Reviewerly/exHarmony huggingface dataset:
Socialization in telecollaboration: The smart use of WhatsApp to develop social presence in the HI-UB project

This study focuses on the identification and analysis of social presence (Garrison, 2009) in the interaction established during the development of a language learning task based on the “smart” use of Whatsapp. This digital task is the second of the HI-UB telecollaboration project amongst learners of Spanish from the University of Island (HI) and pre-service teachers from the University of Barcelona (UB). The task aims for the HI students to practise the language and for the UB pre-service teachers to reinforce the initial encounters of the previous videoconferencing task, to create a climate of confidence trying to consolidate a sense of belonging to a language learning community and to explore mobile instant messaging (MIM) as a pedagogical tool.
Semantic Stabilization Tools for Managing the Cognitive Activity of the Subject

The paper considers a model of knowledge extraction based on the conceptual modeling of user interaction with a domain-oriented virtual environment. The environment is modeled as a network of information graphs that changes its structure over time. This allows us to set the task of supporting the modeling of the cognitive activity of the subject in a changing environment. The description of changes is made on the basis of a parameterized computational model using the construction of a variable domain. The paper shows that a given set of domain variables can be considered as nested in a topos, which provides a natural construction of program structures. The paper offers a tool for working with variable domains, which provides the task of semantically stable fragments of information graphs. The tool is a specialized applicative-type evaluator that provides computations in a changing environment. The build-up of network vertices and connections can lead to system contradictions, for the resolution of which it is necessary to include special handlers, the number of which can grow excessively. The evaluator provides a solution to the problem of managing handlers. Attempts to solve the mentioned difficulties and contradictions in practice lead to the idea of a multi-layer network architecture and semantic adapters.
Detecting Spliced Image Based on Simplified Statistical Model

The goal of this paper is to design an effective forensic detector for image splicing forgery detection. The detector is designed based on a simplified noise model of JPEG image under the framework of hypothesis testing theory, where the model parameters can serve as camera fingerprints for image forgery detection. By specifying the false alarm rate, the designed detector can achieve high detection performance. Meanwhile, the decision threshold can be set independently of image content. In addition, with the support of solid theoretical explanations, the statistical performance of detector can be analytically established. Numerical results validate the effectiveness of our detector and the superiority compared with the current comparison detectors.
Local Path Planning of Unmanned Vehicles Based on Improved RRT Algorithm

research-article Share on Local Path Planning of Unmanned Vehicles Based on Improved RRT Algorithm Authors: Peicheng Shi Anhui Polytechnic University, China Anhui Polytechnic University, ChinaView Profile , Zhiqiang Liu Anhui Polytechnic University, China Anhui Polytechnic University, ChinaView Profile , Guangzhong Liu Anhui Polytechnic University, China Anhui Polytechnic University, ChinaView Profile Authors Info & Claims APIT 2022: 2022 4th Asia Pacific Information Technology ConferenceJanuary 2022 Pages 231–239https://doi.org/10.1145/3512353.3512387Online:14 March 2022Publication History 0citation27DownloadsMetricsTotal Citations0Total Downloads27Last 12 Months27Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Bound on local minimum-error discrimination of bipartite quantum states

We consider the optimal discrimination of bipartite quantum states and provide an upper bound for the maximum success probability of optimal local discrimination. We also provide a necessary and sufficient condition for a measurement to realize the upper bound. We further establish a necessary and sufficient condition for this upper bound to be saturated. Finally, we illustrate our results using an example.
Quantum Signatures in Quench from Chaos to Superradiance

The driven-dissipative Dicke model features normal, superradiant, and lasing steady states that may be regular or chaotic. We report quantum signatures of chaos in a quench protocol from the lasing states. Within the framework of a classical mean-field perspective, once quenched, the system relaxes either to the normal or to the superradiant state. Quench from chaos, unlike quench from a regular lasing state, exhibits erratic dependence on control parameters. In the quantum domain, this sensitivity implies an effect that is similar to universal conductance fluctuations.
Performance and quality assurance of medical image using hybrid thresholding wavelet transform with Wiener filter

Medical images like X-ray, computed tomography, ultrasound, and magnetic resonance imaging (MRI) are produced using different techniques; during this process, noise is added that decreases the image quality and image analysis. Image denoising is an important task in image processing; use of wavelet transform improves the quality of an image and reduces noise level. We propose in this research, a denoising approach basing on discrete wavelet transform (DWT) using Hybrid Thresholding (bayesShrink) with Wiener filter technique for enhancing the quality image. This technique improved a better balance between smoothness and accuracy than the traditional wavelet DWT and are less redundant than stationary wavelet transform (SWT). In addition, the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) were used to analyse the denoised images quality.
Smart Contracts: A Way to Modern Digital World

