Reviewerly/exHarmony huggingface dataset:
Design of information systems of reporting the performance of honorary employees at the web based office of communication and informatics in Blitar Regency

Abstract The Office of Communication and Informatics of Blitar Regency carries out its main tasks supported by temporary personnel with their respective work targets. To find out the number of performance targets that have been carried out and performance appraisals per year, the temporary staff must make daily and monthly performance reports to the leadership. So far, technical reporting uses Microsoft Excel which must be printed one by one before being reported. If there are errors on the report must edit one by one. Each sheet stores daily reports so that many sheets are used to store reports in one month. Reports submitted to the leadership are still in copy which is prone to loss. This has a negative impact on the deadlines for collecting reporting and monitoring by the leadership. Based on these problems, a Web-Based Performance Reporting Information System was built to optimize performance reporting. The system is built with 3 access rights: admin, leadership, and non-permanent personnel, employee mastering and performance targets with targets, as well as daily and monthly reporting transactions. From the test results, the system with UAT 89.8% has been able to optimize performance reporting with the output of performance values per month and per year completed with graphs of performance targets.
NLP Semi-supervised PU Learning with Reduced Number of Labeled Examples

This paper presents an approach to text-based data classification when only a limited number of positive samples are provided with additional challenge that classes are not mutually exclusive. The main goal is to develop an algorithm which can address a specific case of overlapping classes. The proposed solution encompasses a set of steps to be taken in order to achieve high classification accuracy with very few examples. The proposed Negative Case Search (NCS) algorithm based on selective centroid allocation allows to perform training data selection semi-supervised way to enrich the dataset. The system described in the work may be used as framework where building blocks such as AWD-LSTM language model can be replaced with alternative solutions in order to improve its performance without change of an underling processing flow. It was tested on a dedicated custom Polish dataset and achieved 98.89% of accuracy on 0.5% of the dataset size. We also run a range of experiments for English and the model reached 77.86% of accuracy on 5.6% of AG News dataset. Furthermore, our approach reached 85.52% accuracy in rec.sport.hockey class of 20 News groups. Presented results are not far from less restricted solutions.
An Overview of Data Locality Based Scheduler in Hadoop MapReduce Environment

MapReduce is a processing framework used extensively for substantial set of data set. Default MapReduce schedulers are not efficient enough to process data sets of heterogeneous nature. Thereby, it leads to low performance and reduced data locality. Working in a heterogeneous environment require more memory, power and advance architecture and computation. This paper provides the outline on MapReduce scheduling, how it is affected by the data locality and heterogeneous data sets. Examination on data locality based scheduling mechanism is done, that aims in reducing the job's running time or CPU time and increases the efficiency, bandwidth and throughput of the cluster i.e. enhancing the overall performance of the Hadoop cluster. In addition to these merits, the lack point of this data locality based scheduling approach is also reviewed.
Gray-Version Invariant Reversible Data Hiding Scheme Based on 2D Histogram Modification for Color Images

In this paper, a new color image-based reversible data hiding scheme for gray-version invariance using two-dimensional histogram modification is proposed. The proposed scheme first decomposes the color images into its components, i.e., R, G, and B and then secret data is embedded into its two components, namely G and B using proposed embedding strategy. The proposed embedding strategy basically uses prediction error expansions and pairwise embedding for secret data hiding. To maintain the same grayscale version as of actual colored cover image, the pixel intensity values of R component are adjusted according to the modification done in the G and B channels. Therefore, the proposed scheme embeds an impressive amount of data and also result in better image quality while retaining the gray pixel intensities. The experimental results also prove that the proposed technique has shown greater embedding enactment than the existing interrelated arrangements in terms of visual quality at different embedding capacities.
Fast digital image displacement measurement algorithm: theoretical analysis and simulation

Many image processing applications require a quantitative estimation of underlying correspondence (of relative position in space) between two or more images. Here, we propose a novel, less numerically intensive method, which we refer to as the Subtraction Method, to measure in-plane object motion with sub-pixel accuracy. This method can be usefully employed in conjunction with correlation to provide fine motion information very quickly (100 times faster). In this paper we demonstrate, explain, and examine the proposed method using simulated digital image data. Subpixel motion of discrete Gaussian function, four different digital images and speckle image are determined. Performance is compared to that of correlation.
Codeword Detection, Focusing on Differences in Similar Words Between Two Corpora of Microblogs

