Reviewerly/exHarmony huggingface dataset:
An End-to-End Preprocessor Based on Adversiarial Learning for Mongolian Historical Document OCR

In Mongolian historical document recognition, preprocessing mainly involves image binarization and denoising. This is a challenging task and greatly effects the accuracy of the recognition result. Concerning the fact that image binarization and denoising are both image-to-image tasks, this paper proposes an end-to-end preprocessor for Mongolian historical document OCR. The preprocessor is trained in an adversarial learning fashion and deal with binarization and denoising simultaneously. The input of the preprocessor is the color image of Mongolian document images, and the output is the clean binary images which can be used for word recognition. The preprocessor was trained on a limited dataset and performed better than the combination of binarization and denoising methods used in earlier Mongolian historical document OCR systems.
Geoscience Australia: The year in review

Under Geoscience Australia’s (GA) Strategy 2028, our priorities continue to focus on:1) building Australia’s resource wealth;supporting Australia’s community safety;securing Australia’s water resou...
OPEN ONLINE COURSES IN TEACHING STUDENTS «PHYSICAL CULTURE» DISCIPLINE

Problem and purpose. The article analyzes the development of massive open online courses (MOOC), their structure and application in the practice of higher education, the introduction of new experience and strategy of distance learning at the Department of Physical Culture of the Siberian Federal University. The purpose of the article is to analyze the stages of the emergence and development of one of the areas of distance learning – massive open online courses (MOOC) and to analyze their application in the educational process of the University. The methodology of the research is based on the analysis of theoretical materials and research works of foreign and domestic scientists on the development of massive open online courses and the experience in the implementation of massive open online courses. Results. The article is devoted to the theory and experience of using MOОС in the world and in the educational process of the University. The authors describe the main intermediate results of the massive open online courses introduction in the educational process at the Department of Physical Culture of the Siberian Federal University. The problems, errors in the work with the MOОС and the aspects correcting the errors in future are analyzed. Conclusion. The results obtained so far allow us to conclude that the possible option of using distance learning technologies in the form of MOОС in teaching the discipline of „Physical Culture” is effective but requires additional research and further considerations.
On Share Conversions for Private Information Retrieval

Beimel et al. in CCC 12’ put forward a paradigm for constructing Private Information Retrieval (PIR) schemes, capturing several previous constructions for k ≥ 3 servers. A key component in the paradigm, applicable to three-server PIR, is a share conversion scheme from corresponding linear three-party secret sharing schemes with respect to a certain type of “modified universal” relation. In a useful particular instantiation of the paradigm, they used a share conversion from ( 2 , 3 ) -CNF over Z m to three-additive sharing over Z p β for primes p 1 , p 2 , p where p 1 ≠ p 2 and m = p 1 · p 2 . The share conversion is with respect to the modified universal relation C S m . They reduced the question of whether a suitable share conversion exists for a triple ( p 1 , p 2 , p ) to the (in)solvability of a certain linear system over Z p . Assuming a solution exists, they also provided a efficient (in m , log p ) construction of such a sharing scheme. They proved a suitable conversion exists for several triples of small numbers using a computer program; in particular, p = p 1 = 2 , p 2 = 3 yielded the three-server PIR with the best communication complexity at the time. This approach quickly becomes infeasible as the resulting matrix is of size Θ ( m 4 ) . In this work, we prove that the solvability condition holds for an infinite family of ( p 1 , p 2 , p ) ’s, answering an open question of Beimel et al. Concretely, we prove that if p 1 , p 2 &gt; 2 and p = p 1 , then a conversion of the required form exists. We leave the full characterization of such triples, with potential applications to PIR complexity, to future work. Although larger (particularly with m a x ( p 1 , p 2 ) &gt; 3 ) triples do not yield improved three-server PIR communication complexity via BIKO’s construction, a richer family of PIR protocols we obtain by plugging in our share conversions might have useful properties for other applications. Moreover, we hope that the analytic techniques for understanding the relevant matrices we developed would help to understand whether share conversion as above for C S m , where m is a product of more than two (say three) distinct primes, exists. The general BIKO paradigm generalizes to work for such Z m ’s. Furthermore, the linear condition in Beimel et al. generalizes to m’s, which are products of more than two primes, so our hope is somewhat justified. In case such a conversion does exist, plugging it into BIKO’s construction would lead to major improvement to the state of the art of three-server PIR communication complexity (reducing Communication Complexity (CC) in correspondence with certain matching vector families).
Data Mining: Analysis and Comparative Study of Supervised Techniques

