Reviewerly/exHarmony huggingface dataset:
Deterministic quantum cyclic controlled teleportation of arbitrary multi-qubit states using multi-qubit partially entangled channel

In this paper, we present a deterministic four-party quantum cyclic controlled teleportation (QCYCT) scheme, by using a multi-qubit partially entangled state as the quantum channel. In this scheme, Alice can teleport an arbitrary [Formula: see text]-qubit state to Bob, Bob can teleport an arbitrary [Formula: see text]-qubit state to Charlie and Charlie can teleport an arbitrary [Formula: see text]-qubit state to Alice under the control of the supervisor David. We utilize rotation gate, Hadamard gates and controlled-NOT (CNOT) gates to construct the multi-qubit partially entangled channel. Only Bell-state measurements, single-qubit von-Neumann measurement and proper unitary operations are required in this scheme, which can be realized in practice easily based on the present quantum experiment technologies. The direction of cyclic controlled teleportation of arbitrary multi-qubit states can also be changed by altering the quantum channel. Analysis demonstrates that the success probability of the proposed scheme can still reach 100% although the quantum channel is non-maximally entangled. Furthermore, the proposed four-party scheme can be generalized into the case involving [Formula: see text] correspondents, which is more suitable for quantum communication networks. We also calculate the intrinsic efficiency and discuss the security of the proposed scheme. Compared with the existing QCYCT schemes which realized cyclic controlled teleportation of arbitrary single-qubit states, specific two-qubit and three-qubit states, the proposed scheme is of general significance.
Agent Programmability Enhancement for Rambling over a Scientific Dataset

Agent-based modeling (ABM), while originally intended for micro-simulation of individual entities, (i.e., agents), has been adopted to operations research as biologically-inspired algorithms including ant colonial optimization and grasshopper optimization algorithm. Observing their successful use in traveling salesman problem and K-means clustering, we promote this trend in ABM to distributed data analysis. Our approach is to populate reactive agents on a distributed, structured dataset and to have them discover the dataset's attributes (e.g., the shortest routes and the best cluster centroids) through agent migration and interaction. We implemented this agent-based approach with the multi-agent spatial simulation (MASS) library and identified programming features for agents to best achieve data discovery. Of importance is ease of describing when and how to have agents traverse a graph, ramble over an array, and share the on-going computational states. We have responded to this question with two agent-descriptivity enhancements: (1) event-driven agent behavioral execution and (2) direct inter-agent broadcast. The former automatically schedules agent actions before and after agent migration, whereas the latter informs all agents of up-to-date global information, (e.g., the best slate of centroids so far). This paper presents our design, implementation, and evaluation of these two agent descriptivity enhancements.
Various Approaches for Video Inpainting: A Survey

In digital era, Inpainting has wide scope in terms of video restoration. It is also known as video completion where the missing or lost region is filled with its surrounding region restored by applying some sophisticated inpainting algorithms. Inpainting can be done for many applications such as film and entertainment production, removing scratches or dust, creating video synopsis etc. Removal of object from image or video is one of the applications which can be possible by image inpainting or video inpainting techniques. Video inpainting is extension of Image Inpainting. This paper describes the overview of video inpainting for object removal. Also this paper shows dataset, methods used by various authors for video inpainting with its advantages and limitations.
One Realization of the Generalized (L, G)-Codes

In this work we consider one possible approach of realization of the generalized (L, G)-codes with a separable polynomial G(x) and rational functions with a denominator degree greater than 1 as the locators set L.
Modeling the Effect of Spending on Cyber Security by Using Surplus Process

In this paper, we assume the security level of a system is a quantifiable metric and apply the insurance company ruin theory in assessing the defense failure frequencies. The current security level of an information system can be viewed as the initial insurer surplus; defense investment can be viewed as premium income resulting in an increase in the security level; cyberattack arrivals follow a Poisson process, and the impact of attacks is modeled as losses on the security level. The occurrence of cyber breach is modeled as a ruin event. We use this framework to determine optimal investment in cyber security that minimizes the total cyber costs. We show by numerical examples that there is an optimal allocation of total cyber security budget to (1) IT security maintenance/upkeep spending versus (2) external cyber risk transfer.
An Introduction to Quantum Machine Learning Algorithms

