Reviewerly/exHarmony huggingface dataset:
Squeezed state metrology with Bragg interferometers operating in a cavity

Bragg interferometers, operating using pseudospin-1/2 systems composed of two momentum states, have become a mature technology for precision measurements.State-of-the-art Bragg interferometers are rapidly surpassing technical limitations and are soon expected to operate near the projection noise limit set by uncorrelated atoms.Despite the use of large numbers of atoms, their operation is governed by single-atom physics.Motivated by recent proposals and demonstrations of Raman gravimeters in cavities, we propose a scheme to squeeze directly on momentum states for surpassing the projection noise limit in Bragg interferometers.In our modeling, we consider the unique issues that arise when a spin squeezing protocol is applied to momentum pseudospins.Specifically, we study the effects of the momentum width of the atomic cloud and the coupling to momentum states outside the pseudospin manifold, as these atoms interact via a mode of the cavity.We show that appreciable levels of spin squeezing can be demonstrated in suitable parameter regimes in spite of these complications.Using this setting, we show how beyond mean-field techniques developed for spin systems can be adapted to study the dynamics of momentum states of interacting atoms.Our scheme promises to be feasible using current technology and is experimentally attractive because it requires no additional setup beyond what will be required to operate Bragg interferometers in cavities.
3D multi-UAV cooperative velocity-aware motion planning

Motion planning is a crucial topic with multi-UAV applications of search and rescue missions, transportation missions, etc. The concerns of motion planning focus on path planning and inter-UAV collision avoidance. Model based on Lyapunov present great solutions. However, setting reasonable parameters for the model is usually based on experience. Moreover, UAVs controlled by the models usually converge to destinations slowly. Heuristic planning algorithms are also mainstream approaches to guide UAVs. However, they hardly consider kinetics of UAVs. This paper proposes distributed velocity-aware algorithm and collision avoidance algorithm to serve motion planning of multiple UAVs. The velocity-aware algorithm generates paths with acceleration vectors that converge to the predefined destinations. The collision avoidance algorithm will be triggered to protect UAVs from collisions when path conflicts are predicted. Compared with hierarchical control model and Lyapunov-like control laws, our approach could improve success possibility of mission achievement for UAVs. At the same time, the algorithms help UAVs take shorter paths and less time to move to destinations safely.
Semantic-aware multi-keyword ranked search scheme over encrypted cloud data

Traditional searchable encryption schemes mostly adopt the TF-IDF (term frequency - inverse document frequency) model which ignores the semantic association between keywords and documents. It is a challenge to design an effective and secure semantic-aware search scheme. The topic model is based on "high-order co-occurrence", i.e., how often words co-occur in different contexts. Hence, it can be utilized for modeling the latent semantics among texts. In this paper, we propose a novel privacy-persevering searchable encryption scheme based on the Latent Dirichlet Allocation (LDA) topic model. Documents are modeled by LDA, and the concept of topics is utilized to generate a document-topic relevance matrix and query topic vectors. The matrix is used as the index for the proposed scheme. The secure inner product operation is adopted to encrypt the index and query topic vectors, which provides accurate topic relevance score calculation between encrypted index and trapdoors. To improve the efficiency of our basic scheme, we adopt a special complete binary tree and use the "Greedy Depth First Search" algorithm. Our evaluation results demonstrate the effectiveness of our scheme.
A robust steganography method for HEVC based on secret sharing

This paper presents a robust steganography method for HEVC based on secret sharing. To prevent intra-frame distortion drift, three Classes of intra-frame prediction are defined. To improve the robustness of the secret message, the embedded message has been encoded into n sub-secrets by using (t, n)-threshold secret sharing. Then the encoded message is embedded into the multi-coefficients of the selected 4 × 4 luminance DST blocks which meet the Classes mentioned above. When the test video faced with different loss-rates, the survival rate of the proposed steganography method has increased by around 24.27%, 27.38%, 61.91% and 32.26% compared with Swati, Hayat, and Shahid (2014), respectively; the survival rate of the proposed steganography method has increased by around 28.74%, 34.60%, 56.05% and 41.40% compared with Liu, Liu, and Zhao (2017), respectively; when the loss-rate is less than 15%, the proposed steganography method can get 100% survival rate. And the experiments prove that the proposed method can achieve high performance than previously video steganography methods based on HEVC, especially for visual quality and robustness performance.
Can individuals’ neutralization techniques be overcome? A field experiment on password policy