Recently, the use of microblogs in drug trafficking has surged and become a social problem. A common method applied by cyber patrols to repress crimes, such as drug trafficking, involves searching for crime-related keywords. However, criminals who post crime-inducing messages maximally exploit “codewords” rather than keywords, such as enjo kosai, marijuana, and methamphetamine, to camouflage their criminal intentions. Research suggests that these codewords change once they gain popularity; thus, effective codeword detection requires significant effort to keep track of the latest codewords. In this study, we focused on the appearance of codewords and those likely to be included in incriminating posts to detect codewords with a high likelihood of inclusion in incriminating posts. We proposed new methods for detecting codewords based on differences in word usage and conducted experiments on concealed-word detection to evaluate the effectiveness of the method. The results showed that the proposed method could detect concealed words other than those in the initial list and to a better degree than the baseline methods. These findings demonstrated the ability of the proposed method to rapidly and automatically detect codewords that change over time and blog posts that instigate crimes, thereby potentially reducing the burden of continuous codeword surveillance.
Haar Features and JEET Optimization for Detecting Human Faces in Images

Face detection is a topic of immense interest in the field of computer vision. This paper presents a method of improving the success rate and reducing the complexity of human face detection in images, using morphology and Otsu thresholding for pre-processing, Haar features and a proposed Jump-based Eye-position Estimation Template (JEET) algorithm for detecting faces in images using a neural network. The Haar JEET algorithm is shown to require far fewer iterations than the traditional Viola Jones algorithm due to iterating selected centroids on the image, rather than iterate the entire image.
Science and culture centre concept with energy-saving architecture approach

Abstract Science and Culture Center is a place that become a center of educational activities, development of science and technologies, and also a meeting place, an interacting place, and a place for cultural exchange between communities. The problem raised in solving design problems is the lack of special designs that meet the needs of design objects in terms of learning and developing culture and science. The main purposes is as a forum for the local community to meet the needs of the center of non-formal educational activities and culture that can be a place for people to gather, move, learning science and culture. In this study, qualitative methods were used in literature studies and comparative study of similar objects involving technical factors and building requirements. Field observations are also carried out, by directly observing field conditions and collecting data by direct observation and interviews with relevant parties. The problems obtained were further analyzed so as to produce a design concept with the theme of Energy-Saving Architecture, including the use of renewable sources, solar cell power plants, techniques for using plants for roofs, rainfed parks, using gravel compacted for pavement areas, etc. The results in the form of design concepts and applications on the design object as a guide to continue the design of the Center.
TERRABOT—A Multitasking AI

We live in a world where parallelism is above all. The ability to parallelly work on things without disturbing the ongoing is the need of an hour. A bot that not just converses with us but also helps us in the scheduled task basis set of commands is a possible solution for the above. Today with the concept of Deep Learning and NLP, a computer can anticipate our needs and complete the task proactively. With the advent of refined speech recognition and Natural language understanding, we have made a multi-tasking bot that understands Natural language and the intent behind it to work for the associated output. This paper explains about such a multitasking bot that does not limit the idea of the bot to a chatbot but a lot more. It is called TerraBot. It is a computer program that simulates human conversation, understands the command, and picks up the intent behind and proceeds further to fulfil it. It will allow its user to assign itself basic tasks and work in parallel to make the overall experience smart and productive. It can be used to do five basic functionalities like sending an email or playing particular music. The commands are taken by the Bot and further intent is recognized. A knowledgebase is maintained consisting of intent. It generates a response or calls up a specific sub action to generate a particular output.
Evaluating different Kinematic Models of Mobile robots using Linear and Non-linear controls

This paper highlights different control algorithms for reaching a goal position with least possible error with different kinematics models like Ackerman Steering and Differential Drive mobile robots. Linear and non-linear control approaches for both models will be discussed. In analyzing Ackerman mobile robots, the primary focus will be on its steering mechanism as it is the main source of reaching to its final position with little emphasis on its speed variable. However, while working on a Differential Drive, no such provisions will be considered as both rear wheels are responsible for linear as well as angular velocities. Further, the comparative analysis of different controllers will be presented. The efficacy of the controllers with both the models are validated in the CoppeliaSim simulator connected to Matlab via remote API.
Approaching probabilistic and deterministic nomic truths in an inductive probabilistic way

