Reviewerly/exHarmony huggingface dataset:
Circuit centric quantum architecture design

With the development in the field of quantum physics, several methods for building a quantum computer have emerged. These differ in qubit technologies, interaction topologies, and noise characteristics. In this article, insights are given into the circuit-centric architecture design of Noisy Intermediate-Scale Quantum (NISQ) devices. The dependence of the circuit size, circuit depth on the interaction and connection between different qubits present in quantum hardware are discussed. A noise-aware procedure is presented which helps in determining the optimal interactions between different qubits of a quantum chip to execute a given circuit in the most efficient way possible. In this article, the 5-qubit hardware in a noiseless setting is illustrated with an example. Also, a benchmark-driven analysis is performed to show the importance of noise adaptivity in determining the hardware reliability. It is concluded that a generalized and flexible procedure such as this approach can aid in determining the design of hardware accurately for which the circuit runs efficiently, that is, with the least number of clock cycles, the lowest gate operations, and noise-based errors.
Correspondence Learning for Deep Multi-Modal Recognition and Fraud Detection

Deep learning-based methods have achieved good performance in various recognition benchmarks mostly by utilizing single modalities. As different modalities contain complementary information to each other, multi-modal based methods are proposed to implicitly utilize them. In this paper, we propose a simple technique, called correspondence learning (CL), which explicitly learns the relationship among multiple modalities. The multiple modalities in the data samples are randomly mixed among different samples. If the modalities are from the same sample (not mixed), then they have positive correspondence, and vice versa. CL is an auxiliary task for the model to predict the correspondence among modalities. The model is expected to extract information from each modality to check correspondence and achieve better representations in multi-modal recognition tasks. In this work, we first validate the proposed method in various multi-modal benchmarks including CMU Multimodal Opinion-Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) sentiment analysis datasets. In addition, we propose a fraud detection method using the learned correspondence among modalities. To validate this additional usage, we collect a multi-modal dataset for fraud detection using real-world samples for reverse vending machines.
Design and Feasibility Analysis of an Artificial Intelligence Based Mobile App for Emergency Ambulance

In this paper, we introduce a mobile application named EMS BD. EMS BD can be used to find an ambulance at the time of emergency. In an emergency user just need to press the help button and the app will find the nearest driver using artificial intelligence. This is done with the help of numerous features that are available in this application. With the change of differently customized control advancement, an online emergency vehicle framework has ended up being progressively indispensable these days. Also, free ambulance drivers can earn some extra money using the app. This framework is an application-based rescue vehicle framework using a one of a kind mobile application, GPS. The framework is significantly less costly than the standard ambulance, which gives security from various perspectives. The objective of this examination is to manufacture a direct and sensible, yet quicker rescue vehicle benefit. In this paper, model design, performance analysis, and control of EMS BD are elaborately discussed.
A Cloudedge-Combined Data Placement Strategy Based On User Access Regions

To improve the availability of data in the cloud and avoid vendor lock-in risk, multi-cloud storage is attracting more and more attentions. However, accessing data from the cloud usually has some disadvantages such as higher latency and more bandwidth-consuming. Edge computing, which has become popular in recent years, could effectively solve the above problems while it usually reduces availability. Storage only in the cloud or edge environment is limited by many factors. To this end, we build mathematical model which is abstracted from real access mode of users in consideration of multi-cloud and edge. This paper presents a method combined NSGAII with multi-group method which has better ability of global search to help users determine cloud and edge services to store and access data object. A large number of experiments using real service provider information demonstrate that the proposed algorithm outperforms existing methods and effectiveness of it.
How Can Blockchain Shape Digital Transformation: A Scientometric Analysis and Review for Financial Services

As a rapidly emerging and iteratively developed technology, the blockchain and its derived technologies evince promising revolution potential in the financial field. In this state-of-the-art-paper, we organized 1,361 literature from the web of science (WoS) database and analyzes their annual trend, topics, publications, and disciplines. Base on the exploratory scientometric analysis, we also mapping on co-occurrence clusters and demonstrate the possibilities and application areas in the related field. The study reveals after 2015, notions beyond "bitcoin" gradually occurs, and expand relevant topics to a new level. Their majority of topics are in the traditional business field and tend to be applied rather than technology or theory. Although "blockchain", "bitcoin", and "cryptocurrency" are correlative concepts, they are classified into different clusters, in which the red blockchain cluster is more capable of redefining financial services with digital resources. These insights will help future researchers understand the evolving state of blockchain technology and respond to digital transformation trend in a more effective way.
Efficient large-scale face clustering using an online Mixture of Gaussians

