addition, evaluation results for the tradeoff between AD performance and scalability show that our method offers significant advantages over competing methods.
Improving plagiarism detection in text document using hybrid weighted similarity

Plagiarism is a misconduct, which refers to the use of scientific and literary content contained in other sources without reference to them. Today, the rise of plagiarism has become a serious problem for publishers and researchers. Many researchers have discussed this problem and tried to identify types of plagiarism; however, most of these methods are not effective in detecting intelligent plagiarism. In other words, most of these methods focus on direct copying. Therefore, in this study, two methods are proposed to identify Extrinsic plagiarism. In both methods, to limit the search space, two stages of filtering based on the bag of word (BoW) technique are used at the document level and at the sentence level, and plagiarism is investigated only in the outputs of these two stages. In the first method to detect similarities in suspicious documents and sentences, the combination of pre-trained network technique of words embedding FastText and TF-IDF weighting technique to form two structural and semantic matrices and in the second method to form the two matrices, WordNet ontology and weighting TF-IDF is used. After forming the above matrices and calculating the similarity between the pairs of matrices of each sentence, using the Dice similarity and the structural similarity of the weighted composition, two similarity values are calculated. By comparing the similarity of suspicious sentences with the minimum threshold, the document containing the suspicious sentence receives the label of plagiarism or non-plagiarism. Experimental results on the PAN-PC-11 database show that the first method has achieved 95.1% precision and the second method 93.8% precision, which shows that the use of word embedding network compared to WordNet ontology can be more successful in detecting Extrinsic plagiarism.
Attribute-Based Encryption With Blockchain Protection Scheme for Electronic Health Records

In medical scenarios supported by edge clouds, it is difficult for patients to truly gain ownership of their electronic health records (EHRs). However, it is easy for doctors to modify hospital data to deny incorrect treatment records, which makes it difficult to protect the rights of patients. To improve patient control over EHRs, an attribute-based encryption protection scheme named CEC-ABE for EHRs combined with a blockchain is proposed to protect EHRs in edge cloud environments. In this scheme, the agreement process between the patient and the hospital is completed before the ABE stage, and the treatment information, including the treatment time, treatment doctor and additional information, is confidentially transmitted through an encryption algorithm. By storing the uploaded encrypted data in the blockchain in the form of transaction records, the integrity of the data is guaranteed, which facilitates the traceability of EHR generation. Access to EHRs is controlled by the ABE scheme of the outsourced ciphertext policy, and fine-grained attribute revocation can be employed to ensure the security of the ciphertext. The CEC-ABE algorithm, CP-ABE algorithm and other algorithms are experimentally tested, and the computational cost of each stage of the algorithms and the computational delay of each role are compared. CEC-ABE can significantly improve performance in key generation, outsourced decryption and other stages. Compared with the algorithm whose performance is second only to CEC-ABE, it reduces the computational overhead by 1.73% and 5.2%. The results show that the overall comprehensive performance of the CEC-ABE algorithm is better than that of the other algorithms.