Reviewerly/exHarmony huggingface dataset:
Quadrotor UAV Control for Transportation of Cable Suspended Payload

Abstract Payload transportation with UAV’s (Unmanned Aerial Vehicles) has become a topic of interest in research with possibilities for a wide range of applications such as transporting emergency equipment to otherwise inaccessible areas. In general, the problem of transporting cable suspended loads lies in the under actuation, which causes oscillations during horizontal transport of the payload. Excessive oscillations increase both the time required to accurately position the payload and may be detrimental to the objects in the workspace or the payload itself. In this article, we present a method to control a quadrotor with a cable suspended payload. While the quadrotor itself is a nonlinear system, the problem of payload transportation with a quadrotor adds additional complexities due to both input coupling and additional under actuation of the system. For simplicity, we fix the quadrotor to a planar motion, giving it a total of 4 degrees of freedom. The quadrotor with the cable suspended payload is modelled using the Euler-Lagrange equations of motion and then partitioned into translation and attitude dynamics. The design methodology is based on simplifying the system by using a variable transformation to decouple the inputs, after which sliding mode control is used for the translational and pendulum dynamics while a feedback linearizing controller is used for the rotational dynamics of the quadrotor. The sliding mode parameters are chosen so stability is guaranteed within a certain region of attraction. Lastly, the results of the numerical simulations created in MATLAB/Simulink are presented to verify the effectiveness of the proposed control strategy.
Modeling of Lightning Flashes in Thunderstorm Front by Constructive Production of Fractal Time Series

Using the tools of structural-synthesizing modeling, a set of constructors was developed. Implementing parametric multi-character constructors allows to form fractal sequences of characters. Constructor-converter from the character string to time series creates fractal time series, which determine the location, magnitude and decay rate of lightning discharges. Model video images of lightnings in the thunderstorm front are formed in accordance with the implementation of the constructor-assembler. All constructors are developed on the basis of the generalized constructor that was previously presented and repeatedly tested. The model adequacy of the model is confirmed by comparing the video image of the model with the image, what was obtained by NASA satellite. This approach can be the basis for solving the dynamic problems on lightning protection of engineering constructions and civil objects, and development of strategy of aircraft behavior in order to mitigate the risks of lightning strokes in the conditions of movement in the thunderstorm front.
Evaluating Augmented and Virtual Reality in Education Through a User-Centered Comparative Study

Augmented and virtual reality proved to be valuable solutions to convey contents in a more appealing and interactive way. Given the improvement of mobile and smart devices in terms of both usability and computational power, contents can be easily conveyed with a realism level never reached in the past. Despite the tremendous number of researches related with the presentation of new fascinating applications of ancient goods and artifacts augmentation, few papers are focusing on the real effect these tools have on learning. Within the framework of SmartMarca project, this chapter focuses on assessing the potential of AR/VR applications specifically designed for cultural heritage. Tests have been conducted on classrooms of teenagers to whom different learning approaches served as an evaluation method about the effectiveness of using these technologies for the education process. The chapter argues on the necessity of developing new tools to enable users to become producers of contents of AR/VR experiences.
The Book of Endless History: Authorial Use of GPT2 for Interactive Storytelling

We present The Book of Endless History, an infinite Wikipedia of fantasy stories written in the style of Borges and Calvino, exploring the use of structural conditioning on GPT2 to generate text with explicit subjects and embedded web-links. Users are presented with a Wikipedia-like interface, containing a short fantasy description of the topic and containing embedded web-links to other related subjects. Users may click on links to learn more about different topics, following an endless trail of generated pages. The GPT2 architecture is a text completion model – it has no explicit understanding of structure and it can be a challenge to integrate it with authorial intent. Nevertheless, through this work we show that it can be conditioned to learn to write about a subject and additionally to generate the topology for an encyclopedia. We refer to this technique as subject conditioning, or more generally, structural conditioning.
A signaling game approach to mitigate co-resident attacks in an IaaS cloud environment

