Reviewerly/exHarmony huggingface dataset:
Single-Equipment with Multiple-Application for an Automated Robot-Car Control System

The integration of greater functionalities into vehicles increases the complexity of car-controlling. Many research efforts are dedicated to designing car-controlling systems that allow users to instruct the car just to show it what it should do; however, for non-expert users, controlling the car with a remote or a switch is complicated. So, keeping this in mind, this paper presents an Arduino based car-controlling system that no longer requires manual control of the cars. Two main contributions are presented in this work. Firstly, we show that the car can be controlled with hand-gestures, according to the movement and position of the hand. The hand-gesture system works with an Arduino Nano, accelerometer, and radio-frequency (RF) transmitter. The accelerometer (attached with the hand-glove) senses the acceleration forces that are produced by the hand movement, and it will transfer the data to the Arduino Nano that is placed on hand glove. After receiving the data, Arduino Nano will convert it into different angle values in ranges of 0°–450° and send the data to the RF receiver of the Arduino Uno, which is placed on the car through the RF transmitter. Secondly, the proposed car system is to be controlled by an android based mobile-application with different modes (e.g., touch buttons mode, voice recognition mode). The mobile-application system is the extension of the hand-gesture system with the addition of Bluetooth module. In this case, whenever the user presses any of the touch buttons in the application, and/or gives voice commands, the corresponding signal is sent to the Arduino Uno. After receiving the signal, Arduino will check this against its predefined instructions for moving forward, backward, left, right, and brake; then it will send the command to the motor module to move the car in the corresponding direction. In addition, an automatic obstacle detection system is introduced to improve the safety measurements to avoid any hazards with the help of sensors placed at the front of the car. The proposed systems are designed as a lab-scale prototype to experimentally validate the efficiency, accuracy, and affordability of the systems. The experimental results prove that the proposed work has all in one capability (hand-gesture, touch buttons and voice-recognition with mobile-application, obstacle detection), is very easy to use, and can be easily assembled in a simple hardware circuit. We remark that the proposed systems can be implemented under real conditions at large-scale in the future, which will be useful in automobiles and robotics applications.
Iterative Refinement for <i>ℓ<sub>p</sub></i>-norm Regression

We give improved algorithms for the ℓp-regression problem, minx ‖x‖p such that Ax = b, for all p ∊ (1, 2) ∪ (2, ∞). Our algorithms obtain a high accuracy solution in iterations, where each iteration requires solving an m × m linear system, with m being the dimension of the ambient space.Incorporating a procedure for maintaining an approximate inverse of the linear systems that we need to solve at each iteration, we give algorithms for solving ℓp-regression to 1/poly(n) accuracy that runs in time Õp(mmax{ω, 7/3}), where ω is the matrix multiplication constant. For the current best value of ω > 2.37, this means that we can solve ℓp regression as fast as ℓ2 regression, for all constant p bounded away from 1.Our algorithms can be combined with nearly-linear time solvers for linear systems in graph Laplacians to give minimum ℓp-norm flow / voltage solutions to 1/poly(n) accuracy on an undirected graph with m edges in time.For sparse graphs and for matrices with similar dimensions, our iteration counts and running times improve upon the p-norm regression algorithm by [Bubeck-Cohen-Lee-Li STOC'18], as well as general purpose convex optimization algorithms. At the core of our algorithms is an iterative refinement scheme for ℓp-norms, using the quadratically-smoothed ℓp-norms introduced in the work of Bubeck et al. Formally, given an initial solution, we construct a problem that seeks to minimize a quadratically-smoothed ℓp norm over a subspace, such that a crude solution to this problem allows us to improve the initial solution by a constant factor, leading to algorithms with fast convergence.
A Metamodel for Agile Requirements Engineering

Value delivery is becoming an important asset for an organization due to increasing competition in industry.Therefore, companies apply Agile Software Development (ASD) to be more competitive and reduce time to market.Using ASD for the development of systems implies that established approaches of Requirements Engineering (RE) undergo some changes in order to be more flexible to changing requirements.To this end, the field of agile RE is emergent and different process models for agile RE have arisen.The aim of this paper is to build an abstract layer about the variety of existing process models by means of a metamodel for agile RE.It has been created in several iterations and relies on the evaluation of related process models.Furthermore, we have derived process models for agile RE in industry by presenting instances of the metamodel in two different cases: one is based on Scrum whereas the other is based on Kanban.This paper contributes to the software development body of knowledge by delivering a metamodel for agile RE that supports researchers and practitioners modeling and improving their own process models.We can conclude that the agile RE metamodel is highly relevant for the industry as well as for the research community, since we have derived it following empirical research in the field of ASD.
A multi-image Joint Re-ranking framework with updateable Image Pool for person re-identification