Abstract Theories of truth approximation in terms of truthlikeness (or verisimilitude) almost always deal with (non-probabilistically) approaching deterministic truths, either actual or nomic. This paper deals first with approaching a probabilistic nomic truth, viz. a true probability distribution. It assumes a multinomial probabilistic context, hence with a lawlike true, but usually unknown, probability distribution. We will first show that this true multinomial distribution can be approached by Carnapian inductive probabilities. Next we will deal with the corresponding deterministic nomic truth, that is, the set of conceptually possible outcomes with a positive true probability. We will introduce Hintikkian inductive probabilities, based on a prior distribution over the relevant deterministic nomic theories and on conditional Carnapian inductive probabilities, and first show that they enable again probabilistic approximation of the true distribution. Finally, we will show, in terms of a kind of success theorem, based on Niiniluoto’s estimated distance from the truth, in what sense Hintikkian inductive probabilities enable the probabilistic approximation of the relevant deterministic nomic truth. In sum, the (realist) truth approximation perspective on Carnapian and Hintikkian inductive probabilities leads to the unification of the inductive probability field and the field of truth approximation.
On delegatability of MDVS schemes

In a designated verifier signature (DVS) scheme, a signer (Alice) generates a signature which can only be verified by a designated verifier (Bob) chosen by her. Moreover, Bob cannot transfer his conviction about Alice’s signature to any third party. A DVS scheme provides the capability of authenticating Alice to Bob without disrupting her privacy. A multi designated verifier signature (MDVS) scheme is an extension of a DVS which consists of multiple designated verifiers. Non-delegatability is an essential property of a DVS scheme in scenarios where the responsibility of a signer (Alice) is important and she must not be able to delegate the signing rights to another entity. In this paper, we discuss on all MDVS schemes proposed up to now (to the best of our knowledge) and show that all of them are delegatable. As a result, proposing a non-delegatable MDVS scheme is an open research problem in the literature.
A New Genetic-Based Hyper-Heuristic Algorithm for Clustering Problem

Observations over recent studies indicate that most of the methods and algorithms used to deal with clustering problems are based on hybrid metaheuristic and metaheuristic algorithms to rectify the solutions. However, these approaches are restricted by the number of heuristics. Hyperheuristic algorithms are new generation of metaheuristic algorithm that use a collection of low-level search strategies and high-level heuristics which works in heuristic space and solution spaces, while metaheuristic algorithms just work in solution space to find better solutions. The main goal of this research is to propose a hyperheuristic framework for clustering problem which is able to optimize the difference of all data objects of one cluster from their respective cluster centres. The proposed hyperheuristic clustering algorithm has used from pool of meta-heuristic and heuristic approaches and mapping between solution space and heuristic spaces is one of the prevalent and powerful methods in the optimization domains. By mapping the solution spaces into heuristic space, it would be possible to make easy decision to settle data clustering problems. Our suggested hyperheuristic clustering works into three major spaces including high-level space, low-level space and problem space. The experiments of this study have proven that the suggested method has successfully generated efficient and robust clustering results.
Augmenting transportation-related environmental review activities using distributed multimedia

The implementations provide a method of interacting with planning analysis tools using graphical interfaces that are implemented through World Wide Web and stand-alone multimedia systems for group decision support. This chapter is intended to provide an overview of how spatial multimedia has been applied to a problem related to representations of automobile traffic. Given the ongoing nature of this research, the chapter simply describes a problem context and a given method of intervention. The CPS uses a multimedia information base that is projected on the wall of a meeting room. The computer's output is translated into concepts that the human can understand through the use of multimedia representational aids such as sound and images. Many potential urban developments are evaluated for their impact on a community's existing transportation infrastructure. Thus, many communities without the resources necessary to perform exhaustive analyses can learn from the experiences of others.
Image Processing in Long-distance Race Based on Humanoid Robot

Abstract This article mainly focuses on the path planning problems encountered by robots in long-distance races. In view of the uncertainty of the route of the long-distance running field, the image is processed in black and white according to the actual situation, the camera is reselected, and the program is rewritten. After the above steps, the problem of route recognition by the long-distance running robot is finally solved. And finally achieved good results.
Development of Technology for Creating High-Quality Abstracts