Individuals’ lack of adherence to password security policy is a persistent problem for organizations. This problem is especially worrisome because passwords remain the primary authentication mechanism for information systems, and the number of passwords has been increasing. For these reasons, determining methods to improve individuals’ adherence to password-security policies constitutes an important issue for organizations. Extant research has shown that individuals use neutralization techniques, i.e., types of rationalizations, to disregard organizational information-security policies. What has not been determined from extant information security research is whether these neutralizations can be changed through educational training interventions. We argue that training based on principles of cognitive dissonance theory is a promising method for reducing individuals’ use of neutralization techniques. We contribute by showing empirically that training based on cognitive dissonance theory can reduce the use of neutralization techniques when such training is designed to counter such techniques. Using a quasi-experimental design at an organization, individuals received training on neutralization techniques in the context of password security. Using a quasi-experimental design, we found that individuals who received our training treatment exhibited substantially less intent to use neutralization techniques and were significantly more likely to use secure passwords. Additionally, a follow-up measurement three weeks after the training session showed that the experimental treatment retained its effectiveness, i.e., the experimental group exhibited substantially less intent to use neutralization techniques and a greater likelihood of using strong passwords in the future. Additionally, intent was significantly greater in the experimental group. Implications for practice and future research are discussed.
Named Entity Recognition in Bengali Text Using Merged Hidden Markov Model and Rule Base Approach

Named Entity Recognition (NER) is the subtask of Natural Language Processing (NLP) which tries to achieve human level on a specific domain (e.g. newspaper) to identify named entities. It seeks to locate and classify named entities (Person Name, Location, Organization names etc.), which is the most vital step of Information Extraction (IE). In many cases Machine Learning (ML) is mostly used to perform NER. Apart from that, another method is applied which is known as Rule Base approach. This paper presents a method which is using both ML and Rule Base approach together for NER basing on Bengali language. Mainly the rule based approach has been merged with ML. For ML Hidden Markov Model (HMM) and for rule base approach Regular Expression has been used. A Named Entity (NE) tagged corpus has been developed by using Bengali newspaper, which consists of 10k words that has been manually annotated with seven tags. This paper concludes with experimental results which shows two distinctive ways of our proposed model.
Aerial Grasping with a Lightweight Manipulator Based on Multi-Objective Optimization and Visual Compensation

Autonomous grasping with an aerial manipulator in the applications of aerial transportation and manipulation is still a challenging problem because of the complex kinematics/dynamics and motion constraints of the coupled rotors-manipulator system. The paper develops a novel aerial manipulation system with a lightweight manipulator, an X8 coaxial octocopter and onboard visual tracking system. To implement autonomous grasping control, we develop a novel and efficient approach that includes trajectory planning, visual trajectory tracking and kinematic compensation. Trajectory planning for aerial grasping control is formulated as a multi-objective optimization problem, while motion constraints and collision avoidance are considered in the optimization. A genetic method is applied to obtain the optimal solution. A kinematic compensation-based visual trajectory tracking is introduced to address the coupled affection between the manipulator and octocopter, with the advantage of discarding the complex dynamic parameter calibration. Finally, several experiments are performed to verify the effectiveness of the proposed approach.
Real-Time Augmented Reality Physics Simulator for Education

Physics education applications using augmented reality technology, which has been developed extensively in recent years, have a lot of restrictions in terms of performance and accuracy. The purpose of our research is to develop a real-time simulation system for physics education that is based on parallel processing. In this paper, we present a video see-through AR (Augmented Reality) system that includes an environment recognizer using a depth image of Microsoft’s Kinect V2 and a real-time soft body simulator based on parallel processing using GPU (Graphic Processing Unit). Soft body simulation can provide more realistic simulation results than rigid body simulation, so it can be more effective in systems for physics education. We have designed and implemented a system that provides the physical deformation and movement of 3D volumetric objects, and uses them in education. To verify the usefulness of the proposed system, we conducted a questionnaire survey of 10 students majoring in physics education. As a result of the questionnaire survey, 93% of respondents answered that they would like to use it for education. We plan to use the stand-alone AR device including one or more cameras to improve the system in the future.
Information-Theoretic Privacy Watchdogs

Given a dataset comprised of individual-level data, we consider the problem of identifying samples that may be disclosed without incurring a privacy risk. We address this challenge by designing a mapping that assigns a "privacy-risk score" to each sample. This mapping, called the privacy watchdog, is based on a sample-wise information leakage measure called the information density, deemed here lift privacy. We show that lift privacy is closely related to well-known information-theoretic privacy metrics. Moreover, we demonstrate how the privacy watchdog can be implemented using the Donsker-Varadhan representation of KL-divergence. Finally, we illustrate this approach on a real-world dataset.
Perturbation Analysis of Learning Algorithms: Generation of Adversarial Examples From Classification to Regression

