Reviewerly/exHarmony huggingface dataset:
Ships Target Detection on Water Based on Improved Faster RCNN

The intelligent research of watercraft detection is of great significance to the construction of marine transportation industry. Based on the Faster RCNN algorithm, this paper proposes a watercraft target detection algorithm based on the improved Faster RCNN. Due to the serious loss of low-level feature information during feature extraction. The fusion path between high-level and low-level features is longer, which increases the difficulty of information positioning. Specially increase the side fusion path network to strengthen the fusion of low-level features and high-level information from bottom to top. In view of the situation where the gradient of the Intersection over Union (IoU) bounding box loss is 0 and the intersection method not judged, the Distance-IoU loss function is introduced to improve the process of NMS filtering out repeated target boxes, improve the accuracy of position regression and improve the missed and false detection situations. Experiments show that the improved Faster RCNN algorithm in different weather environments has mean Average Precision (mAP) of 86.12% on ship images, which is an increase of 13.24% compared with the Faster RCNN algorithm, and the average IoU has increased by 6.94%. The overall detection accuracy is rate is improved.
Research on internal control of accounting information system based on BP neural network

In this paper, qualitative and quantitative evaluation criteria can be used to evaluate the internal control system. Qualitative evaluation is influenced by the subjective judgment of evaluators and often lacks objectivity, while quantitative evaluation is welcomed by audit institutions because of its scientific, accurate and comparable characteristics. This paper constructs the internal control evaluation index, takes the characteristic information describing the internal control status of accounting information as the input vector of the neural network, and takes the value representing the corresponding comprehensive evaluation result as the output of the neural network, and trains the network with enough samples to make different input vectors get different output values, This paper makes a quantitative evaluation on the control status of accounting information system.
Online Calling Cards and Professional Profiles in Cybersecurity From Social Media

Demand is very high for people to work in various cybersecurity professions and ceteris paribus that demand may well continue into the near term. While there are more formal trails for employment, such as higher-educational pathways, performance in cybersecurity competitions, participation in professional conferences, and social media presentations may all offer less conventional paths into cybersecurity hiring. Through a convenience sample across a number of social media platforms and bottom-up coding, this work explores some aspects of cybersecurity professional profiles (“calling cards”) available on the open Social Web and what may be learned about respective skills and capabilities from these glimmers of the person(s) behind the profiles. These profiles are assessed based on a 2x2 axis with focuses on (1) target skills and (2) personhood attributes. From these analyses, some tentative insights are shared about the cybersecurity calling cards and how informative they may be for recruitment and retention of cybersecurity workers.
Management of Encrypted Data and De-Duplication of Big Data in Cloud Computing

Cloud computing is the accessibility of computer resources which includes storing of data and power to compute the data, when there is a demand of it for several applications. It is used to represent the data centers to users in the end over the Internet. The user cannot directly manage it actively. The challenging problem is the integrity of outsourced data. The encryption and decryption of data must be secure to overcome this problem. Here we use MD5 encryption algorithm to secure the outsourced data. Data replication is avoided by using de-duplication technique; thus the efficiency of memory storage is enhanced and time for processing is minimized.
A Generative Machine Learning Approach to Policy Optimization in Pursuit-Evasion Games

We consider a pursuit-evasion game [1] played between two agents, ‘Blue’ (the pursuer) and ‘Red’ (the evader), over T time steps. Red aims to attack Blue’s territory. Blue’s objective is to intercept Red at time T and thereby limit the success of Red’s attack. Blue must plan its pursuit trajectory by choosing parameters that determine its course of movement (speed and angle in our setup) such that it intercepts Red at time T. We show that Blue’s path-planning problem in pursuing Red, can be posed as a sequential decision making problem under uncertainty. Blue’s unawareness of Red’s action policy renders the analytic dynamic programming approach intractable for finding the optimal action policy for Blue. In this work, we are interested in exploring data-driven approaches to the policy optimization problem that Blue faces. We apply generative machine learning (ML) approaches to learn optimal action policies for Blue. This highlights the ability of generative ML model to learn the relevant implicit representations for the dynamics of simulated pursuit-evasion games. We demonstrate the effectiveness of our modeling approach via extensive statistical assessments. This work can be viewed as a preliminary step towards further adoption of generative modeling approaches for addressing policy optimization problems that arise in the context of multi-agent learning and planning [2].
Fall-Perceived Action Recognition of Persons With Neurological Disorders Using Semantic Supervision

