Reviewerly/exHarmony huggingface dataset:
An Instance-Optimal Algorithm for Bichromatic Rectangular Visibility.

Afshani, Barbay and Chan (2017) introduced the notion of instance-optimal algorithm in the order-oblivious setting. An algorithm A is instance-optimal in the order-oblivious setting for a certain class of algorithms 𝒜 if the following hold: - A takes as input a sequence of objects from some domain; - for any instance σ and any algorithm A' ∈ 𝒜, the runtime of A on σ is at most a constant factor removed from the runtime of A' on the worst possible permutation of σ. If we identify permutations of a sequence as representing the same instance, this essentially states that A is optimal on every possible input (and not only in the worst case).We design instance-optimal algorithms for the problem of reporting, given a bichromatic set of points in the plane S, all pairs consisting of points of different color which span an empty axis-aligned rectangle (or reporting all points which appear in such a pair). This problem has applications for training-set reduction in nearest-neighbour classifiers. It is also related to the problem consisting of finding the decision boundaries of a euclidean nearest-neighbour classifier, for which Bremner et al. (2005) gave an optimal output-sensitive algorithm.By showing the existence of an instance-optimal algorithm in the order-oblivious setting for this problem we push the methods of Afshani et al. closer to their limits by adapting and extending them to a setting which exhibits highly non-local features. Previous problems for which instance-optimal algorithms were proven to exist were based solely on local relationships between points in a set.
Sistem Pakar dengan Menggunakan Metode Naive Bayes dalam Mengidentifikasi Penyakit Karies pada Gigi Manusia

Caries disease in human teeth is a disease that permanently destroys the inner walls of teeth and forms small holes in the teeth. The Indonesian people lack the knowledge to find information and identify tooth decay, which makes many people unaware of the consequences and dangers of this disease. Tooth decay disease is usually caused by three factors. The first factor is teeth and saliva, which are the hosts of microorganisms in the oral cavity. Bacteria and food are the second and third factors. The purpose of this research is to help the public find information about tooth decay, thus forming a branch of artificial intelligence, the expert system. Artificial intelligence is a science that allows you to build computer systems that display intelligence in different ways. An expert system is a computer program or information system that uses some knowledge of an expert. The methods used in this study are the Naive Bayes method and the model's view controller, which are implemented as a database in the PHP Codeigniter framework and MySQL. The data processed in this study is knowledge about the symptoms of dental caries obtained from experts. The test results of this method provide patients with the knowledge necessary to prevent tooth decay, with an accuracy rate of 83.61%. This expert system helps the public to recognize and obtain information about tooth decay. The Expert System can also be used to take the first step in preventing tooth decay.
Face Recognition Attendance System

Attendance system is very important in schools and colleges because Manual attendance system has many problems for example it may be less accurate and difficult to maintain. So, attendance system using face recognition technique increases the accuracy and also it requires less time than other methods. There are many existing systems for attendance such as face recognition using IoT, PIR sensors and so on. For face recognition, hardware devices are also helpful, but the challenge is to maintain all the sensors properly without getting them damaged. After studying all methods and techniques, we are trying to implement a system with Haar Cascade Algorithm which has the highest accuracy among all. It is able to capture the images from 50-70cm. We are creating graphical user interface which capture the images, create the dataset and train the dataset on single click. After recognizing a face it will display name of student and roll number. That information will be stored in attendance sheet automatically with time and date.
Neural Network Instruction Set Extension and Code Mapping Mechanism

PDF HTML XML Export Cite reminder Neural Network Instruction Set Extension and Code Mapping Mechanism DOI: 10.21655/ijsi.1673-7288.00251 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:In recent years, Convolutional Neural Network (CNN) has received widespread attention in the field of machine learning due to its high-accuracy performance in character recognition and image classification. Nevertheless, the compute-intensive and memory-intensive characteristics of CNN have posed huge challenges to the general-purpose processor, which needs to support various workloads. Therefore, a large number of CNN-specific hardware accelerators have emerged to improve efficiency. Though significantly efficient, previous accelerators are not flexible enough. In this study, classical CNN models are analyzed and a domain-specific instruction set of 10 matrix instructions, called RV-CNN, is design based on the promising RISC-V architecture. By abstracting CNN computation into instructions, the proposed design can provide sufficient flexibility for CNN and possesses a higher code density than the general ISA. On this basis, a code-to-instruction mapping mechanism is proposed. By using the RV-CNN to build different CNN models on the Xilinx ZC702, this paper found that compared to x86 processors, RV-CNN has an average of 141 times energy efficiency and 8.91 times the code density; compared to GPU, it has an average of 1.25 times energy efficiency and 1.95 times the code density. In addition, compared to previous CNN accelerators, the design supports typical CNN models while at high energy efficiency. Reference Related Cited by
DIGITAL INSTRUMENTS IN THE EDUCATIONAL ENVIRONMENT