This paper focuses on creating high-quality notes. It is based on a modified version of the LeNet5 model, which allows to recognize individual characters, with the function of loading your own datasets, which can improve the accuracy of recognition, due to the characteristics of each handwriting. A neural network containing recurrent, convolutional, and fully connected layers was used to define words. To select a single word, the CTPN machine learning model was used. During the research, it was found that this solution for recognizing handwritten text is the most optimal for the task at hand. Also, an equally important part is to define the characteristic objects of the notes, such as headings, definitions, and formulas. The solution to this problem also used machine-learning algorithms. At the final stage, the main methods for working with file graphics were used to create the visual component of the note.
Data-Driven Laser Plane Optimization for High-Precision Numerical Calibration of Line Structured Light Sensors

Line structured light sensor has been applied in various three dimensional (3D) measurement scenes with the advantages of non-contact, low cost and high speed. Its accuracy, which is directly determined by the calibration method, needs to be further improved to fulfill the measuring tasks of precision parts. Here, we proposed a numerical method that can eliminate the errors of the model based methods through two strategies. One is the establishment of the numerical mapping relationship between stripe pixel coordinates and their world coordinates through piecewise cubic interpolation. Corner points of a checkerboard target are used to obtain sufficient interpolating nodes. This target can be manually aligned with the laser plane and the alignment error would be eliminated via the point projection. The other is the data-driven laser plane optimization. The data set of reference interval distance is computed based on the invariance of cross ratio. The optimization model is to minimize the root mean squared error of measured interval distances by adjusting the laser plane coefficients. After the optimization, a higher numerical mapping relationship can be achieved. It bypasses the camera and the distortion models and reaches a calibration error of only 0.005mm. The comparison studies and the measurement of the steps further validate the proposed method.
Web based recommendation system using Multi-attribute collaborative filtering for user satisfaction

In the wide area of personalized user based recommendation, although resource attribute is one the biggest important factors in recognizing user preferences, the researchers take into consideration among the user interest differences in resource attribute. As per previous research, both prediction and similarity computation are not extremely precise. There are some areas which have some space for improvements. In terms of accuracy, this paper proposed a modified ratio based multi-attribute method to calculate the similarity, providing us a new evaluation model of user interest based on resource multi-attribute. By comparing the multi attribute values we can find similarity between users and items using PARAFAC algorithm which is also responsible to handle large dataset and parallel computation among multi-attribute. This proposed method is also able to evaluate performance using this large data set of real web services whose experimental results explain that proposed method, in this paper, achieve better prediction and take less time in computation than various references considered.
Infini – A Keyword Recognition Chatbot

In this age of modernized era where each and every detail of the organization and its segments are publicized on their respective website, third parties looking for any information have to ransack through the entire website for the right information. Nonetheless, to tackle this issue the governing organizations need to spend resources on manpower which then assist the user when needed. But manpower isn't the only viable solution, another efficient and lucrative solution for this problem is to develop an AI Chatbot that can do the work of providing sustaining results on behalf. A Chatbot is a software application that mimic genuine person for reenacting a conversation. The main aim of the chatbot developed is to reduce the time that user spends in finding the correct information. In this paper we demonstrate the working of keyword recognition chatbot which reads the query from the user and provides the most suitable answer from its database.
Comparative Analysis of Authentication Techniques with Mechanism Implementing HoneyPot Over Cloud

In the field of Cloud Computing, there have been many data security techniques proposed over the years by researchers. Apart from it, there are many authentication mechanisms or techniques laid out in the literature for strong authentication. As part of this paper, the initial idea is to make an analysis of some of those authentication techniques given in the literature based on some identified criteria taken from the literature itself. This study or analysis would prove to be a helping hand to researchers and others in the community trying to make use of the given authentication techniques for their work. The second perspective of this work covers the idea of honey pots discussed in the literature for transferring the control of attackers or malicious users to some honey traps based on some worked-out logic. After reviewing the literature, it has been figured out that easy to work with methods for shifting the control of attacker to honey traps are either very few and others available are complex and difficult to achieve. This work covers two perspectives in the area of cloud, one is the comparative analysis of authentication techniques based on different criteria points identified after studying related literature. And another one would be providing a suitable and easy to work out a mechanism for redirecting users to honey traps preventing attackers to gain control of the original data servers.