Reviewerly/exHarmony huggingface dataset:
Neural networks in business applications

Abstract In this paper shows a present vision of neural systems that are propelled by neural frameworks to give viable models to measurable examination. Their most essential part in neural system is the capacity to “learn”, depend in a set number of observation. With regards to neural systems, The articulation “Picking up” subsidizing that the learning picked up from the example can be outlined as tactile reconnaissance. In this regard, fake neural systems are regularly alluded to as the learning machine. In that capacity, counterfeit neural systems might be considered as images for operators who take in the reliance of their condition and make their conduct techniques subject to a predetermined number of perceptions. This exploration does not have to finish up from the natural sources of neural systems. Be that as it may, this is an absolutely scientific model and factual application. ever after the comming of PC insight, The craft of working together needs to experience uncommon changes. After some time, numerous information based registering frameworks have entered a substantial number of organizations and their utilization has turned out to be progressively across the board. With the colossal advances in innovation, the administration of data identified with cutting edge counterfeit neuroscience has turned into a basic part of business insight. In this article, we portray the key of neural systems and in addition We will audit the work done in counterfeit neural systems applications in numerous organizations. The association of this diary is as per the following. The initial segment exhibits a general prologue to neural systems. The second part features the business utilizations of neural systems. The third part takes a gander at work done in the field of insolvency determining, trailed by work in the zones of Mastercard extortion identification. The fourth part investigate the Back spread calculation – a numerical methodology and work done in the zones of securities exchange forecast, trailed by a survey of money related bookkeeping work. Area five examines The connection among ANN and Statistical strategies, Finally, we finish up this article in segment 6th pursued by references and the glossary.
Plenoptic camera calibration based on microlens distortion modelling

Light field cameras have become a popular device for handheld, passive depth estimation. The geometry of the captured light rays can be equivalently described as a virtual camera array, where each view is taken from a different point on a regular grid. With this paper, we propose describing the effect of the main lens and microlens distortion on both the captured views, and on their position on this grid. It is showed that the microlens distortion affects the grid shape, implying a non-constant baseline between each view, unlike assumed in previous light field geometrical models. Moreover, we propose an end-to-end calibration method based on grid point detection. The detection is performed on raw hexagonal lattice sub-aperture images, to avoid a loss of precision in re-sampling operations. The calibration pipeline is tested on a Lytro Illum camera and is compared to the previous state of the art in this field. It is shown that the quality of the reconstructed 3D is significantly improved with our method, thanks to the fine-level description of the baseline between the virtual views. Our model is resilient to a change in the focusing distance of the main lens, making it applicable in a wide variety of scenarios.
Energy aware simulation and testing of smart-spaces

A smart-space SS typically consists of many rooms, with temperature and humidity environment attributes, devices, and software components that communicate with each other to satisfy certain test purposes that need to be checked over various realistic exterior environment weather conditions. We present a novel energy-aware approach for the validation of smart-spaces while minimizing the energy consumption encountered during testing. A framework for deriving minimal (energy) cost tests is provided. It includes SS, a controlled environment Env that depicts the exterior conditions, and a Tester that can control SS and Env, derive and runs tests, and observe relevant SS attributes in order to release a success verdict whenever a test purpose is met. A simulator is proposed for deriving tests by appropriately exploring part of the SS behavior employing several cost functions for computing the estimated cost and duration of test events. The framework is deployed in a real SS environment which is used to assess the actual energy consumption of derived tests in practice. Experiments show that the actual power consumption of the derived tests is close to the ones estimated by the simulator. A case study that assesses the gains in using energy aware tests in comparison to non energy-aware alternatives is also provided. The obtained results highlight the importance of considering power consumption in the development and testing of smart-spaces.
The Algorithm of Web Services Composition of Group Decision-making Based on Fuzzy Numbers

In order to improve the efficiency of web services, it is necessary to evaluate the integrative performance of web services composition. Aimed at the problem of the diversity of quality evaluation as well as the description of the vagueness of the language, in view of the fuzziness and diversity of service evaluation attributes, this paper proposes a group-decision service composition method based on fuzzy numbers, which can realize service evaluation by triangular fuzzy numbers. Unified processing and quantitative description of service composition are carried out, and multi-attribute group decision-making method based on cut-set matrix evaluation is adopted to optimize service composition. Finally, it proves the reliability of the algorithm through the web services combination of case analysis and simulation experiment, and improves the service quality of the combination.
A User Behavior Credibility Calculation Method Based on Behavior Co-occurrence

