Reviewerly/exHarmony huggingface dataset:
An Improved Sum of Squared Difference Algorithm for Automated Distance Measurement

In recent years, with improvement of photoelectric conversion efficiency and accuracy, photoelectric sensor was arranged to simulate binocular stereo vision for 3D measurement, and it has become an important distance measurement method. In this paper, an improved sum of squared difference (SSD) algorithm which can use binocular cameras to measure distance of vehicle ahead was proposed. Firstly, consistency matching calibration was performed when images were acquired. Then, Gaussian blur was used to smooth the image, and grayscale transformation was performed. Next, the Sobel operator was used to detect the edge of images. Finally, the improved SSD was used for stereo matching and disparity calculation, and the distance value could be obtained corresponding to each point. Experimental results showed that the improved SSD algorithm had an accuracy rate of 95.06% when stereo matching and disparity calculation were performed. This algorithm fully meets the requirements of distance measurement.
Study on the Types of Deviant Workplace Behavior in Croatian IT Companies

This paper deals with the concept of deviant workplace behavior. The paper presents the results of an empirical study on the types of deviant workplace behavior in Croatian companies in the Information Technology sector (IT). The difference in the occurrence of deviant workplace behavior by gender and age was analyzed, after which the correlation between the types of deviant behavior was examined.
Don't Get Stuck in the "Con" Game

"Eventual consistency" is a popular phrase with a fuzzy definition. People are even inconsistent in their use of consistency. But two other terms, "convergence" and "confluence", that have crisper definitions and are more easily understood.
Library services in digital space and information technology development

Starting with the creation of the global information space, there are two forces that can really influence information processes. The first side is the technological side, the other side is the state structures based on social and humanitarian issues. The creators of global networks and the owners of key technologies advocate that everything remains as it is, based on freedom of speech, and in humanitarian terms, states are trying to control this process based on the implementation of legislation. The first side is based on free speech, controls the advertising market and dodges taxes, the other side wants to deal with “concerned groups” and also fight social ailments. In this confrontation, libraries, as institutions influencing the information environment, stand next to the state and try not to lose their attractiveness. In this respect, libraries can act as a bridge between local and regional as well as global networks. Libraries, academic and university libraries currently operating in the world and meeting modern challenges are the main participants in the authoritative information cycle. Coordinating the information activities of these agencies can lead to a significant improvement in the information environment. The main goal of the study is to identify the optimal forms and methods of using social networks in libraries. It is also exploring the reasons why libraries use social media to ensure an effective and widespread information service.
Hierarchical Modeling of Label Dependency and Label Noise in Fine-grained Entity Typing

Fine-grained entity typing (FET) aims to annotate the entity mentions in a sentence with fine-grained type labels. It brings plentiful semantic information for many natural language processing tasks. Existing FET approaches apply hard attention to learn on the noisy labels, and ignore that those noises have structured hierarchical dependency. Despite their successes, these FET models are insufficient in modeling type hierarchy dependencies and handling label noises. In this paper, we directly tackle the structured noisy labels by combining a forward tree module and a backward tree module. Specifically, the forward tree formulates the informative walk that hierarchically represents the type distributions. The backward tree models the erroneous walk that learns the noise confusion matrix. Empirical studies on several benchmark data sets confirm the effectiveness of the proposed framework.
Construction Method of Tender Document Based on Case-based Reasoning

The core activities of tender documents compilation are to collect similar historical tender documents, select compilation templates of tender documents and revise templates of tender documents partially. However, when the historical tender documents have accumulated to a certain amount, it becomes extremely difficult for compilers to summary, reuse and revise templates artificially in traditional compiling methods. Based on casebased reasoning (CBR), this paper studied the content recommendation method in the process of tender document construction. Firstly, a structured model of tender documents was constructed, and similar tender cases were retrieved from the tender case database according to the characteristics of tender cases; Secondly, the non-interference sequence index was used to measure the similarity of clauses used in similar tender cases, and the recommended sequences of reference template and content module of tender documents were constructed, which realized the recommendation of compiling templates of tender documents and partial revision of templates; Finally, the knowledge of the new tender case was updated. The empirical analysis shows that the construction method of tender documents based on case-based reasoning not only proposes a suitable strategy for compiling tender documents, but also improves the compilation efficiency of tender documents.
Prob-CLR: A probabilistic approach to learn discriminative representation