Machine learning, as a collection of powerful data analytical methods, is widely used in classification, face recognition, nature language processing, etc. However, the efficiency of machine learning algorithms is seriously challenged by big data. Fortunately, it is found that quantum mechanics properties can help overcome this problem. In this paper, we introduce typical ideas and methods of quantum machine learning to show how quantum algorithms improve the performance of machine learning process. These quantum machine learning methods can generally be divided into four categories: the efficient calculation methods of classical distances on a quantum computer, the construction of quantum models, the reformulation of traditional machine learning by a quantum system, and quantum dimensionality reduction algorithms. Finally, the challenges and opportunities of quantum machine learning are discussed.
System approach to evaluation of tourism cluster companies’ environment and performance

The survey is aimed to justify system approach to assess companies’ interaction and environment in tourism clusters. Intangibility of tourism product is determined by the specifics of economic entity and territorial authorities’ interaction, that makes it a prerequisite to use system approach for companies’ interaction within regional tourism cluster. The paper identifies institutional environment of business development in tourism cluster taking into accountthe rapid development of communication and information technologies. To achieve this goal the authors analysed the theoretical frameworks of institutional environment of tourism and the process of its formation and factors. The proposed technique is based on system approach and allows to indicate the deficiency in tourism cluster models, taking such critical issues into account as interconnection between cluster actors, regional competition policy, investment climate, development of standards and systems for the services certification, availability of qualified personnel in the region’s labour market, tourism infrastructure, financial and tax policies in the region, network of competitive local suppliers, public policies on tourism, tourism support programs and communication problems. As a result, the rationale of using system approach to evaluate effectiveness of subjects’ interaction in tourism clusters to determine weaknesses in regional tourism clusters was formed
Effects of Part-of-Speech on Thai Sentence Classification to Wh-Question Categories using Machine Learning Approach

In the last decade, question classification is a strong signal for answer selection and help to find the structure of question sentences from sentences. For this paper, we evaluated the proposed pre-processing method for classifying the simple sentence to wh-question categories ("What", "When", "Who", "Where", and "How") on Thai texts by considering Part-of-Speech tagging (POS). The performances are evaluated using classification accuracy obtained from traditional classification models including Naïve Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and neural networks which employs Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). We compared traditional models and neural networks; the experimental results showed that the result of the neural networks models better than the traditional model. The accuracy of the proposed model using the LSTM model with pre-trained word embedding is improved with an average F1 of 79.60%.
Model-based optimization with concept drifts

Model-based Optimization (MBO) is a method to optimize expensive black-box functions that uses a surrogate to guide the search. We propose two practical approaches that allow MBO to optimize black-box functions where the relation between input and output changes over time, which are known as dynamic optimization problems (DOPs). The window approach trains the surrogate only on the most recent observations, and the time-as-covariate approach includes the time as an additional input variable in the surrogate, giving it the ability to learn the effect of the time on the outcomes. We focus on problems where the change happens systematically and label this systematic change concept drift. To benchmark our methods we define a set of benchmark functions built from established synthetic static functions that are extended with controlled drifts. We evaluate how the proposed approaches handle scenarios of no drift, sudden drift and incremental drift. The results show that both new methods improve the performance if a drift is present. For higher-dimensional multimodal problems the window approach works best and on lower-dimensional problems, where it is easier for the surrogate to capture the influence of the time, the time-as-covariate approach works better.
Advanced statistical analysis of empirical performance scaling

Theoretical running time complexity analysis is a widely adopted method for studying the scaling behaviour of algorithms. However, theoretical analysis remains intractable for many high-performance, heuristic algorithms. Recent advances in statistical methods for empirical running time scaling analysis have shown that many state-of-the-art algorithms can achieve significantly better scaling in practice than expected. However, current techniques have only been successfully applied to study algorithms on randomly generated instance sets, since they require instances that can be grouped into "bins", where each instance in a bin has the same size. In practice, real-world instance sets with this property are rarely available. We introduce a novel method that overcomes this limitation. We apply our method to a broad range of scenarios and demonstrate its effectiveness by revealing new insights into the scaling of several prominent algorithms; e.g., the SAT solver lingeling often appears to achieve sub-polynomial scaling on prominent bounded model checking instances, and the training times of scikit-learn's implementation of SVMs scale as a lower-degree polynomial than expected (≈ 1.51 instead of 2).
Internet Provider Service Value Delivery Index Problem: Case Study of the NetHost

