Reviewerly/exHarmony huggingface dataset:
Adversarial concept drift detection under poisoning attacks for robust data stream mining

Continuous learning from streaming data is among the most challenging topics in the contemporary machine learning. In this domain, learning algorithms must not only be able to handle massive volume of rapidly arriving data, but also adapt themselves to potential emerging changes. The phenomenon of evolving nature of data streams is known as concept drift. While there is a plethora of methods designed for detecting its occurrence, all of them assume that the drift is connected with underlying changes in the source of data. However, one must consider the possibility of a malicious injection of false data that simulates a concept drift. This adversarial setting assumes a poisoning attack that may be conducted in order to damage the underlying classification system by forcing an adaptation to false data. Existing drift detectors are not capable of differentiating between real and adversarial concept drift. In this paper, we propose a framework for robust concept drift detection in the presence of adversarial and poisoning attacks. We introduce the taxonomy for two types of adversarial concept drifts, as well as a robust trainable drift detector. It is based on the augmented restricted Boltzmann machine with improved gradient computation and energy function. We also introduce Relative Loss of Robustness—a novel measure for evaluating the performance of concept drift detectors under poisoning attacks. Extensive computational experiments, conducted on both fully and sparsely labeled data streams, prove the high robustness and efficacy of the proposed drift detection framework in adversarial scenarios.
How efficient is transport of quantum cargo through multiple highways?

Quantum states can be efficiently transferred over a long distance if the entire quantum channel can be divided into several small blocks. We consider a scenario in which each block consists of two copies of a multiparty state -- one is used for distributing an arbitrary quantum state to multiple parties while the other channel is required to concentrate it back to a single party. Both in noiseless and local noisy scenarios, we find one-shot quantum capacities of these channels in terms of fidelity, when the initial shared states in each block are the generalized Greenberger-Horne-Zeilinger and the generalized W states. We also consider a situation where optimal local measurements transform multipartite states to bipartite ones which can then be used as single-path channels for quantum state transmission in each segment. We show that in some parameter ranges, the former protocol provides strictly better fidelities than that of the latter, thereby establishing the importance of distributing and concentrating arbitrary quantum states via multipartite entangled states in long distance quantum communication, over the local measurement based protocol. Moreover, we show that in presence of bit flip or bit-phase flip noise, shared generalized Greenberger-Horne-Zeilinger states possess an inherent noise detection and correction mechanism, leading to the same fidelity as in the noiseless case. We consider further noise models also, which do not enjoy the same mechanism. In addition to the fidelity based advantages, the multipath scheme is shown to be useful when one considers a situation in which the completion of the teleportation needs to be delayed. We also find the efficiencies of a quantum channel when a quantum state is transferred over long distances and the entire channel is divided into several small blocks.
A New Measure to Characterize the Degree of Self-Similarity of a Shape and Its Applicability

We propose a new measure (Γ) to quantify the degree of self-similarity of a shape using branch length similarity (BLS) entropy which is defined on a simple network consisting of a single node and its branches. To investigate the properties of this measure, we computed the Γ values for 70 object groups (20 shapes in each group) in the MPEG-7 shape database and performed grouping on the values. With relatively high Γ values, identical groups had visually similar shapes. On the other hand, the identical groups with low Γ values had visually different shapes. However, the aspect of topological similarity of the shapes also warrants consideration. The shapes of statistically different groups exhibited significant visual difference from each other. Also, in order to show that the Γ can have a wide variety of applicability when properly used with other variables, we showed that the finger gestures in the (Γ, Z) space are successfully classified. Here, the Z means a correlation coefficient value between entropy profiles for gesture shapes. As shown in the applications, Γ has a strong advantage over conventional geometric measures in that it captures the geometrical and topological properties of a shape together. If we could define the BLS entropy for color, Γ could be used to characterize images expressed in RGB. We briefly discussed the problems to be solved before the applicability of Γ can be expanded to various fields.
Three-Dimensional Thyroid Assessment from Untracked 2D Ultrasound Clips