One of the core objectives of unsupervised representation learning is to learn discriminative representations without human supervision. Generally speaking, discriminative representations should be efficiently assigned to desired clusters because of their small intra-class distances and considerable inter-class distances. However, representations learned by most of the previous works cannot be clustered effectively and need to be fine-tuned for clustering. These fine-tuning processes usually take advantage of the dataset’s prior knowledge, which may not be easy to acquire in real-world scenarios. Besides, the fine-tuning network needs to be retrained when the prior knowledge changes, making the network lack adaptability. This paper proposed a probability-based contrastive learning method (Prob-CLR) that aims to learn discriminative representations without the prior knowledge of the given dataset. Prob-CLR works by encoding input data into multivariate Gaussian distributions and imposing contrastive learning from two different probability perspectives, namely distribution-wise and sample-wise regulations. The learned representations are discriminative and can be signed into desired clusters by K-means. Extensive experimental results show that Prob-CLR outperforms state-of-the-art methods on five benchmarks. Besides, Prob-CLR has stable performance in multi-grained clustering.
Control and Monitoring Automatic Floodgate based on NodeMCU and IOT with Fuzzy Logic Testing

Flood is still a problem that always been a mayor obstacle in several big cities in Indonesia such as DKI Jakarta. If the rainfall so high and river can be accommodate the rainfall, there will be flooding. So that river water doesn’t overflow, it is necessary to make a floodgate in the river flow. One of the previous studies that has been carried out regarding the control and monitoring of water dam gates is researched by Fahruddin. 2014 from the Faculty of Science and Technology, Makassar State Islamic University: Describes the design of a device capable of monitoring the height of water activity in the reservoir and controlling the floodgates automatically according to the height of water activity. The research that the writer is currently carrying out is the design method which consists of a series of sensors, NodeMCU microcontrollers, and Stepper Motors. The workflow of this system is when the water rises or falls, it send a signal to the sensor. And then the signal will be input for the microcontroller which is then processed to turn on the motor, so that it can open or close the floodgates, and send information to the indicator screen and Telegram application. After the experiment is carried out, it can be said that the system can work properly, namely when the ultrasonic sensor experiment, it was found that the sensor has been sent an output signal that was in accordance with the water level in actual condition. Based on testing using the fuzzy method, it was found that opening value at the floodgate obtained during the test had an average accuracy rate of 91% with an error difference of 9%.
Modeling a social media-based solution for Marketing Library Services in Sri Lanka: A User Librarian Collaborative Model (ULCM)

The use of social media platforms is in a very low capacity in the provision of their services in university libraries of Sri Lanka.Though a very limited number of university libraries utilize social media concepts, they do not use it as a marketing tool.The library is the place where all new information and knowledge is deposited.However, most of the students are unaware of or have little knowledge of what is available in the library.Social media can be used to increase the awareness of the library's services among university students.This paper attempted to introduce a social media-based solution for marketing library services in Sri Lanka.To support this, a research study was carried out to look into the possibilities of using social media to market university libraries.A survey method was adapted and questionnaire was used collect the data from the academic library professionals working under higher education institutions in Sri Lanka.A total of 123 library professionals (N) were invited to respond to a survey questionnaire that was given to them through email.The totally field completed surveys were returned by 113 library professionals (n) who work at various libraries.The study identified Facebook as the most important and powerful tool used by the library professionals in Sri Lanka.Facebook was also revealed to be the most effective platform for marketing university libraries.This positive attitude of academics on Facebook can be converted into a usable tool to market the services and resources in the libraries.A User-Librarian collaborative model which can be implemented to market university libraries in Sri Lanka through social media platforms was suggested as a result of the research findings of this study.The proposed model, connects the major parties of the library, users, and library management, efficiently and effectively to function daily activities of the system smoothly.Further, it would address the basic requirements that should be fulfilled and satisfied by both parties in the process.Additionally, the model provides some functions from both points of view.
Realization of Mobile Augmented Reality System Based on Image Recognition

