Reviewerly/exHarmony huggingface dataset:
Particle Swarm Optimization Algorithm Implementation for Multiple Drones Control in Continuous Task Simulation

Robot swarms are an alternative approach that is standing out due to increased processing capacity, improvements in miniaturization, and reduced robot costs. It has led to increased the use of drones in an ever-increasing range of scenarios. This work proposes the development and implementation of a solution tested in simulations that can be replicable in a real flight to plan the path of multiple drones and perform a task continuously. Experiments have shown that the proposed solution proved to be a useful functional tool with the ability to generate a satisfactory and accurate trajectory for robots that detect and avoid collisions in their trajectories by satisfactorily performing an independent robot relay without harming the task.
Simultaneous Detection and Removal of Dynamic Objects in Multi-view Images

Consider a set of images of a scene consisting of moving objects captured using a hand-held camera. In this work, we propose an algorithm which takes this set of multi-view images as input, detects the dynamic objects present in the scene, and replaces them with the static regions which are being occluded by them. The proposed algorithm scans the reference image in the row-major order at the pixel level and classifies each pixel as static or dynamic. During the scan, when a pixel is classified as dynamic, the proposed algorithm replaces that pixel value with the corresponding pixel value of the static region which is being occluded by that dynamic region. We show that we achieve artifact-free removal of dynamic objects in multi-view images of several real-world scenes. To the best of our knowledge, we propose the first method which simultaneously detects and removes the dynamic objects present in multi-view images.
Novel Approach for Power Analysis in Microcontrollers

Security showcases a major breakthrough in the history of the embedded systems, as the connections move beyond computing devices, from intelligent traffic management systems to missile control system. The drift of Internet protocol from version 4 to version 6 has greatly expanded the number of devices that could be connected over the Internet and it is estimated that it could accommodate three times the number of devices currently existing in the world. With this growing pace in embedded systems, security issues are an area of great concern. Objective of this approach examines the vulnerability of the commonly used ARM processor to a simple distributive embedded security—power analysis attack for difference and also making the role of service provider active by modifying the conventional security model.
Virtual Keyboard and Fingers

The problem of data entry in mobile devices is considered. The shortcomings of virtual projected keyboards are analyzed. The new input technology called VKF (Virtual Keyboard and Fingers) is offered. It is shown that VKF is more convenient not only in comparison with projected virtual keyboards, but also with data entry methods including use of on-screen keyboards.
STRATEGIES FOR BUILDING COMMUNICATION AND NEGOTIATION COMPETENCIES FOR PROSPECTIVE ACCOUNTANTS

Purpose of the study: The objective of the study is to evaluate the effectiveness of the existing strategies employed by the department of accounting in equipping its students with communication and negotiation competencies (CNC). Classroom, extracurricular, and internship are the main strategies to build students’ CNC. This study aims to identify the impacts of classroom learning, extra-curricular activities, internship and community service on CNC.&#x0D; Methodology: The study employs a quantitative approach by using accounting students at Universitas Negeri Semarang as a population. The study employs a five-point-Likert scale to collect data from accounting students in the fourth year. Student engagements are used to measure classroom, extra-curricular activities, internship and community service. Descriptive, correlation and regression techniques are used to analyze data.&#x0D; Main Findings: Classroom, extracurricular, internship and community service engagement positively and significantly influence communication and negotiation competencies. The study also shows that accounting student learns more about CNC in the internship and community service activities.&#x0D; Applications of this study: ASEAN member countries have ratified Mutual Recognition Arrangement (MRA) where accountants from a member country can operate their business in other member countries. To ensure prospective accountants would work well in this region, they should have good CNC. The results of the study would provide information to accounting departments on how to equip their graduates with these competencies.&#x0D; Novelty/Originality of this study: Research on CNC for prospective accountants seems to be limited in number since the departments of accounting pay more attention to generic accounting competencies. The study uses engagement to measure extracurricular, internship, and community service activities.
Inference Ability Assessment of Modified Differential Neural Computer

In this paper we propose three modifications of Differential Neural Computer aiming at the improvement of convergence and speed of the network training. The first one relies on simplifying the mechanism of erasing old information; the second increases the influence of the link between data, and the last one increases the utility of the memory module. We evaluate the proposed modifications using five bAbI tasks. The results analysis gave some insights into modification effects, and the most promising results achieved the DNC modification without erasing vector.
A User Centered Model Driven Service Oriented Ubiquitous Government Design Approach