This chapter presents an introduction that covers the background of the study includes traditional contract system and its limitations. In the past five four years, a large number of businesses, organizations, governments, and communities have been paying much attention to blockchain technology, as it opens up a new model of the legal contract known as smart contract. The smart contract is one of the applications of blockchain technology next to bitcoin, which can find a wide spectrum of potential applications in the digital economy including financial, insurance, health care, supply chain, trade, commerce, retail, and Internet of Things that transforms the world into a modern digital world. Blockchain technology enables us to execute, maintain, and monitor the processes of the contract system digitally from the beginning to the closure. Further, explained the importance of smart contracts in the modern digital world. Besides, briefly explain what a smart contract is and mention the elements of the smart contract. In addition, mention the evolution of various types of smart contract technology platforms. Moreover, present the life cycle of the smart contract and explain the functioning of each phase. In addition, briefly touch upon the major characteristics and benefits of a smart contract. Furthermore, mention three open challenges in developing smart contracts. Finally, discuss possible future trends and directions of smart contracts. Additional reading material is provided such as relevant books and papers that explore smart contracts in greater depth.
Adversarial Knowledge Distillation Based Biomedical Factoid Question Answering

Biomedical factoid question answering is an essential application for biomedical information sharing. Recently, neural network based approaches have shown remarkable performance for this task. However, due to the scarcity of annotated data which requires intensive knowledge of expertise, training a robust model on limited-scale biomedical datasets remains a challenge. Previous works solve this problem by introducing useful knowledge. It is found that the interaction between question and answer (QA-interaction) is also a kind of knowledge which could help extract answer accurately. This research develops a knowledge distillation framework for biomedical factoid question answering, in which a teacher model as the knowledge source of QA-interaction is designed to enhance the student model. In addition, to further alleviate the problem of limited-scale dataset, a novel adversarial knowledge distillation technique is proposed to robustly distill the knowledge from teacher model to student model by constructing perturbed examples as additional training data. By forcing the student model to mimic the predicted distributions of teacher model on both original examples and perturbed examples, the knowledge of QA-interaction can be learned by student model. We evaluate the proposed framework on the widely used BioASQ datasets, and experimental results have shown the proposed method's promising potential.
YOLO-based Tricycle Counting in Aid of Traffic Analysis

Traffic management is one of the challenging issues that need to be addressed by any urban area and the need to employ traffic measures is imperative. In any kind of traffic monitoring system, vehicle detection and counting are basic functionalities. In this paper, a YOLO-based model is utilized in the counting of tricycles, which is a unique kind of public transportation in the city of Tagbilaran, located in the province of Bohol, Philippines. The said model is used as the object detection model, which is integrated in the module that imports the Simple Online and Realtime Tracking (SORT) algorithm. Data collected are saved in .csv files which are then manually processed to generate a graph representation of the traffic status. Results in the tricycle count reveal that vehicles are minimal during mid-morning or late afternoon. Furthermore, the counting module was tested for performance where a value of 59.34 counting percentage was generated. This was based on the percentage of the automatic count compared to the manual count of the ground truth. A continuous enhancement in tracking the tricycles is recommended. The inclusion of various angles when counting vehicles going to different directions in the videos may generate a more practical data that will support any traffic reduction initiative. Likewise, traffic conditions such as weather and possible occlusions should be considered in the study for practical reasons.
Leveraging Google’s Publisher-Specific IDs to Detect Website Administration

Digital advertising is the most popular way for content monetization on the Internet. Publishers spawn new websites, and older ones change hands with the sole purpose of monetizing user traffic. In this ever-evolving ecosystem, it is challenging to effectively answer questions such as: Which entities monetize what websites? What categories of websites does an average entity typically monetize on and how diverse are these websites? How has this website administration ecosystem changed across time? In this paper, we propose a novel, graph-based methodology to detect administration of websites on the Web, by exploiting the ad-related publisher-specific IDs. We apply our methodology across the top 1 million websites and study the characteristics of the created graphs of website administration. Our findings show that approximately 90% of the websites are associated each with a single publisher, and that small publishers tend to manage less popular websites. We perform a historical analysis of up to 8 million websites, and find a new, constantly rising number of (intermediary) publishers that control and monetize traffic from hundreds of websites, seeking a share of the ad-market pie. We also observe that over time, websites tend to move from big to smaller administrators.
A Second Look at the ASCAD Databases