Based on software system user historical access data, this paper studies user visiting access behaviors and habits, Through the construction of co-occurrence model of user behavior, the credibility calculation method of user behavior is proposed. The co-occurrence model of user behavior consists of co-occurrence probability matrix and synchronization distance matrix. These two matrices are still based on user history access data, through the data processing process, the behavior sequence data is converted into the form of matrix. The calculation method of user's behavior credibility is based on the above two matrices. This method can effectively quantify the trust degree of users' behavior sequences compared with their historical behavior habits by dividing the behavior confidence interval and setting the trust threshold. Finally, the simulation results show that the method is effective.
System of Feature Extraction for Video Pattern Recognition on FPGA

A system is proposed for image recognition in a video stream, such as inscriptions, road signs. It is able to recognize the patterns in the complex lighting conditions due to processing the high dynamic range (HDR) signals. The image feature extraction is based on the method, which originated from the Retinex method but is adapted to the HDR images. Due to the new method, the bilateral filter is exchanged to the 2D edge-preserving adaptive filter. The filter output gives information for the feature extraction detectors. The experimental HDR video camera with the feature extraction is built around the Lattice HDR-60 board.
Instance of Interest Detection

In this paper, we propose a novel task named Instance of Interest Detection (IOID) to provide instance-level user interest modeling for image semantic description. IOID focuses on extracting the instances which are beneficial to represent image content, while other related tasks such as saliency analysis, attention model and instance segmentation extract the regions attracting visual attention or with a predefined category. To this end, we propose a Cross-influential Network for IOID, which integrates both visual saliency and semantic context. Moreover, we contribute the first dataset IOID evaluation, which consists of 45,000 images from MSCOCO with manually annotated instances of interest. Our method outperforms the state-of-the-art baselines on this dataset.
Large-Scale Dynamic Graph Updating Algorithm in Distributed Computing System

Distributed graph computing technology for processing large-scale graph data has been widely used in social network, communication network and so on. The foundation of distributed graph computing is reasonable partitioning of large-scale graph in distributed system. Most proposed graph partitioning algorithms cannot achieve the goals of load balance and minimizing the number of edge-cuts at the same time. This paper constructs a cost function to measure the efficiency of partitioning large-scale graphs in a distributed system, where the graph is dynamically updated in real time. Based on the cost function, the update algorithm is proposed for the addition of vertex and edge. Experimental results show that the proposed algorithm can yield significant reduction in load imbalance and number of edge-cuts.
Shooter Localization Using Social Media Videos

Nowadays a huge number of user-generated videos are uploaded to social media every second, capturing glimpses of events all over the world. These videos provide important and useful information for reconstructing events like the Las Vegas Shooting in 2017. In this paper, we describe a system that can localize the shooter location only based on a couple of user-generated videos that capture the gunshot sound. Our system first utilizes established video analysis techniques like video synchronization and gunshot temporal localization to organize the unstructured social media videos for users to understand the event effectively. By combining multimodal information from visual, audio and geo-locations, our system can then visualize all possible locations of the shooter in the map. Our system provides a web interface for human-in-the-loop verification to ensure accurate estimations. We present the results of estimating the shooter's location of the Las Vegas Shooting in 2017 and show that our system is able to get accurate location using only the first few gunshots. The full technical report, all relevant source code including the web interface and machine learning models are available.
Optimisation of linear dependence energy for object co‐segmentation in a set of images with heterogeneous contents

This work proposes a framework for simultaneously segmenting foreground objects in a collection of images having heterogeneous contents. Rather than resorting to image co-segmentation to segment similar objects in multiple images, which requires the use of categorised images, the authors’ idea disseminates segmentation information within images. In this way, it becomes easier to detect foreground objects in all of them simultaneously, mainly under the hypothesis of using similar or different images. General information is aggregated, on foregrounds as well as on backgrounds, from a set of images for joint segmentation of category-independent objects. The key idea is to estimate the linear dependence of the foreground histograms of the input images to optimise a Markov random field-based energy function. Iterative optimisation of each image permits after that the enhancement of the final segmentation results. Extensive experiments demonstrate that the proposed method (PM) enables full-object segmentation of foreground objects within a collection of images composed of different classes. Indeed, the validation of the accuracy on five challenging datasets (iCoseg, Oxford Flowers, MicroSoft Research Cambridge (MSRC), Caltech101 and Berkeley) shows that the PM achieves satisfactory results as compared with state-of-the-art methods. Besides, it has the challenging ability to efficiently deal with uncategorised objects.
Evaluating Efficiency of Methods for Automatic Orientation Solar Panel Batteries to Position with Maximum Output Electrical Power