With the development of computation technology, the augmented reality (AR) is widely applied in many fields as well as the image recognition.However, the AR application on mobile platform is not developed enough in the past decades due to the capability of the mobile processors.In recent years, the performance of mobile processors has changed rapidly, which makes it comparable to the desktop processors.This paper proposed and realized an AR system to be used on the Android mobile platform based on the image recognition through EasyAR engine and Unity 3D development tools.In this system, the image recognition could be done locally and/or using cloud recognition.Test results show that the cloud-based recognition is more efficient and accuracy than the local recognition for the mobile AR when there are more images to be recognized at the same time.
ADVANTAGES AND DISADVANTAGES OF QR CODE IN TAX INVOICES IN PORTUGAL

The systematization of the information underlying the electronic invoice, published in the Decree-Law no. 28/2019 of 15 February in Portugal, brought legislative changes leading to the introduction of a QR-Code on the invoice creating the conditions for the document's dematerialization. This article´s purpose is to present the results of a set of surveys applied to the various stakeholders in the process, in order to validate the advantages and constraints of an aggregating Approach, designed to systematize such stakeholders in a process of this nature. The methodology adopted was Design Science Research and proved to be adequate for the design of the artefact. The elaborated approach and the results of the surveys are also presented. In 2021, invoices and other tax documents must contain a two-dimensional bar code, containing all information related to the invoice, allowing the consumer to digitally send this information to his electronic invoice without his Fiscal ID introduction.
Ontology-Based Approach of E-government for Interoperability

There is a need for the exchange and sharing of knowledge between the department of government in the e-government system. Therefore, this paper ‘Ontology-Based Approach of E-government’ will discuss the scope of interoperability. With the e-government ontology, there will be proper semantics by using web ontology language (OWL) which helps to give clearer relation and semantics. To have an ontology, knowledge management is important and architecture/framework design is essential. The development of the e-government system is to serve citizens and organizations. However, e-government systems with heterogeneous database and distributed in nature have made difficult to integrate or interoperate. Therefore, developing a knowledge base (KB) is the major task that e-government focuses on. With KB definition and description, it will ensure clarity about e-government services. Knowledge management is important for e-government and the use of ontology is an effective way in semantic technologies. This ontology will enhance the processing of services and data between different departments in government. This kind of ontology will give a common understanding of knowledge and interoperability between the different departments of government. It will also offer effective and efficient value towards the e-government services by which citizens will be benefitted eventually.
Small Longest Tandem Scattered Subsequences

We consider the problem of identifying tandem scattered subsequences within a string. Our algorithm identifies a longest subsequence which occurs twice without overlap in a string. This algorithm is based on the Hunt-Szymanski algorithm, therefore its performance improves if the string is not self similar, which occurs naturally on strings over large alphabets. Our algorithm relies on new results for data structures that support dynamic longest increasing sub-sequences. In the process we also obtain improved algorithms for the decremental string comparison problem.
Analysis of Psychometric Properties and Validation of the Personal Learning Environments Questionnaire (B-PLE) in Higher Education Students

We are living through a cultural transition, characterized by technological disruption and the erosion of the modern ideology, which are redefining the behavior of citizens in their physical and digital spaces. Virtuality emerges as a new human dimension, making it necessary to rethink social and educational paradigms for a new two-dimensional citizen. In this context, the psychometric features and validation procedure of an instrument (B-PLE) for analyzing Personal Learning Environments (PLE) in students of higher education institutions in the Biobío Region of Chile are described. There were four phases to the validation method: (i) content validity, as determined by six experts in education and ICT; (ii) pilot test, with a non-probabilistic sample of 327 subjects; (iii) principal components analysis (PCA); and (iv) confirmatory factor analysis (CFA). The results of the dimensional analysis made it possible to define the structure of the new instrument, explaining 72% of the total variance. The reliability analysis yielded an alpha coefficient of 0.92. The confirmatory factor analysis showed fit indexes that support the proposed theoretical model. In conclusion, the instrument was composed of three latent variables: Open learning (OL), with six questions, Information management (IM), with two questions, and Knowledge creation and transfer (KCT), with three questions.
Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas–Kanade Algorithm

