Reviewerly/exHarmony huggingface dataset:
Quantum Pontryagin principle under continuous measurements

In this paper, we develop the theory of the quantum Pontryagin principle for continuous measurements and feedback. The analysis is carried out under the assumption of compatible events in the output channel. The plant is a quantum system, which generally is in a mixed state, coupled to a continuous measurement channel. The Pontryagin maximum principle is derived in both the Schrödinger picture and Heisenberg picture, in particular, in statistical moment coordinates. To avoid solving stochastic equations, we derive a linear-quadratic-Gaussian scheme, which is more suitable for control purposes. Finally, we use the quantum harmonic oscillator as a concrete example to illustrate the performance of the controller.
Predicting effective control parameters for differential evolution using cluster analysis of objective function features

A methodology is introduced which uses three simple objective function features to predict effective control parameters for differential evolution. This is achieved using cluster analysis techniques to classify objective functions using these features. Information on prior performance of various control parameters for each classification is then used to determine which control parameters to use in future optimisations. Our approach is compared to state-of-the-art adaptive and non-adaptive techniques. Two accepted bench mark suites are used to compare performance and in all cases we show that the improvement resulting from our approach is statistically significant. The majority of the computational effort of this methodology is performed off-line, however even when taking into account the additional on-line cost our approach outperforms other adaptive techniques. We also investigate the key tuning parameters of our methodology, such as number of clusters, which further support the finding that the simple features selected are predictors of effective control parameters. The findings presented in this paper are significant because they show that simple to calculate features of objective functions can help to select control parameters for optimisation algorithms. This can have an immediate positive impact on the application of these optimisation algorithms on real world problems, where it is often difficult to select effective control parameters.
Empirical assessment of the effects of cashless policy on financial inclusion in the Nigerian emerging economy

The present study examined the effects of cashless policy on financial inclusion in the Nigerian emerging economy.The necessary data were collected from the Central Bank of Nigeria Economic Reports as well as Statistical Bulletin and the ordinary least squares method was applied to analyze the data.The findings reveal that the cashless policy maintained a nonsignificant relationship with financial inclusion both in urban and rural areas of Nigeria.The findings show that the cashless policy had a significant effect on increasing customers' deposits in commercial banks of Nigeria.Volume of Automated Teller Machine (ATMVL) maintains a positive and significant effect on financial inclusion indicators in urban center compared with the rural areas.Volume of Point of Sales (POSVL) shows more significant and positive influence on financial inclusion in urban area than the rural area.However, Web Based Technique (WBTVL) reveals a non-significant effect on financial inclusion both in urban and rural centers.The study therefore recommends that more branches of commercial banks and ATM centers/outlets should be opened in rural urban centers with efficient network systems and security in order to enhance banking penetration and by extension effectiveness of the financial inclusion and poverty reductions.
Sketch Face Recognition: P-HOG Multi-Features Fusion

With the development of biometric recognition technology, sketch face recognition has been widely applied to assist the police to confirm the identity of the criminal suspect. Most of the present recognition methods use the image features directly, in which the key parts can’t be used sufficiently. This paper presents a sketch face recognition method based on P-HOG multi-features weighted fusion. Firstly, the global face image and the local face image which contains key components of the face are divided into patches based on spatial scale pyramid, and then the global P-HOG features and local P-HOG features are extracted, respectively. After that, the dimensions of global and local features are reduced using PCA and NLDA. Finally, the features are weighted based on sensitivity and fused. The nearest neighbor classifier is used to complete the final recognition. The experimental results on different databases show that the proposed method outperforms state-of-the-art methods.
Camera Selection in Far-Field Video Surveillance Networks

Since camera networks for surveillance are becoming extremely dense, finding the most informative and desirable views from different cameras are of increasing importance. In this paper, we propose a camera selection method to achieve the goal of providing the clearest visibility possible and selecting the cameras which exactly capture targets for the far-field surveillance. We design a benefit function that takes into account image visibility and the degree of target matching between different cameras. Here, visibility is defined using the entropy of intensity histogram distribution, and the target correspondence is based on activity features rather than photometric features. The proposed solution is tested in both artificial and real environments. A performance evaluation shows that our target correspondence method well suits far-field surveillance, and our proposed selection method is more effective at identifying the cameras that exactly capture the surveillance target than existing methods.
Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India