The paper considers the main methods of automatic orientation of solar panel batteries perpendicular to the flow of solar radiation. The attention is focused on the problem of using solar energy and the economic efficiency of using stationary installation solar panel batteries. The output power by a stationary solar battery and a batteries subjected to two-axial orientation is calculated. The description is given of the experimental swivel installation. Experimentally determined the power of a two-axis swivel installation, spent for horizontal and vertical rotation at a given angle. Possible tracking algorithms for finding the optimal point for orienting a solar panel battery are considered. Based on the data obtained, the optimal positioning algorithm is determined and its economic efficiency is evaluated in comparison with a stationary battery. In addition, the system payback period is estimated.
Internet of Things Enabled Innovation Constructs in Third-Party Logistics—An Empirical Validation

Logistics is an important construct of supply chain management. Advancements in the logistics domain with specialized services and external pressures such as globalization and customer demand volatility have triggered the use of third-party logistics. It proves to be a unique and effective engagement creating a win—win for all the stakeholders. The need for real-time data is irrefutable and therefore researchers have proposed the implementation of the Internet of Things. The findings of the study have cemented the concept of amalgamation of Internet of Things and 3PL. The benefits include cost savings, real-time visibility of material as well as vehicle, just in time, flexible manufacturing systems, and most importantly customer satisfaction and an increase in market share.
Towards Distinction of Rock Art Pecking Styles with a Hybrid 2D/3D Approach

We present a qualitative study on the distinction of pecking styles in prehistoric rock art captured by high-resolution 3D scans. Different pecking styles result from different shapes, sizes, depths and spatial distributions of individual peck marks. Pecking style distinction enables inter alia automated detection of superimpositions. To our knowledge, this is the first attempt towards an automatic analysis and characterization of pecking styles in rock art. We model pecking style similarity by local descriptions of the surface joining full 3D and 2D (image-space) representations. Our results show that different pecking styles can be efficiently retrieved and distinguished by combined 3D/2D surface analysis.
The Evolution of Open-Source Blockchain Systems

Blockchain enjoys a rapid development over current years, penetrating multiple areas of application. However, despite the active evolvement of blockchain systems, no special attention is attached to such a hot spot. To gain a clear image of their evolution process, we conducted an empirical study on six open-source blockchain projects with long life span, covering a total of 504 versions. We attempted to verify whether Lehman's Laws are still applicable to blockchain applications over the passage of time with multiple metrics, and found that there do exist laws like declining quality are not confirmed. We raised our new findings---the centralized trends of revisions and non-smooth growth based on the experimental results as well. By this paper, we hope to provide future researcher on blockchain with an overview of its evolution and reveals the points on which special effort should pay during the periods of development and maintenance.
A Correlation Filter Target Tracking Algorithm Combining LK Optical Flow

Correlation filtering is currently one of the important methods in the field of target tracking. However, due to occlusion, deformation, motion and other factors, the correlation filtering will learn too much background information, which makes the tracking failure. Therefore, this kind of algorithm still needs to be studied. We propose a correlation filter tracking algorithm that combines LK optical flow (Lucas-Kanade, LK). The key feature points are resulted by matching the FB (forward and backward) optical streams of the video sequence to determine whether the target is occluded. For the occluded target, the optical flow tracking is used to get the predicted position of the target; if no occlusion occurs, the position is obtained by the correlation filtering algorithm with high computational efficiency. In addition, after determining the target prediction position, the selected key feature points are used to achieve the adaptive change of the target scale. The above algorithm is evaluated by selecting the OTB public video sequence. The experimental results show that the proposed algorithm can effectively deal with various occlusion and scale problems, and has higher accuracy and robustness than other advanced algorithms.
DISTINCTIONS OF THE RESULTS OF AUTOMATED ANALYSIS AND MICROSCOPIC BLOOD INVESTIGATION (EXAMPLES OF CLINICAL CASES)

