Reviewerly/exHarmony huggingface dataset:
Hedging static saliency models to predict dynamic saliency

In recent years, many computational models for saliency prediction have been introduced. For dynamic scenes, the existing models typically combine different feature maps extracted from spatial and temporal domains either by following generic integration strategies such as averaging or winners take all or using machine learning techniques to set each feature’s importance. Rather than resorting to these fixed feature integration schemes, in this paper, we propose a novel weakly supervised dynamic saliency model called HedgeSal, which is based on a decision-theoretic online learning scheme. Our framework uses two pretrained deep static saliency models as experts to extract individual saliency maps from appearance and motion streams, and then generates the final saliency map by weighted decisions of all these models. As visual characteristics of dynamic scenes constantly vary, the models providing consistently good predictions in the past are automatically assigned higher weights, allowing each expert to adjust itself to the current conditions. We demonstrate the effectiveness of our model on the CRCNS, UCFSports and CITIUS datasets.
Waveform selection for multistatic target tracking with Cartesian estimates

This chapter is concerned with multistatic tracking using Cartesian estimates, where only desired measurements from two receivers in the system are selected and utilized to compute a Cartesian state estimate for target dynamics which is then processed by a linear Kalman tracker. Joint adaptive selection of radar waveform and Cartesian estimate is presented to optimize tracking performance. The Cramér–Rao lower bounds for Cartesian estimates are derived and exploited in the development of the joint adaptive selection scheme. The tracking system under consideration provides three important advantages; viz., (i) minimal requirement for bandwidth and power consumption in transmitter-receiver communication links by utilizing only one Cartesian estimate for target tracking, (ii) performance optimization brought about by the joint adaptive selection of radar waveform and Cartesian estimate, and (iii) inherent benefits of linear estimation, e.g., stability, from the use of linear Kalman filter. The performance advantages of the presented tracking system are demonstrated via simulation examples.
A 10 kN portable electromagnetic vibrator for near-surface studies

SummaryIn a previous paper, we described how a small electromagnetic vibrator could be manufactured using commercially available components for less than US$2,000. The basis of the unit was a set of four low-frequency actuators designed for use in home-theatre systems. In this paper we describe a new unit with the number of actuators increased from four to ten and improved quality electronic components.The maximum output of the vibrator, as measured using load-cells, was more than 10 kN; when operating the unit in the field we noticed that the weight of the vehicle was not sufficient to prevent it decoupling. Variation between the load-cell and accelerometer measurements, consistent with similar studies conducted using hydraulic vibrators, suggests that the new unit has considerable potential as a research tool looking at issues such as baseplate flexure. A VSP acquired using distributed acoustic sensing showed signal to a depth of 850 m for a single sweep.
Sistem Informasi Penjualan Produk Berbasis Web dan Penyaluran Donasi Untuk Animal Shelter

Now these online purchases have become convenience for buyers. Pet shop owners sell food products and other animal-related products using desktop-based programs so that not many people know about the products being sold. Many animal shelters require funds for daily needs and medical care. If the animals are well maintained and healthy, they will be adopted by potential adopters who can care for them. The result of this adoption is one way to obtain funds for fees and other animal care at the shelter. The shop owner also wants to invite shoppers to help abandoned animals by offering donations. This design application is made to sell products needed by animal-based websites, as well as designing donations that will be offered to customers. The donation begins with the purchase of products in the form of pet food and other products. The donations will be distributed to animal shelters that can care for and maintain animal health. Customers can also see the donation bookkeeping based on the expenditure of funds from the donations collected and display animals that have been treated. In the development of this application program made with several stages of software development using the Waterfall Method which will be discussed sequentially according to the stages. As for making this application using the PHP programming language and Javascript, with the CodeIgniter framework, and for the database using MySQL. With the sale of products and web-based donations, this can provide more extensive information about the products being sold and at the same time invite customers to take care of abandoned animals.
Computational Complexity and Tort Deterrence

