Reviewerly/exHarmony huggingface dataset:
DASA : an open-source design, analysis and simulation framework for automotive image-based control systems

Image-Based Control (IBC) systems are a class of data-intensive feedback control systems whose feedback is provided by image-based sensing using a camera. IBC has become popular with the advent of efficient image processing systems and low-cost CMOS cameras with high resolution. The combination of the camera and image processing (sensing) gives necessary information on parameters such as relative position, geometry, relative distance, depth perception and tracking of the object-of-interest. This enables the effective use of low-cost camera sensors to enable new functionality or replace expensive sensors in cost-sensitive industries like automotive. The state-of-the-art design, analysis, and simulation of IBC assumes that the sensing algorithm is executing correctly with an assumed or estimated worst-case delay. The sensing algorithm is simulated and validated using static pre-captured image streams and is normally decoupled from the control algorithm. However, in reality, the camera is fixed to the vehicle body and any steering change would affect the region captured by the image. This dynamism cannot be captured in a static image stream and a dynamic image stream that considers the change in vehicle dynamics due to IBC actuation is needed. We present an open-source design, analysis, and simulation framework for automotive IBC systems that can consider the change in vehicle dynamics in real-time and produces real-time dynamic image stream as per the control algorithm. Our framework models the 3D environment in 3ds Max, simulates the vehicle dynamics, camera position, environment and traffic in V-REP and computes the control output in Matlab. Our framework runs Matlab as a server and V-REP as a client in synchronous mode. We show the effectiveness of our framework using a vision-based lateral control system.
Impact of Shadow Detection and Removal on Object Recognition Using Machine Learning from Images

Object recognition is an important research subfield in the area of Image analysis. Any algorithm to perform object recognition with near-human accuracy is still a challenging task especially from real life images captured by general purpose camera. i.e; images which are not captured in a very constrained environment as far as lighting and/or pose are concerned. Such images are bound to have some shadow component. This shadow component is to be correctly detected and removed by an image analysis algorithm so that they can be analyzed for the target application. Failure in doing so will lead to many problems. For e.g; It may result in wrong segmentation leading to increase in number of segments obtained. These segments may, in turn,, be treated wrongly as objects in the image and will, in turn, have an incorrect computation of features. So the shadow detection and removal is an inevitable pre-processing step for object recognition. Many approaches have been proposed in the literature for shadow detection and removal. The main contribution of this paper is twofold; first, it performs comparative study of three shadow detection and removal approaches. And second, manifests empirically, the need of shadow detection and removal process, as one of the pre-processing step, on the success and accuracy of object recognition algorithm. For the purpose of experiment fruit images are considered and they are recognized. Results are presented and conclusions are drawn.
THE KUPYNA HASH FUNCTION CRYPTANALYSIS WITH THE MERKLE TREES SIGNATURE SCHEMES

The paper is devoted to the security analysis of the Kupyna (DSTU 7564:2014) hash function applied to Merkle tree signature schemes. The paper lists possible attacks on the hash, and their application for signature schemes. The results show expediency of using the Kypuna hash in Merkel schemes based on the performance, security levels and strength against known cryptanalytic attacks.
Night Attendance Information System Development at International Islamic Boarding School KH Mas Mansyur

International Islamic Boarding School KH Mas Mansyur has administrators called Staff, Room coordinator and Security. The room coordinator is tasked with checking Students attendance every night. Students who have needs outside the dormitory between 22.00 up to 04.00 must get a permit at the Staff Office, the form permission is shown to the Security to get out or enter the Dormitory. The oversight process that still uses manual recording and the lack of connection of information between Administrators can cause misinformation among the Administrators. Based on these problems this study aims to create an information system based on Android and the web in order to facilitate the process of supervising the presence of Students so that it is more effective and efficient and can provide information on the presence of Students to each Administrators. This system will be designed using the PHP and Java programming languages and MySql as a database processor. The final result of this study is a web-based information system that can provide information on the presence of students to each administrator and an android-based information system that can facilitate the data collection process carried out by the Room coordinator.
A k-Nearest Neighbor Approach towards Multi-level Sequence Labeling