Cloud service providers (CSPs) offer a variety of services that are opening the door to the infinite possibilities of cloud computing. Despite numerous benefits offered by the CSPs, there are, however, some security issues that may dissuade users. In cloud computing, different virtual machines (VMs) often share the same physical resources, which are known as co-resident VMs. The shared physical resources pose a significant threat to the users as resources may belong to competing organizations as well as unknown attackers. From the perspective of a cloud user, there is no guarantee whether the co-resident VMs are trustworthy. The shared resources make privacy and perfect isolation implausible, which paves the way for co-resident attacks in which a VM attacks another co-resident VM through a covert side channel that can be used to extract another user’s secret information or launch denial of service attacks. The attack campaign becomes more damaging when multiple co-resident VMs collaborate. In this paper, we analyze the co-resident attacks and corresponding defense strategies, with respect to benign and malicious VMs and the defender, i.e., the VM monitor (VMM), using a signaling game model. The solutions to the game provide optimal defense strategies for the VMM with respect to the expected number of malicious VMs in collaboration. We evaluate the game results through simulations on various synthetic attack scenarios. The results show that the defender can effectively resist co-resident attacks by distinguishing the benign and malicious VMs.
Extended hierarchical key assignment scheme (E-HKAS): how to efficiently enforce explicit policy exceptions in dynamic hierarchies

In this paper, we focus on practically motivated flexibility requirements for the hierarchical access control model, namely transitive exception and anti-symmetric exception. Additionally, we motivate a new flexibility requirement called "class delegation with descendant(s) safety" in a practical application scenario. We propose our extended hierarchical key assignment scheme (E-HKAS) that satisfies all three aforementioned flexibility requirements in a dynamic hierarchy of security classes. To propose a generic E-HKAS, we model the hierarchical access control policy as a collection of access groups. E-HKAS enforces transitive and anti-symmetric exceptions using an efficient group-based encryption scheme. To enforce class delegation with descendant(s) safety, we propose a novel cryptographic primitive called group proxy re-encryption (GPRE) that supports proxy re-encryption between two access groups. We present an IND-CPA-secure construction of our proposed GPRE scheme and formally prove its security. Performance analysis shows that the proposed E-HKAS enforces explicit transitive and anti-symmetric exceptions more efficiently than the existing approaches in the literature. Computation cost for key derivation is constant and does not depend on the depth of the hierarchy. Also, to enforce class delegation with descendant(s) safety, the proposed E-HKAS requires constant number of computational steps to be executed.
New models for generating hard random boolean formulas and disjunctive logic programs

We propose two models of random quantified boolean formulas and their natural random disjunctive logic program counterparts. The models extend the standard models of random k-CNF formulas and the Chen-Interian model of random 2QBFs. The first model controls the generation of programs and QSAT formulas by imposing a specific structure on rules and clauses, respectively. The second model is based on a family of QSAT formulas in a non-clausal form. We provide theoretical bounds for the phase transition region in our models, and show experimentally the presence of the easy-hard-easy pattern and its alignment with the location of the phase transition. We show that boolean formulas and logic programs from our models are significantly harder than those obtained from the standard k-CNF and Chen-Interian models, and that their combination yields formulas and programs that are "super-hard" to evaluate. We also provide evidence suggesting that formulas from one of our models are well suited for assessing solvers tuned to real-world instances. Finally, it is noteworthy that, to the best of our knowledge, our models and results on random disjunctive logic programs are the first of their kind.
Analysis on Web Frameworks

Abstract while creating a website choosing the best framework for the required application is the most important step. Since there are different types of frameworks it is not an easy task. Besides, if the wrong framework is chosen it could negatively impact the website. With the evolution of the web many frameworks like PHP, Java servlets, struts, Stencil, Rebel, Ruby on rails, Flask and Falcon have been developed. Large amounts of personal information and data are being sent and recovered on a daily basis from web applications. Every application contains confidential information and therefore has its own integrity policies. There can be a huge negative impact on the financial status of the company involved, if these policies are violated. Although, implementing them is very difficult even for the programmer’s with good security framework. The environment of the web consists of a huge number of different technologies and components; therefore, web applications have an extraordinary surface of attack. Security defects stored in web browser may expose authentication details and sensitive information stored in the website. This may breach the integrity and confidentiality of the session and errors in the code may lead to the addition of destructive contents in the web pages. Complex elements such as databases, operating systems, firewalls, servers and other software’s functioning of a web application. But for it to run properly, persistent maintenance and configuration is required. Injection attacks normally takes control over the websites database by data injection into the application. In this paper we discuss about different Frameworks, its types, benefits, the security provided and its challenges. We have also compared five of the leading frameworks in aspects of complexity and security.
Action recognition to estimate Activities of Daily Living (ADL) of elderly people