In this work, we address the problem of large-scale online face clustering: given a continuous stream of unknown faces, create a database grouping the incoming faces by their identity. The database must be updated every time a new face arrives. In addition, the solution must be efficient, accurate and scalable. For this purpose, we present an online gaussian mixture-based clustering method (OGMC). The key idea of this method is the proposal that an identity can be represented by more than just one distribution or cluster. Using feature vectors (f-vectors) extracted from the incoming faces, OGMC generates clusters that may be connected to others depending on their proximity and their robustness. Every time a cluster is updated with a new sample, its connections are also updated. With this approach, we reduce the dependency of the clustering process on the order and the size of the incoming data and we are able to deal with complex data distributions. Experimental results show that the proposed approach outperforms state-of-the-art clustering methods on large-scale face clustering benchmarks not only in accuracy, but also in efficiency and scalability.
Spammer Detection Using Graph-level Classification Model of Graph Neural Network

The popularity of social networks has made spammers a ubiquitous presence on all platforms. Spammers occupy the limited hardware resources and information transfer channel, even pose multiple security risks to legitimate users. In this paper, the graph-level classification model of graph neural network is used to detect spammer on social network platform. We convert the behavior pattern of each user into a graph and extract the graph features for model training. Bayesian optimization framework is used in hyperparameters tuning. We obtain node features from account information, relation-graph and behaviour-sequence, and principal component analysis is used for feature selection. We have performed 10-fold cross-validation experiments on the Tagged dataset and get good results. The results show that graph neural network model has higher recognition accuracy for spammers than traditional classifiers, such as gradient tree classifier and random forest classifier.
Simulation of the Floating PV System to Supply Electricity Demand for the City of Surabaya, Indonesia

Solar photovoltaic (PV) is one of the promising renewable energy for replacing conventional fossil fuels. One of the limitation use of the solar PV system is it requires land or open space for mounting of the solar panel. The Floating Photovoltaic (FPV) would be the appropriate solution for a rapid expansion of solar PV. This paper studies the possibility of implementation of the FPV system to supply electricity demand for the city of Surabaya, Indonesia. The studies include FPV reviews, PV simulation, interpolation, and FPV estimation based on the energy demand in Surabaya. It is found that a 1,255 MWp capacity of the FPV system would be needed for electricity demand. The coastal area at the Northwest of the city is considered suitable for the FPV panel area for Surabaya.
Intelligent fault analysis of transmission line using phasor measurement unit incorporating auto-reclosure protection scheme

Abstract The power system stability and reliability are stimulated by the faults on the transmission line. Many researchers have explored the performance of the transmission system under various kinds of faults. Specifically, the arrival of expeditious and effective data acquisition systems with high rate of sampling has set down the foundation for successful real-time monitoring. Using the LabVIEW and the data acquisition system’s of National Instruments (NI), virtual systems have been developed for obtaining optimal paradigmatic data with appropriate characterization and quality transmission. The primary objective of the work is to perceive and comprehend the transmission line faults with the aid of synchronized phasor measurements obtained from the phasor measurement unit (PMU) as well as protecting the system using auto-reclosing signal. The developed algorithms include phaselet coefficients for perception as well as comprehension. In order to increase the accuracy, particle swarm optimized extreme learning machine technique has also been used for comprehension. A protection scheme is employed using auto-reclosing to minimize the power loss and quick reconnection the power line in case of temporary fault. Developed algorithms have been validated on a practical laboratory transmission line using NI PMU. As the LabVIEW platform has been used for simulations, it is composed of visual displays such that the system operator can efficiently perform the planning and control decisions.
Ensemble summarization of bio-medical articles integrating clustering and multi-objective evolutionary algorithms