In this paper we present a new method for intent recognition for complex dialog management in low resource situations. Complex dialog management is required because our target domain is real world mixed initiative food ordering between agents and their customers, where individual customer utterances may contain multiple intents and refer to food items with complex structure. For example, a customer might say “Can I get a deluxe burger with large fries and oh put extra mayo on the burger would you?” We approach this task as a multi-level sequence labeling problem, with the constraint of limited real training data. Both traditional methods like HMM, MEMM, or CRF and newer methods like DNN or BiLSTM use only homogeneous feature sets. Newer methods perform better but also require considerably more data. Previous research has done pseudo-data synthesis to obtain the required amounts of training data. We propose to use a k-NN learner with heterogeneous feature set. We used windowed word n-grams, POS tag n-grams and pre-trained word embeddings as features. For the experiments we perform a comparison between using pseudo-data and real world data. We also perform semi-supervised self-training to obtain additional labeled data, in order to better model real world scenarios. Instead of using massive pseudo-data, we show that with only less than 1% of the data size, we can achieve better result than any of the methods above by annotating real world data. We achieve labeled bracketed F-scores of 75.46, 52.84 and 49.66 for the three levels of sequence labeling where each level has a longer word span than its previous level. Overall we achieve 60.71F. In comparison, two previous systems, MEMM and DNN-ELMO, achieved 52.32 and 45.25 respectively.
Contribution of human capital in technological innovation An Empirical Study of the General Company for the manufacture of medicines and medical supplies in Nineveh

The purpose of this research is to focus on the relationship between human capital and innovation at the level of the company , many companies are gaining competitive advantage of human capital , so you should invest it and keep it , the company was selected public for the pharmaceutical industry and medical supplies in Mosul to invest human capital and interest individuals talented and creative people to improve the quality of medicines provided to customers of doctors , pharmacists, patients , hospitals and health centers , as the sample consisted search of (49) respondent  was chosen as the key variables of human capital represented by (b driving practices , optimizing the workforce, easy access to information , energy education , post workers ) and to find the relationship between human capital and innovation , research has come to a set of conclusions and recommendations
Blockchain and Public Companies: A Revolution in Share Ownership Transparency, Proxy-Voting and Corporate Governance?

Under the traditional paradigm, the shareholder was the one in whose name company shares were registered. However, for public companies in the US, this system became highly inefficient by the 1960s due to high numbers of transactions. As a result, shares began to be “immobilized” at central securities depositories (“CSD”s) and held through the Indirect Holding System (“IHS”), with share transactions settled through “book entries,” first in the US and then in other major markets. Although market liquidity benefited, the system broke the direct relationship between companies and shareholders, introducing also a discrepancy between “recorded shareholders” and “beneficial shareholders.” Communication solutions were developed to bridge this discrepancy and allow “beneficial shareholders” to cast their votes through proxies. However, they currently rely on highly intermediated “pass-it-along” architectures, which cause several inefficiencies, the costs of which are borne by the shareholders themselves and raise questions in the context of collateralization. By increasing share “ownership transparency,” blockchain has the capacity to streamline the entire share ownership architecture. Indeed, blockchain could enable the tracking of share ownership through the complete settlement cycle, enhancing the “shareholder democracy” of listed companies, and benefiting their corporate governance and the market in their shares. However, blockchain also brings risks, including those related to greater ownership transparency. Consequently, a management system for the digital identity of share transactions is necessary to foster the benefits of such blockchain-based voting architectures, while reducing the risks.
Associative Classification in Big Data through a G3P Approach

The associative classification field includes really interesting approaches for building reliable classifiers and any of these approaches generally work on four different phases (data discretization, pattern mining, rule mining, and classifier building). This number of phases is a handicap when big datasets are analysed. The aim of this work is to propose a novel evolutionary algorithm for efficiently building associative classifiers in Big Data. The proposed model works in only two phases (a grammar-guided genetic programming framework is performed in each phase): 1) mining reliable association rules; 2) building an accurate classifier by ranking and combining the previously mined rules. The proposal has been implemented on Apache Spark to take advantage of the distributed computing. The experimental analysis was performend on 40 well-known datasets and considering 13 algorithms taken from literature. A series of non-parametric tests has also been carried out to determine statistical differences. Results are quite promising in terms of reliability and efficiency on high-dimensional data.
How does Hukou Status Transformation Affect Rural Migrants’ Subjective Well-being? An Empirical Study Based on CLDS Micro-data

The deepening reform of the hukou status system will enable more rural migrants to realize the hukou status transformation and become permanent migrants in cities. Whether rural migrants can achieve the integration of subjective well-being in the city is related to the quality of new human-oriented urbanization. This paper utilizes CLDS2016 data to explore whether and how hukou status transformation affects rural migrants’ subjective well-being in urban China. Data summary shows that hukou status transformers, i.e. permanent migrants, have significantly higher happiness score than their rural migrant counterparts who have no hukou status transformation. Step regression and OB decomposition both indicate that the improvement of labor market, social security and social capital may be the main reasons why hukou status transformation can improve the subjective well-being of rural migrants. Comparatively speaking, the traditional factors such as income and demographic characteristics cannot explain the impact of hukou status transformation on the subjective well-being of rural migrants. An investigation of the subjective well-being status of permanent migrants and urban aborigines shows that there is no significant difference in happiness between permanent migrants and urban aborigines, that is, permanent migrants realize the urban integration of subjective well-being.
Optimizing Power and Energy Efficiency in Cloud Computing