Frequent uncertain falls is one of the common cause of injury among elderly adults and persons suffering from the neurological disorder. It will be costlier to go through <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$24\times 7$ </tex-math></inline-formula> medical monitoring if we monitor a person suffering from the early stage of the neurological disorder. An “uncertain” action classification model can be a less costly and easily scalable. It can help to regularly monitor a person suffering from neurological declines and how frequent it relapse. In this article, we propose a video-based action recognition with fall detection architecture, FallNet, which learns the features of uncertain actions related to day-to-day activities. FallNet first incorporates semantic supervision using the per-class weight of uncertain action through class-wise weighted focal loss. It addresses both the class imbalance problem and the weak interclass separability issue. We design a joint training model to train the overall architecture efficiently in an end-to-end manner. We utilize benchmark data sets, OOPS, HMDB51, and Kinetics-600, for experimentation that has less falling action videos. Therefore, we have collected videos to create a data set, denoted by FallAction, that consists of different 15 falling action classes with an average of 100 videos per class. The proposed network gain an accuracy of 13.2% in OOPs, 2% in HMDB51, and 0.2% in Kinetics-600 data set.
ACO-Based Resource Allocation Hybrid Algorithm for Cloud Environment

Distributed computing is the result of the advancement of data enumeration. The more the exploration and applications on cloud computing more will be the innovation of processing gets more extensive. The cloud processing has a huge gathering of user. A huge number of errands are managed it. The principle issue in distributed computing emerges while allotting equipment assets to the assignments and planning the undertakings to the resource pool. This paper delivers to the current circumstance of asset portion strategy and occupation planning calculations under cloud condition [1, 2]. Matrix figuring, so, as to help complex issues uses the conveyed heterogeneous assets. Lattice can be categorized into two sorts: figuring matrix and information network. Employment planning for registering lattice is a significant issue. For effective use of networks, we need a productive employment planning calculation for the task of occupations to assets in matrices. Characteristic environment put center around the ants that they have a gigantic capacity to shape a group and to locate an ideal pathway to food resources. The conduct of ants is reproduced by an Algorithm. In this paper, the prime commitments of our undertaking are load balancing for the whole framework while attempting to limit the make span of a given arrangement of occupations. ACO can beat other employment booking calculations as per the test results when analyzed.
Logarithmic Morphological Neural Nets Robust to Lighting Variations

Morphological neural networks allow to learn the weights of a structuring function knowing the desired output image. However, those networks are not intrinsically robust to lighting variations in images with an optical cause, such as a change of light intensity. In this paper, we introduce a morphological neural network which possesses such a robustness to lighting variations. It is based on the recent framework of Logarithmic Mathematical Morphology (LMM), i.e. Mathematical Morphology defined with the Logarithmic Image Processing (LIP) model. This model has a LIP additive law which simulates in images a variation of the light intensity. We especially learn the structuring function of a LMM operator robust to those variations, namely: the map of LIP-additive Asplund distances. Results in images show that our neural network verifies the required property.
Recognizing Sentences Concerning Research Methods of Citation from the Full Text of JASIST

Citing behavior is the basic academic behavior of researchers, which can reflect the situation that researchers refer to previous research results, show the inheritance and communication of Science, and also show respect for others' research results. Citation analysis has always been an important research method in bibliometrics and is often used in hot spot research and trend analysis of disciplines. In this paper, deep learning method is used to fine-tune a pre-training model to predict whether sentence-level data in academic full-text is a citation research method. The average effect of the model is 85.47%, which proves that the model can effectively identify citation methods in academic full-text.
Improved Secure Identification-Based Multilevel Structure of Data Sharing in Cloud Environments

The Cloud Computing Environment (CCE) developed for using the dynamic cloud is the ability of software and services likely to grow with any business.It has transformed the methodology for storing the enterprise data, accessing the data, and Data Sharing (DS).Big data frame a constant way of uploading and sharing the cloud data in a hierarchical architecture with different kinds of separate privileges to access the data.With the requirement of vast volumes of storage area in the CCEs, capturing a secured data access framework is an important issue.This paper proposes an Improved Secure Identification-based Multilevel Structure of Data Sharing (ISIMSDS) to hold the DS of big data in CCEs.The complex file partitioning technique is proposed to verify the access privilege context for sharing data in complex CCEs.An access control Encryption Method (EM) is used to improve the encryption.The Complexity is measured to increase the authentication standard.The active attack is protected using this ISIMSDS methodology.Our proposed ISIMSDS method assists in diminishing the Complexity whenever the user's population is increasing rapidly.The security analysis proves that the proposed ISIMSDS methodology is more secure against the chosen-PlainText (PT) attack and provides more efficient computation and storage space than the related methods.The performance of the proposed ISIMSDS methodology provides more efficiency in communication costs such as encryption, decryption, and retrieval of the data.
BISRAC Banking Information Security Risk Assessment and Compliance Model