The rapid exponential growth of scientific literature of bio-medicine, genomics, and other bio-sciences increases difficulties to access useful information timely and efficiently. The medical researchers, physicians and clinicians need to extract the gist of the novel articles effectively to gather knowledge and apply it properly for better treatment of the patients. In the paper, the bio-medical articles available in an open access source, PubMed MEDLINE, are considered for generating their extractive summaries using clustering and multi-objective evolutionary algorithm based ensemble summarization technique. Initially, we have extracted only the medical terms of an article and determined the concepts of all medical terms using Unified Medical Language System (UMLS) to represent the article in terms of a set of concepts. Next, extractive summaries of the article have been generated using various popularly used clustering algorithms and different centrality measures. The generated summaries are referred to as the base summaries of the article. Finally, multi-objective evolutionary algorithm is applied on these base summaries for generating an ensemble summary of the given article. The evolutionary algorithm uses two competent objective functions to measure the fitness value of each chromosome in the population. One objective function is the linear combination of jaccard similarity and Word2Vec similarity between the original article and the ensemble summary generated by the chromosome. The other objective function is defined as the information gain of the chromosome about the article by generating the ensemble summary. The best pareto optimal solution of the final population of the evolutionary algorithm provides the ensemble summary of the given article. The proposed ensemble method is compared with some state-of-art methods to demonstrate that it is both effective and statistically significant.
The basics of natural language processing

One of the milestones in human civilization is the advent of language. For thousands of years, almost all human knowledge has been shared via the language medium. Natural language processing (NLP) is a discipline that uses computer science and artificial intelligence techniques to analyze and understand human languages. Among the various categories of AI, the understanding of natural language is among the most difficult tasks because of its complexity and ambiguity. With the rapid progress of machine learning, statistics, and deep learning, the field of NLP has seen many breakthroughs. This chapter will introduce basic concepts in NLP, and methods to analyze and comprehend text. We will start with the basic concept of word tokenization, and then dive into other important topics such as word vectors, linguistic tagging, and language models. All of these technologies are commonly used in machine reading comprehension.
CineMPC: Controlling Camera Intrinsics and Extrinsics for Autonomous Cinematography

We present CineMPC, an algorithm to autonomously control a UAV-borne video camera in a nonlinear Model Predicted Control (MPC) loop. CineMPC controls both the position and orientation of the camera-the camera extrinsics-as well as the lens focal length, focal distance, and aperture-the camera intrinsics. While some existing solutions autonomously control the position and orientation of the camera, no existing solutions also control the intrinsic parameters, which are essential tools for rich cinematographic expression. The intrinsic parameters control the parts of the scene that are focused or blurred, the viewers' perception of depth in the scene and the position of the targets in the image. CineMPC closes the loop from camera images to UAV trajectory and lens parameters in order to follow the desired relative trajectory and image composition as the targets move through the scene. Experiments using a photo-realistic environment demon-strate the capabilities of the proposed control framework to successfully achieve a full array of cinematographic effects not possible without full camera control.
Blind 360-degree image quality assessment via saliency-guided convolution neural network

With the rapid development of virtual reality (VR) technologies, quality assessment of 360-degree images has become increasingly urgent. Unlike the traditional 2D images, the distortion is not evenly distributed in the 360-degree images, e.g., the projection distortion in the polar region of the Equirectangular projection (ERP) is more serious than that in other regions. Thus, the traditional 2D quality model cannot be directly applied to 360-degree images. In this paper, we propose a saliency-guided CNN model for blind 360-degree image quality assessment (SG360BIQA), which is mainly composed of a saliency prediction network (SP-Net) and a feature extraction network (F-Net). By training the whole network with the two sub-networks together, more discriminant features can be extracted and the mapping from feature representations to quality scores can be established more accurately. Besides, due to the lack of sufficient large 360-degree images database, we use a pre-trained network model instead of the initial random parameter model to overcome the limitation. Experimental results on two public 360-IQA databases demonstrate that our proposed model outperforms state-of-the-art full-reference and no-reference IQA metrics in terms of generalization ability and evaluation accuracy.
Digital Competences in European University Teaching Staff: Teaching and Guidance

Nowadays, we live in a practically digitised world. Therefore, education has been one of the first aspects to have the need to update itself digitally. This, together with exceptional situations, such as COVID-19 (coronavirus diseases), have meant that the only way to teach is through information and communication technology (ICT). As an objective of this communication, we propose to analyse the level of digital competences of European university teaching staff, in addition to detecting good practices in teaching and guidance using ICT. This is a mixed-method research study in which 918 teachers participated in the quantitative part and 76 in the qualitative part. As an instrument, the scale Necesidades formativas en estrategias orientadoras y tutoriales (NFEOT-17) has been used for quantitative research, and the techniques of discussion group, nominal group and the SWOT (Strengths, Weaknesses, Opportunities and Threats) technique have been used for qualitative research. The results show that low digital competence is manifested in relation to teaching and university orientation. Furthermore, determining factors have been detected that characterise good ICT practices. In conclusion, the need to develop digital competencies in university teachers is highlighted so that they can be part of the new digital era and face exceptional situations that may arise.
Tracer study information system for higher education