The diagnostic quantification of thyroid gland, mostly based on its volume, is commonly done by ultrasound. Typically, three orthogonal length measurements on 2D images are used to estimate the thyroid volume from an ellipsoid approximation, which may vary substantially from its true shape. In this work, we propose a more accurate direct volume determination using 3D reconstructions from two freehand clips in transverse and sagittal directions. A deep learning based trajectory estimation on individual clips is followed by an image-based 3D model optimization of the overlapping transverse and sagittal image data. The image data and automatic thyroid segmentation are then reconstructed and compared in 3D space. The algorithm is tested on 200 pairs of sweeps, and shows that it can provide fully automated, but also more accurate and consistent volume estimations than the standard ellipsoid method, with a median volume error of \(11\%\).
Image Dehazing Method of Transmission Line for Unmanned Aerial Vehicle Inspection Based on Densely Connection Pyramid Network

The quality of the camera image directly determines the accuracy of the defect identification of the transmission line equipment. However, complex external factors such as haze can seriously affect the image quality of the aircraft. The traditional image dehazing methods are difficult to meet the needs of enhanced image inspection in complex environments. In this paper, the image enhancement technology in haze environment is studied, and an image dehazing method of transmission line based on densely connection pyramid network is proposed. The method uses an improved pyramid network for transmittance map calculation and uses an improved U-net network for atmospheric light value calculation. Then, the transmittance map, atmospheric light value, and dehazed image are jointly optimized to obtain image dehazing model. The method proposed in this paper can improve image brightness and contrast, increase image detail information, and can generate more realistic deblur images than traditional methods.
Full thermalization of a photonic qubit

The generalized amplitude damping (GAD) quantum channel implements the interaction between a qubit and an environment with arbitrary temperature and arbitrary interaction time. Here, we implement a photonic version of the GAD for the case of infinite interaction time (full thermalization). We also show that this quantum channel works as a thermal bath with controlled temperature.
Security evaluation of quantum key distribution with weak basis-choice flaws

Abstract Quantum key distribution (QKD) can share an unconditional secure key between two remote parties, but the deviation between theory and practice will break the security of the generated key. In this paper, we evaluate the security of QKD with weak basis-choice flaws, in which the random bits used by Alice and Bob are weakly controlled by Eve. Based on the definition of Li et al. (Sci Rep 5:16200, 2015) and GLLP’s analysis, we obtain a tight and analytical bound to estimate the phase error and key rate for both the single photon source and the weak coherent source. Our approach largely increases the key rate from that of the original approach. Finally, we investigate and confirm the security of BB84-QKD with a practical commercial devices.
A Secure Data Sharing Using IDSS CP-ABE in Cloud Storage

In day-to-day life, cloud computing plays an important role in storing and retrieving the huge volumes of data. Data’s which are taken from social media (Whatsapp, Facebook, Twitter etc.), Organizaitons (MNC companies), Business data (Sensex, Forbes Global, Forbes cloud, TS Historical bus cycles etc.,) and other datas from our day to day life records are not enough memory to store in Big Data. While the storage size is increasing, the security problem also increases simultaneously. Therefore, there is a security issue in cloud computing. There are large numbers of studies conducted to improve security in cloud computing. However, most of the survey is not applicable for cloud providing security in mobile devices because of less power consuming and computing resources. The issue in the existing mobile application is no proper security of data on cloud storage. In this paper, we propose a lightweight data sharing scheme for mobile cloud computing. It accesses a control from the ciphertext-policy attribute-based encryption technology which is used in the basic environment of cloud and makes some changes in the structure of the access control tree that is suitable for mobile cloud computing. This technology in the mobile cloud will remove the major portions in the access control tree, and it transforms by using the external proxy servers. Additionally, it is used to reduce the cost of user revocation. The experimental results of our LDSS system can efficiently reduce the mobile device overhead.
Security-aware dynamic VM consolidation

The explosive growth of cloud usage encourages several challenges, especially high energy consumption of Cloud Data Centers (CDCs), new security risks to Virtual Machines (VMs) resulting from co-residency with other risky VMs on the same Physical Machine (PM), and the Quality of Service (QoS) degradation due to sharing resources. Many recent studies have proposed Dynamic VM Consolidation (DVMC) to save energy with minimum degradation of the QoS. However, due to the lack of reliable security measurements and consolidating VMs without any awareness of their security risk degrees, the overall security risk of the CDC may be increased. To tackle these challenges, this study presents a Security-aware DVMC (SDVMC) that consists of a Security Monitoring Module (SMM) and a SDVMC module. The SMM utilizes a three-dimensional security assessment model, while in the SDVMC module we propose a novel VM placement algorithm called Minimum Risk Increase (MRI) with Risk Increase Threshold (RITH). The proposed MRI with RITH VM placement algorithm selects the host that leads to minimum risk increase to the overall security risk while maintaining the risk increase for each VM does not exceed the value of the proposed RITH constraint; which is set according to the aims of the cloud provider. Simulation results show that using our approach with RITH 0.8 results in security improvement, overall risk was decreased by 2% to 5%, without negative impact on energy consumption or QoS. Moreover, using our approach with RITH less than 0.8 enables the tradeoff between energy consumption and the overall security risk. The maximum overall risk decrease ranged from 10% up to 40%, according to the intensive of the communication overhead between the VMs, while the used energy in its maximum was less than half if we used a non-power-ware VM allocation policy.
On the convergence of projective-simulation–based reinforcement learning in Markov decision processes