The ASCAD databases marked the starting point for a large amount of research regarding deep learning-based (SCA). While most work focuses on the analysis of different architectures, little attention has been paid to the datasets used for training and evaluation. In this paper, we provide a detailed analysis of the ASCAD datasets that examines all 16 bytes of the targeted AES implementation and reveals leakage from intermediate values of interest for attribution of Machine Learning (ML)-based SCA. We show that some bytes exhibit first-order or univariate second-order leakage that is unexpected for a protected implementation. Subsequently, we investigate how training on the fixed key we provide a detailed analysis of the ASCAD database is an easier task for (CNNs) based on two different hyperparameter architectures. Our findings suggest that results based on the we provide a detailed analysis of the ASCAD fix dataset should be revisited and that the more recent ASCAD variable dataset with variable key training should be used in future work. Finally, we investigate the attack success for all bytes. Performance differences with the same network architecture for different bytes highlight that even traces of identical operations on the same dataset pose challenges to CNNs. This highlights the possibility to use different bytes of the ASCAD dataset in order to evaluate the robustness of ML approaches in future work.
Knowledge Augmented Bert Mutual Network in Multi-Turn Spoken Dialogues

Modern spoken language understanding (SLU) systems rely on sophisticated semantic notions revealed in single utterances to detect intents and slots. However, they lack the capability of modeling multi-turn dynamics within a dialogue particularly in long-term slot contexts. Without external knowledge, depending on limited linguistic legitimacy within a word sequence may overlook deep semantic information across dialogue turns. In this paper, we propose to equip a BERT-based joint model with a knowledge attention module to mutually leverage dialogue contexts between two SLU tasks. A gating mechanism is further utilized to filter out irrelevant knowledge triples and to circumvent distracting comprehension. Experimental results in two complicated multi-turn dialogue datasets have demonstrate by mutually modeling two SLU tasks with filtered knowledge and dialogue contexts, our approach has considerable improvements compared with several competitive baselines.
Speeding up quantum adiabatic processes with a dynamical quantum geometric tensor

For adiabatic controls of quantum systems, the nonadiabatic transitions are reduced by increasing the operation time of processes. Perfect quantum adiabaticity usually requires the infinitely slow variation of control parameters. In this paper, we propose the dynamical quantum geometric tensor, as a metric in the control parameter space, to speed up quantum adiabatic processes and reach quantum adiabaticity in relatively short time. The optimal protocol to reach quantum adiabaticity is to vary the control parameter with a constant velocity along the geodesic path according to the metric. For the system initiated from the $n\mathrm{th}$ eigenstate, the transition probability in the optimal protocol is bounded by ${P}_{n}(t)\ensuremath{\le}4{\mathcal{L}}_{n}^{2}/{\ensuremath{\tau}}^{2}$ with the operation time $\ensuremath{\tau}$ and the quantum adiabatic length ${\mathcal{L}}_{n}$ induced by the metric. Our optimization strategy is illustrated via two explicit models: the Landau-Zener model and the one-dimensional transverse Ising model.
Application of Machine Learning to Badminton Action Decomposition Teaching

The study was aimed at realizing the identification of athletes’ actions in badminton teaching. The teaching process is segmented into many independent actions to help learners standardize their movements in badminton play, improving the national physical quality. First, the principle and advantages of machine vision sensing are introduced. Second, the images and videos about the action decomposition of badminton teaching are collected, and the image data are extracted by Haar-like. Subsequently, badminton players’ actions are recognized and preprocessed, and a dataset is constructed. Furthermore, a new algorithm model is implemented and trained by using Haar-like and Adaptive Boosting (AdaBoost). Finally, the badminton players’ action recognition algorithm is tested and compared with the traditional hidden Markov model (HMM) and support vector machine (SVM). The results show that action images improved by machine vision can process the captured actions effectively, making the computer better identify different badminton teaching actions. The proposed method has a recognition rate of more than 90% for each action, the average recognition accuracy of actions reaches 95%, the average recognition rate of the same person’s actions is 96.5%, and the average recognition rate of different people’s actions is 94.8%. The badminton teaching action recognition model based on Haar-like and AdaBoost can recognize and classify badminton actions and improve the quality of badminton teaching. This study shows that the image processing technology can effectively process the players’ static images, which gives the direction for physical education (PE) under artificial intelligence (AI).
Taming Cyclops: Mixed Reality Head-Mounted Displays as Laser Safety Goggles for Advanced Optics Laboratories