Climate change is the biggest challenge faced by the world. It has already started affecting the weather patterns leading to disruption of normal life. Detecting change helps to monitor and plan the Earth’s resources in an efficient manner. An important factor in climate change is the change in the green cover. Non-availability of standard datasets and limited labeled data points makes it difficult to attain high accuracies in change detection. In this paper, we have proposed a modified ensemble of extreme learning machines (mAEELM) for change detection in green cover to increase the accuracy of the change detection process. It uses Extreme Learning Machine (ELM) as the base classifier. Different ELMs are trained with different configuration so that they have different learning capabilities. These ELMs are combined to create an ensemble and it is then adapted based on the accuracy of the individual ELMs. The ensemble is then pruned to eliminate the ELMs which are not contributing towards the overall result of the ensemble, to make it more efficient. The proposed algorithm has been applied for detecting change on two areas of Gandhuan, Punjab and Chaparkaura Kham, Uttar Pradesh, India. The algorithm shows an average accuracy of 97.8% on both the datasets.
A Markov Prediction-Based Privacy Protection Scheme for Continuous Query

The query probability of a location which the user utilizes to request location-based service (LBS) can be used as background knowledge to infer the real location, and then the adversary may invade the privacy of this user. In order to cope with this type of attack, several algorithms had provided query probability anonymity for location privacy protection. However, these algorithms are all efficient just for snapshot query, and simply applying them in the continuous query may bring hazards. Especially that, continuous anonymous locations which provide query probability anonymity in continuous anonymity are incapable of being linked into anonymous trajectories, and then the adversary can identify the real trajectory as well as the real location of each query. In this paper, the query probability anonymity and anonymous locations linkable are considered simultaneously, then based on the Markov prediction, we provide an anonymous location prediction scheme. This scheme can cope with the shortage of the existing algorithms of query probability anonymity in continuous anonymity locations difficult to be linked, and provide query probability anonymity service for the whole process of continuous query, so this scheme can be used to resist the attack of both of statistical attack as well as the infer attack of the linkable. At last, in order to demonstrate the capability of privacy protection in continuous query and the efficiency of algorithm execution, this paper utilizes the security analysis and experimental evaluation to further confirm the performance, and then the process of mathematical proof as well as experimental results are shown.
Dynamical Properties of the Finite-size Dicke Model Coupled to a Thermal Reservoir

We investigate the dynamical properties of the finite-size Dicke model coupled to a photon reservoir in the dispersive regime. The system–reservoir coupling in our Hamiltonian includes counter-rotating terms, which are relevant in the strong atom–cavity coupling. Because the dispersive regime is considered, the dynamics of low-energy states are described sufficiently accurately within the finite-dimensional subspace of the dressed states. Using the separation of the time scales between the system and the reservoir, we derive the Markovian quantum master equation in the subspace without ignoring the counter-rotating terms. The temporal evolution of the expectation of the cavity mode shows that the bifurcation of the long-lived state corresponds to the superradiant transition in the isolated model. The master equation explicitly gives the steady state solution. The numerical results for the first-order correlation function on the steady state indicate that the strong atom–cavity coupling enhances the coherence and softens the dephasing in the superradiant region.
Necessary and sufficient condition of separability for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">D</mml:mi></mml:math> -symmetric diagonal states

For multipartite states, we consider a notion of $D$ symmetry. For a system of $N$ qubits, it coincides with the usual permutational symmetry. In the case of $N$ qudits ($d\ensuremath{\ge}3$), the $D$ symmetry is stronger than the permutational one. For the space of all $\mathrm{D}$-symmetric vectors in ${({\mathbb{C}}^{d})}^{\ensuremath{\bigotimes}N}$, we define a basis composed of vectors ${|{R}_{N,d;k}\ensuremath{\rangle}:\phantom{\rule{0.16em}{0ex}}0\ensuremath{\le}k\ensuremath{\le}N(d\ensuremath{-}1)}$ which are analogs of Dicke states. The aim of this paper is to discuss the problem of separability of $D$-symmetric states which are diagonal in the basis ${|{R}_{N,d;k}\ensuremath{\rangle}}$. We show that if $N$ is even and $d\ensuremath{\ge}2$ is arbitrary then a positive partial transposition property is a necessary and sufficient condition of separability for $D$-invariant diagonal states. In this way, we generalize results obtained by Yu [Phys. Rev. A 94, 060101(R) (2016)] and Wolfe and Yelin [Phys. Rev. Lett. 112, 140402 (2014)]. Our strategy is to use some classical mathematical results on a moment problem.
A HYBRID METAHEURISTIC NAVIGATION ALGORITHM FOR ROBOT PATH ROLLING PLANNING IN AN UNKNOWN ENVIRONMENT