This work proposes a method, and preliminary experimental results to detect and recognize a set of Activities of Daily Living, carried out by elderly people in a residential context, by analyzing video of actions, recorded using an RGB Camera. The proposed solution is based on the creation of neural network models, in particular Convolutional Neural Networks (CNN), which are trained on data extracted and preprocessed from the “Moments in Time” dataset, a resource released by MIT-IBM Watson AI Lab that includes a collection of a million labeled 3-second videos from hundreds of categories. The performances of the models obtained following two different approaches are also described, the one using Auto Machine Learning, which was also necessary in order to have an idea of the achievable performances and the transfer learning approach. One of the main drivers of our research activity was also to explore and challenge the Auto Machine Learning approach in the context of ADL and evaluate its initial accuracy baseline concerning Transfer Learning approaches.
Performance Evaluation and Social Optimization of an Energy-Saving Virtual Machine Allocation Scheme Within a Cloud Environment

Abstract Achieving greener cloud computing is non-negligible for the open-source cloud platform. In this paper, we propose a novel virtual machine allocation scheme with a sleep-delay and establish a corresponding mathematical model. Taking into account the number of tasks and the state of the physical machine, we construct a two-dimensional Markov chain and derive the average latency of tasks and the energy-saving degree of the system in the steady state. Moreover, we provide numerical experiments to show the effectiveness of the proposed scheme. Furthermore, we study the Nash equilibrium behavior and the socially optimal behavior of tasks and carry out an improved adaptive genetic algorithm to obtain the socially optimal arrival rate of tasks. Finally, we present a pricing policy for tasks to maximize the social profit when managing the network resource within the cloud environment.
Harmony: An Approach for Geo-distributed Processing of Big-Data Applications

Big-data application processing is increasingly geo-distributed, a paradigm shift from the traditional cluster-based processing frameworks. As the communication time for data movement across geo-distributed data centers is not a design criterion for traditional cluster-based processing approaches, there are research gaps in the algorithms used for staging and scheduling big-data applications for geo-distributed clusters. We address these gaps by proposing Harmony, an approach consisting of both staging and scheduling strategies to minimize an application’s total execution time. The staging strategy of Harmony exploits the intra-stage parallelism by having concurrent operators within a stage in contrast to the traditional Apache spark which uses fine-grained stages, thus reducing the computation time within each stage. Secondly, the scheduling strategy of Harmony reduces data transfers between geo-distributed data centers by exploiting data locality and thus reducing communication time and total execution time. The proposed approach Harmony achieves a speedup of two times with respect to geo-distributed Apache Spark. In addition, Harmony achieves a speedup of 1.6 times and 2.1 times when compared with the state-of-the-art framework Iridium for geo-distributed analytics over five locations with uniform and non-uniform network link bandwidths respectively.
Near-term Quantum Algorithms for Quantum Many-body Systems

Abstract There has been a good deal of work on algorithms to simulate quantum many-body systems with fault-tolerant quantum computers- those with full error correction. Fault-tolerant quantum computers of scale requisite to achieve computational advantage for these problems are likely over a decade away. Moreover, devices that we can build in the near term, called Noisy Intermediate Scale Quantum computers (NISQ), have too much noise to implement the long circuits required by these algorithms. We review heuristic, short-depth quantum algorithms more suited to NISQ computers; specifically, their scaling properties when applied to electronic and nuclear structure calculations, including Hamiltonian complexity with particle number, ansatz state preparation, convergence, and noise. We will present examples of actual quantum structure calculations with NISQ computers, as well as a newly-developed error mitigation technique that significantly improves accuracy. We end with an outlook for “advantage” – when NISQ systems might excel conventional HPC approaches for comparable problems.
Determination of borders between objects on satellite images using a two-proof doubly stochastic filtration

Abstract The text deals with the possibility of using a random process of the algorithm based on the two-pass application of the Kalman filter modification to determine the instant of disorder. A technique is proposed for generalizing this algorithm to the case of processing a random process with slowly varying statistical and correlation properties based on the application of a doubly stochastic filter. The consistency of the approach for determining the borders between objects on two-dimensional digital images is shown.
Object Detection in VHR Image Using Transfer Learning with Deformable Convolution

In the field of deep learning, finetuning the pretrained networks to get a good classifier is a common way of transfer learning. Unlike the traditional way, we insert deformable convolutional layers into the pretrained networks, and finetune the new networks. As a result, we find it performs as well as the normal one in classification, and when we construct a plane detection pipeline based on the two classifiers respectively, the one with deformable convolution shows a better result than the other.
Towards Adaptive Anomaly Detection in Buildings with Deep Reinforcement Learning

In this paper, we present early results on the use of deep reinforcement learning (DRL) for maximizing anomaly detection performance in buildings. We conjecture that DRL can improve performance by exploring the entire parameter space for all sensors, individually. Many anomaly detection algorithms are designed to use a single parameter for ease of use, however there are usually many parameter values that are pre-set, a priori. We hypothesize that a single threshold cannot work well for all sensors and propose the use of DRL to explore the entire parameter space. We use a deterministic policy gradient algorithm - Deep Deterministic Policy Gradient (DDPG)[4] - and use a building-specific anomaly detection algorithm, Strip, Bind, and Search (SBS) [2].
Implementation of TensorFlow and Caffe Frameworks: in View of Application