Abstract A good education system has a looping system in which the responsibility of higher education for students does not end at graduation but also related to the sustainability of graduates careers so that they are better prepared to work in the community. The purpose of this study is to develop a one-door Tracer Study Information System managed by universities to improve the quality of graduates through improving the education process. The results of the tracer study as feedback are expected to be an input for the education system, curriculum, student activity direction, and Universitas Negeri Jakarta (UNJ) policies which include the development of student hard skills, soft skills, and life skills. A reliable tracer study system is due to the availability of adequate resources, namely information systems, survey teams, publications, also the reward and punishment given to respondents. The prototype was developed using the waterfall method which went through several stages specifically analysis, design, implementation, testing and maintenance. The prototype was tested using black box testing. Based on the functional test, it is known that all functions of the system prototype are running according to plan.
Hyper-Parameter Tuning of Classification and Regression Trees for Software Effort Estimation

Classification and regression trees (CART) have been reported to be competitive machine learning algorithms for software effort estimation. In this work, we analyze the impact of hyper-parameter tuning on the accuracy and stability of CART using the grid search, random search, and DODGE approaches. We compared the results of CART with support vector regression (SVR) and ridge regression (RR) models. Results show that tuning improves the performance of CART models up to a maximum of 0.153 standardized accuracy and reduce its stability radio to a minimum of 0.819. Also, CART proved to be as competitive as SVR and outperformed RR.
Deep Semi-Supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection

Deep learning has achieved unprecedented success in various object detection tasks with huge amounts of labeled data. However, obtaining large-scale annotations for medical images is extremely challenging due to the high demand of labour and expertise. In this paper, we propose a novel deep semi-supervised metric learning method to effectively leverage both labeled and unlabeled data for cervical cancer cell detection. Specifically, our model learns a metric space and conducts dual alignment of semantic features on both the proposal level and the prototype levels. On the proposal level, we align the unlabeled data with class proxies derived from the labeled data. We further align the prototypes of the labeled and unlabeled data to alleviate the influence of possibly noisy pseudo labels generated at the proposal alignment stage. Moreover, we adopt a memory bank to store the labeled prototypes, which significantly enrich the metric learning information from larger batches. Extensive experiments show our proposed method outperforms other state-of-the-art semi-supervised approaches consistently, demonstrating the efficacy of our proposed deep semi-supervised metric learning with dual alignment.
Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Tweets for Emergency Services

During the onset of a natural or man-made crisis event, public often share relevant information for emergency services on social web platforms such as Twitter. However, filtering such relevant data in real-time at scale using social media mining is challenging due to the short noisy text, sparse availability of relevant data, and also, practical limitations in collecting large labeled data during an ongoing event. In this paper, we hypothesize that unsupervised domain adaptation through multi-task learning can be a useful framework to leverage data from past crisis events for training efficient information filtering models during the sudden onset of a new crisis. We present a novel method to classify relevant social posts during an ongoing crisis without seeing any new data from this event (fully unsupervised domain adaptation). Specifically, we construct a customized multi-task architecture with a multi-domain discriminator for crisis analytics: multi-task domain adversarial attention network (MT-DAAN). This model consists of dedicated attention layers for each task to provide model interpretability; critical for real-word applications. As deep networks struggle with sparse datasets, we show that this can be improved by sharing a base layer for multitask learning and domain adversarial training. The framework is validated with the public datasets of TREC incident streams that provide labeled Twitter posts (tweets) with relevant classes (Priority, Factoid, Sentiment) across 10 different crisis events such as floods and earthquakes. Evaluation of domain adaptation for crisis events is performed by choosing one target event as the test set and training on the rest. Our results show that the multi-task model outperformed its single-task counterpart. For the qualitative evaluation of interpretability, we show that the attention layer can be used as a guide to explain the model predictions and empower emergency services for exploring accountability of the model, by showcasing the words in a tweet that are deemed important in the classification process. Finally, we show a practical implication of our work by providing a use-case for the COVID-19 pandemic.