In this paper, a new method for robot path rolling planning in a static and unknown environment based on grid modelling is proposed. In an unknown scene, a local navigation optimization path for the robot is generated intelligently by ant colony optimization (ACO) combined with the environment information of robot’s local view and target information. The robot plans a new navigation path dynamically after certain steps along the previous local navigation path, and always moves along the optimized navigation path which is dynamically modified. The robot will move forward to the target point directly along the local optimization path when the target is within the current view of the robot. This method presents a more intelligent sub-goal mapping method comparing to the traditional rolling window approach. Besides, the path that is part of the generated local path based on the ACO between the current position and the next position of the robot is further optimized using particle swarm optimization (PSO), which resulted in a hybrid metaheuristic algorithm that incorporates ACO and PSO. Simulation results show that the robot can reach the target grid along a global optimization path without collision.
Mobile Applications in Higher Education

In a previous study, a practicum-based approach to bridge the gap between industry expectations and Information Systems (IS) graduates' skills was discussed from the students' point of view. The practicum project was initiated to enable students to experience real work in the IS domain in accordance with their professional aspirations and their desired specialty. To complete the whole picture, in this article, we present the employers' point of view as regards to the benefits the students gained from the project and compare between the two points of view. Semi-structured questionnaire and in-depth interviews with employers who participated in the project in the last four years were conducted in order to reveal their perceptions. Many similarities exist between the employers' and the students' perceptions but also some differences stemming from different perspectives on the process were found. The employers' perspectives were discussed and also the differences between the students'; and the employers'; perceptions.
Monitoring Water Siltation Caused by Small-Scale Gold Mining in Amazonian Rivers Using Multi-Satellite Images

The small-scale mining techniques applied all over the Amazon river basin use water from streams, including digging and riverbed suctioning, rarely preventing environmental impacts or recovery of the impacted areas.As a consequence, thousands of tons of inorganic sediment (which can contain mercury) have been discharged directly into the rivers creating sediment plumes that travel hundreds of kilometers downstream with unknown consequences to the water quality and aquatic biota.We hypothesize that because of intensification of mining activities in the Brazilian Amazon, clear water rivers such as the Tapajós and Xingu rivers and its tributaries are becoming permanently turbid waters (so-called white waters in the Amazonian context).To investigate this hypothesis, satellite images have been used to monitor the sediment plume caused by gold mining in Amazonian rivers.Given the threat of intense water siltation of the Amazonian rivers combined with the technological capacity of detecting it from satellite images, the objective of this chapter is to inform the main activities carried out to develop a monitoring system for quantifying water siltation caused by small-scale gold mining (SSGM) in the Amazon rivers using multi-satellite data.
Classical Simulation of Quantum Circuits by Half Gauss Sums

We give an efficient algorithm to evaluate a certain class of exponential sums, namely the periodic, quadratic, multivariate half Gauss sums. We show that these exponential sums become $$\#{\mathsf {P}}$$ -hard to compute when we omit either the periodicity or quadraticity condition. We apply our results about these exponential sums to the classical simulation of quantum circuits, and give an alternative proof of the Gottesman–Knill theorem. We also explore a connection between these exponential sums and the Holant framework. In particular, we generalize the existing definition of affine signatures to arbitrary dimensions, and use our results about half Gauss sums to show that the Holant problem for the set of affine signatures is tractable.
Learning Diverse Bayesian Networks

Much effort has been directed at developing algorithms for learning optimal Bayesian network structures from data. When given limited or noisy data, however, the optimal Bayesian network often fails to capture the true underlying network structure. One can potentially address the problem by finding multiple most likely Bayesian networks (K-Best) in the hope that one of them recovers the true model. However, it is often the case that some of the best models come from the same peak(s) and are very similar to each other; so they tend to fail together. Moreover, many of these models are not even optimal respective to any causal ordering, thus unlikely to be useful. This paper proposes a novel method for finding a set of diverse top Bayesian networks, called modes, such that each network is guaranteed to be optimal in a local neighborhood. Such mode networks are expected to provide a much better coverage of the true model. Based on a globallocal theorem showing that a mode Bayesian network must be optimal in all local scopes, we introduce an A* search algorithm to efficiently find top M Bayesian networks which are highly probable and naturally diverse. Empirical evaluations show that our top mode models have much better diversity as well as accuracy in discovering true underlying models than those found by K-Best.
Design and Structural Analysis of the Sun Ray Double Axis Tracking Device

To improve the solar energy utilization in the photovoltaic power, the sun ray double axis tracking device is designed and its tracking method is introduced. Using the finite element method, the parameterized analysis model is built and the static calculation is performed in different conditions for the device. The design feasibility of the device is verified by evaluating the stress. The lightweight of the device is made on the premise that the design satisfies the intensity and this provides the basis for manufacturing the prototype. The sun's rays tracing experiment is carried out by the prototype. Results show that the device design is reasonable and meets the design requirements; the key working conditions for the solar tracking design are found; the lightweight is obvious and the weight is reduced by 14%; the average errors of azimuth angle and height angle are within 5 deg; compared with the fixed device, the increasing proportion of solar energy one day is up to 52.6%; and the device works steadily and has good mechanical properties.
Time evolution of entanglement and trace distance in an atom-cavity system described with random walk and non-random walk states