With the exponential growth in cloud computing data centers, power consumption due to the use of physical and virtual machines is becoming a challenge. The amount of power consumed by these systems is increasing day by day. In this context, we have done a study on optimizing the power and energy efficiency of physical and virtual machines in a cloud computing environment. By using different tools, the effect of workloads on power consumption and energy efficiency is investigated. This paper presents the findings from our study which provides a good understanding of how different workloads affect power and energy efficiency. Also, the methods and frameworks provided can be used in any cloud environment and of any size in order to investigate and improve energy efficiency.
Knowledge Erosion and Knowledge Protection in the Age of Increasing Data Flow

The article deals with the problem of knowledge erosion as the result of data and information explosive growth. The conception of knowledge and knowledge core as a system of established and accepted scientific (“textbook”) knowledge necessary to be transferred for sustainable social development is presented. The way of protecting knowledge core from destruction and unauthorized change is proposed with the use of Unified Electronic Educational Environment (UEEE) based on a set of learning objects constituting the Unified Bank of Learning Objects. Permitted blockchain algorithm as a basic means of protecting Unified Bank of Learning Objects is considered.
Efficient Cloud Storage with Data Partitioning and Replication Using Advanced Encryption Standard

Cloud computing associate the computing and storage resources controlled by different operating systems to make available services such as large-scaled data storage and high performance computing to users. The benefits of low-cost, negligible management (from a user's perspective), and greater flexibility come with increased security concerns is one of the most crucial aspects among those prohibiting the wide-spread adoption of cloud computing. The data outsourced to a public cloud must need to be secured. This work gives Division and Replication of Data (DROPs) inside the Cloud for Optimal Performance and Security that judicially fragments user files into portions and replicates them at strategic places in the cloud. The division of a files into fragments is achieved based on a given consumer standards such that the individual fragments do not comprise any meaningful facts. The node separation is ensured by the means of the Grid Topology algorithm. To further improve the retrieval time, replicate fragments over the nodes that generate the highest read/write requests. The data encrypted using AES encryption algorithm. Duplication checking is implementing to provide efficient storage and time based access control for secure file access system.
Közlekedési táblák felismerése neurális hálózat alkalmazásával

Artificial intelligence is one of the most dynamically developing areas of science today.Although it is not yet an integral part of our lives to use artificial intelligence solutions, it can be seen in terms of development, that it will become available to everyone in the coming decades, and not be exclusive to the richest.An important part of artificial intelligence research are the so-called soft computing methods, the most important of which are fuzzy logic, genetic algorithms and neural networks.In this article, the authors present a method of identifying certain traffic signs with the help of the neural networks.
Is Agent Software More Complex than Other Software

We empirically investigate agent software repositories using commonly used software metrics, which are used in software engineering literature to quantify meaningful characteristics of software based on its source code. We contrast the measurements with those of software in other categories. Analyzing hundreds of software projects, we find that agent software is clearly and significantly different from other types of software of comparable size.
Modeling for Optimal Investment Strategy of Educational Donation

Nowadays, educational donation plays an important role in the development of education. Since the effectiveness of education donations is affected by many factors, this paper presents a model to find an optimal investment strategy by processing big data. First, a criterion of investment is established by using TOPSIS method. Based on the criterion, schools worth investing are selected through SVM method. After determining return of investment and prosperity index with method of Principal Component Analysis and Data Envelopment Analysis, this model can allocate funds to invest institutions properly for the next five years, taking both indexes into consideration. Finally, this paper extends the model to make it more realistic.
Kajian Kapasitas Saluran Drainase Kel. Bayur Kec. Samarinda Utara Kota Samarinda