Technological innovations have improved bank’s capabilities, by enhancing many of their customer-facing, frontend operations with digital solutions. Increased adoption of information technology applications by Indian banks leads to automation of various banking services which, in turn acts as effective way to reduce the cost incurred and time requirement for financial deployment. In this paper, a tool "BISCRAC" has been specifically developed for Indian banks which can aid banks in performing information security risk assessment and check compliance against information security best practices as per RBI guidelines. "BISRAC" has been deployed in real-time and mandates the banks to prepare their incident reporting database which is further used by the tool to calculate information security risks. The results in turn help banks to assess their current information security posture and associated risks. Failure Mode Effect Analysis (FMEA) model has been customized for banks and is used as a pedestal model for performing risk assessment by generating Risk Priority Numbers (RPN). The proposed model has been tested across three nationalized banks. Even though banks have implemented technical and operational controls, but still there is a lack of awareness about information security risks and compliance to RBI guidelines. "BISRAC" can be used iteratively in banks to aid them to assess current information security posture, adopt measures to neutralize or reduce associated risk and again measure the enhanced security posture both quantitatively as well as qualitatively.
Comparison on Object Detection Algorithms: A Taxonomy

Visual object detection is a popular task, which categorizes all the defined objects in the whole images. With the emerging of numerous object detection frameworks, many detection methods have been proposed. In this paper, we aim to survey several distinguished detection methods, which consists of two-stage based methods and one-stage based methods. In specific, we summarize 9 different object detection papers, including RCNN based methods, YOLO, M2Det and CornerNet, etc. All these papers play important roles and obtain state-of-the-art performances. We summarize all the frameworks, motivations and training steps for all methods. We hope that our review can give an in-depth guidance for the beginners for recognizing this search fields.
ML &amp; AI Application for the Automotive Industry

The dawn of Industry 4.0 fosters new opportunities for manufacturing companies in order to improve their operational efficiency and competitiveness through the use of Machine Learning (ML) and Artificial Intelligence (AI) applications. The automotive industry has always been a leading industry in applying new methodologies and R&D results to all the steps of its value chain. Due to the variety and typology of data available through these different steps, new data processing architectures shall be applied in order to empower novel data analytics applications. This chapter presents three use cases related to the application of ML and AI solutions in manufacturing processes from the automotive sector: (I) Real-time quality prediction, (ii) one-off raw material optimization and (iii) real-time industrial robot anomalous behaviour detection. Data analytics allow to identify in advance the generated product quality and speed up the production process and reduce waste, while ensuring the quality of all parts generated for each batch. In this case, this system is working in real-time while the plant is being producing. In other cases, the analysis is done once and there is no need to re-measure the parameters in real time. This is the case of the identification of the lower hydrogen consumption for a minimum product quality accepted by the client. Some industries include robots from different manufacturers. The management becomes complex due to the isolated reporting platforms. A system is presented for capturing data in real-time, analyzing and warning anomalies.
Multi-Operator based Saliency Detection

Considering the changed size and aspect ratio of the display devices, it is required to resize the presentation of images of those devices. As displays become less expensive and are consolidated into an ever-increasing number of gadgets, there is an expanded spotlight on the image resizing systems to fill a picture to subjective size of the screen. Removing vigorous visual saliency guides and picture editing are central issues in PC vision, designs, and many more. It isn't easy to distinguish and trim the whole remarkable object from pictures with a complex foundation. In this paper, the different techniques for image resizing and preserving important objects are discussed. This paper also presents novel methods for image resizing based on solving the current problem with it.
Investigation of Susceptible Characteristics in Network MLM and Big Data Prevention

As a new form of crime in the Internet age, college students are the key targets of network MLM. In order to study the characteristics of the susceptible population of MLM, this article takes college students in Nanchang as an example, collects research data using questionnaires, and conducts actions on 800 college students. Measurements, using methods such as single-factor analysis of variance, multiple comparisons of LSD, and self-analysis of big data documents. The study found that the difference in grade, major, and employment pressure of college students will significantly affect their susceptibility to MLM. Students facing high employment pressure are more susceptible to MLM. Sophomores and juniors in grades are more susceptible than seniors. Professional management Classes and agronomy classes are less susceptible than other majors. Finally, relevant big data visualization, knowledge map and other prevention and control suggestions are putting forward.
Multispectral Image Compression Algorithm Based on Silced Convolutional LSTM