An atom-cavity system consists of an atom or group of atoms inside a cavity. When an atom in cavity is stimulated by a laser pump, it is affected by the atom-field interaction shows the qusi- random walk behavior. This can change the entanglement and trace distance of system. In this work, the change entanglement and trace distance of system in two different cases -namely- the random walk and non-random walk is considered. The descriptive system is a two-level atom in the electrodynamics cavity based on the Jayne’s-Cummings model, which is stimulated by two longitudinal and transverse laser pumps. The results show that the consideration of the random walk case for the atom changes in the amount of entanglement can be seen as the phenomenon of sudden death and birth of entanglement. It results in its rate of changes to be increased. In contrast to the non-random walk case, this rate is increased and decreased more quickly compared to the random walk case. The maximum amount of entanglement in each case is the same, but the minimum in random walk case is less than that of non-random walk case.
Multilevel Steganography for Data Protection

Nowadays, the world is changing very fast. In this world, there are many technologies that are being developed to protect the data from the outer world. Here the outer world is referred to a hacker or the third man who is in between one end user and the other end user. There is many software’s which protects and transfers the data from the one machine to another machine or from one user to the other user, as in mobile phones, laptop, IoT devices, cloud, etc. There are also many algorithms and techniques, which protect data from unauthorized accesses. One of the techniques is called stenography. Two levels of data protection are carried out in the current proposed model. One is image stenography and another one is video stenography. Here, the text is encrypted by using digital encryption system (DES). The encrypted data is embedded in the image by using the LSB technique. The stegano image is converted to the base 16 in text format and embedded on to the video by using the frame converting technique. This process is called the multilevel stenography where the data is processed by two stenography techniques, i.e., image stenography and video stenography. This increases the security of data and the data is easily transmitted from one machine to the other machine providing multilevel security.
Quantum Conditional Random Field

Conditional random field (CRF) is an important probabilistic machine learning model for labeling sequential data, which is widely utilized in natural language processing, bioinformatics and computer vision. However, training the CRF model is computationally intractable when large scale training samples are processed. Since little work has been done for labeling sequential data in the quantum settings, we in this paper construct a quantum CRF (QCRF) model by introducing well-defined Hamiltonians and measurements, and present a quantum algorithm to train this model. It is shown that the algorithm achieves an exponential speed-up over its classical counterpart. Furthermore, we also demonstrate that the QCRF model possesses higher Vapnik Chervonenkis dimension than the classical CRF model, which means QCRF is equipped with a higher learning ability.
Using single conspiracy number for long term position evaluation

Single Conspiracy Number (SCN) is a variant concept of conspiracy number and proof number which indicates the difficulty of a root node changing its MIN/MAX value to a certain score. It makes up the drawbacks of conspiracy number on computing complexity, and can be easily applied into different sea rch frameworks. This paper explores the potential usage of SCN as a long-term position evaluation to understand in-depth game progress patterns. Chinese Chess is chosen as a testbed for this study, whereas a strong AI engine ‘Xiangqi Wizard’ is used. It is implemented with alpha-beta search and modified to produce SCNs during the search process. Experiments are conducted on different types of positions including tactical positions, drawn positions and opening positions. The experimental results show that SCN is more consistent and accurate for long-term position evaluation than the conventional way using evaluation function values only, and using SCN together with evaluation function values enables us to better understand game progress patterns.
A NOVEL METHOD FOR STRATEGIC DECISION MAKING BASED ON PROJECT MANAGEMENT MATURITY

This paper presents a novel method to evaluate the maturity of projects and organizations in order to minimize the risks. It is assumed that the higher the project management maturity (PMM) level is, the higher the performance of projects is, but a high PMM level requires investment. So far, the maturity of the major stakeholders to ensure they have enough competence to perform successfully, project complexity and success have not been considered by maturity evaluation methods. in this paper we go a step forward and investigate the relationship between those elements. As a result of this research, we present a novel model to evaluate the maturity of the Client and the Contractor and the complexity of the projects.. We have applied our model in qualitative research studies and demonstrate that it is a concise and useful framework for companies that seek optimum maturity levels. Our method is useful when the trust among organizations is low, and it provides stronger position to organizations facing a business opportunity. Keywords: Project management maturity, project complexity, business strategy