Abstract In recent years, the interest in leveraging quantum effects for enhancing machine learning tasks has significantly increased. Many algorithms speeding up supervised and unsupervised learning were established. The first framework in which ways to exploit quantum resources specifically for the broader context of reinforcement learning were found is projective simulation. Projective simulation presents an agent-based reinforcement learning approach designed in a manner which may support quantum walk-based speedups. Although classical variants of projective simulation have been benchmarked against common reinforcement learning algorithms, very few formal theoretical analyses have been provided for its performance in standard learning scenarios. In this paper, we provide a detailed formal discussion of the properties of this model. Specifically, we prove that one version of the projective simulation model, understood as a reinforcement learning approach, converges to optimal behavior in a large class of Markov decision processes. This proof shows that a physically inspired approach to reinforcement learning can guarantee to converge.
About Epistemic Negation and World Views in Epistemic Logic Programs

Abstract In this paper we consider Epistemic Logic Programs, which extend Answer Set Programming (ASP) with “ epistemic operators” and “ epistemic negation”, and a recent approach to the semantics of such programs in terms of World Views . We propose some observations on the existence and number of world views. We show how to exploit an extended ASP semantics in order to: (i) provide a characterization of world views, different from existing ones; (ii) query world views and query the whole set of world views.
Photonic realization of quantum resetting

Contrary to the usual assumption of at least partial control of quantum dynamics, a surprising recent result proved that an arbitrary quantum state can be probabilistically reset to a state in the past by having it interact with probing systems in a consistent but uncontrolled way. We present a photonic implementation to achieve this resetting process, experimentally verifying that a state can be probabilistically reset to its past with a fidelity of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>0.870</mml:mn> <mml:mo>±<!-- ± --></mml:mo> <mml:mn>0.012</mml:mn> </mml:math> . We further demonstrate the preservation of an entangled state, which still violates a Bell inequality, after half of the entangled pair was reset. The ability to reset uncontrolled quantum states has implications in the foundations of quantum physics and applications in areas of quantum technology.
Cognitively-Inspired Agent-Based Service Composition for Mobile and Pervasive Computing

Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host’s mobility, and time constraints to tailor composition plans. Thus, our main contribution is the development of a cognitively-inspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. Our approach exhibits features such as distributedness, modularity, emergent global functionality, and robustness, which endow it with capabilities to perform decentralized service composition by orchestrating manifold service providers and conflicting goals from multiple users. The evaluation of our approach shows promising results when compared against state-of-the-art service composition models.
Quasidistribution of phases in Raman process with weak and strong pumps

Nonclassicality is studied through a quasidistribution of phases for the Raman process under both weak and strong pump conditions. In the former case, the solution is applicable to both resonant and off-resonant Raman processes, while strong classical pump is assumed at resonance. Under weak pump conditions (i.e., in a complete quantum treatment), the phase difference of phases described by single nonclassical modes is required to be filtered to describe a regular distribution function, which is not the case with strong pump. Compound Stokes-phonon mode shows nonclassical features of phases in both weak and strong pumping, which effect is similar to that for compound pump-phonon (Stokes-anti-Stokes) mode with weak (strong) pump. While anti-Stokes-phonon mode is observed to be classical and coherence conserving in strong pump case, pump-Stokes mode shows similar behavior in a special case in quantum treatment.
CPMetric: Deep Siamese Networks for Metric Learning on Structured Preferences