Despite the tremendous success of deep neural networks in various learning problems, it has been observed that adding intentionally designed adversarial perturbations to inputs of these architectures leads to erroneous classification with high confidence in the prediction. In this work, we show that adversarial examples can be generated using a generic approach that relies on the perturbation analysis of learning algorithms. Formulated as a convex program, the proposed approach retrieves many current adversarial attacks as special cases. It is used to propose novel attacks against learning algorithms for classification and regression tasks under various new constraints with closed-form solutions in many instances. In particular, we derive new attacks against classification algorithms which are shown to be top-performing on various architectures. Although classification tasks have been the main focus of adversarial attacks, we use the proposed approach to generate adversarial perturbations for various regression tasks. Designed for single pixel and single subset attacks, these attacks are applied to autoencoding, image colorization and real-time object detection tasks, showing that adversarial perturbations can degrade equally gravely the output of regression tasks <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Map-optimize-reduce: CAN tree assisted FP-growth algorithm for clusters based FP mining on Hadoop

Over the past era, Frequent Pattern Mining (FPM) is emerging as a significant approach to discover fascinating knowledge concealed in the data. However, preceding works failed to address the validation of FPM with user queries and also achieving better scalability and execution time is still bottleneck owing to difficulties in handling large dataset. To address this downside, our proposed work establishes FPM using extend version of MapReduce framework in Hadoop environment. Our proposed work comprises of five processes that are: 1) Preprocessing 2) Affinity Propagation (AP) based Clustering 3) Load Balancing 4) Map-Optimize-Reduce 5) Mining User Queries. Primarily, our proposed work performs preprocessing to remove data redundancy. To speed up the MapReduce framework, we propose AP clustering which generates effective clusters from the given dataset. Load balancing is executed to balance load among different blocks concerning where reputation is computed. To avoid oversight in scanning and minimal searching space in MapReduce, optimizer is included between Mapper and Reducer where Emperor Penguin Colony (EPC) optimization is used. Frequent patterns are mined using CANonical order (CAN) tree based Frequent Pattern (FP) growth which reduces execution time and frequent tree construction. User provides Mining_Request to the Hadoop and frequent patterns are mined for given query which is send back to the user. If user given query is not present in the CAN tree, then it sends Relevance Feedback as a recommendation to the user. Finally, we validate our proposed work performance with the previous works for succeeding metrics that are Execution Time, Response Time, Load Balancing Rate, and Scalability.
Predictive Auto-Scaling of Multi-Tier Applications Using Performance Varying Cloud Resources

The performance of the same type of cloud resources, such as virtual machines (VMs), varies over time mainly due to hardware heterogeneity, resource contention among co-located VMs, and virtualization overhead. The performance variation can be significant, introducing challenges to learn workload-specific resource provisioning policies to automatically scale the cloud-hosted applications to maintain the desired response time. Moreover, auto-scaling multi-tier applications using minimal resources is even more challenging because bottlenecks may occur on multiple tiers concurrently. In this paper, we address the problem of using performance varying VMs for gracefully auto-scaling a multi-tier application using minimal resources to handle dynamically increasing workloads and satisfy the response time requirements. The proposed system uses a supervised learning method to identify the appropriate resources provisioning for multi-tier applications based on the prediction of the application response time and the request arrival rate. The supervised learning method learns a state transition configuration map which encodes a resource allocation states invariant to the underlying VMs performance variations. This configuration map helps to use performance varying resources in predictive autoscaling method. Our experimental evaluation using a real-world multi-tier web application hosted on a public cloud shows an improved application performance with minimal resources compared to conventional predictive auto-scaling methods.
A Minimum-Skew Clock Tree Synthesis Algorithm for Single Flux Quantum Logic Circuits

This article presents a synchronous minimum-skew clock tree synthesis algorithm for single flux quantum circuits considering splitter delays and placement blockages. The proposed methodology improves the state-of-the-art by accounting for splitter delays and creating a fully balanced clock tree structure in which the number of clock splitters from the clock source to all the sink nodes is identical. Additionally, a mixed integer linear programming based algorithm is presented that removes the overlaps among the clock splitters and placed cells (i.e., placement blockages) and minimizes the clock skew, simultaneously. Using the proposed method, the average clock skew for 17 benchmark circuits is 4.6 ps, improving the state-of-the-art algorithm by 70%. Finally, a clock tree synthesis algorithm for imbalanced topologies is presented that reduces the clock skew and the number of clock splitters in the clock network by 56% and 37%, respectively, compared with a fully balanced clock tree solution.
Optimal Privacy-Utility Trade-off under a Rate Constraint

