Reviewerly/exHarmony huggingface dataset:
Two-mode Gaussian states as resource of secure quantum teleportation in open systems

For secure quantum teleportation (SQT) of coherent states two conditions are necessary to be fulfilled: Gaussian-state resources with two-way steering and teleportation fidelity higher than 2/3. We investigate and compare squeezed thermal states and squeezed vacuum states as initial resource states for SQT in an open quantum system, consisting of two uncoupled harmonic oscillators interacting with a thermal environment. The evolution of the open system is obtained in terms of the covariance matrix, by using the Gorini-Kossakowski-Lindblad-Sudarshan master equation. The SQT conditions are satisfied in a longer period of time in the case of initial squeezed vacuum states, therefore these states are better resource states for SQT than squeezed thermal states. We show that the admissible time for SQT decreases by increasing temperature, dissipation coefficient and average number of thermal photons, while for greater values of the squeezing parameter, SQT conditions are satisfied in a longer period of time.
Assessing Teacher’s Discourse Effect on Students’ Learning: A Keyword Centrality Approach

The way that content-related keywords co-occur along a lesson seems to play an important role in concept understanding and, therefore, in students’ performance. Thus, network-like structures have been used to represent and summarize conceptual knowledge, particularly in science areas. Previous work has automated the process of producing concept networks, computed different properties of these networks, and studied the correlation of these properties with students’ achievement. This work presents an automated analysis of teachers’ concept graphs, the distribution of relevance amongst content-related keywords and how this affects students’ achievement. Particularly, we automatically extracted concept networks from transcriptions of 25 physics classes with 327 students and compute three centrality measures (CMs): PageRank, Diffusion centrality, and Katz centrality. Next, we study the relation between CMs and students’ performance using multilevel analysis. Results show that PageRank and Katz centrality significantly explain around 75% of the variance between different classes. Furthermore, the overall explained variance increased from 16% to 22% when including keyword centralities of teacher’s discourse as class-level variables. This paper shows a useful, low-cost tool for teachers to analyze and learn about how they orchestrate content-related keywords along with their speech.
An Improved Adaptive Genetic Algorithm for Mobile Robot Path Planning Analogous to the Ordered Clustered TSP

The material transportation planning with a mobile robot can be regarded as the ordered clustered traveling salesman problem. To solve such problems with different priorities at stations, an improved adaptive genetic simulated annealing algorithm is proposed. Firstly, the priority matrix is defined according to station priorities. Based on standard genetic algorithm, the generating strategy of the initial population is improved to prevent the emergence of non-feasible solutions, and an improved adaptive operator is introduced to improve the population ability for escaping local optimal solutions and avoid premature phenomena. Moreover, to speed up the convergence of the proposed algorithm, the simulated annealing strategy is utilized in mutation operations. The experimental results indicate that the proposed algorithm has the characteristics of strong ability to avoid local optima and the faster convergence speed.
Artificial Intelligence and the Copyright Survey

Artificial intelligence has emerged as a key contributor to American social, economic, and cultural development. Intelligent software increasingly plays a greater role in every creative industry. These industries rely on intellectual property protections to maintain equilibrium between productivity, remuneration, and competitiveness. American policymakers, however, have paid little attention to the intersection of artificial intelligence and copyright protection. This study collects data from fifty-seven AI scientists, tech policy experts, and copyright scholars through a survey and questionnaire. The data shows that while intelligent software is an important contributor to American cultural development, half of respondents believe that the US Copyright Office is not prepared to deal with an influx of computer-generated works. In light of rapid developments in artificial intelligence, this could present a serious challenge to the American copyright system and future advancements in the AI industry.
AbhAS: A Novel Realistic Image Splicing Forensics Dataset

This paper proposes a realistic image splicing dataset named AbhAS for evaluating various image forensic algorithms. We evaluate the performance of our proposed AbhAS dataset against existing benchmark datasets by extracting high-energy coefficients from images belonging to each dataset with the application of Kekre and discrete cosine transforms (DCT). Thus, we obtain feature sets of sizes 12, 24, and 48 respectively which are passed through various machine learning classifiers. RandomForest (with DCT) and Bagging (with Kekre transform) provide the highest detection accuracy. We believe this dataset could add value to the existing work in the area of image forensics.
Incomplete Information Management Using an Improved Belief Entropy in Dempster-Shafer Evidence Theory