Abstract Common problems in service delivery is might related to the issue of timing, marketing cost, user experience, generating lead, identifying prospect, forecasting demand, value proposition, security, privacy, identity management and many more. Unfortunately, company often decided on the wrong context and concept lead to misrepresentation and misinterpretation on the requirement for the service improvement. Thus, the direct intervention through certain criteria in the characteristics and attribute of service delivery should be conducted with judgment assessment. It is done in order to identify the target and examine the policy used to support the outcomes. Thus, this study explore the model used for particular company, which in this case NetHost to provide insights on how the assessment can be fruitful in the long term investment for intangible aspects of service delivery.
Identification of Tuberculosis (TB) Disease Based on Lung X-Rays using Extreme Learning Machine

Abstract Tuberculosis is a disease caused by a bacterium called Mycobacterium Tuberculosis . Most Tuberculosis bacteria not only attack the lungs, but also attack other organs. Tuberculosis disease can spread and contaminate through the salivary and bacterial when the sufferer coughs and sneezes. Thus, the contagion of tuberculosis can take place either through contact directly with the sufferer or through the air. The examination system of Tuberculosis is still done manually. Therefore, a computational method is necessary to examine and identify Tuberculosis suffered by the patient through X-ray images of human lungs. The method used in this research is the Extreme Learning Machine. Lung X-ray images are used as input for image processing. The steps taken before identification were image pre- processing and Thresholding. The external results of this research include HKI in the form of software (registered), international conference presenter (done). In addition, the results of this study can assist public in determining the types of tuberculosis suffered so that it will be more efficient in early treatment of the disease.
Protocol-Based Particle Filtering and Divergence Estimation

Network protocols are applied to reduce the amount of data which are transmitted simultaneously over the network. In this article, round-robin protocol (RRP) and try-once-discard protocol (TODP) are studied for state estimation over network where information of different sensor nodes are fused. Using these protocols, at each time sample, only one sensor node can send its information over the network and consequently the required bandwidth is significantly reduced. Particle filters (PF) are able to estimate states of generally any nonlinear and non-Gaussian systems. Therefore, in this article, the development of particle filtering in networked systems under RRP and TODP protocols is studied. Toward this goal, two sequential importance sampling resampling algorithms under RRP and TODP are proposed where their corresponding marginal posterior pdfs are approximated by sequentially computations of weights. For the case of RRP, likelihoods of previous measurements are included in the weight computations while for TODP case, only the latest likelihood appears. Moreover, the approximated marginal posterior pdfs under RRP and TODP are compared with the normal posterior pdf using Kullback-Leibler divergence measure. This measure computes the difference between two probability distributions. Finally, the efficiency of the proposed method is demonstrated for a networked interconnected four-tank system.
Teachers’ perceptions of their use of SPADA Brightspace: A professional education for in-service teacher context

Abstract The Directorate General of Learning and Student Affairs developed a program named professional education for in-service teachers. This program applies a hybrid learning model. The adoption of the hybrid learning model is considered appropriate in the industrial revolution 4.0 era. The teacher professional development program is no longer just a face-to-face course but also can be held in the online form. The online course has been performed using SPADA Brightspace, an e-learning system. This study aims to analyze teachers’ perceptions of using the SPADA Brightspace for online learning. A questionnaire survey technique was used for collecting data. The study results showed that the teachers have positive acceptance towards SPADA Brightspace. The teachers perceived the usefulness of SPADA Brightspace to support their online learning process in the in-service teacher professional development program (PPG DALJAB). The use of SPADA Brightspace increases the productivity of teachers because they can manage their time and control their own learning pace. The teachers are also easier to do knowledge sharing by active participation in online discussion forums.
Pattern-avoiding permutation powers

Recently, Bóna and Smith defined strong pattern avoidance, saying that a permutation π strongly avoids a pattern τ if π and π2 both avoid τ. They conjectured that for every positive integer k, there is a permutation in Sk3 that strongly avoids 123⋯(k+1). We use the Robinson–Schensted–Knuth correspondence to settle this conjecture, showing that the number of such permutations is at least kk3∕2+O(k3∕logk) and at most k2k3+O(k3∕logk). We enumerate 231-avoiding permutations of order 3, and we give two further enumerative results concerning strong pattern avoidance. We also consider permutations whose powers all avoid a pattern τ. Finally, we study subgroups of symmetric groups whose elements all avoid certain patterns. This leads to several new open problems connecting the group structures of symmetric groups with pattern avoidance.
A Gamified Solution to the Cold-Start Problem of Intelligent Tutoring System