Data mining techniques are used more and more in the economic field.Such as the prediction of certain economic indicators, the discovery of hidden information, problems or finding problems in the industrial sector, as well as in relations with customers through the study of their data and behaviors to improve the cost-effectiveness of customer relationships or attract new customers.Data Mining techniques are classified into two categories: supervised and unsupervised.This paper focus only on the first techniques for solving Data Mining tasks such as: Decision Trees, Regression, Neural Networks and Support vector machines (SVM).The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of implementation of each technique.Finally, the paper has presented some valuable recommendations in this field.
A Blind Image Information Hiding Algorithm in the HSI Color Space Based on BEMD and DWT

For sake of guaranteeing the equilibrium betwixt robustness and invisibility in image hiding, an image steganography algorithm based on two-dimensional empirical mode decomposition (BEMD) and wavelet transform is proposed in the HSI color space. In this algorithm, the initiate layer of intrinsic mode function is selected for hiding, and the local correlation of 8 neighborhood pixels is used to complete the embedding and blind extraction of secret information. The experimental outcome testify that the stego image has good robustness, visual quality and imperceptibility, and can resist certain noise and compression. In the previous method, the problem of requiring the original image to participate in the extraction is improved. And the algorithm finally achieves blind extraction.
Semantic Model-Driven PLM Data Interoperability: An Application for Aircraft Ground Functional Testing with Eco-Design Criteria

The latest developments in Model-Based Systems Engineering (MBSE) and Product Life-Cycle Management (PLM) are playing a role in the evolution of the aeronautical industry. Despite the reluctance of this domain to accept the introduction of technology leaps in the production process - mostly due to safety reasons - aircraft manufacturers are slowly moving to a new digital factory concept. The deployment of a PLM Tool for Aircraft Ground Functional testing with Eco-design criteria can be leveraged to improve both sustainability of the assembly line and efficiency of the Ground System Tests process end to end, however, heterogeneous data interoperability represents one of the major challenges in this framework. The ontology-based solution proposed in this work addresses this challenge, thus, shows how semantics can be exploited to streamline the data pipeline throughout a PLM digital platform.
Effective Parallel Computing via a Free Stale Synchronous Parallel Strategy

As the data becomes bigger and more complex, people tend to process it in a distributed system implemented on clusters. Due to the power consumption, cost, and differentiated price-performance, the clusters are evolving into the system with heterogeneous hardware leading to the performance difference among the nodes. Even in a homogeneous cluster, the performance of the nodes is different due to the resource competition and the communication cost. Some nodes with poor performance will drag down the efficiency of the whole system. Existing parallel computing strategies such as bulk synchronous parallel strategy and stale synchronous parallel strategy are not well suited to this problem. To address it, we proposed a free stale synchronous parallel (FSSP) strategy to free the system from the negative impact of those nodes. FSSP is improved from stale synchronous parallel (SSP) strategy, which can effectively and accurately figure out the slow nodes and eliminate the negative effects of those nodes. We validated the performance of the FSSP strategy by using some classical machine learning algorithms and datasets. Our experimental results demonstrated that FSSP was 1.5-12× faster than the bulk synchronous parallel strategy and stale synchronous parallel strategy, and it used 4× fewer iterations than the asynchronous parallel strategy to converge.
SPEAKER RECOGNITION SYSTEM USING SIGNAL PROCESSING TOOL IN MATLAB

Speech Recognition is the process in which certain words of a particular speaker will automatically recognized based on the information included in individual speech waves. Speech is one of the most important medium by which a communication can take place. The invention and widespread use of mobiles, telephones, data storage devices etc. have provided a major help in speech communication and its analysis. These also brought about ever-increasing need to authenticate and identify individuals automatically. Biometrics which identifies the physical traits and behavioural characteristics that make each of us unique therefore becomes necessary as a natural choice for identity verification. Advances in Biometric technology promises an effective solution to the world security needs as it can accurately identify or verify individuals based upon their unique physical or behavioural characteristics. This paper demonstrates a speech recognition system using signal processing tool in MATLAB. MATLAB programming was used to develop code that compares the pitch and formant vectors of a known speech signal with the bunch of other unknown speech signals and identifies appropriate match. The system was repeated several times to test it accuracy and was found to always make a perfect match.
Learning Controllable Face Generator from Disjoint Datasets

Recently, GANs have become popular for synthesizing photorealistic facial images with desired facial attributes. However, crucial to the success of such networks is the availability of large-scale datasets that are fully-attributed, i.e., datasets in which the Cartesian product of all attribute values is present, as otherwise the learning becomes skewed. Such fully-attributed datasets are impractically expensive to collect. Many existing datasets are only partially-attributed, and do not have any subjects in common. It thus becomes important to be able to jointly learn from such datasets. In this paper, we propose a GAN-based facial image generator that can be trained on partially-attributed disjoint datasets. The key idea is to use a smaller, fully-attributed dataset to bridge the learning. Our generator (i) provides independent control of multiple attributes, and (ii) renders photorealistic facial images with target attributes.
Hardware Implementation of Efficient Elliptic Curve Scalar Multiplication using Vedic Multiplier