Delivering government services using information and communication technologies has gained great success, and raised citizens' need to access the presented services in ubiquitous ways. Leading governments and institutes in this field have already started to invest in this field and dispute that it has been over twenty years since the presentation of the concept of ubiquity there are no adaptable and reusable frameworks for creating large ubiquitous systems, since the developed ones were small and destined to create specific systems. In this article, the authors present a development approach that combines XP fast development, MDA's automated development and ease of modifying and updating, and the domain-oriented development that allows for the creation of a virtual image of governments agencies with a focus on active involvement of future system's users.
Semantic Segmentation Network with Local Multi-Scale Attention for Locating Traffic Sign

Semantic segmentation network is able to detect multi-scale traffic sign effectively. However, probabilities that different scale features participate in decision are equal in the network. In this work, we propose a local multi-scale attention module based on channel-attention, which extracts local features generated by dilated convolution and weights scale features. We demonstrate that adopting local multi-scale attention module on a semantic segmentation network achieves a better performance for locating multi-scale traffic signs than state-of-art convolutional networks by the experimental results on a Chinese traffic sign dataset.
Q-DFN

Traditional approaches to each image simply depend on detectors of the classification network and the bounding box regression network. These approaches are, although working well, fail to exploit the satisfied accuracy for specific object detection or many methods sacrifice the accuracy for the speed of real-time detection. The reason for the unsatisfied results is that the relationship between the various objects is not effectively considered. In this paper, we propose a decisive fusion network for object detection. After the network extracts the features from the backbone first, the last five layers are treated as the feature pyramid structure. Each layer is responsible for the bounding box by the border regression algorithm, and the Deep Q-Learning (DQN) algorithm is responsible for the classification of the target. The fusion function connects the DQN algorithm with the object detection network, and considers the selection relationship between objects through the DQN algorithm to improve the overall accuracy. Through a lot of experiments, the parameters of DQN are better adjusted. The experiments show our method is better than tradition approaches.
IoT based detection of adulteration in Gold using ANN

Abstract According to the old sayings, “All the glitters are not gold”. We should be very aware of this quote. Nowadays we don’t get a pure crystalline of gold (Au). The Gold (Au) cannot itself be made into proper jewellery, so it needs some impurities as such to make and sell them into a product. So, in olden days to till now the gold (Au) is mixed with Copper (Cu) to make it into a nice wearing’s and products. The Copper (Cu) is not only mixed with Gold (Au) it is also mixed with silver, Palladium, Platinum and more. Nowadays the iridium is mixed with gold to enhance the weight of the gold. Thus the gold sellers sell the iridium (Ir) mixed gold (Au) to the traders. Unknowingly the traders get the gold (Au) as of such in a high cost. The iridium (Ir) cannot be identified. It can be identified only when the gold (Au) is melted. The nature and property of gold remains the same when mixed with iridium, but the mass of the gold (Au) gets inflated. Thus the proposed system uses the x-ray to identify the Proportion of iridium (Ir) in gold (Au). The Controller senses the image captured the by the x-ray machines and matches them with the other samples taken from the pure Gold (Au) through Back Propagation algorithm and finds the error. The identification of such these will not cost more. It is highly efficient and traces the impurity in gold and provides the traders with at most satisfaction.
A regolith geochemical anomaly formed by accumulation and corrosion of resistant minerals from Scuddles VMS primary ore, Western Australia

Cassiterite, rutile and gahnite from Scuddles VMS primary ore have accumulated residually in silcrete and in ferruginous lateritic zones (duricrust and gravel) that cap the weathering profile at Scuddles. Cassiterite and rutile contain inclusions of base metal sulfides and sulfosalts which are stable under alkaline and reducing conditions in the supergene sulfide enrichment zone, and alkaline to neutral conditions in the supergene oxide enrichment zone. However, cassiterite and rutile have been intensively corroded in the silcrete and lateritic zones under oxidizing conditions by acidic fluids derived by infiltration of acidic meteoric water and the oxidation of sulfide inclusions. This has resulted in releasing a suite of pathfinder elements, including Sn, Cu, Zn, Cd, Hg, Pb, Sb, Bi, W, Mo, Se and Te. The released Cu, Zn and Cd tend to be mobile under these conditions and are leached from the silcrete and lateritic zones. However, these elements are elevated at certain stratigraphic zones in silcrete and form surface anomalies in exposed saprolite. The other, less mobile elements, particularly Pb, Sb, Bi, W and Mo are concentrated in silcrete and ferruginous duricrust to form a variety of secondary microcrystalline phases of mixed or of unestablished mineralogy, including QRAZ (quartz, rutile, anatase, and zircon) and more complex Pb-, Ag-, Bi- and Sb-bearing phases. These pathfinder elements form a narrow, near-surface anomaly that delineates the Scuddles VMS deposit. This paper also reinforces findings that silcrete, ferruginous duricrust and ferruginous gravel are anomalous in pathfinder elements over the Scuddles deposit and, furthermore, are important sampling media in mineral exploration in general, particularly where gossans may not have formed or have been eroded.
Automated Health Care Analysis using Network Structure and Sprial Analysis