Today's intelligent tutoring systems provide more intelligent but complex services to learners. These systems encounter two critical issues: 1) the initial lack of new learner's information for running complex services, and 2) new learners failing to actively interact with these complex services due to the initial unfamiliarity. We define such issues as the "cold-start" problem in today's intelligent tutoring systems, and propose a gamified solution to tackle it. By leveraging on the established MDA model, a three-layer framework with narrative elements and task-oriented elements is designed and implemented in an ITS for mathematics. The preliminary evaluations show its effectiveness, and the proposed solution is generally applicable to other ITS systems.
Decentring the Human in Digital Making - Towards Embodied Mattering

The key objective for this research is to understand how materials shape the outcome of craft practice to create embodied ways of making and being with technology, that supports our material existence as bodies in a more-than-human world. To achieve this, I will use a mixed-method approach consisting of ethnography, experimental lab work, as well as research-through design and making workshops. The first half of the PhD is concerned with understanding the relationship woodworkers and hand carvers have with their material as well as values connected with their work and craft process. An ethnography of woodcarvers lays the foundation for a lab experiment using a custom-built apparatus for augmenting hand carving experience. I will draw on insights gained in the first part of the PhD to develop novel processes for embodied making with machines using research-through-design methods.
Look ahead to improve QoE in DASH streaming

When a video is encoded with constant quality, the resulting bitstream will have variable bitrate due to the inherent nature of the video encoding process. This paper proposes a video Adaptive Bitrate Streaming (ABR) algorithm, called Look Ahead, which takes into account this bitrate variability in order to calculate, in real time, the appropriate quality level that minimizes the number of interruptions during the playback. The algorithm is based on the Dynamic Adaptive Streaming over HTTP (DASH) standard for on-demand video services. In fact, it has been implemented and integrated into ExoPlayer v2, the latest version of the library developed by Google to play DASH contents. The proposed algorithm is compared to the Müller and Segment Aware Rate Adaptation (SARA) algorithms as well as to the default ABR algorithm integrated into ExoPlayer. The comparison is carried out by using the most relevant parameters that affect the Quality of Experience (QoE) in video playback services, that is, number and duration of stalls, average quality of the video playback and number of representation switches. These parameters can be combined to define a QoE model. In this sense, this paper also proposes two new QoE models for the evaluation of ABR algorithms. One of them considers the bitrate of every segment of each representation, and the second is based on VMAF (Video Multimethod Assessment Fusion), a Video Quality Assessment (VQA) method developed by Netflix. The evaluations presented in the paper reflect: first, that Look Ahead outperforms the Müller, SARA and the ExoPlayer ABR algorithms in terms of number and duration of video playback stalls, with hardly decreasing the average video quality; and second, that the two QoE models proposed are more accurate than other similar models existing in the literature.
Geometric quantum discord of Heisenberg model with dissipative terms

Abstract In this paper, we study a system with two interacting qubits described by the Heisenberg model with dissipative terms, and analyze decay dynamics and the steady-state of geometric quantum discords. Our results indicate that we can ignore the interaction force in the z -direction and adjust the parameters to change the loss of quantum correlation with time when the initial state satisfies some conditions. Moreover, we show that after a long enough period of time, unlike other parameters, the energy and the intensity of the non-uniform magnetic field do not affect the steady-state.
Integrated navigation approaches of vehicle aided by the strapdown celestial angles

The integrated navigation method based on star sensor celestial angles (altitude angle and azimuth angle) is proposed to serve the need for rapidly responsive, reliable, and precise of a hypersonic vehicle under a sophisticated environment. An integrated navigation algorithm suitable for large azimuth misalignment is established under launching point inertial coordinate and local geographical coordinate system based on altitude angle and azimuth angles. Meanwhile, a Bayesian method for data dropouts aided by the strapdown celestial angles is presented for the rapid variability in the celestial star angle with active galaxies. A nonlinear Bayesian filter is applied to implement the simulation on account of the nonlinear feature of the state and measurement equations. The simulation results showed that the Bayesian method for integrated navigation data dropouts could be accomplished by altitude angle and azimuth angle aiding in both launching point inertial coordinate and local geographical coordinate systems, which converge to 1′ in 10 s. The method indicated that the integrated navigation significant errors derived from initial localization and initial attitude alignment could be modified by the strapdown inertial navigation system (SINS) supported by the star sensor’s celestial angle in the local geographic coordinate system in the early launch stage. For the seconds of the flight phase, the integrated navigation aided by celestial angles in the launching point inertial coordinate system was guaranteed for the feasibility and validity. During the flight, the feasibility and validity of integrated navigation were guaranteed aided by celestial angles in launching point inertial coordinate system.