Standard formulations of the economic model of tort deterrence constitute the injurer as the unboundedly rational bad man. Unbounded rationality implies that the injurer can always compute the solution to his care-taking problem. This in turn implies that optimal liability rules can provide robust deterrence, for they can always induce the injurer to take socially optimal care. In this paper I examine the computational complexity of the injurer's care-taking problem. I show that the injurer's problem is computationally tractable when the precaution set is unidimensional or convex, but that it is computationally intractable when the precaution set is multidimensional and discrete. One implication is that the standard assumptions of unidimensional and convex care, though seemingly innocuous, are pivotal to ensuring that tort law can provide robust deterrence. It is therefore important to recognize situations with multidimensional discrete care, where robust tort deterrence may not be possible.
Attention-Guided Model for Robust Face Detection System

Face detection is a basic computer vision task which is required by various higher level applications including surveillance, authentication, and security system. To satisfy the demand on a high quality face detection method, this paper proposes a robust system based on deep learning model which utilize an attention-based training mechanism. This strategy enables the model to not only predicting the bounding boxes of faces but also outputs a heatmap that corresponds to the existence of faces on a given input image. The proposed method was trained on the most popular face detection dataset and the results show that it produces comparable performance to the existing state of the arts methods.
A Technique to Eliminate Cloud of RS Images

Uneven illumination phenomenon and the cloud are common factors in lower quality aerial images, which will lead to the ground cover image, tonal change, distribution of color and brightness in RS images. This paper presents a technique to eliminate the cloud of the RS images, which uses filter dodging and information compensation to the processed images, in order to achieve a clear representation of the ground cover information.
Basics of programming in the context of software engineering

The article discusses the use of a constructive approach to building a program that is systematically cultivated in software engineering and made possible by a number of fundamental results obtained in programming theory. On the first, based on the structural theorem, reasonably was refused to use the go to operator and proposed a structured programming method, which provided a real way to create understandable programs. On the second, the concept of a subroutine, although it was used only to reduce routine work in the programming process, was the first means of modular presentation of programs. Later, the block and the subroutine formed the basis of block-oriented (procedural, subroutine) languages and the procedural (subroutine) programming method. In the third, to answer questions related to the definition of boundaries, sizes, and building module, the concept of connecting parts that make up a module and connections to indicate the connection between modules; specific modularization criteria; proposed a device module based on the concept of information hiding were introduced. The module is implemented in the programming language Modula, and later Modula-2. The concept of a module based on the modular (compositional) programming method. In the Simula 67 language, the foundations of object-oriented languages were introduced. They developed through the work on the concepts of inheritance, late binding and links, and were completed by the development of object-oriented languages and object-oriented (classification) programming. Thus, a framework for reuse, systematic reuse and component software development was created. Now these works are developing in the direction of research and creation of software as system of systems, using the link between system analysis and software engineering, and developing software system engineering. In the article, for learning the basics of programming, as a tool that allows clarifying the concept of program design, classification is used. As a classification feature - the level of encapsulation, which is based on the principles of software engineering – encapsulation and multi-level presentation -, is used. Applying the principle of encapsulation at different levels of representation program structure corresponding to different degrees of software abstraction, the concept of encapsulation level was obtained. Using this concept, you can find out the types of software constructions and the corresponding methods of programming (construction) programs. Using introduced the concepts and the constructive approach to building the program, the author created the didactics of the basics of programming, which was deployment through lectures for students of the specialty “Software Engineering” (121) and a textbook of the author for students and post-graduate students of the specialty. Problems in programming 2019; 3: 45-57
Hacker’s Multiple-Precision Integer-Division Program in Close Scrutiny

Before the era of ubiquitous computers, the long-division method was presented in primary schools as a paper-and-pencil technique to do whole-number division. In the book “Hacker’s Delight” by Warren [2nd edition, 2013], an implementation of this algorithm was given using the C programming language. In this paper we will report our experiences when converting this program to a generic program-library routine. The highlights of the paper are as follows: (1) We describe the long-division algorithm—this is done for educational purposes. (2) We outline its implementation—the goal is to show how to use modern C++ to achieve flexibility, portability, and efficiency. (3) We analyse its computational complexity by paying attention to how the digit width affects the running time. (4) We compare the practical performance of the library routine against Warren’s original. It is pleasure to announce that the library routine is faster. (5) We release the developed routine as part of a software package that provides fixed-width integers of arbitrary length, e.g. a number of type (editor’s note: the non-transliterated form used in the code is ) has 2019 bits and it supports the same operations with the same semantics as a number of type .
Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia

This paper describes the study of overlapping leukaemia cells based on geometric features for identification and classification. Geometric features of blood cells are proposed to identify and classify overlapping cells into groups based on different overlapping degrees and the number of overlapped cells. In the proposed method, the percentage of average accuracy for identifying overlapping cells reached 98 percent. The proposed method can segment white blood cells from the overlapping cells with an overlapping degree of 70 percent. Improved Watershed Algorithm successfully increased 36.89 percent of accuracy in WBC segmentation. It reduced 46.12 percent of the over-segmentation problem. Tests of cell counting are conducted in the two stages, which are before and after the process of identification and classification of overlapping cells. The average percentage of total cell count is 83.31 percent, the average percentage of WBC counting is 84.8 percent, and the average percentage of RBC counting is 60.55 percent. The proposed method is efficient in identifying and classifying overlapping cells for increasing the accuracy of cell counting.
Agent-based MR Simulation as a Tool for Combat Search and Rescue Command and Control

Combat search and rescue(CSAR) is a significant part of joint combat operation. The isolated personnel in hostile territory is of great value to both sides. For the sake of extremely high cost and massive consumption of time in military exercises, virtual reality is a preferred way to conduct the experiment. Mixed reality, known as MR, allows users to interact with the virtual objects, as well as the real objects. With MR devices, commanders get a better awareness of both virtual combat and real environment. Agent-based modelling is a relatively new approach to modelling systems composed of interacting objects. By analyzing the decision-making process of agents, codes are written to drive the simulation. Finally, the whole system is applied to HoloLens to provide a holographic demonstration.
An IoT based distortion invariant motion detection using raspberry pi with mean shift algorithm

Motion detection is one of the most commonly used image processing technologies because of its high availability. To detect motion, a special camera is used to prevent image distortion due to noise from the external environment such as sunlight in practice. However, the cost and complicated installation can be the problem.
Detection of Causative Attack and Prevention Using CAP Algorithm on Training Datasets

Machine learning is the scientific study of algorithms, which has been widely used for making automated decisions. The attackers change the training datasets by using their knowledge, it cause impulses to implement the malicious results and models. Causative attack in adversarial machine learning explores certain security threat against carefully executed poisonous data points into the training datasets. This type of attacks are caused when the malicious data gets injected on training data to efficiently train the model. Defense techniques have leveraged robust training datasets and prevents accuracy on evaluating the machine learning algorithms. The novel algorithm CAP is explained to substitute trusted data instead of the untrusted data, which improves the reliability of machine learning algorithms.
Modeling tuberculosis patients using loglinear models

Abstract Tuberculosis (TB) patients usually get infected by tuberculosis bacteria called Mycobacterium tuberculosis which can spread from one person to another through air. TB cases normally infect the lungs (Pulmonary TB) and in some other cases it infects other organs such as brain, heart, spine, lymph nodes, adrenal gland and intestines (Extrapulmonary TB). However, patients who are infected with tuberculosis bacteria may have complications due to other factors that can delay the process of healing. Using loglinear modelling, this study will examine whether types of TB is strongly associated with patient’s age, gender, whether or not they smoke, have HIV and into drug use. Records of 477 TB patients obtained from a previous related study are used in the modelling process where five loglinear models are compared to find the most parsimonious model that will determine the strength of the association between the variables. Likelihood ratio G 2 , p-values, expected values and standardized residuals are used in the analysis. Based on the comparisons of the loglinear models, homogeneous model is found to be the most parsimonious compared to independence and joint models. The analysis based on the homogenous loglinear model show that that the estimated odds of patients who smoke is 2 times more likely to have a Pulmonary TB compared to the non-smokers. Also patients who are drug users are 8 times more likely to have a Pulmonary TB compared to patients who are non-drug users. Hence, a recommendation for the healthcare providers is to give more attention to patients in these categories.
Data security based on homographic function

In this paper, we adapt a homographic function to encrypt digital images, which takes into account the value of the pixel as well as its position in the image. The homographic function is a function represented as a quotient of two affine functions in a switching field K; it is represented by four different coefficients of zeros, which must verify certain conditions. One take the field K equal Z/257Z switching field (257 is a prime number), (all its non-zero elements are invertible, we take into account the following condition if we have 0 as the pixel value we put 256), 256 is the inverse of 256 in Z/257Z. We will show that this a homophonic substitution, which can represent the confusion part in a modern cryptographic system. We strengthened this proposal by a diffusion method and, we have used the Hénon attractor of is 2-dimensional; each pixel will be permuted to another position of the encrypted image. We managed to build a cryptosystem who passed all the statistic tests, and that has a secret key of 253 bits in length.
Named entity recognition in steel field based on BiLSTM-CRF model