The article discusses the issues of introducing digital technologies into education. Examples of some online platforms of educational programs of universities, digital tools that allow to optimize the learning process, characteristics of digital educational resources and models of the needs of the educational process used in the modern learning process for the continuous educational process are presented. The problems arising in the course of digitalization of the educational environment are outlined. The need to improve the regulatory framework of the research topic and stimulate the development of digital legal literacy is noted.
ON ARTIFICIAL INTELLIGENCE TALENT CULTIVATION IN CHINA FROM CASE STUDY

Through the case analysis of Carnegie Mellon University in the field of artificial intelligence in America. the similarities and differences of the above university in artificial intelligence talent cultivation were obtained from four dimensions: length of learning and degree, enrollment requirements, staff force construction and the offered curriculum. The conclusion could support the suggestions and advice for domestic policymakers and decision makers as follows: to promulgate the document of artificial intelligence talent cultivation pertinently as the guideline; to promote the new institutes construction of artificial intelligence and update the research centers of artificial intelligence; to improve the supporting incentive mechanisms such as scholarships, competitions and academic conference grants for the students in the direction of artificial intelligence.
Cloud Computing Based Security Enhancement

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. In the cloud, the data is transferred among the server and client. High speed is the important issue innetworking. Cloud security is the current discussion in the IT world. This research paper helps in securing the data without affecting the network layers and protecting the data from unauthorized entries into the server, the data is secured in server based on users’ choice of security method so that data is given high secure priority. Cloud Computing has been selected as the next generation architecture of IT Enterprise. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, Cloud Computing moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. This unique attribute, however, poses many new security challenges which have not been well understood.
Telescoping Filter: A Practical Adaptive Filter.

Filters are small, fast, and approximate set membership data structures. They are often used to filter out expensive accesses to a remote set S for negative queries (that is, filtering out queries x ∉ S). Filters have one-sided errors: on a negative query, a filter may say present with a tunable false-positive probability of e. Correctness is traded for space: filters only use log (1/e) + O(1) bits per element.The false-positive guarantees of most filters, however, hold only for a single query. In particular, if x is a false positive, a subsequent query to x is a false positive with probability 1, not e. With this in mind, recent work has introduced the notion of an adaptive filter. A filter is adaptive if each query is a false positive with probability e, regardless of answers to previous queries. This requires fixing false positives as they occur.Adaptive filters not only provide strong false positive guarantees in adversarial environments but also improve query performance on practical workloads by eliminating repeated false positives.Existing work on adaptive filters falls into two categories. On the one hand, there are practical filters, based on the cuckoo filter, that attempt to fix false positives heuristically without meeting the adaptivity guarantee. On the other hand, the broom filter is a very complex adaptive filter that meets the optimal theoretical bounds.In this paper, we bridge this gap by designing the telescoping adaptive filter (TAF), a practical, provably adaptive filter. We provide theoretical false-positive and space guarantees for our filter, along with empirical results where we compare its performance against state-of-the-art filters. We also implement the broom filter and compare it to the TAF. Our experiments show that theoretical adaptivity can lead to improved false-positive performance on practical inputs, and can be achieved while maintaining throughput that is similar to non-adaptive filters.
Efficiently Verifiable Computations on User Data for Behavior Analysis with Privacy Preservation

