Reviewerly/exHarmony huggingface dataset:
Visual Communication with UAV: Use Cases and Achievements

This paper illustrates an alternative approach towards mission control of UAV beyond the usage of radio data link. It is based on bidirectional visual communication employing onboard computer vision to recognize the operator’s gestures and providing visible feedback to him. This work presents use cases and summarizes the achievements made so far at the Institute of Flight Systems with respect to functional concepts, system architectures, sensor technology, data processing and experimental validation.
Recessive Social Networking: Preventing Privacy Leakage against Reverse Image Search

This work investigates the image privacy problem in the context of social networking under the threat of reverse image search. We introduce a new concept called recessive social networking. Unlike conventional privacy-preserving social networking, in our setting, the aim is to deceive machine learning algorithms that used in reverse image search, while still enabling unaffected ubiquitous social networking among humans. We, for the first time, ultilize adversarial example technique as a defensive mechanism to protect image privacy against content-based image search algorithms in the context of social networking. Finally, rigorous evaluations are conducted to demonstrate the effectiveness, transferability, and robustness of the proposed countermeasure.
Experimenting With Non-Interactive Range Proofs Based on the Strong RSA Assumption

Range proofs are proofs that a committed number m belongs to a range [a, b] for public constants a, b, without leaking any information about the value m. In this work, we evaluate and analyze the performance of existing techniques for range proofs based on the strong RSA assumption while varying the range sizes. We first group the techniques into two classes. Our experiments show that the first class, being built on finding sums of squares (e.g., Groth's range proof), has sharply decreasing performance trends as the range size increases. Thus, solutions in this class seem to be useful primarily for small ranges. The second class, which relies on a direct proof (e.g., Boudot's range proof), exposes that the performance degradation slopes are not as steep as the range size grows, compared to solutions in the first group. However, this class's main drawback is that these methods require considerably more modular arithmetic than the first class. Concretely, the Groth and Boudot protocols achieve the best performance when the range sizes are less than and greater than 1410 bits, respectively. As part of this work, we consider an extension by combining the strong points of existing solutions and examine the result efficiency. Interestingly, however, our experimental results report that this extension outperforms either Groth's or Boudot's protocol for certain ranges, but there is no range for which the extension outperforms both.
A Sensitivity Analysis for Harmony Search with Multi-Parent Crossover Algorithm

Harmony search algorithm with multi-parent crossover (HSA-MPC) is a hybrid algorithm that relies on benefiting from the crossover operation to combine more than one harmony to generate a new harmony. The picked harmonies are taken from an archive pool with best harmonies. In a previous study, the algorithm proves its efficiency when compared to other harmony search algorithms. In this paper, we will study the effect of harmony memory size (HMS), harmony memory consideration rate (HMCR), multi-parent crossover rate (MPCR), and the archive pool size on the quality of the generated solution. Eleven different scenarios are evaluated using a set of eight real-world numerical optimization problems introduced for CEC 2011 evolutionary algorithm competition. The analysis provides fixed values for all operators except the one under investigation. The obtained results prove the sensitivity of the algorithm to these operators and suggest a set of recommendations to improve the algorithm performance.
Applying User-Centered Techniques in the Design of a Usable Mobile Musical Composition Tool

In this study, we present the music composition tool Flow and how an interaction was designed that led towards introducing balance in the work of musicians across all stages in musical composition. Observation and user research led to having a deeper understanding of the various needs, gains and pain points musicians encounter when composing. Musicians and composers who participated in the study, came from varying levels of expertise from beginner (those with less than 7 years) and veteran (those with beyond 10 years experience). An iterative process of design and development was continuously employed which led to improving the interaction design within the prototype. The processes described in this study show how insights were uncovered from a comprehensive set of usability tests and inspections done. These insights led to the development of a more usable and acceptable musical composition tool as seen from the results in the user tests. It can be observed that varying levels of expertise in music composition leads to different expectations and needs with regards to a music composition prototype. Results of the user tests show that Flow achieved a level of satisfaction and usability at par with the industry-standard tools.
Discriminative Deep Attention-Aware Hashing for Face Image Retrieval

Although the power of hashing methods has been proved in image retrieval, they cannot effectively extract discriminative features for face image retrieval as the discriminative differences in face regions are subtle and the background information interferes with the feature expression. To solve this problem, we propose an end-to-end deep hashing method with attention mechanisms to learn discriminative hash codes. Specifically, a face spatial network is designed to enhance the discrimination of face features from the spatial aspect. With a specially designed face spatial loss, it can automatically mine differentiated facial regions, and reduce the interference of background information. Furthermore, an attention-aware hash network, in which facial features could be enhanced by fusing strategy and channel attention module, is designed to learn compact and discriminative hash codes. Experimental results on two widely used datasets demonstrate the inspiring performance over several state-of-the-art hashing methods.
Use case specifications: How complete are they?