Real-world video surveillance has increasing demand for person re-identification. Existing multi-shot works usually aggregate single sample features by computing the average features or using time series model. The Multi-image Joint Re-ranking framework with updateable Image Pool that we are proposing will give a different approach. First, we defined the term ‘Image Pool’ to store image samples for each pedestrian. Next, the updating rules of Image Pool has been defined in order to optimize the representativeness of it. Second, we compute initial ranking lists of every sample in Image Pool, and propose the ‘Multiple-image Joint Re-ranking’ algorithm to aggregate initial ranking lists. We calculate the rank score of partial elements of initial ranking lists. In the end, we get final ranking list by ascending the order of the rank scores. We validated our re-ranking results on Market-1501, iLIDS-VID, PRID-2011 and our ITSD datasets, and the results outperform other methods.
An effective solution for large scale single machine total weighted tardiness problem using lunar cycle inspired artificial bee colony algorithm

Single machine total weighted tardiness problem (SMTWTP) is one of the fundamental combinatorial optimization problems. The problem consists of a set of independent jobs with distinct processing times, weights, and due dates to be scheduled on a single machine. The goal of the problem is to minimize the total weighted tardiness. Several swarm intelligence (SI) motivated techniques have been proposed to solve SMTWTP. Still, the solution for large scale SMTWTP instances within a reasonable amount of time is a challenging task. Artificial bee colony (ABC) algorithm is one of the efficient SI based techniques to solve real world optimization problems. This article presents an effective amended ABC based strategy to solve SMTWTP. A local search (LS) approach, influenced from the lunar cycle is proposed and hybridized with ABC to escalate the exploitation capacity of the algorithm. The proposed LS approach is titled as the lunar inspired LS (LLS) approach and the proposed hybridized strategy is known as lunar inspired ABC (LuABC) algorithm. The proposed LuABC algorithm has been applied on 25 large SMTWTP instances of job size 1000. The obtained outcomes prove that the proposed algorithm obtains the optimum solutions for all the considered instances within a reasonable amount of time.
A Systematic Mapping Study of Cloud Resources Management and Scalability in Brokering, Scheduling, Capacity Planning and Elasticity

Cloud computing allows for resource management through various means.Some of these include brokering, scheduling, elasticity and capacity planning and these processes helps in facilitating service utilization.Determining a particular research area especially in terms of resources management and scalability in the cloud is usually a cumbersome process for a researcher, hence the need for reviews and paper surveys in identifying potential research gaps.The objective of this work was to carry out a systematic mapping study of resources management and scalability in the cloud.A systematic mapping study offers a summarized overview of studies that have been carried out in a particular area of interest.It then presents the results of such overviews graphically using a map.Although, the systematic mapping process requires less effort, the results are more coarse-grained.In this study, analysis of publications were done based on their topics, research type and contribution facets.These publications were on research works which focused on resource management, scheduling, capacity planning, scalability and elasticity.This study classified publications into research facets viz., evaluation, validation, solution, philosophical, option and experience and contribution facets based on metrics, tools, processes, models and methods used.Obtained results showed that 31.3% of the considered publications focused on evaluation based research, 19.85% on validation and 32% on processes.About 2.4% focused on metric for capacity planning, 5.6% focused on tools relating to resource management, while 5.6 and 8% of the publications were on model for capacity planning and scheduling method, respectively.Research works focusing on validating capacity planning and elasticity were the least at 2.29 and 0.76%, respectively.This study clearly identified gaps in the field of resources management and scalability in the cloud which should stimulate interest for further studies by both researchers and industry practitioners.
Perception-Aware Point-Based Value Iteration for Partially Observable Markov Decision Processes

In conventional partially observable Markov decision processes, the observations that the agent receives originate from fixed known distributions. However, in a variety of real-world scenarios, the agent has an active role in its perception by selecting which observations to receive. We avoid combinatorial expansion of the action space from integration of planning and perception decisions, through a greedy strategy for observation selection that minimizes an information-theoretic measure of the state uncertainty. We develop a novel point-based value iteration algorithm that incorporates this greedy strategy to pick perception actions for each sampled belief point in each iteration. As a result, not only the solver requires less belief points to approximate the reachable subspace of the belief simplex, but it also requires less computation per iteration. Further, we prove that the proposed algorithm achieves a near-optimal guarantee on value function with respect to an optimal perception strategy, and demonstrate its performance empirically.
Micrography QR Codes

