Reviewerly/exHarmony huggingface dataset:
LSUN-Stanford Car Dataset: Enhancing Large-Scale Car Image Datasets Using Deep Learning for Usage in GAN Training

Currently there is no publicly available adequate dataset that could be used for training Generative Adversarial Networks (GANs) on car images. All available car datasets differ in noise, pose, and zoom levels. Thus, the objective of this work was to create an improved car image dataset that would be better suited for GAN training. To improve the performance of the GAN, we coupled the LSUN and Stanford car datasets. A new merged dataset was then pruned in order to adjust zoom levels and reduce the noise of images. This process resulted in fewer images that could be used for training, with increased quality though. This pruned dataset was evaluated by training the StyleGAN with original settings. Pruning the combined LSUN and Stanford datasets resulted in 2,067,710 images of cars with less noise and more adjusted zoom levels. The training of the StyleGAN on the LSUN-Stanford car dataset proved to be superior to the training with just the LSUN dataset by 3.7% using the Fréchet Inception Distance (FID) as a metric. Results pointed out that the proposed LSUN-Stanford car dataset is more consistent and better suited for training GAN neural networks than other currently available large car datasets.
Pose measurement approach based on two-stage binocular vision for docking large components

Abstract Large components docking is an important step in industrial manufacturing. During the docking process, the relative pose of joining components needs real-time and accurate acquisition. Existing methods rely on expensive and complex optical instruments. Photogrammetry has the advantages of low cost and fast measurement speed, but its measurement accuracy decreases sharply along with the increase of the measurement range. This paper proposes a two-stage binocular vision which consists of two sets of binocular vision systems with different accuracy levels in the same coordinate system. Binocular system with corresponding structural parameters is designed for pose measurement at corresponding stages to solve the contradiction between the range and accuracy in measurement. A triangulation and spatial plane fitting method is proposed to calculate relative poses without introducing coordinate transformation errors. We adopt a novel 3D optimization method to further improve the accuracy. Experiment results show that this method can meet the measurement range and accuracy requirements for large components docking. Compared with traditional combined vision measurement station based on multi-vision sensor, the proposed method reduces coordinate transformation errors and overcomes the contradiction between the measurement range and accuracy, which can improve the accuracy and meet the requirements of engineering application.
Robust Prediction of Punctuation and Truecasing for Medical ASR

Automatic speech recognition (ASR) systems in the medical domain that focus on transcribing clinical dictations and doctor-patient conversations often pose many challenges due to the complexity of the domain.ASR output typically undergoes automatic punctuation to enable users to speak naturally, without having to vocalise awkward and explicit punctuation commands, such as "period", "add comma" or "exclamation point", while truecasing enhances user readability and improves the performance of downstream NLP tasks.This paper proposes a conditional joint modeling framework for prediction of punctuation and truecasing using pretrained masked language models such as BERT, BioBERT and RoBERTa.We also present techniques for domain and task specific adaptation by finetuning masked language models with medical domain data.Finally, we improve the robustness of the model against common errors made in ASR by performing data augmentation.Experiments performed on dictation and conversational style corpora show that our proposed model achieves ∼5% absolute improvement on ground truth text and ∼10% improvement on ASR outputs over baseline models under F1 metric.
Parallelizing the queries in a bucket-brigade quantum random access memory

Quantum algorithms often use quantum RAMs (QRAM) for accessing information stored in a database-like manner. QRAMs have to be fast, resource efficient and fault-tolerant. The latter is often influenced by access speeds, because shorter times introduce less exposure of the stored information to noise. The total execution time of an algorithm depends on the QRAM access time which includes: 1) address translation time, and 2) effective query time. The bucket brigade QRAMs were proposed to achieve faster addressing at the cost of exponentially many ancillae. We illustrate a systematic method to significantly reduce the effective query time by using Clifford+T gate parallelism. The method does not introduce any ancillae qubits. Our parallelisation method is compatible with the surface code quantum error correction. We show that parallelisation is a result of advantageous Toffoli gate decomposition in terms of Clifford+T gates, and after addresses have been translated, we achieve theoretical $\mathcal{O}(1)$ parallelism for the effective queries. We conclude that, in theory: 1) fault-tolerant bucket brigade quantum RAM queries can be performed approximately with the speed of classical RAM; 2) the exponentially many ancillae from the bucket brigade addressing scheme are a trade-off cost for achieving exponential query speedup compared to quantum read-only memories whose queries are sequential by design. The methods to compile, parallelise and analyse the presented QRAM circuits were implemented in software which is available online.
RLINK: Deep reinforcement learning for user identity linkage