In this poster paper, we present a mixed reality application for laser eye protection based on a video see-through head-mounted display. With our setup, laser lab users perceive the real environment through the head-mounted display, using it as a substitute for laser safety goggles required by health and safety regulations. We designed and evaluated our prototype with a human-centered computing approach at the Deutsches Elektronen-Synchrotron where there exists some of the most advanced and extreme optics laboratory working conditions. We demonstrated that virtual reality headsets can be an attractive future alternative to conventional laser safety goggles.
Quantum Cloning of Steering

Quantum steering in a global state allows an observer to remotely steer a subsystem into different ensembles by performing different local measurements on the other part. We show that, in general, this property cannot be perfectly cloned by any joint operation between a steered subsystem and a third system. Perfect cloning is viable if and only if the initial state is of zero discord. We also investigate the process of cloning the steered qubit of a Bell state using a universal cloning machine. Einstein–Podolsky–Rosen (EPR) steering, which is a type of quantum correlation existing in the states without a local-hidden-state model, is observed in the two copy subsystems. This contradicts the conclusion of no-cloning of quantum steering (EPR steering) [C. Y. Chiu et al. , npj Quantum Inf. 2 , 16020 (2016)] based on a mutual information criterion for EPR steering.
Context-Unsupervised Adversarial Network for Video Sensors

Foreground object segmentation is a crucial first step for surveillance systems based on networks of video sensors. This problem in the context of dynamic scenes has been widely explored in the last two decades, but it still has open research questions due to challenges such as strong shadows, background clutter and illumination changes. After years of solid work based on statistical background pixel modeling, most current proposals use convolutional neural networks (CNNs) either to model the background or to make the foreground/background decision. Although these new techniques achieve outstanding results, they usually require specific training for each scene, which is unfeasible if we aim at designing software for embedded video systems and smart cameras. Our approach to the problem does not require specific context or scene training, and thus no manual labeling. We propose a network for a refinement step on top of conventional state-of-the-art background subtraction systems. By using a statistical technique to produce a rough mask, we do not need to train the network for each scene. The proposed method can take advantage of the specificity of the classic techniques, while obtaining the highly accurate segmentation that a deep learning system provides. We also show the advantage of using an adversarial network to improve the generalization ability of the network and produce more consistent results than an equivalent non-adversarial network. The results provided were obtained by training the network on a common database, without fine-tuning for specific scenes. Experiments on the unseen part of the CDNet database provided 0.82 a F-score, and 0.87 was achieved for LASIESTA databases, which is a database unrelated to the training one. On this last database, the results outperformed by 8.75% those available in the official table. The results achieved for CDNet are well above those of the methods not based on CNNs, and according to the literature, among the best for the context-unsupervised CNNs systems.
Structural physical approximation of partial transposition makes possible to distinguish SLOCC inequivalent classes of three-qubit system

Detection and classification of entanglement properties of a multi-qubit system is a topic of great interest. This topic has been studied extensively and thus we found different approaches for the detection and classification of multi-qubit entangled states. We have applied partial transposition operation on one of the qubit of the three-qubit system and then studied the entanglement properties of the three-qubit system, which is under investigation. Since the partial transposition operation is not a quantum operation so we have approximated partial transposition operation in such a way so that it represent a completely positive map. The approximated partial transposition operation is also known as structural physical approximation of partial transposition (SPA-PT). We have studied in detail the application of SPA-PT on a three qubit system and provided explicitly the matrix elements of the density matrix describing SPA-PT of a three qubit system. Moreover, we propose criterion to classify all possible stochastic local operations and classical communication(SLOCC) inequivalent classes of a pure as well as mixed three qubit state through SPA-PT map, which makes our criterion experimentally realizable. We have illustrated our criterion for detection and classification of three-qubit entangled states by considering few examples.
Bayesian Optimization Using Hamiltonian Dynamics for Sparse Artificial Neural Networks

Artificial Neural Networks (ANN) are being widely used in supervised Machine Learning (ML) to analyse signals or images for many applications. Using a learning database, one of the main challenges is to optimize the network weights. This optimization step is generally performed using a gradient-based approach with a back-propagation strategy. For the sake of efficiency, regularization is generally used. When non-smooth regularizers are used especially to promote sparse networks, this optimization becomes challenging. Classical gradient-based optimizers cannot be used due to differentiability issues. In this paper, we propose an MCMC-based optimization scheme formulated in a Bayesian frame-work. Hamiltonian dynamics are used to design an efficient sampling scheme. Promising results show the usefulness of the proposed method to allow ANNs with low complexity levels reaching high accuracy rates.