This paper presents a novel algorithm to generate micrography QR codes, a novel machine-readable graphic generated by embedding a QR code within a micrography image. The unique structure of micrography makes it incompatible with existing methods used to combine QR codes with natural or halftone images. We exploited the high-frequency nature of micrography in the design of a novel deformation model that enables the skillful warping of individual letters and adjustment of font weights to enable the embedding of a QR code within a micrography. The entire process is supervised by a set of visual quality metrics tailored specifically for micrography, in conjunction with a novel QR code quality measure aimed at striking a balance between visual fidelity and decoding robustness. The proposed QR code quality measure is based on probabilistic models learned from decoding experiments using popular decoders with synthetic QR codes to capture the various forms of distortion that result from image embedding. Experiment results demonstrate the efficacy of the proposed method in generating micrography QR codes of high quality from a wide variety of inputs. The ability to embed QR codes with multiple scales makes it possible to produce a wide range of diverse designs. Experiments and user studies were conducted to evaluate the proposed method from a qualitative as well as quantitative perspective.
Three Dimensional Point Cloud Compression and Decompression Using Polynomials of Degree One

The availability of cheap depth range sensors has increased the use of an enormous amount of 3D information in hand-held and head-mounted devices. This has directed a large research community to optimize point cloud storage requirements by preserving the original structure of data with an acceptable attenuation rate. Point cloud compression algorithms were developed to occupy less storage space by focusing on features such as color, texture, and geometric information. In this work, we propose a novel lossy point cloud compression and decompression algorithm that optimizes storage space requirements by preserving geometric information of the scene. Segmentation is performed by using a region growing segmentation algorithm. The points under the boundary of the surfaces are discarded that can be recovered through the polynomial equations of degree one in the decompression phase. We have compared the proposed technique with existing techniques using publicly available datasets for indoor architectural scenes. The results show that the proposed novel technique outperformed all the techniques for compression rate and RMSE within an acceptable time scale.
On Evaluating Fault Resilient Encoding Schemes in Software

Cryptographic implementations are often vulnerable against physical attacks, fault injection analysis being among the most popular techniques. On par with development of attacks, the area of countermeasures is advancing rapidly, utilizing both hardware- and software-based approaches. When it comes to software encoding countermeasures for fault protection and their evaluation, there are very few proposals so far, mostly focusing on single operations rather than cipher as a whole. In this paper we propose an evaluation framework that can be used for analyzing the effectivity of software encoding countermeasures against fault attacks. We first formalize the encoding schemes in software, helping us to define what properties are required when designing a fault protection. Based on these findings, we develop an evaluation metric that can be used universally to determine the robustness of a software encoding scheme against bit flip faults and instruction skips. We provide a way to select a code according to user criteria and also a dynamic code analysis method to estimate the level of protection of assembly implementations using encoding schemes. Finally, we verify our findings by implementing a block cipher PRESENT, protected by encoding scheme based on anticodes, and provide a detailed evaluation of this implementation using different codes.
DETECTION OF CITIES VEHICLE FLEET USING YOLO V2 AND AERIAL IMAGES

Abstract. Recent progress in deep learning methods has shown that key steps in object detection and recognition can be performed with convolutional neural networks (CNN). In this article, we adapt YOLO (You Only Look Once) to a new approach to perform object detection on satellite imagery. This system uses a single convolutional neural network (CNN) to predict classes and bounding boxes. The network looks at the entire image at the time of the training and testing, which greatly enhances the differentiation of the background since the network encodes the essential information for each object. The high speed of this system combined with its ability to detect and classify multiple objects in the same image makes it a compelling argument for use with satellite imagery.
Multi-Swarm Single-Objective Particle Swarm Optimization to Extract Multiple-Choice Tests

This paper proposes the use of multi-swarm method in particle swarm optimization (PSO) algorithm to generate multiple-choice tests based on assumed objective levels of difficulty. The method extracts an abundance of tests at the same time with the same levels of difficulty and approximates the difficulty-level requirement given by the users. The experimental results show that the proposed method can generate many tests from question banks satisfying predefined levels of difficulty. Additionally, the proposed method is also shown to be effective in terms of many criteria when compared with other methods such as manually extracted tests, random methods and PSO-based methods in terms of execution time, standard deviation, the number of particles per swarm and the number of swarms.
Double-Layer Cubature Kalman Filter for Nonlinear Estimation