Abstract User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based on the similarity scores. However, most of these methods fail to utilize the results of previously matched identities, which could contribute to the subsequent linkages in following matching steps. To address this problem, we transform user identity linkage into a sequence decision problem and propose a reinforcement learning model to optimize the linkage strategy from the global perspective. Our method makes full use of both the social network structure and the history matched identities, meanwhile explores the long-term influence of processing matching on subsequent decisions. We conduct extensive experiments on real-world datasets, the results show that our method outperforms the state-of-the-art methods.
Team Formation in Software Engineering: A Systematic Mapping Study

Context: Software team formation is an important project management activity.However, forming appropriate teams is a challenge for most of the companies.Objective: To analyze and synthesize the state of the art on the software team formation research.Additionally, we aim to organize the identified body of knowledge in software team formation as a taxonomy.Method: Using a Snowballing-based systematic mapping study, 51 primary studies, out of 2516, were identified and analyzed.We classified the studies considering the research methods used, their overall quality, and the characteristics of the formed teams and the proposed solutions.Results: The majority of the studies use search and optimization techniques in their approaches.Also, technical attributes are the most frequent type considered to build individuals' profiles during the team formation process.Furthermore, we proposed a taxonomy on software team formation.Conclusion: There is a predominant use of search-based approaches that combine search and optimization techniques with technical attributes.However, the adoption of non-technical attributes as complementary information is a tendency.Regarding the research gaps, we highlight the level of subjectivity in software team formation and the lack of scalability of the proposed solutions.
M-BPR: A novel approach to improving BPR for recommendation with multi-type pair-wise preferences

In this paper, we examine the two assumptions of the Bayesian personalized ranking (BPR), a well-known pair-wise method for one-class collaborative filtering (OCCF): (1) a user with the same degree of negative preferences for all her unrated items; and (2) a user always preferring her rated items to all her unrated items. We claim that (A1) and (A2) cause recommendation errors because they do not always hold in practice. To address these problems, we first define fine-grained multi-type pair-wise preferences (PPs), which are more sophisticated than the single-type PP used in BPR. Then, we propose a novel pair-wise approach called M-BPR, which exploits multi-type PPs together in learning users’ more detailed preferences. Furthermore, we refine M-BPR by employing the concept of item groups to reduce the uncertainty of a user’s a single item-level preference. Through extensive experiments using four real-life datasets, we demonstrate that our approach addresses the problems of the original BPR effectively and also outperforms seven state-of-the-art OCCF (i.e., four pair-wise and three point-wise) methods significantly.
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering

Automatic Machine Learning (AutoML) is an area of research aimed at automating Machine Learning (ML) activities that currently require the involvement of human experts. One of the most challenging tasks in this field is the automatic generation of end-to-end ML pipelines: combining multiple types of ML algorithms into a single architecture used for analysis of previously-unseen data. This task has two challenging aspects: the first is the need to explore a large search space of algorithms and pipeline architectures. The second challenge is the computational cost of training and evaluating multiple pipelines. In this study we present DeepLine, a reinforcement learning-based approach for automatic pipeline generation. Our proposed approach utilizes an efficient representation of the search space together with a novel method for operating in environments with large and dynamic action spaces. By leveraging past knowledge gained from previously-analyzed datasets, our approach only needs to generate and evaluate few dozens of pipelines to reach comparable or better performance than current state-of-the-art AutoML systems that evaluate hundreds and even thousands of pipelines in their optimization process. Evaluation on 56 classification datasets demonstrates the merits of our approach.
Gender and social network brokerage: A meta-analysis and field investigation.

In this article, we aim to address 2 important questions: (a) Are women less likely than men to occupy network brokerage positions? And if so, (b) what mechanisms may explain their fewer brokerage roles? Study 1, a meta-analysis examining gender differences in network brokerage, analyzed a cumulative sample of 15,743 individuals (69 independent samples) to show that women were less likely to be brokers in both instrumental and expressive networks, which partly explained their lower career success. Study 2, a follow-up study with 2 independent samples of new employees (n = 150 and 245, respectively), examined both structural opportunity (job-based opportunity and workplace discrimination) and individual agency (proactive networking) as potential mechanisms underlying the relationship between gender and network brokerage. Results of these 2 samples consistently show that proactive networking mediated gender’s effect on network brokerage that was measured 6 months after the new employees entered their organizations. The predictions regarding the mediation effects of job-based opportunity and workplace discrimination were not supported. Our findings offer valuable insights into the relative positions of women and men in informal structures of organizational networks, advance our understanding of gender inequality in career outcomes, and shed new light on the relative importance of individual agency and structural opportunity in explaining individuals’ occupancy of advantageous network positions. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
Multimodel Deep Learning for Person Detection in Aerial Images