Quantifying uncertainty is a hot topic for uncertain information processing in the framework of evidence theory, but there is limited research on belief entropy in the open world assumption. In this paper, an uncertainty measurement method that is based on Deng entropy, named Open Deng entropy (ODE), is proposed. In the open world assumption, the frame of discernment (FOD) may be incomplete, and ODE can reasonably and effectively quantify uncertain incomplete information. On the basis of Deng entropy, the ODE adopts the mass value of the empty set, the cardinality of FOD, and the natural constant e to construct a new uncertainty factor for modeling the uncertainty in the FOD. Numerical example shows that, in the closed world assumption, ODE can be degenerated to Deng entropy. An ODE-based information fusion method for sensor data fusion is proposed in uncertain environments. By applying it to the sensor data fusion experiment, the rationality and effectiveness of ODE and its application in uncertain information fusion are verified.
The patterns of user experience for sticky-note diagrams in software requirements workshops

Software requirements elicitation workshops often make use of a widely used and, on the surface, highly informal, notational medium: the sticky note. Often, in such workshops, facilitators and participants collaborate to construct “diagrams” made up of sticky notes. Methodologies have been developed that prescribe the way that diagram construction can uncover and document software requirements. Because they constitute a collaborative diagrammatic notation, such sticky-note diagrams lend themselves to an evaluation underpinned by theories of visual notation. In this paper we apply an analytic framework, the Patterns of User Experience (PUX), to sticky-note diagrams from two workshop methodologies, “Big Picture Event Storming” (BPES) by Alberto Brandolini and “User Story Mapping” (USM) by Jeff Patton. Our evaluation considers the sticky-note diagrams and the collaborative social processes surrounding them.
Mobile Networked Creativity: Developing a Theoretical Framework for Understanding Creativity as Survival

Abstract Mobile networked creativity is an emergent practice that arises from the ongoing relationships among people and people with technologies—or networked resources. In this article, we propose a concept of creativity as emerging from networked connections, (im)mobility, and situations of hardship. We, thus, make a connection between mobility and space as networked elements of creativity as opposed to individual agent models. We focus on how unplanned or emergent uses of digital technologies reveal how creative practices emerge, particularly in the context of mobile technology use where people are physically mobile and yet connected via the Internet. We define the concept of creativity as a constant process of becoming, a “recursive organization” that can be seen in groups such as migrants, or people living in disenfranchised communities that survive in make-shift locations such refugee camps or slums. Contrary to the affluent and capitalistic-embedded traditional ideas of creativity, mobile networked creativity is a practice that is found mostly in situations of economic hardship, power imbalances, and (im)mobilities.
Einstein-Podolsky-Rosen steering and nonlocality in quantum dot systems

We examine the relationship among the quantum steering, Bell nonlocality and entanglement of formation in a quantum dot system taking into account Coulomb-blocked and electron-electron interaction. We find that the steerability of the quantum dot system state is dependent on the main model parameters and the degradation of the quantum steering can be reduced by a suitable choice of these parameters. We show that the quantum dot system states with Bell nonlocality can violate the entropy uncertainty relation steering inequality, and that not every entangled quantum state of the dot system is steerable with violation of the Bell nonlocality. Moreover, we find that there is an optimal value of the temperature for which the quantum steering, nonlocality and entanglement are maximal. Finally, we display the monotonic dependence of these classes of quantum correlations on the main physical parameters.
Security onboarding

After being placed into a position, it is common for new employees to be acclimated to an organization's culture, rules, and procedures via a process called onboarding. These processes are critical to ensure that employees become valuable assets to an organization and abide by organizational rules and procedures.
Applying Touchscreen Based Navigation Techniques to Mobile Virtual Reality with Open Clip-On Lenses

Recently, a new breed of mobile virtual reality (dubbed as “EasyVR” in this work), has appeared in the form of conveniently clipping on a non-isolating magnifying lenses on the smartphone, still offering a reasonable level of immersion to using the isolated headset. Furthermore, such a form factor allows the fingers to touch the screen and select objects quite accurately, despite the finger(s) being seen unfocused over the lenses. Many navigation techniques have existed for both casual smartphone 3D applications using the touchscreen and immersive VR environments using the various controllers/sensors. However, no research has focused on the proper navigation interaction technique for a platform like EasyVR which necessitates the use of the touchscreen while holding the display device to the head and looking through the magnifying lenses. To design and propose the most fitting navigation method(s) with EasyVR, we mixed and matched the conventional touchscreen based and headset oriented navigation methods to come up with six viable navigation techniques—more specifically for selecting the travel direction and invoking the movement itself—including the use of head-rotation, on-screen keypads/buttons, one-touch teleport, drag-to-target, and finger gestures. These methods were experimentally compared for their basic usability and the level of immersion in navigating in 3D space with six degrees of freedom. The results provide a valuable guideline for designing/choosing the proper navigation method under different navigational needs of the given VR application.
Learning high-dimensional Gaussian linear structural equation models with heterogeneous error variances