The multispectral imaging which are used for remote sensing imaging has a large amount of data, so this paper proposed a deep learning method which is based on sliced convolutional LSTM for multispectral image compression. Compared with other algorithms, the proposed algorithm further compresses the multispectral images by considering the similarity between the spectra and removing the inter-spectral redundancy. The proposed algorithm is based on end to end framework which is consist of encoder, decoder, entropy coding and quantizer. In experiments, the PSNR of proposed model is compared with that of JPEG2000 to evaluate the performance of our algorithm at several different bit rates.
Improve the Appreciation of Literary Works of College Students Based on Virtual Reality Technology Simulation

With the continuous development of education informatization in my country, how to apply VR technology to teaching has become an issue of concern to people. This research mainly discusses how to improve the appreciation of literary works of college students based on virtual reality technology simulation. The control part of the virtual experiment system uses the external dynamic behavior control principle to provide an interactive platform for the virtual experiment scene. The relevant trigger information in EON can realize actions or display information in Visual Basic. The trial target of this system is third-year students in higher vocational colleges. Students use this system to assist in the study of literary works. With the help of VR technology, he walked into ancient poems to describe the scene, reproduced the creative environment, and walked into the artistic conception of poetry creation. The assessment collects data in the form of questionnaire surveys. The significance level of the homogeneity test is 0.38>0.05, so under the 95% confidence level, the literary appreciation ability of the students trained by the virtual experiment system is better than that of the traditional classroom teaching students. This research is of great significance in pioneering informatization teaching.
Real-Time Tracking and Stereoscopic Matching for Mixed Reality

Robotic-Assisted Surgery (RAS) is beginning to unlock its potential. However, despite the latest advances in RAS, the steep learning curve of RAS devices remains a problem. A widely used teaching resource in surgery is the display of videos of previous procedures, which in RAS are almost always stereoscopic. On these videos, it is important to be able to make virtual annotations so that certain elements of the surgical process are tracked and highlighted during the teaching session. Including virtual annotations in stereoscopic videos turn them into Mixed Reality (MR) experiences, in which tissues, tools and procedures are better observed. However, an MRbased annotation of objects requires tracking and some kind of depth estimation. For this reason, this paper proposes a real-time hybrid tracking-matching method for performing virtual annotations in RAS videos. The proposed method is hybrid because combines tracking and stereo matching, avoiding the need to calculate the real depth of the pixels. The method was tested with five different state-of-the-art trackers and assessed with videos of a sigmoidectomy of a sigma neoplasia, performed with a Da Vinci® surgical system. Objective assessment metrics are proposed, presented and calculated for the different solutions. The results show that the method can successfully annotate RAS videos in real-time. Of all the trackers tested for the presented method, the CSRT (Channel and Spatial Reliability Tracking) tracker seems to be the most reliable and robust in terms of tracking capabilities. In addition, in the absence of a ground truth, an assessment with a domain expert using a novel continuous rating method with an Oculus Quest 2 Virtual Reality device was performed, showing that the depth perception of the virtual annotations is good despite the fact that no absolute depth values are calculated.
Towards General Natural Language Understanding with Probabilistic Worldbuilding

Abstract We introduce the Probabilistic Worldbuilding Model (PWM), a new fully symbolic Bayesian model of semantic parsing and reasoning, as a first step in a research program toward more domain- and task-general NLU and AI. Humans create internal mental models of their observations that greatly aid in their ability to understand and reason about a large variety of problems. In PWM, the meanings of sentences, acquired facts about the world, and intermediate steps in reasoning are all expressed in a human-readable formal language, with the design goal of interpretability. PWM is Bayesian, designed specifically to be able to generalize to new domains and new tasks. We derive and implement an inference algorithm that reads sentences by parsing and abducing updates to its latent world model that capture the semantics of those sentences, and evaluate it on two out-of-domain question-answering datasets: (1) ProofWriter and (2) a new dataset we call FictionalGeoQA, designed to be more representative of real language but still simple enough to focus on evaluating reasoning ability, while being robust against heuristics. Our method outperforms baselines on both, thereby demonstrating its value as a proof-of-concept.