Andi Rianto, NPM 11.11.1001.7311.186, 2011, Civil Engineering Study Program, Faculty of Engineering, University of 17 August 1945 Samarinda. Flood is natural disaster that occurs due to the inability of channel of a region to accommodate the high rainfall in the region. One solution to overcome the flood is to improve the dimensions of the drainage channel to be able to accommodate the amount of water debit contained in a region. This research took place in Kel. Bayur, Kec. North Samarinda, Samarinda City. Calculation analysis using Gumbel and Log Person Type III method to calculate drainage channel capacity. Based on the calculation of the existing flood discharge of drainage channel at the research location that is smallest 2,924 m3/dt - biggest 8,667 m3/dt and flood discharge design at Kel. Bayur Samarinda City that is smallest 0,511 m3/dt - biggest 1,013 m3/dt with kala 10 years until year 2027. With the design of dimensional cross-sectional capacity : an average upper width of 3,15 meters width an average of 2,30 meters, and an average height of 1,30 meters. From the calculation results can be concluded that the drainage channel capacity enough to accommodate all the water discharge that exist in the area.
PROJECT CULTURE OF A SCHOOL STUDENT AND STAGES OF ITS FORMATION

The concept of project culture is defined in the article, its structure is suggested. The general model of the gradual formation of the project culture based on the two categories is proposed and considered: the child’s age and the learner’s need for raising the level of difficulty of the researching (project) task. The competencies that a child can master at the level of basic general education in the course of project activities are considered.
Quantitative newspaper sentiment analysis of the 2017 Battle of Marawi

Newspapers are expected to be objective but have often been accused of being biased. In this work, we quantitatively measure the bias of Filipino newspapers by using the TextBlob sentiment analysis tool on the coverage of the 2017 Battle of Marawi in southern Philippines. We see that an overwhelming majority (71.1% to 80.0%) of the articles on Marawi had a positive slant, while only two out of 1,185 articles were perfectly neutral. However, the small polarity absolute values (0.0286 to 0.1106) and the large deviations suggest that the articles may be considered within the neutral range. Moreover, these results are in sharp contrast with our previous work using another sentiment analysis tool, showing that there is still much room for improvement regarding natural language processing.
Programme – UIC nextstation 2019 - Tehran, 11 – 13 November 2019

Sunday - 10 November 2019 Tehran @ Evin Hotel 18:00 Registration, Welcome Coffee/Drinking Monday - 11 November 2019 Tehran @ Evin Hotel 08:00 Registration 09:00 Opening ceremony Recitation of…
eXplainable Modeling (XM): Data Analysis for Intelligent Agents

Intelligent agents perform key tasks in several application domains by processing sensor data and taking actions that maximize reward functions based on internal models of the environment and the agent itself. In this paper we present eXplainable Modeling (XM), a Python software which supports data analysis for intelligent agents. XM enables to analyze state-models, namely models of the agent states, discovered from sensor traces by data-driven methods, and to interpret them for improved situation awareness. The main features of the tool are described through the analysis of a real case study concerning aquatic drones for water monitoring.
Usage of grammarly – Online grammar and spelling checker tool at the health sciences Library, Manipal Academy of Higher Education, Manipal: A study

Accurate use of grammar and writing is an essential aspect of any writing. Today various online grammar spelling checker tools are available for improving writing quality. The popularity of these platforms is increasing among researchers and academicians every day. Aim of the present study is to know the opinion, use and satisfaction level among the users of Grammarly - online grammar and spelling checker tool subscribed by the Health Science Library, Manipal Academy of Higher Education (MAHE), Manipal. Structured questionnaires were distributed among the registered users of Grammarly tool at the Health Sciences Library. The study observed that almost all the users were aware that the Health Science Library is subscribing to Grammarly tool. More than half of the users used this tool because of the limitation of Microsoft word as found in the study. It is also found from the study that the majority of the respondents who participated in the study were using Grammarly for Microsoft Office. The study also observed that users are using Grammarly tool for different purposes like writing research papers, dissertations, theses, books, reports, and course papers. From the present study, it is also observed that most important advantages of Grammarly are - it improves the writing skills, catches contextual spelling and grammar mistakes, integrates with Microsoft Office, shows definitions and synonyms via doubleclicks. Grammarly is one of the tools subscribed by the Health Sciences Library to boost up academic writing, research, and publication of students, faculty members, and research scholars of MAHE, Manipal.
Automatic Generation of Video Game Character Images Using Augmented Structure-and-Style Networks

We propose a fast, flexible approach to automatically generating two-dimensional character images for video games. We treat the generation of character images as a two-part machine learning problem. The first task is to generate structured images that represent common attributes of characters as images. The second task is to add details to the structured images that appear to fit with some overall theme for each image. For both tasks, we employ generative adversarial network architectures, with modifications to improve their performance for their respective tasks. The resulting As2-GAN approach generates character images that are as realistic as those generated with the DCGAN approach, and are more consistent in quality and structural resemblance to images from the dataset. The As2-GAN approach also provides image creators with more control over images than typical one-step methods, while being able to generate high quality images using small datasets.