This paper presents an area efficient and high-speed FPGA implementation of scalar multiplication using a Vedic multiplier. Scalar multiplication is the most important operation in Elliptic Curve Cryptography(ECC), which used for public key generation and the performance of ECC greatly depends on it. The scalar multiplication is multiplying integer k with scalar P to compute Q=kP, where k is private key and P is a base point on the Elliptic curve. The Scalar multiplication underlying finite field arithmetic operation i.e. addition multiplication, squaring and inversion to compute Q. From these finite field operations, multiplication is the most time-consuming operation, occupy more device space and it dominates the speed of Scalar multiplication. This paper presents an efficient implementation of finite field multiplication using a Vedic multiplier. The scalar multiplier is designed over Galois Binary field GF(2233) for field size=233-bit which is secured curve according to NIST. The performances of the proposed design are evaluated by comparing it with Karatsuba based scalar multiplier for area and delay. The results show that the proposed scalar multiplication using Vedic multiplier has consumed 22% less area on FPGA and also has 12% less delay, than Karatsuba, based scalar multiplier. The scalar multiplier is coded in Verilog HDL, synthesize and simulated in Xilinx 13.2 ISE on Virtex6 FPGA.
Interaksi Spasial Kondisi Sosial-Ekonomi Terhadap Kerawanan Kejahatan Di Kota Bandung (Studi Kasus Sumur Bandung)

Crime is an unplanned change due to the process of urban development. The geographical approach is able to analyze spatial interactions of criminogenic factors into crime phenomenon. This study aims to explain the spatial interaction between socio-economic conditions and crime risk in Bandung City. The crime risk is obtained through Kernel Density Estimation (KDE) method based on The Annual Crime Data from Bandung Police Department, whereas the socio-economic condition data is revealed from interview and questioner with 176 respondents who determined by multistage random sampling in 37 hamlets. Variable interactions are analysed based on Geographically Weighted Regression (GWR) with Gaussian Distance Function type of cross validation as optimum weight. The results show the socio-economic conditions has a positive and significant effect on crime risk. This model shows that the independent variable acts as a crime attractor. Partially its known that economic conditions have a stronger interaction with crime risk than the social climate. The strongest interaction between variables are concentrated in the southern of Kebon Pisang Urban Village, precisely in the area passed by arterial roads at southern of Sumur Bandung. Crime prevention efforts are conducted using environmental interventions by the formal security personnels and strengthening the social climate of the community.
Analysis of People Response on Twitter Towards Tidal Wave Disaster in the Southern Coast of Yogyakarta Special Province (Case Studies: Parangtritis Beach, Bantul Regency)

In July 2018 the movement of the wind from Australia to the Indian Ocean gives the impact on season transition from rainy to dry season. As the result, the wave becomes so much higher than normal condition as it hits the coastal area as well as in the southern part of Yogyakarta Special Province where is directly bordered with the Indian Ocean. Some impacted areas are popular tourism spots like Parangtritis Beach. The wave wrecks several shops along the beach owned by the local people. The majority of damaged objects are semi-permanent buildings constructed by traditional bamboo and timber. Moreover the tourism activity has been warned due to the dangerous condition. The advancement of technology becomes one of popular issues including the increasing of online social media usage. Internet and gadgets such as smartphone are recently the part of people lifestyle. The nowadays people prefer to access anything online through their smartphone including to find the news on the website or social media such as Twitter. One of interested news is about disaster particularly in recognizable places as well as about tidal wave disaster in Parangtritis Beach. This study aims to investigate the advantages of Twitter contents related to the tidal wave in Parangtritis Beach on people response about the disaster and the beach. The analysis applies sentiment analysis theory. Furthermore the data being collected in this research is online from Twitter accounts that has divided into three phases of disaster (before tidal wave, during tidal wave, and after tidal wave).
ANALYSIS OF ROUTER ATTACK WITH SECURITY INFORMATION AND EVENT MANAGEMENT AND IMPLICATIONS IN INFORMATION SECURITY INDEX