There are cases of discrepancies between the results of a microscopic examination of blood cells by morphological physicians with data obtained using an automatic analyzer in patients with a hematologic profile. It is shown that in some cases the analyzer does not determine changes in the morphology of erythrocytes, allows errors in calculating the leukoformula, differentiation of cells according to the degree of maturity, and evaluation of the number of platelets. The complementary importance of methods of light microscopy and automated blood analysis in patients with suspected pathology of the blood system is underlined.
Data mining and data driving of harmonic in AC arc furnaces based on functional analysis

Abstract This paper use big data technology, mine synchronous harmonic measuring data of AC arc-furnaces whole production process with functional analysis, verifies that harmonic current released from AC arc-furnaces is in independence and orthogonality approximately between each other. The discovered knowledge shows that multi-arc-furnaces harmonic current can be largely counteracted each other, If these AC arc-furnaces were connected to a PCC in which short-circuit capacity is enough for that the superposed harmonic current of AC arc-furnaces can be electromagnetic compatible, then power quality of the PCC can reach the qualified level discovered knowledge further reveals the model of AC arc-furnace, disorder data stream generated from harmonic measuring data is derived to calculation composition harmonic current at PCC(Point of Common Connection) in which AC arc-furnaces were connected.
A fast stitching method based on verticality calibration using three marker points

The speed of three-dimensional (3D) reconstruction based on structured light is affected by the stitching speed. In order to improve the stitching speed, a fast stitching method based on verticality calibration is proposed. Firstly, vertical calibration is used to place the camera perpendicular to the reference plane; then, the rotation axis is corrected. A normal vector of a plane based on three marker points is determined, and the decomposition vector perpendicular to the corrected rotation axis is obtained. Secondly, the angle between the decomposition vector before and after transformation is calculated. Finally, the rotation (R) matrix and translation (T) matrix are solved, and the stitching is completed. Experiments show that this method can improve the speed of surface stitching in 3D reconstruction based on structured light. The average error of stitching is 0.1103 mm and the standard deviation is 0.0327 mm when the stitching area is 250 mm × 90 mm.
Lavoisier: High-Level Selection and Preparation of Data for Analysis

Most data mining algorithms require their input data to be provided in a very specific tabular format. Data scientists typically achieve this task by creating long and complex scripts, written in data management languages such as SQL, R or Pandas, where different low-level data transformation operations are performed. The process of writing these scripts can be really time-consuming and error-prone, which decreases data scientists' productivity. To overcome this limitation, we present Lavoisier, a declarative language for data extraction and formatting. This language provides a set of high-level constructs that allow data scientists to abstract from low-level data formatting operations. Consequently, data extraction scripts' size and complexity are reduced, contributing to an increase of the productivity with respect to using conventional data manipulation tools.
Automation of Manual Fixing Axle ARM Bush into the ABS Sleeve and Ensuring Safety to the Workers

The main objective of the project is to eliminate repetitious work of hammering the bush into the sleeve and ensuring the safety to the workers by introducing the hydraulic press. Here the manual handling of hammering the bush is converted into partial automation studying the motion of the worker. A 16 ton press is introduced for this function with 100 litres of 68 grade oil and star delta starter with 10 HP capacities. After BS IV norms, ABS(Anti-lock Breaking System) came in an important position of modern world. To facilitate the effective sensing of speed, a bush is inserted into the sleeve of the front axle arm. In front axle assembly this operation of manual hammering is carried out This type of axle arm comprises of different types based on the different model of vehicles. The main aim is to eliminate the manual process into semi-automatic condition considering the safety precautions to the workers. This hammering process includes a 3 to 4 hammering strokes to insert the bush into the sleeve. So we searched for many process through which the manual Hammering can be eliminated. Two emerging process involved in reciprocating motions are1.Using hydraulics.2.Using pneumatics.As pneumatics works under compressed air and it is quite difficult to control the speed, in the case of hydraulics which uses oil as medium it is easy to control the speed. So we choose hydraulic as the best suitable for this operation. Double acting cylinder which is cheap is most suited in cost wise also but managing the resource, we came to know that an old unused hydraulic press is there. So our guide helped us in setting up the hydraulic press. Again we had a problem in holding the axle arm while pressing the bush. We are in a situation to facilitate the holding device to hold all types of axle arm and made a bush holding device. So we displaced the existing ram plate in the hydraulic press as such it can support all types of axle arm. We also designed and fabricated the bush holding devices which effectively supports the bush to its sleeve. The work holding device i.e., axle arm holder and bush holding devices are designed and fabricated for the effective functioning of hydraulic pressing. Due to this semi-automatic function the injuries happening in manual handling is avoided thus safety to the workers is ensured.