The cubature Kalman filter (CKF) has poor performance in strongly nonlinear systems while the cubature particle filter has high computational complexity induced by stochastic sampling. To address these problems, a novel CKF named double-Layer cubature Kalman filter (DLCKF) is proposed. In the proposed DLCKF, the prior distribution is represented by a set of weighted deterministic sampling points, and each deterministic sampling point is updated by the inner CKF. Finally, the update mechanism of the outer CKF is used to obtain the state estimations. Simulation results show that the proposed algorithm has not only high estimation accuracy but also low computational complexity, compared with the state-of-the-art filtering algorithms.
Construction of three-qubit biseparable states distinguishing kinds of entanglement in a partial separability classification

We construct seven kinds of three-qubit biseparable states to show that every class of biseparable states in the partial separability classification proposed by Szalay and K\"ok\'enyesi [S. Szalay and Z. K\"ok\'enyesi, Phys. Rev. A 86, 032341 (2012)] is nonempty.
Dual Video Watermarking Algorithm Based on SIFT and HVS in the Contourlet Domain

We propose dual un-compressed video watermarking algorithms based on human visual system and scale invariant feature transform (SIFT) in the contourlet domain.First, every frame is decomposed by contourlet transform to obtain the low-frequency band and several high-frequency sub-bands in different directions.The energy value of every high-frequency sub-band is calculated.The low-frequency sub-band and the high-frequency sub-bands of the largest energy values are chosen to be embedded into watermarking signals.Second, the first watermarking algorithm is realized to embed watermarking signals by adjusting the size ratio between the coefficient histograms of low-frequency sub-band because the coefficient histogram of low-frequency sub-band possesses invariance to conventional geometric transform.Third, to the second watermarking algorithm, the human visual masking threshold based on contourlet domain is calculated as the maximum embedding tolerance, which is embedded in the largest energy value of the high-frequency sub-band as watermarking signals to guarantee the imperceptibility.Finally, when the watermarked video frame suffered from geometrically attacks, the SIFT and fine corrections are combined to effectively correct and restore the synchronization between geometrically attacked video frames and watermarking signals.The experimental results indicate that: 1) the visual masking based on video motion information in the contourlet domain can effectively improve the transparency and 2) combining the histogram mapping in low-frequency sub-band coefficients with SIFT and fine corrections can resist common geometric attacks and therefore improve the robustness of the watermarking algorithm.INDEX TERMS Dual video watermarking, geometric attacks, human visual system
Machine Translation Research A Scientometric Assessment of Global Publications Output during 2007 16

&#x0D; &#x0D; &#x0D; The present study provides a quantitative and qualitative description of global machine translation research published during 2007-16 and as indexed in Scopus database. The study profiles research in the field on a series of measures, such as publications growth rate, global share, citation impact, share of international collaborative papers and distribution of publications by sub-areas. The study also profiles top contributing countries, organisations and authors in machine translation research on a series of bibliometric indicators. The study further reports characteristics of highly cited papers in the field.&#x0D; &#x0D; &#x0D;
Formalized meta-theory of sequent calculi for linear logics

When studying sequent calculi, proof theorists often have to prove properties about the systems, whether to show that they are “well-behaved”, amenable to automated proof search, complete with respect to another system, consistent, among other reasons. These proofs usually involve many very similar cases, which leads to authors rarely writing them in full detail, only pointing to one or two more complicated cases. Moreover, the amount of details makes them more error-prone for humans. Computers, on the other hand, are very good at handling details and repetitiveness. In this work we have formalized textbook proofs of the meta-theory of sequent calculi for linear logic in Abella. Using the infrastructure developed, the proofs can be easily adapted to other substructural logics. We implemented rules as clauses in an intuitive and straightforward way, similar to logic programming, using operations on multisets for the explicit contexts. Although the proofs are quite big, they use only elementary reasoning principles, which makes the proof techniques fairly portable to other formal reasoning systems.
Strict entanglement monotonicity under local operations and classical communication

Entanglement monotone is defined as a convex measure of entanglement that does not increase on average under local operations and classical communication (LOCC). Here we call an entanglement monotone a strict entanglement monotone (SEM) if it decreases strictly on average under LOCC. We show that, for any convex roof extended entanglement monotone that on pure states is given by a function of the reduced states, if the function is strictly concave, then it is a SEM. Moreover, we prove that the negativity and the relative entropy of entanglement, which are not defined by the convex roof structure, are also SEMs. In addition, if the squashed entanglement could be obtained by some optimal extension, then it is a SEM as well. Our results imply that entanglement is strictly decreasing on average under LOCC.