NER is a basic research in the field of NLP. Most of the Chinese NER tasks are for the recognition of names of people, places, and institutions. The recognized content is single and cannot be applied in many fields. In this paper, we label and build the NER dataset for the steel field. Based on the BiLSTM-CRF model, we proposed character and word combination embedding method. Experiments show that our method reduces the OOV problem, which has an increase in 7.20% and 5.03% in the F1 score compared with the traditional character embedding and word embedding.
Fast Video Clip Retrieval Method via Language Query

The goal of video clip retrieval is to find video clips that match the description of the query in massive video data based on natural language queries. The booming of video-based social media, the increase in the amount of video data and the increasing complexity of video content have created challenges for video retrieval. The existing relevant methods rely on more complex description paragraphs to match corresponding videos then associate each sentence with specifically interest segment, which is need more rich language queries supervision. In this paper, we aim to improve the efficiency by generating proper descriptions from the videos and searching the clips only in the possible videos, which descriptions matches the queries. Specifically, our method is top-down framework, which divides the task into two stages. The upper stage is basically coarse retrieval that selects candidate videos according the description of the videos. The bottom stage is video clips locating that is done by matching the queries with candidate clips through the matching strategy. We tested our method with the existing methods on Charades-STA dataset and the experimental data shows it improves remarkable performance.
CACAO: Conditional Spread Activation for Keyword Factual Query Interpretation

Information retrieval is regarded as pivotal to empower lay users to access the Web of Data. Over the past years, it achieved momentum with a large number of approaches being developed for different scenarios such as entity retrieval, question answering, and entity linking. This work copes with the problem of entity retrieval over RDF knowledge graphs using keyword factual queries. It discloses an approach that incorporates keyword graph structure dependencies through a conditional spread activation. Experimental evaluation on standard benchmarks demonstrates that the proposed method can improve the performance of current state-of-the-art entity retrieval approaches reasonably.
A Study in the Selection Factors of Facility Management(FM) Enterprises Using AHP Analysis

Looking at recent safety accidents in buildings, most of them due to lack of FM expertise, and this is due to human resources. In each all industry areas, some companies are competitive, representational, and leading in the market through long-term expertise and research in their respective fields. In the field of FM industry, some companies offer core-competencies in internal and external innovative building management through competition with others. However, old custom and old management form still exist in the FM field. However, an old custom and old management system still exist in the FM field; it forces cost-cutting and caused by carelessness in technical development competitiveness and safety measures. The government control by safety inspection and facility inspection through legislation of building management, but the management is still weak as a side of in the facility management section due to inspect sth as a mere formality and submit a paper report. These facts exist different kind of usage of the building have a three-party encounter between a building owner, user, and FM enterprises. In this research identify survey, especially building owner (manager)’s choice perception priority for optional factors to choose FM enterprises. Based on this, FM enterprises will be forced to seek a new dimension for better building management. In this light, the purpose of the research is to shape changes in the FM market culture safely and providing better service in building management and understanding building owner's insight through this research's information.
New from Old: Advancing Statewide Geophysics with Company Data

SummaryThe Geological Survey of NSW (GSNSW) are the custodians of both government and company geophysical data throughout NSW. The NSW Government has acquired a suite of over 60 airborne magnetic and ground gravity surveys since the 1990s, the data from which has been merged into statewide grids that are delivered, alongside the original survey data, through our web portal ‘MinView’. These grids are available for Magnetics, Radiometric, Gravity and Digital Elevation Models.Open-file exploration and mining company data dates back over 60 years, spanning a wide range of conventions, formats, and often missing large amounts of information.GSNSW have committed to a large-scale quality assessment of these data to deliver curated open-file company data to the public via MinView and allow incorporation of good quality company data into the statewide geophysical imagery. This will improve the statewide images because the line spacing of the company surveys is typically much smaller than the regional surveys flown by the government.