Abstract The flexibility of the use cases in specifying requirements from informal textual descriptions to more formal ones makes them usable in different contexts and purposes. However, the versatility of use cases regarding their admissible structure raises a natural concern about the specification completeness. This paper reports on the analysis of the eight different variants of use case template regarding their functional completeness, ability to express the set of particular functionalities, by comparing their information contents exercised by comprehensive questionnaires on five different problem specifications. The questionnaire was obtained from two sources: one based on the understanding of the functionality to be implemented and the other based on the functionality documented in eight different use case templates under study. Our results suggested that use cases can be used to specify a complete set of requirements intended to develop a software system. We found that the choice of a use case template influences the completeness of the specification. A more detailed analysis of the eight different variants of use case template resulted in the identification of 21 significant use case elements. We showed that by incorporating these significant use case elements in a given template, the completeness of the specification could be further enhanced.
A high-speed multi-scale kernel correlation filter tracking algorithm

Recently, Kernel Correlation Filter (KCF) is recognized as one of the best visual tracking algorithms which provide excellent tracking performance and high possessing speed. However, how to adapt to the scale change of target in real time and accurately is still an open problem. In this paper, a multi-scale improved method based on one-dimensional scale correlation filtering is proposed. We use the kernel function to solve the regularized least squares classifier, and the translation filter template is obtained to estimate the target position. Then, we collect a series of samples of different scales at the target position to construct a one-dimensional scale filter template for estimating the most suitable scale. The filter is learned and updated online in the tracking process. In scale estimation, we employed the dimension reduction method to reduce the computational complexity, and the interpolation method is used to increase the number of multi-scale to obtain more accurate scale location. A number of experimental results and data show that the target region overlap rate and tracking rate of the improved algorithm are improved by 9% and 60% on average compared with the best of other scale adaptive tracking algorithms. Compared with the KCF algorithm, the average position tracking error is reduced by 23.9% and the overlap rate is increased by 46%.
Fuzzy Optimal State Observers for Takagi-Sugeno Fuzzy State Feedback Control

In this work, a comparative study is developed among different methods for state observers design, analyzing their advantages and drawbacks. Three optimal design methods are considered. First, an observer based on the duality theorem and the Linear Quadratic Regulator (LQR) controller design method. Second, a new optimal observer for noiseless described systems. Third, a Kalman filter. The fuzzy observers are obtained by the linearized Takagi-Sugeno (TS) function at each fuzzy rule, so the optimal observers become suboptimal among the fuzzy interpolation of the rules. Two examples are proposed to evaluate the performance of the observers. The first one is an example of linear discrete system, and the second one is a thermal mixing tank, which is a multivariable, delayed and nonlinear system. The results show that for noiseless described systems, the best and easiest method is the new optimal observer, but the Kalman filter is the best choice for reducing noise.
EPA-RIMM : An Efficient, Performance-Aware Runtime Integrity Measurement Mechanism for Modern Server Platforms

Detecting unexpected changes in a system's runtime environment is critical to resilience. A repurposing of System Management Mode (SMM) for runtime security inspections has been proposed, due to SMM's high privilege and protected memory. However, key challenges prevent SMM's adoption for this purpose in production-level environments: the possibility of severe performance impacts, semantic gaps between SMM and host software, high overheads, overly broad access permissions, and lack of flexibility. We introduce a Runtime Integrity Measurement framework, EPA-RIMM, for both native Linux and Xen platforms, that includes several novel features to solve these challenges. EPA-RIMM decomposes large measurements to control perturbation and leverages the SMI Transfer Monitor (STM) to bridge the semantic gap between hypervisors and SMM, as well as restrict the measurement agent's accesses. We present a design and implementation for a concurrent approach that allows EPA-RIMM to utilize all cores in SMM, dramatically increasing measurement throughput and reducing application perturbation. Our Linux and Xen prototype results show that EPA-RIMM meets performance goals while continuously monitoring code and data for signs of attack, and that it is effective at detecting a number of recent exploits.
Towards Robust Scenarios of Spatio-Temporal Renewable Energy Planning: A GIS-RO Approach