A new approach is presented for learning high-dimensional Gaussian linear structural equation models from only observational data when unknown error variances are heterogeneous. The proposed method consists of three steps: inferring (1) the moralized graph using the inverse covariance matrix, (2) the ordering using conditional variances, and (3) the directed edges using conditional independence relationships. These three problems can be efficiently addressed using inversion of parts of the covariance matrix. It is proved that a sample size of n=Ω(dm2logp) is sufficient for the proposed algorithm to recover the true directed graph, where p is the number of nodes and dm is the maximum degree. It is also shown that the proposed algorithm requires O(p3+pdm4) operations in the worst-case, and hence, it is computationally feasible for recovering large-scale graphs. It is verified through simulations that the proposed algorithm is statistically consistent and computationally feasible in high-dimensional and large-scale graph settings, and performs well compared to the state-of-the-art structural learning algorithms. It is also demonstrated through protein signaling data that our algorithm is well-suited to the estimation of directed acyclic graphical models for multivariate data in comparison to other methods used for normally distributed data.
Applying Cluster-Based Zero-Shot Classifier to Data Imbalance Problems

Imbalanced data classification is an important issue in machine learning. To solve the data imbalance problem, rebalance algorithms are utilized. However, the rebalance algorithm has a lot of problems. Hence, the specific classification algorithm without a rebalancing algorithm is required. In this paper, we apply the zero-shot classifier to imbalance problem. The zero-shot classifier works despite the number of minority class is zero. Hence, the zero-shot classifier should also work in weaker data imbalance problems. We utilize the cluster-based zero-shot learning algorithm to do imbalanced data classification. The proposed method is evaluated using an imbalanced-learn dataset and compared with Decision Tree, K-Nearest Neighbor, and Over-sampling methods.
A Digital Random Number Generator Based on Chaotic Sampling of Regular Waveform

True Random Number Generators (TRNGs) are an integral part of any application that involves information security. In this paper, a new digital TRNG is proposed where a Digital Nonlinear Oscillator (DNO) is used with the chaotic sampling of regular waveform method. While the DNO circuit harvests enough entropy using considerably fewer resources compared to conventional digital TRNG structures, the chaotic sampling of regular waveform method removes the need for post-processing, increasing the quality of the generated random data. The designed circuit is implemented on a Zedboard Zynq development kit and tested using the NIST 800-22 test suite. Test results show that the proposed TRNG successfully generates random bits with only 6 LUTs, 14 flip-flops, and with no postprocessing.
Attack and Defense Strategies in Cyber War Involving Production and Stockpiling of Zero-Day Cyber Exploits

Abstract Two players strike balances between allocating resources for defense and production of zero-day exploits. Production is further allocated into cyberattack or stockpiling. Applying the Cobb Douglas expected utility function for equivalent players, an analytical solution is determined where each player’s expected utility is inverse U shaped in each player’s unit defense cost. More generally, simulations illustrate the impact of varying nine parameter values relative to a benchmark. Increasing a player’s unit costs of defense or development of zero-days benefits the opposing player. Increasing the contest intensities over the two players’ assets causes the players to increase their efforts until their resources are fully exploited and they receive zero expected utility. Decreasing the Cobb Douglas output elasticity for a player’s stockpiling of zero-days causes its attack to increase and its expected utility to eventually reach a maximum, while the opposing player’s expected utility reaches a minimum. Altering the Cobb Douglas output elasticities for a player’s attack or defense contests towards their maxima or minima causes maximum expected utility for both players.
Design and analysis of a potential-based controller for safe robot navigation in unknown GPS-denied environments with strictly convex obstacles

In this paper, we propose an obstacle avoidance controller for a disk-shaped holonomic robot with double-integrator dynamics and local sensing. The control objective is for the robot to converge to a target velocity while avoiding collisions with strictly convex obstacles in an unbounded environment. We assume that the robot has no information about the location and geometry of the obstacles, has no localization capabilities, and can only measure its own velocity and its relative position vector to the closest point on any obstacles in its sensing range. We first propose a potential-based controller for the case with a single obstacle, and we prove that the robot safely navigates past the obstacle and attains the desired velocity. For the case with multiple obstacles, we propose a switching control scheme in which the robot applies the single-obstacle controller for the closest obstacle at each instant. We investigate the correctness of this switching control law and demonstrate the absence of local stable equilibrium points that would trap the robot. We validate our analytical results through simulations of a robot that uses the proposed controllers to successfully navigate through an environment with strictly convex obstacles of various shapes and sizes.
Stepping ahead based hybridization of meta-heuristic model for solving Global Optimization Problems