Along with development of internet and web, online social network are becoming important information propagation platform with hundreds of million users worldwide. Online social network attract thousands of million users to use it every day for different purpose. So that tons of user behavior data is generated on internet. Developing endeavors have been committed to mining the inexhaustible behavior data to extract significant information for research purposes to inquire about that, or analyst to develop better ecommerce strategies for business purpose. However the concern arises with this data is security, which is going to be presented to third parties. The most recent decade has seen an assortment of look into works endeavoring to perform information conglomeration in a privacy protecting manner. Most by far of existing techniques give protection to users information yet at the cost of very limited data aggregation operations like calculating sum and mean of particular query, which barely fulfill the requirement of behavior analysis. So that, proposed system mainly focuses on privacy preservation and behavior analysis of online user data. In this paper we use general accumulation and specific collection for behavior analysis. Using cryptographic algorithm we prevent privacy disclosure from both third party data aggregator and analyst. We have executed our technique and assessed its execution utilizing a relational dataset. The results of the experiment shows that this research scheme handle both overall queries and various selective aggregate queries with acceptable computation, privacy, and overheads of the communication effectively.
MODERATING EFFECT OF FOREIGN-DOMESTIC OWNERSHIP RATIO ON FIRM ATTRIBUTES AND ENVIRONMENTAL DISCLOSURE IN NIGERIAN OIL AND GAS QUOTED COMPANIES

The focus of this study is on the assessment of environmental disclosure: the moderating effect offirm attributes and foreign-domestic ownership ratio with specific interest on the role of firm size,leverage and profitability. Secondary data retrieved from the annual reports of oil and gas quotedcompanies on the stock exchange of Nigeria was employed in the study. The study period spans from2010-2018 and the Generalized Least Squares (GLS) regression was used for the estimation of thespecified models. The findings of the study show that profitability has a significant impact onenvironmental reporting of oil and gas quoted companies in Nigeria while leverage and companysize have no significant impact on environmental reporting of oil and gas quoted companies inNigeria. The study further revealed a significant moderating effect of foreign-domestic ownershipratio on the relationship between firm size, leverage, Profitability and environmental reporting. Thestudy recommends that firms that are well to do financially should pay more attention toenvironmental reporting and firms should improve their environmental performance irrespective oftheir leverage. The study further recommends that both small and big firms need to improve theirenvironmental performance and the presence of more foreign-domestic ownership should lead tomore robust disclosures of environmental issues.
Mathematics of casino carnival games

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Predicting Dynamic Visual Target Detection Performance From Brain Networks of Searching State

This paper describes a novel weighted voting tree classification scheme for breast density classification. Breast parenchymal density is an important risk factor in breast cancer. Moreover, it is known that mammogram interpretation is more difficult when dense tissue is involved. Therefore, automated breast density classification may aid in breast lesion detection and analysis. Several classification methods have been compared and a novel hierarchical classification procedure of combined classifiers with linear discriminant analysis (LDA) is proposed as the best solution to classify the mammograms into the four BIRADS tissue classes. The classification scheme is based on 298 texture features. Statistical analysis to test the normality and homoscedasticity of the data was carried out for feature selection. Thus, only features that are influenced by the tissue type were considered. The novel classification techniques have been incorporated into a CADe system to drive the detection algorithms and tested with 1459 images. The results obtained on the 322 screen-film mammograms (SFM) of the mini-MIAS dataset show that 99.75% of samples were correctly classified. On the 1137 full-field digital mammograms (FFDM) dataset results show 91.58% agreement. The results of the lesion detection algorithms were obtained from modules integrated within the CADe system developed by the authors and show that using breast tissue classification prior to lesion detection leads to an improvement of the detection results. The tools enhance the detectability of lesions and they are able to distinguish their local attenuation without local tissue density constraints.
An Extended PEE-HS Based Method for Reversible Data Hiding of Depth Images

An RGB-D image has extra information, i.e., depth information, which represents the distance between the camera and the objects. We extend a PEE-HS (prediction-error expansion with histogram shifting) based reversible data hiding method for RGB-D images to embed the depth information into the RGB layer without visible artifacts. The proposed method achieves the compatibility between the RGB-D and normal RGB images using a single marked image containing the depth information. Our experimental results show that the marked images have high quality without serious distortion.
Values And Trends In Educational Communities

This study draws our attention to the existence of educational communities that allow teachers to continuously develop their abilities and work together in order to face the challenges presented by the educational system. We will discuss three case studies â€“ ANPRO, Aspire Teachers, Merito â€“ and the information obtained in an empirical evidence, to highlight the dynamics and variables at work in these communities in Romania.
Advances in meta-heuristic methods for large-scale black-box optimization problems