As bio and health technology continues to grow rapidly and become more and more ubiquitous, the need to pay attention to health concerns and the quality of medical care has increased. The first step in a healthcare analysis that will tackle these concerns is to analyze data from people who are interested in the health sectors via individual or group interviews. However, the traditional approach to this kind of health care analysis is based on person to person conversations followed by an expert's decision, this has inherent limitations in terms of data scalability and the bias from experts' prior knowledge. In this paper, we propose an Automated HealthCare Analysis (AHCA) approach using network structures and spiral analysis. AHCA generates network structures from real world datasets (health care interview results from real people) and specifies important keywords and their properties in terms of network structure. Then AHCA evaluates the impact on these keywords by removing the keywords iteratively. In this paper, an application of AHCA is designed, evaluated and analyzed for possible use as a new framework by using real world data.
Decision Making of Mobile Robot based on Multimodal Fusion

To solve the problem of multimodal information fusion, this paper proposes a method based on the filling of scene main components, and evaluates the channel information according to the knowledge base. The modal channel of this paper chooses vision and hearing, which is more suitable for the information transmission in the actual communication. Firstly, the single mode information is identified by the neural network. After processing, the image and audio expression are transformed into text expression, and the component value describing the scene is filled in according to text analysis. According to the knowledge base, the evaluation model is established to calculate the confidence of each channel when the information conflicts. After getting the scene, query the content of the prior knowledge base again and send the corresponding action instructions to the robot. The experimental results show that this paper can correct the modal fusion results under the guidance of prior knowledge base, and achieve effective human-computer cooperation in specific scenes. It reduces the dependence on single channel information in the interaction process, increases fault tolerance mechanism, and improves user experience evaluation.
Modified Cyclotomic Polynomial and Its Irreducibility

Finding irreducible polynomials over Q (or over Z ) is not always easy. However, it is well-known that the mth cyclotomic polynomials are irreducible over Q . In this paper, we define the mth modified cyclotomic polynomials and we get more irreducible polynomials over Q systematically by using the modified cyclotomic polynomials. Since not all modified cyclotomic polynomials are irreducible, a criterion to decide the irreducibility of those polynomials is studied. Also, we count the number of irreducible mth modified cyclotomic polynomials when m = p α with p a prime number and α a positive integer.
Foreign Object Detection between PSDs and Metro Doors Using Deep Neural Networks

At present, many cities have established sophisticated metro transportation systems, and metro passenger traffic accounts for about 50 to 80 percent of the total passenger traffic. Unfortunately, there are hundreds of incidents of train delays and even casualties caused by foreign objects in metros of various cities every year. The detection of foreign objects between metro doors and Platform Screen Doors (PSDs) has become an urgent problem to be solved. The main contribution of this paper is that this is the first attempt to use deep neural networks to solve the problem of foreign object detection between the PSDs and metro doors. In this paper, we first introduce the object detection framework called You Only Look Once (YOLO series) and Single Shot MultiBox Detector (SSD). We further explore the performance of YOLO v3 and SSD by implementing their models to do the foreign object detecting task in our dataset (from real-world data). The experimental results demonstrate that deep neural networks achieve good detection accuracy in the task of foreign object detection between the PSDs and metro doors. In addition, the deep neural network can also give the category information of foreign objects, which greatly reducing the time of artificial judgment and relieving traffic pressure. We hope our paper will serve as a solid baseline and help ease future research in foreign object detection between the PSDs and metro doors.
Deep Neural Network-Based Algorithm Approximation via Multivariate Polynomial Regression

Many communication tasks have been formulated as optimization problems that can be solved by iterative algorithms. However, these algorithms are usually computationally intensive. To enable real-time processing of communication algorithms, in this paper, we propose a new deep neural network (DNN) architecture for algorithm approximation. Based on the idea of deep unfolding, we expand the iterative algorithm into a cascade network structure of basic blocks, with each network block corresponding to an iteration. Then, instead of directly approximating each iteration through network layers, we introduce the multivariate polynomial regression (MPR) as a bridge between the iterations and layers for constructing the unfolded network blocks. Specifically, we use a multivariate polynomial to approximate an iteration of the original iterative algorithm, and further construct a shallow neural network with one hidden layer to realize the polynomial, where the order of the polynomial provides theoretical guidance for determining the size of the network and controls the tradeoff between the approximation accuracy and computation speed. As empirical justifications, we apply the proposed DNN architecture in approximating the classic WMMSE algorithm for wireless interference management, showing that the proposed approach can achieve a good approximation accuracy with a much faster computation speed.
Design of Carry Select Adder with Online Testability Using Reversible Gates