Solar-based energy is an intermittent power resource whose potential pattern varies in space and time. Planning the penetration of such resource into a regional power network is a strategic problem that requires both to locate and bound candidate parcels subject to multiple geographical restrictions and to determine the subset of these and their size so that the solar energy production is maximized and the associated costs minimized. The problem is also permeated with uncertainty present in the estimated forecast energy demand, resource potential and technical costs. This paper presents a novel combination of Geographic Information Systems (GIS) and Robust Optimization (RO) to develop strategic planning scenarios of a collection of parcels that accounts for their spatio-temporal characteristics, and specifically their hourly radiation patterns that are location dependent, to best fit the network temporal demand and minimize technical costs. The problem is formulated as a GIS spatial placement problem and a RO fractional knapsack problem to plan the effective power penetration and geographical suitability of new PV facilities. The combination GIS-RO generates an excellent decision support system that allows for the computation of optimized parcel scenarios (locations, sizes and power). The qualitative and quantitative effectiveness of the approach is demonstrated on real data on the French Guiana region. Results show that the proposed approach provides reliable fine grained planning that also accounts for the risk adversity of the decision maker towards forecast demand and solar potentials.
Efficient and Accountable Oblivious Cloud Storage with Three Servers

As the adoption of cloud storage service has been pervasive, more and more attentions have been paid to the related security and privacy risks, among which, data access pattern privacy is an important aspect. Lots of solutions have been proposed, but most are infeasible due to high overheads in communication and storage. In this paper, we propose a new solution to address the limitations by leveraging the moderate storage capacity in the increasingly popular cloud storage gateways and the existence of multiple competing and independent cloud storage servers. Extensive analysis and evaluation have shown that, our proposed system can simultaneously attain the features of provable protection of data access pattern, low data query delay, low server storage overhead, low communication costs, and accountability.
Human Rescue Based on Autonomous Robot KUKA YouBot with ROS and Object Detection

The rescue processes that must be carried out in environments that present contamination levels are limited by the exposure time to which rescuers can be submitted, due to this the use of robotic platforms has been taken as a feasible option since it allows to increase the precision when carrying out the operations and to reduce the time in the decision making. In this paper is presented a system which uses the Kuka Youbot mobile platform to perform navigation processes, exploration and detection of people, in order to generate a map which contains the locations of individuals detected. The processes are executed through the combination of Robot Operating System and Open Source Computer Vision Library, in addition the complete system is simulated within the environment of Gazebo. The detection of individuals is done by implementing the Single Shot Detector algorithm and using a trained neural network. The simulation of the system made it possible to detect people inside a building in spite of occlusions in the captured images and it was possible to save the coordinates of their locations to later generate marks inside a 2D generated map.
Pose estimation of a single circle based on 1D homography

Circular target has an important application in pose estimation based on vision by the virtue of anti-occlusion, anti-noise and easy recognition on the image. This paper focuses on the pose estimation problem when the projection of circle center is determined, and a pose calculation method is proposed based on 1D homography. Firstly, under the normalized image coordinates, a homography matrix is determined from three points, one is the projection of circle center, the other two are the intersections of the line through the projected center and the projected ellipse. According to the geometrical properties of the homography matrix, a linear method for computing the circular pose from multiple homography matrices is presented. Secondly, the reprojection errors of image points are taken as the objective function to optimize the linear solution results. Finally, the proposed method is compared with the two existing pose estimation methods through experiment. The experimental results show that the proposed method is slightly superior to the existing methods in terms of anti-noise performance and has obvious advantages in remote pose estimation.
THE ANALYSIS OF INFORMATION TECHNOLOGIES USAGE FOR ACADEMIC PURPOSES IN ENGLISH LANGUAGE TEACHING AND LEARNING AT UNIVERSITAS BANGKA BELITUNG

The use of Information Technologies (IT) tools has become very important requirements for teachers and students nowadays in achieving academic goals in higher education. The harness of computers, internet, e-mail, multi-media and other IT tools is indispensable and has a great influence on the process of language teaching and learning in an effort to improve the quality of education and to produce best human resources that not only having good competence in foreign language but also possessing great capabilities in the use of Information Technology to face the Industrial Revolution 4.0 era. The study seeks to find out the frequency and the purposes of IT usage by students and lecturers in the English learning environment. A questionnaire containing questions about the frequency and the use of IT in the teaching and learning process was disseminated to the students and the lecturers of the English Literature department at the University of Bangka Belitung. The study reveals that the lecturers and students show a positive attitude towards the use of IT tools and the harness of the tools in the process of teaching and learning is believed to be very useful.
Mobile Learning and Its Impact on Learning English Vocabulary