Preferences are central to decision making by both machines and humans. Representing, learning, and reasoning with preferences is an important area of study both within computer science and across the social sciences. When we give our preferences to an AI system we expect the system to make decisions or recommendations that are consistent with our preferences but the decisions should also adhere to certain norms, guidelines, and ethical principles. Hence, when working with preferences it is necessary to understand and compute a metric (distance) between preferences – especially if we encode both the user preferences and ethical systems in the same formalism. In this paper we investigate the use of CP-nets as a formalism for representing orderings over actions for AI systems. We leverage a recently proposed metric for CP-nets and propose a neural network architecture to learn an approximation of the metric, CPMetric. Using these two tools we look at how one can build a fast and flexible value alignment system (This is an expanded version of our paper, “Metric Learning for Value Alignment” [38]. In this version we have added the classification and regression results and significantly expanded the description of the CPMetric network.).
Dependency-aware release planning for software projects using fuzzy graphs and integer programming

Software Release Planning (SRP) is to find, for the software, a subset of the requirements with the highest value while respecting the budget. The value of a requirement however may, to various degrees, depend on selecting or ignoring other requirements. However, existing SRP models ignore either Value-Related Dependencies altogether or the strengths of those dependencies even if they consider them. This paper presents an Integer Programming model for software release planning, which considers the variances of strengths of positive and negative value-related dependencies among software requirements. To capture the imprecision associated with strengths of value-related dependencies we have made use of the algebraic structure of fuzzy graphs. We have further, contributed a scalable technique for automated identification of value-related dependencies based on user preferences for software requirements. The validity of the work is verified through simulations.
New Rewriter Features in FGL

FGL is a successor to GL, a proof procedure for ACL2 that allows complicated finitary conjectures to be translated into efficient Boolean function representations and proved using SAT solvers. A primary focus of FGL is to allow greater programmability using rewrite rules. While the FGL rewriter is modeled on ACL2's rewriter, we have added several features in order to make rewrite rules more powerful. A particular focus is to make it more convenient for rewrite rules to use information from the syntactic domain, allowing them to replace built-in primitives and meta rules in many cases. Since it is easier to write, maintain, and prove the soundness of rewrite rules than to do the same for rules programmed at the syntactic level, these features help make it feasible for users to precisely program the behavior or the rewriter. We describe the new features that FGL's rewriter implements, discuss the solutions to some technical problems that we encountered in their implementation, and assess the feasibility of adding these features to the ACL2 rewriter.
Optimal Estimation of Parameters Encoded in Quantum Coherent State Quadratures

In the context of multiparameter quantum estimation theory, we investigate the construction of linear schemes in order to infer two classical parameters that are encoded in the quadratures of two quantum coherent states. The optimality of the scheme built on two phase-conjugate coherent states is proven with the saturation of the quantum Cramér–Rao bound under some global energy constraint. In a more general setting, we consider and analyze a variety of n-mode schemes that can be used to encode n classical parameters into n quantum coherent states and then estimate all parameters optimally and simultaneously.
Fock majorization in bosonic quantum channels with a passive environment

We introduce a class of quantum channels called passive-environment bosonic channels. These channels are relevant from a quantum thermodynamical viewpoint because they correspond to the energy-preserving linear coupling of a bosonic system with a bosonic environment that is in a passive state (no energy can be extracted from it by using a unitary transformation) followed by discarding the environment. The Fock-majorization relation defined in [New J. Phys. 18, 073047 (2016)] happens to be especially useful in this context as, unlike regular majorization, it connects the disorder of a state together with its energy. Our main result here is the preservation of Fock majorization across all passive-environment bosonic channels. This implies a similar preservation property for regular majorization over the set of passive states, and it also extends to passive-environment channels whose Stinespring dilation involves an active Gaussian unitary. Beyond bosonic systems, the introduced class of passive environment operations naturally generalizes thermal operations and is expected to provide new insights into the thermodynamics of quantum systems.
How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization

Event detection (ED) aims to identify and classify event triggers in texts, which is a crucial subtask of event extraction (EE).Despite many advances in ED, the existing studies are typically centered on improving the overall performance of an ED model, which rarely consider the robustness of an ED model.This paper aims to fill this research gap by stressing the importance of robustness modeling in ED models.We first pinpoint three stark cases demonstrating the brittleness of the existing ED models.After analyzing the underlying reason, we propose a new training mechanism, called context-selective mask generalization for ED, which can effectively mine contextspecific patterns for learning and robustify an ED model.The experimental results have confirmed the effectiveness of our model regarding defending against adversarial attacks, exploring unseen predicates, and tackling ambiguity cases.Moreover, a deeper analysis suggests that our approach can learn a complementary predictive bias with most ED models that use full context for feature learning.