With ever increasing demand for applications of Deep Learning (DL) in various fields namely industry and academic areas, the demand for success rate of application of Deep Learning frameworks is also increasing at a rapid rate. There are wide range of DL frameworks available, each having its own functions and characteristics. The combination of frameworks and algorithms together provides the desired output. For this purpose, analysing of frameworks along with various algorithms in different application must be known to select the best framework for a specific application. In this paper, two frameworks such as TensorFlow and Caffe are implemented in specific applications and results are analysed to choose the best framework based on application.
User Perception of Robot's Role in Floor Projection-based Mixed-Reality Robotic Games

Within the emerging research area represented by robotic gaming and, specifically, in application domains in which the recent literature suggests to combine commercial off-the-shelf (COTS) robots and projected mixed reality (MR) technology in order to develop engaging games, one of the crucial issues to consider in the design process is how to make the player perceive the robot as having a key role, i.e., to valorize its presence from the user experience point of view. By moving from this consideration, this paper reports efforts that are being carried out with the aim to investigate the impact of diverse game design choices in the above perspective, while at the same time extracting preliminary insights that can be exploited to orient further research in the field of MR-based robotic gaming and related scenarios.
Statistical Models for Authorship Attribution

A combination of three hypothesis methods has been proposed for authorship attribution. These are: the Student’s t-test, the Kolmogorov-Smirnov’s test and the chi-square test. This combination of methods can perfect adequacy of authorship attribution. With the help of these methods, the quantity of phoneme groups is minimized. The texts can be differentiated by one or three phoneme groups. The minimization of phoneme groups facilitates authorship attribution. On the basis of the results obtained, statistical models have been built. The models show author-differentiating capability of phoneme groups in authorship attribution. This way the highest author-differentiating capability of phoneme groups is determined. New software realizes the models applied. The program makes transforming a text into transcription faster. The transcription variants of words available in the program are used.
Joint learning of Siamese network with top-down modulation and hard example mining for visual tracking

The development of deep convolutional neural networks (CNNs) has facilitated a type of Siamese network-based tracking methods. Such kind of trackers generally include two basic modules, i.e., the appearance model for target representation and the discriminative classifier to determine the location. In the existing methods, much attention has been paid on feature fusion for more robust appearance representation. However, they ignore the intrinsic relationship between features from multiple layers. Furthermore, the problem of data imbalance between positive and negative samples in offline training limits the discriminative ability of the CNN-based classifier. We investigate the joint learning of representation and discriminative classifier under the Siamese network for robust tracking. We integrate the top-down modulation for feature fusion by considering the intrinsic relationship between features from different layers. To solve the data imbalance problem, we propose an advanced hinge loss objective function to mine the hard examples in offline training, which helps to improve the robustness of the similarity measure. Experimental results on the public tracking benchmark datasets show that the proposed method obtains favorable tracking accuracy against the state-of-the-art trackers with a real-time tracking speed.
Identifying Facet Mismatches In Search Via Micrographs

E-commerce search engines are the primary means by which customers shop for products online. Each customer query contains multiple facets such as product type, color, brand, etc. A successful search engine retrieves products that are relevant to the query along each of these attributes. However, due to lexical (erroneous title, description, etc.) and behavioral irregularities (clicks or purchases of products that do not belong to the same facet as the query), some mismatched products are often included in search results. These irregularities can be detected using simple binary classifiers like gradient boosted decision trees or logistic regression. Typically, these binary classifiers use strong independence assumptions between the results and ignore structural relationships available in the data, such as the connections between products and queries. In this paper, we use the connections that exist between products and query to identify a special kind of structure we refer to as a micrograph. Further, we make use of Statistical Relational Learning (SRL) to incorporate these micrographs in the data and pose the problem as a structured prediction problem. We refer to this approach as structured mismatch classification (\SMC). In addition, we show that naive addition of structure does not improve the performance of the model and hence introduce a variation of \SMC, strong \SMC~(\SSMC), which improves over the baseline by passing information from high-confidence predictions to lower confidence predictions. In our empirical evaluation we show that our proposed approach outperforms the baseline classification methods by up to 12% in precision. Furthermore, we use quasi-Newton methods to make our method viable for real-time inference in a search engine and show that our approach is up to 150 times faster than existing ADMM-based solvers.