Intelligent optimization algorithms based on swarm principles have been widely researched in recent times. The Firefly Algorithm (FA) is an intelligent swarm algorithm for global optimization problems. In literature, FA has been seen as one of the efficient and robust optimization algorithm. However, the solution search space used in FA is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although, there are a lot of modifications and hybridizations of FA with other optimizing algorithms, there is still a room for improvement. Therefore, in this paper, we first propose modification of FA by introducing a stepping ahead parameter. Second, we design a hybrid of modified FA with Covariance Matrix Adaptation Evolution Strategy (CMAES) to improve the exploitation while containing good exploration. Traditionally, hybridization meant to combine two algorithms together in terms of structure only, and preference was not taken into account. To solve this issue, preference in terms of user and problem (time complexity) is taken where CMAES is used within FA's loop to avoid extra computation time. This way, the structure of algorithm together with the strength of the individual solution are used. In this paper, FA is modified first and later combined with CMAES to solve selected global optimization benchmark problems. The effectiveness of the new hybridization is shown with the performance analysis.
Multifactorial Genetic Fuzzy Data Mining for Building Membership Functions

Association mining is a famous data mining technology because its form is explainable by human beings. Innovating fuzzy set theory to associations mining provides a solution to quantitative database, where membership function plays an important role in mining fuzzy associations. Genetic algorithm (GA) has been successfully applied to the optimization of membership functions. Based on the spirit of divide-and-conquer, GA optimizes the membership functions for each item separately. Nevertheless, the cooperation among different items in the course of evolution was never considered. Evolutionary multitasking optimization (EMO) is an emerging searching paradigm which dedicates to solving multiple tasks simultaneously for improving the search efficiency. This study introduces the EMO into genetic fuzzy data mining to address the above issue. Specifically, this study incorporates a state-of the-art genetic fuzzy data mining method, the structure-based representation genetic algorithm, with the well-known multifactorial evolutionary algorithm (MFEA). A series of experiments is conducted to validate the effectiveness and efficiency of the proposed method. The results indicate that the proposed method improves the structure-based representation genetic algorithm in terms of convergence speed and solution quality on all sizes of datasets. The results also show that the proposed method is about 20 times faster than the structure-based representation genetic algorithm with respect to the exploited number of evaluations.
A survey and research challenges of anti-forensics: Evaluation of game-theoretic models in simulation of forensic agents’ behaviour

Digital forensic investigators’ aim is identifying, collecting and presenting reliable, accurate, and admissible evidence in court. However, anti-forensics manipulate, obfuscate, hide, and remove the remaining piece of evidence in a compromised system. Anti-forensics interrupt investigation procedures; thus, the investigators require specific defensive strategies (counter-anti-forensics) against anti-forensics. This paper mounts a survey to explore existing anti-forensic research, and constitute a taxonomy on behaviour of anti-forensics and another taxonomy on further research tasks of anti-forensics. The knowledge of interactions between forensic agents' (an investigator and an attacker) in a forensic environment helps the investigator to evaluate the existing counter-anti-forensics, and enables him/her to design and develop more advanced counter-anti-forensics. Therefore, in this paper, first, we formulate a set of characteristics to model interactions between the attacker and the investigator (players) in a realistic forensic environment. Next, we propose a game-theoretic approach to model the players' interactions. The attacker uses anti-forensics (i.e. rootkits) and the investigator employs counter-anti-forensics (i.e. anti-rootkits). We select and evaluate a set of game-theoretic models and algorithms to simulate the players' interactions. Results of the evaluation show that a gradient play algorithm has satisfactory performance, among the selected game-theoretic models and algorithms to simulate the interactions in the forensic environment. The gradient play algorithm identifies the investigator's most stable and desired strategies after spending 10.0E-4 s and consuming 5.8 KB.
A Hybrid TLBO-Particle Filter Algorithm Applied to Remaining Useful Life Prediction in the Presence of Multiple Degradation Factors

One of the end goals of a Prognostic and Health Monitoring (PHM) algorithm is to provide accurate Remaining Useful Life (RUL) predictions for the monitored component or system. Most of the PHM algorithms found in the literature are based on the assumption that the degradation process is governed by only one degradation factor. However, some components and systems may be subject to multiple degradation factors. In this paper, we propose a hybrid algorithm that incorporates a Teaching-Learning Based optimization (TLBO) step into a Particle Filter (PF) framework. PF is an algorithm that can handle multiple degradation factors. However, it has some drawbacks such as sample degeneracy and sample impoverishment. The hybrid TLBO-PF algorithm proposed in this paper improves the performance of the standard PF algorithm by reducing the effects of sample degeneracy and sample impoverishment. A case study is presented to evaluate the performance of the proposed algorithm for estimating the degradation factors and predicting the RUL of a Lithium-ion battery, which is affected by two degradation factors. The results show that the proposed algorithm presented a better performance for both the tasks (degradation factor estimation and RUL prediction) when compared with the standard Particle Filter algorithm.