Information security is a need to secure organizational information assets. The government as the regulator issues an Information Security Management System (ISMS) and Information Security Index (US) as a measure of information security in the agency of a region. Security Information and Event Management (SIEM) is a security technology to secure information assets. SIEM is expected to provide information on attacks that occur on the router network and increase the value of the Indeks KAMI of government agencies. However, the use of SIEM is still questionable whether it can recognize a router attack and its impact on the value of our index. This research simulates attacks on routers with 8 attacks namely Mac Flooding, ARP-Poisoning, CDP Flooding, DHCP Starvation, DHCP Rogue, SYN Flooding SSH Bruteforce and FTP Bruteforce. 8 types of attacks followed by digital forensic analysis using the OSCAR method to see the impact on routers and SIEM. Also measured is index KAMI before and after the SIEM to be able to measure the effect of SIEM installation on the value of index KAMI. It was found that the use of SIEM to conduct security monitoring proved successful in identifying attacks, but not all were recognized by SIEM. SIEM only recognizes DHCP Starvation, DHCP Rogue, SSH Bruteforce and FTP Bruteforce. Mac Flooding, ARP-Poisoning, CDP Flooding, SYN Flooding attacks are not recognized by SIEM because routers do not produce logs. Also obtained is the use of SIEM proven to increase our index from the aspect of technology
A Promising Partnership

Though library science internships for undergraduates are not as well documented as those for MLIS students, internships for undergraduates can be impactful for both the intern and the library. Undergraduate students have firsthand, immersive experience with their major curriculum and this expertise can benefit science librarians making collection development decisions and creating content for research guides. This article discusses two such projects undertaken by an undergraduate mathematics major as part of a science librarianship internship.
Application of Analytics in Machine Vision using Big Data

The Bag-of-Words (BoW) approach has been successfully applied in the context of category-level image classification. To incorporate spatial image information in the BoW model, Spatial Pyramids (SPs) are used. However, spatial pyramids are rigid in nature and are based on pre-defined grid configurations. As a consequence, they often fail to coincide with the underlying spatial structure of images from different categories which may negatively affect the classification accuracy.The aim of the paper is to use the 3D scene geometry to steer the layout of spatial pyramids for category-level image classification (object recognition). The proposed approach provides an image representation by inferring the constituent geometrical parts of a scene. As a result, the image representation retains the descriptive spatial information to yield a structural description of the image. From large scale experiments on the Pascal VOC2007 and Caltech101, it can be derived that SPs which are obtained by the proposed Generic SPs outperforms the standard SPs.
PENERAPAN MODEL INKUIRI TERBIMBING UNTUK MENINGKATKAN HASIL BELAJAR SISWA DALAM PEMBELAJARAN IPS

This research is motivated by the problem of the low student learning outcomes in social studies subjects in SD Negeri Babakan II, one of the causes is the teaching methods and techniques that are less varied, and the application of learning models that are less appropriate causes the results low learning.This is seen when children feel bored in learning.So students lack confidence and are afraid to express opinions.This research was conducted to improve student activity and student learning outcomes in class IV SD Negeri Babakan II.This research is a classroom action research conducted by teachers and researchers, the research action is carried out in two cycles.The subjects of this study were grade IV students at SD Negeri Babakan II, as many as 22 students consisting of 9 male students and 13 female students.Data collection tools used by researchers in research are tests in the form of items, observations in the form of teacher performance observation sheets and student activity observation sheets and documentation.Data in the form of test results as primary data, observation results and documentation as supporting data.The results of this study indicate that the implementation of learning by applying the guided inquiry learning model can improve the learning outcomes of fourth grade students.This is indicated by an increase in student learning outcomes in the first cycle 55% percentage with an average grade obtained 74.50, while in the second cycle with an average value obtained 85.41 with a percentage of 86%.Thus the application of guided inquiry models in class IV social studies learning can improve student learning outcomes.From these studies it can be concluded that the application of the guided inquiry learning model can improve the learning outcomes of Grade IV SD Negeri Babakan II students.
Intelligent Risk Management framework

The Internet of Things(IoT) is rapidly increasing and enhancing today’s world by introducing a large set of interconnected devices. Several beneficial services are produced by these devices as for area monitoring and process control. However, IoT security is still a major problem. In fact, IoT’ security beggings largely whith an effective Risk Management process. However, the essense of this process is to acquire a risk inventory cibling the IoT devices. Nevertheless, it is quite difficult to obtaining this latter which significantly adds complication issues to the Risk Management.Without the ability of holisticly identify the IoT critical devices, inaccurate Risk Management is achieved which leads unfortunately to novel risk exposures. Traditional Risk-based approaches fails drastically at apprending IoT’ potential attacks. The dynamic structure, the heteregouns nature of devices, the various security objectives and infrastructure pervasiveness are key factors impacting the overall perfomance. Thus, a holistic Risk Management witihin the IoT is indispensable. Accordingly, we propose an intelligent Risk Management framework using Mobile Agents in order to deliver preventive and responsive assessment.
New doubly even self-dual codes having minimum weight 20

In this note, we construct new doubly even self-dual codes having minimum weight 20 for lengths 112,120 and 128. This implies that there are at least three inequivalent extremal doubly even self-dual codes of length 112.