Mobile learning is nowadays becoming a well-established methodology in the institutions of higher learning. The key reasons comprise easy portability and accessibility of mobile devices, as well as ubiquity of wireless environment, and the opportunity of informal and personalized approaches to learning. The purpose of this article is to discuss whether the use of a mobile application developed for English vocabulary learning for students of Management of Tourism at the Faculty of Informatics and Management in Hradec Kralove, Czech Republic, is effective. The methodology of this study was based on an experiment, which consisted of a group of students who used the app and of those who did not. The findings of this study show that the use of mobile app for English vocabulary learning is effective as suitable support to traditional, face-to-face classes.
Automation of Teaching Processes on e-Learning Platforms Using Recommender Systems

The aim of this paper is to present the difficulties and limitations of working on e-learning platform. Two models of e-learning applications will be discussed: the classical model and the model realized by the new teaching paradigm in distance learning aspect. Next, the areas of automation of teaching processes on e-learning platforms will be presented as well as problems which result from the implementation of such models. Next, the concept of using recommendation systems in the extended version of the application will be presented. After a brief description of the recommendation algorithms, the principles of positioning the system user in accordance with his knowledge and methods for recommending content for him will be described. The solutions provided are proprietary solutions. The most important relationships between the user’s behavior and the construction of his knowledge model will be described. In the last step, conclusions will be drawn regarding the selection of the best solution for the processes of knowledge automation on distance learning platforms.
Bi-Abductive Inference for Shape and Ordering Properties

In separation logic, bi-abduction - a combination of abductive inference and frame inference - is the key enabler for compositional reasoning, helping to scale up verification significantly. Indeed, the success of bi-abduction led to the development of Infer, the tool used daily to verify Facebook's codebase of millions of lines of code. However, this success currently stays largely within the shape domain. To extend this impact towards the combination of shape and arithmetic domains, in this work, we present a novel one-stage bi-abductive procedure for a combination of data structures and ordering values. The procedure is designed in the spirit of the Unfold-and-Match paradigm where the inference is utilized to derive any mismatched portion. We demonstrate our proposal through several interesting examples to show that it is promising for an automated verification of heap-manipulating programs.
FANet: An End-to-End Full Attention Mechanism Model for Multi-Oriented Scene Text Recognition

In this paper, we proposed An end-to-end Multi-Oriented Scene Text Recognition Model with Full Attention Mechanism. Attention mechanism is adopted in both encoder and decoder sides of the model. At the coding end, the idea of residual attention can not only be easily combined with the current most advanced recognition model structure, but also can easily increase the depth of the network without causing model crash, so as to extract image features for encoding more pertinently. The idea of seq2seq attention translation model is adopted in the decoding end, so as to translate image features into recognized words better. This model changes the general method of detecting-slicing-recognition, but directly carries out end-to-end training to get the recognition results. The test results of the model on the two data sets show that the model can achieve even better results using the detecting-slicing-recognition method on the basis of greatly simplifying the model training steps. We call this network FANet.
FIRST-ORDER HOMOTOPICAL LOGIC

Abstract We introduce a homotopy-theoretic interpretation of intuitionistic first-order logic based on ideas from Homotopy Type Theory. We provide a categorical formulation of this interpretation using the framework of Grothendieck fibrations. We then use this formulation to prove the central property of this interpretation, namely homotopy invariance . To do this, we use the result from [8] that any Grothendieck fibration of the kind being considered can automatically be upgraded to a two-dimensional fibration, after which the invariance property is reduced to an abstract theorem concerning pseudonatural transformations of morphisms into two-dimensional fibrations.
Circle Graph Isomorphism in Almost Linear Time

Circle graphs are intersection graphs of chords of a circle. In this paper, we present a new algorithm for the circle graph isomorphism problem running in time $${\mathcal {O}}((n+m)\alpha (n+m))$$ where n is the number of vertices, m is the number of edges and $$\alpha $$ is the inverse Ackermann function. Our algorithm is based on the minimal split decomposition [Cunnigham, 1982] and uses the state-of-art circle graph recognition algorithm [Gioan, Paul, Tedder, Corneil, 2014] in the same running time. It improves the running time $${\mathcal {O}}(nm)$$ of the previous algorithm [Hsu, 1995] based on a similar approach.