The optimal design of complex engineering equipment usually faces high-complexity, high-dimensional optimization problems – the so-called black-box optimization problems (LBOPs) – which are characterized by unavailable mathematical expressions of objective functions and/or constraint functions, and high dimensionality of design variables. The LBOPs have attracted the interest of scholars in various fields in recent years, and meta-heuristic algorithms are considered effective methods for solving these problems. This paper comprehensively summarizes recent research progress in meta-heuristic algorithms for solving LBOPs, including meta-heuristic algorithms with and without decomposition strategies, and meta-heuristic algorithms for handling computationally expensive large-scale optimization problems. Finally, possible future research directions of meta-heuristic methods for solving LBOPs are proposed.
Detection Of Leukemia Using Digital Image Processing And Artificial Intelligence

ABSTRACT The main aim of this paper is to detect leukemia and its staging using image processing and artificial intelligence. This evaluation ought to be done various blood slides of cancerous and noncancerous blood slide microscopic images through simulation using MATLAB along with artificial intelligence. It is also desirable to compare the results obtained with different parameters such as morphological operations like erosion, opening, shutting middle filtering, sharpening and applying Hough transform to converted blood slide images into grayscale. The thesis also includes the goal to determine a simulation environment that could be used as a platform for further studies within the area of leukemia detection using artificial intelligence. Keywords; MATLAB, Artificial neural networks, Digital image processing, Image segmentation, Artificial Intelligence (AI). Cite this Article: Khan mohammed Shahnawaz, Mukesh kumar Saini. Detection of leukemia using Digital image processing and artificial intelligence. International Journal of VLSI design and technology . 2019; 1(2): 13–21p.
Pelatihan Penggunaan Sistem Informasi Keuangan Sekolah Muhammadiyah (SIKeSMu) Pada Majelis Pendidikan Dasar dan Menengah Muhammadiyah Kota Medan

The Medan City Muhammadiyah Elementary and Secondary Education Council is an institution or organization whose activities are related to religion. One of the activities of the Medan City Muhammadiyah Elementary and Secondary Education Council is to supervise reporting and carry out Monitoring and Evaluation (Monev) of financial reports from Muhammadiyah schools that are members of the assembly so that financial problems in these schools become transparent. Based on the analysis of the situation of the target audience that has been stated above, the problems faced so far have occurred in two directions, where the assembly and the school both have problems in monitoring and evaluating the financial statements so that the right solution is needed to resolve both the problem. The solution to the existing problems is to build the Muhammadiyah School Financial Information System Application (SIKeSMu) and provide training for the use of the application so that it is hoped that later it can simplify reporting and facilitate monitoring of financial statements in schools that are members of the Primary and Secondary Education Council. Muhammadiyah Medan City.
SECURITY ENHANCEMENT OF DATA ON CLOUD USING LSB TECHNIQUE AND SPLITTING TECHNIQUE

Security is vital for sharing sensitive information in the cloud. Using the genuine key performs the structure share the fragile information without moving Keys for every record. This structure occupations Hilter kilter encryption standard for encrypting all the information is trailed by open key encryption. The clients will get their information recovered once they put the private key and a public key they got from the sender. If the key is hacked during transmission, the suspect attacker won't get the information to decode as the private key gets the information. It isn't obligatory to share keys for each document since they can decrypt the record utilizing a private key. The special secret key concentrates all data and information. So the information is secured in better places, and clients can use it anywhere.
Lithological Mapping and Identification of Argillic and Propylitic Alteration Zones at Anjerd Volcanic Terrain

Anjerd area is part of Arasbaran volcanic-magmatic terrain in northwestern Iran. The area is covered by multiple intrusive bodies of diverse compositions, volcanic, volcaniclastic, sedimentary and young alluvial deposits. The usefulness of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for the lithologic mapping of the area and identification of alteration zones is investigated in this study. To carry out the study, one ASTER frame was atmospherically calibrated by Internal Average Relative Reflectance (IARR). The False Color Composite (FCC) and Principal Component Analysis (PCA) images were used for discriminating lithologic units along with Spectral Angle Mapper (SAM) and Matched Filtering (MF). The pure endmember image spectra were extracted from Pixel Purity (PPI) algorithm. The results showed that PCA and FCC can be used to discriminate sedimentary rocks from magmatic and volcanic rocks. Because feldspars and quartz are relatively featureless in shortwave infrared (SWIR) spectral region the methods were not capable to distinguish between various magmatic rocks except for basalts with darker color and higher pyroxene content. The MF gave better results than SAM algorithm and therefore MF is recommended over SAM for studying similar terrains. The argillic and propylitic alteration zones were detected by band ratios. The results showed that MF algorithm in combination with band ratios can be used to distinguish magmatic rocks from sedimentary rocks and delineate the alteration zones