In this paper, we propose a novel method for person detection in aerial images of nonurban terrain gathered by an Unmanned Aerial Vehicle (UAV), which plays an important role in Search And Rescue (SAR) missions. The UAV in SAR operations contributes significantly due to the ability to survey a larger geographical area from an aerial viewpoint. Because of the high altitude of recording, the object of interest (person) covers a small part of an image (around 0.1%), which makes this task quite challenging. To address this problem, a multimodel deep learning approach is proposed. The solution consists of two different convolutional neural networks in region proposal, as well as in the classification stage. Additionally, contextual information is used in the classification stage in order to improve the detection results. Experimental results tested on the HERIDAL dataset achieved precision of 68.89% and a recall of 94.65%, which is better than current state-of-the-art methods used for person detection in similar scenarios. Consequently, it may be concluded that this approach is suitable for usage as an auxiliary method in real SAR operations.
The safety of IoT-enabled system in smart cities of India: do ethics matter?

Purpose The purpose of this study is to analyze the impact of ethics and technology towards safety of internet of things (IoT)-enabled system in smart cities of India (SCI). Design/methodology/approach The determinants that would impact on securing IoT-enabled system in SCI have been identified by the studies of literature. Some hypothesis has been formulated. A conceptual model has been developed. Hypotheses and conceptual models have been tested by a statistical approach through survey works considering the feedbacks of 331 usable respondents. The results have been discussed followed by explaining the implications of this study. A comprehensive conclusion has been provided at the end. Findings The validated results show that the trust factor has insignificant impacts on the system and technology use, as well as on the behavioural intention. The model provided could achieve 79% explanative power. Practical implications For ensuring safety and security of IoT-enabled devices in SCI, the ethical sense of the stakeholders of SCI has considerable impact on securing safety of IoT-enabled devices in SCI. The practitioner should, as such, be sincere to motivate the stakeholders to ameliorate the ethical sense of the stakeholders for securing safety of IoT-enabled devices in SCI. Originality/value A few studies in the areas of Indian smart cities, IoT and related ethical issues have been conducted. In that sense, this approach is deemed to be a novel attempt.
Interface Transparency Issues in Teleoperation

Transferring skills and expertise to remote places, without being present, is a new challenge for our digitally interconnected society. People can experience and perform actions in distant places through a robotic agent wearing immersive interfaces to feel physically there. However, technological contingencies can affect human perception, compromising skill-based performances. Considering the results from studies on human factors, a set of recommendations for the construction of immersive teleoperation systems is provided, followed by an example of the evaluation methodology. We developed a testbed to study perceptual issues that affect task performance while users manipulated the environment either through traditional or immersive interfaces. The analysis of its effect on perception, navigation, and manipulation relies on performances measures and subjective answers. The goal is to mitigate the effect of factors such as system latency, field of view, frame of reference, or frame rate to achieve the sense of telepresence. By decoupling the flows of an immersive teleoperation system, we aim to understand how vision and interaction fidelity affects spatial cognition. Results show that misalignments between the frame of reference for vision and motor-action or the use of tools affecting the sense of body position or movement have a higher effect on mental workload and spatial cognition.
Topic Modeling Coherence: A Comparative Study between LDA and NMF Models using COVID’19 Corpus

Topic modeling is a method for finding abstract topics in a large collection of documents.With it, it is possible to discover the mixture of hidden or "latent" topics that varies from document to document in a given corpus.As an unsupervised machine learning approach, topic models are not easy to evaluate since there is no labelled "ground truth" data to compare with.However, since topic modeling typically requires defining some parameters beforehand (first and foremost the number of topics k to be discovered), model evaluation is crucial in order to find an "optimal" set of parameters for the given data.Latent Dirichlet allocation (LDA) and Non-Negative Matrix Factorization (NMF) are the two most popular topic modeling techniques.LDA uses a probabilistic approach where as NMF uses matrix factorization approach.In this paper we want to assess which most relevant technique for topic coherence using c_v measure, we have chosen citations's Covid'19 Corpus for experimentations.
An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm

Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithreshold OTSU calculates the fitness in the initial population and makes the initial stage basically stable. Tsallis entropy calculates the fitness quickly. The DE algorithm can solve the local optimal solution of GWO. The performance of the DE-OTSU-GWO algorithm was tested using a CEC2005 benchmark function (23 test functions). Compared with existing particle swarm optimizer (PSO) and GWO algorithms, the experimental results showed that the DE-OTSU-GWO algorithm is more stable and accurate in solving functions. In addition, compared with other algorithms, a convergence behavior analysis proved the high quality of the DE-OTSU-GWO algorithm. In the results of classical agricultural image recognition problems, compared with GWO, PSO, DE-GWO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm had accuracy in straw image recognition and is applicable to practical problems. The OTSU algorithm improves the accuracy of the overall algorithm while increasing the running time. After adding the DE algorithm, the time complexity will increase, but the solution time can be shortened. Compared with GWO, DE-GWO, PSO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm has better results in segmentation assessment.
Curved light surface model for calibration of a structured light 3D modeling system based on striped patterns

Structured light is an optical 3D surface measurement technique with the merits of high speed and high robustness. However, the huge size of traditional digital light processing (DLP) projectors limits its convenience in numerous applications. In this paper, a one-axis MEMS mirror is used as the structured light projector in 3D modeling systems, and has the advantages of small volume and low cost. Limited by the inability to project orthogonal patterns and projection distortion, it is difficult for the one-axis MEMS mirror based 3D modeling system to obtain high accuracy through existing calibration methods. This paper proposed a calibration method for structured light 3D modeling systems that can only project stripes in one direction with projection distortion. A curved surface equation called curved light surface model was proposed to replace the ideal plane equation as the mathematic model of the projected structured light stripes. Experiment results verified that this method can significantly reduce the effect of projection distortion and an accuracy of 0.11 mm was achieved when measuring a standard dumbbell-shaped object with 201.10 mm center-to-center distance.
The Roles of Device Ownership and Infrastructure in Promoting E-Learning and M-Learning in Indonesia

In developing countries, where most people have cell phones (even when they don't have reliable electricity or internet service), e-learning and m-learning offer great opportunities for educating a vast populace, particularly in rural communities. This article presents an analysis of primary data from over one thousand secondary school students in Indonesia, the fourth most populated nation in the world, to determine how they are using e-learning and m-learning and how device ownership influences that. The findings from the logistic regression models show that mobile devices are more important than traditional PCs in promoting e-learning, while tablets are better than smartphones for promoting m-learning. Having access to free wi-fi is equally important. Students who prefer m-learning on mobile devices over traditional e-learning activities on PCs tend to be more active and collaborative learners. These findings can help inform policy makers in improving the educational attainment in Indonesia and other developing countries.
Proposing High-Smart Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet

The security and reliability of Arabic text exchanged via the Internet have become a challenging area for the research community. Arabic text is very sensitive to modify by malicious attacks and easy to make changes on diacritics i.e. Fat-ha, Kasra and Damma, which are represent the syntax of Arabic language and can make the meaning is differing. In this paper, a Hybrid of Natural Language Processing and Zero-Watermarking Approach (HNLPZWA) has been proposed for the content authentication and tampering detection of Arabic text. The HNLPZWA approach embeds and detects the watermark logically without altering the original text document to embed a watermark key. Fifth level order of word mechanism based on hidden Markov model is integrated with digital zero-watermarking techniques to improve the tampering detection accuracy issues of the previous literature proposed by the researchers. Fifth-level order of Markov model is used as a natural language processing technique in order to analyze the Arabic text. Moreover, it extracts the features of interrelationship between contexts of the text and utilizes the extracted features as watermark information and validates it later with attacked Arabic text to detect any tampering occurred on it. HNLPZWA has been implemented using PHP with VS code IDE. Tampering detection accuracy of HNLPZWA is proved with experiments using four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks of experimental datasets. The experimental results show that HNLPZWA is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection.
A choices framework for the responsible use of AI

Popular press and media often make us believe that artificial intelligence technology is ethical or unethical by itself. In this paper, we will argue that organizations that develop or apply AI have certain choices they can make that will lead to a more or less responsible use of AI. By approaching those choices in a methodological way, organizations can make better decisions toward the ethical use of this powerful technology.