We study the privacy-utility trade-off in data release under a rate constraint. An agent observes random variable X and reveals information U to the utility provider over a rate-constrained channel, such that I(X; U) ≤ R, in return for utility I(U; Y ), where Y denotes a latent random variable correlated with X. While the objective is to maximize the utility, the agent also wants to protect a private information S, also correlated with X and Y from the utility provider. The trade-off between rate, utility and private information leakage is studied. This problem can be thought of as a generalization of both the information bottleneck and privacy funnel problems, reducing to either of the two problems in special cases. A necessary and sufficient condition for the existence of positive utility under zero private information leakage (or perfect privacy) is established. Subsequently, the problem of maximizing the utility subject to perfect privacy constraint is shown to be a linear program when the rate constraint is inactive. Also, the maximum value of the ratio of utility to infinitesimal private information leakage for an arbitrary rate constraint is obtained.
Improved Veron Identification and Signature Schemes in the Rank Metric

It is notably challenging to design an efficient and secure signature scheme based on error-correcting codes. An approach to build such signature schemes is to derive it from an identification protocol through the Fiat-Shamir transform. All such protocols based on codes must be run several rounds, since each run of the protocol allows a cheating probability of either 2/3 or 1/2. The resulting signature size is proportional to the number of rounds, thus making the 1/2 cheating probability version more attractive. We present a signature scheme based on double circulant codes in the rank metric, derived from an identification protocol with cheating probability of 2/3. We reduced this probability to almost 1/2 to obtain the smallest signature among code-based signature schemes based on the Fiat-Shamir paradigm, around 22 KBytes for 128 bit security level. Furthermore, among all code-based signature schemes, our proposal has the lowest value of signature plus public key size, and the smallest secret and public key sizes. We provide a security proof in the Random Oracle Model, implementation performances, and a comparison with the parameters of similar signature schemes.
Sampling-based robot motion planning

To address the computational challenges that arise when planning for robotic systems, traditional CS algorithms, tools, and paradigms must be revisited.
Celebrity abuse on Twitter: The impact of tweet valence, volume of abuse, and dark triad personality factors on victim blaming and perceptions of severity

Celebrities are increasingly utilizing social media platforms to establish their brand and interact with their fan base, but in doing so they often become the targets of online abuse. While such abusive acts are known to cause severe consequences in the general population little is known about how celebrity abuse is perceived by observers. This study investigated observers' impressions of the severity of online abuse on Twitter, the blame attributed to celebrities for the abuse they received, and the role of the dark triad of observers' personality factors (Machiavellianism, narcissism, and psychopathy) in these decisions. We manipulated celebrity tweet content (negative, neutral, positive) and the volume of abusive comments (high, low) the tweets received. Celebrities received more blame the more negative their initial tweet was, and incidents were perceived as least severe following a negative tweet with a high volume of abuse. Observer impressions were influenced by their dark triad personality factors. Following negative tweets, as observer narcissism increased, victim blame increased and perceived severity decreased. Following positive tweets, as observer psychopathy increased, perceived severity decreased. Results are discussed in the context of the Warranting Theory of online impression formation and the ramifications for celebrity social media use are explored.
A New Re-Ranking Method Based on Convolutional Neural Network and Two Image-to-Class Distances for Remote Sensing Image Retrieval

With the rapid growth of remote sensing image data, it has become necessary to effectively and efficiently retrieve images from a big image database for managing and exploiting such data.This paper presents a novel method for content-based remote sensing image retrieval (CBRSIR) that re-ranks the initial retrieval result using two image-to-class distances, which are the similarity between an image and an image class.One is the image-to-training-class distance between an image and each image class of the training dataset, which uses the information included in the train samples with known-label.It is used to calculate the weight of the image classes and estimating the class of an image.The other is the image-to-queryclass distance between an image and the query class that is obtained using the k-nearest neighbor (kNN) method.The proposed method first obtains the initial retrieval results via sorting the distances of the CNN feature between the query image and each retrieved image in ascending order.Then, the image-to-query-class distance is calculated according to the initial result, and the weight of each class is calculated according to the class probability of images obtained by CNN and the image-to-training-class distances.Finally, the initial result is re-ranked to get the final retrieval result via the weighted image-to-class distances.The performance of the proposed method is tested on UCMD and PatternNet databases.Experimental results show the effectiveness of the proposed re-ranking scheme in image retrieval in comparison to other state-of-the-art techniques.