Now a days, Reversible Logic is winding up increasingly more conspicuous innovation having its applications in different fields like Nanotechnology, Low Power CMOS, Optical Computing. Reversibility plays prominent role for building energy efficient circuits. In this paper, reversible carry select adder is proposed and the performance analysis is verified interms of Ancilla, Quantum Cost and Garbage outputs. Online testing feature is added to reversible carry select adder which detects single stuck-at faults in a circuit. The proposed design is better as compared to basic carry select adder interms of Quantum cost and Ancilla.
SUPERVISION IN THE SOCIAL PRACTICE OF ART THERAPY AND COUNSELING

Supervision is viewed within the process of art-therapy and counseling in social work practice as an applied field of social pedagogy. The consulting and counseling within the helping professions in the social sphere is viewed as an important issue in the university training of specialists working with individuals, families and groups with different problems. The specifics of the consulting and counseling in the social sphere have been defined. On the basis of the analysis and the study of one’s own teaching experience as well as the empirical study of the feedback from students` opinions conclusions have been made about the problems of the methodology of the preparation for the helping professions. Attitudes towards professional counseling, self-estimation of the level of education and expectations for realization in the social sphere have been viewed as indirect orientations. In the end some generalization are made about the practice of supervision in art-therapy and counseling in social practice and teaching.
Objective Reduced reference Stereoscopic image quality assessment

In the last years, 3D imaging is becoming a vast area of research focused on several disciplines such as industry and scientific applications. Nevertheless, the evaluation of 3D quality does not follow the technical evolution. This paper investigates the evaluation of the quality of stereoscopic 3D images. Although several methods dedicated to 2D quality assessment have been presented in the literature, there was not as much effort for evaluating the quality of 3D images. It's in this perspective and in the aim to introduce an objective method for evaluating the quality of 3D images, we suggest to combine the estimation of each 2D quality assessment to extend it to 3D case. Thus different metric have been tested on LIVE and IRCCyNIVC databases, the obtained results show that reduced reference methods are the most appropriate.
A Study on Use Cases and Business Aspects of Cloud Stock Exchange

Current trends in telecommunication are influenced by Network Function Virtualization (NFV) and Software Defined Networks (SDN) which aim to create elastic networks. Furthermore, new constraints are emerging like ultra-low (~1-5ms) latency which requires geographically/topologically optimized solutions in relation of the communication path between the infrastructure and the end-users. In this context, the Multi-Access Edge Computing (MEC) concept allows to put computing resources as close to the actual places of application as possible. However, current networks are fragmented from countries/regions point of view, so there should be new ways to move service endpoints closer to the users across countries e.g., in roaming situations. There are ways which ensure inter-provider resource instantiation. This is also a resource booking problem and this paper proposes a method known from stock exchanges to manage resource allocation especially while addressing pricing questions concerning the location and time of the demand. Furthermore, the presented approach could simplify charging and billing of resource usage.
Research on Exploratory Modeling of Combat Capability of Weapon SoS Based on Synergy

The combat capability of the weaponry system reflects the overall characteristics of the weapon system. It is a non-linear and cooperative complex system. First of all, we study the characteristic mechanism of weapon and equipment system synergy and propose the basic idea of exploratory synergy modeling. According to the exploratory idea, we model the hierarchical structure of the equipment system, abstract the main unit of combat capability, give the method of quantifying the underlying data based on the mean method, and briefly introduce the regression calculation method of partial least squares method to determine the relationship between the main bodies.
Stochastic Text Sentiment Analysis of Modern Chinese Literature Using Fast Fourier Transform

With the rapid expansion of the commentary information, it is difficult to deal with such massive information by manual methods, and it is necessary to rely on computers to assist in acquisition and processing. In this paper, an algorithm based on two-way sentiment analysis is proposed, and an actual system is constructed to perform real-time analysis of the emotional needs of users and recommend literary works. On the one hand, a feature classifier is constructed based on the modern literary audio in the frequency domain. The feature classifier is used to complete the emotional classification of literary works. On the other hand, the real-time text information of users in the social network is obtained to analysis the users based on the fast Fourier transform. Current emotional needs ultimately lead to a list of recommended literary works for users, which allows real-time literary work recommendations based on contextual awareness. The experiments show that the use of personalized recommendation algorithm has higher accuracy, and the user community can obtain a more satisfactory experience.