The purpose of this research is to study the application effect of Lucas-Kanade algorithm in right ventricular color Doppler ultrasound feature point extraction and motion tracking under the condition of scale invariant feature transform (SIFT). This study took the right ventricle as an example to analyze the extraction effect and calculation rate of SIFT algorithm and improved Lucas-Kanade algorithm. It was found that the calculation time before and after noise removal by the SIFT algorithm was 0.49 s and 0.46 s, respectively, and the number of extracted feature points was 703 and 698, respectively. The number of feature points extracted by the SIFT algorithm and the calculation time were significantly better than those of other algorithms (P < 0.01). The mean logarithm of the matching points of the SIFT algorithm for order matching and reverse order matching was 20.54 and 20.46, respectively. The calculation time and the number of feature points for the SIFT speckle tracking method were 1198.85 s and 81, respectively, and those of the optical flow method were 3274.19 s and 80, respectively. The calculation time of the SIFT speckle tracking method was significantly lower than that of the optical flow method (P < 0.05), and there was no statistical difference in the number of feature points between the SIFT speckle tracking method and the optical flow method (P > 0.05). In conclusion, the improved Lucas-Kanade algorithm based on SIFT significantly improves the accuracy of feature extraction and motion tracking of color Doppler ultrasound, which shows the value of the algorithm in the clinical application of color Doppler ultrasound.
New approach to dehaze single nighttime image

This paper focus on the dehazing of a single image captured at nighttime. The current state-of-the-art nighttime dehazing approaches usually suffer from the color shift problem due to the fact that the assumptions enforced underdaytime cannot get applied to the nighttime image directly. The classical dehazing methods try to estimate the transmission mapand accurate light to dehaze a single image. The present basic idea is to firstly separate the light layer from the hazy image and thetransmission map can be computed afterwards. A new layer separation method is proposed to solve the non-globalatmospheric light problem. The present method on some real datasets to show its superior performance is validated.
Augmented Reality-Based Medicine Plants Learning Applications

This study aims to design and build learning applications for medicinal plants based on Augmented reality so that they can be used as interactive learning media. The research methods used are qualitative and quantitative methods. Data collection methods used in this study are observation, interview, documentation, questionnaires and literature study and use the Prototype method as a software development method. This Augmented Reality-based medicinal plant learning application was built using the C# and Blender programming language to create 3-dimensional image designs. The result of developing this application is a learning media that can be run on a Smartphone by utilizing interactive Augmented Reality technology. When this application is opened, the main page will appear containing several menus, namely the play, guide, learning, and quite menus. The features contained in Augmented Reality-Based Medicinal Plant Learning, namely the 3D object scan feature, can find out information about plants with scanned images. This application can help people to get to know medicinal plants and their properties. And it can be an effective learning medium. Based on the results of the questionnaire, the application made by the author received a positive response from the respondents, in many category, such as the display, educational, satisfaction, interactive and music
Exact Acceleration of K-Means++ and K-Means||

K-Means++ and its distributed variant K-Means|| have become de facto tools for selecting the initial seeds of K-means. While alternatives have been developed, the effectiveness, ease of implementation,and theoretical grounding of the K-means++ and || methods have made them difficult to "best" from a holistic perspective. We focus on using triangle inequality based pruning methods to accelerate both of these algorithms to yield comparable or better run-time without sacrificing any of the benefits of these approaches. For both algorithms we are able to reduce distance computations by over 500×. For K-means++ this results in up to a 17×speedup in run-time and a551×speedup for K-means||. We achieve this with simple, but carefully chosen, modifications to known techniques which makes it easy to integrate our approach into existing implementations of these algorithms.
Penerapan Pengenalan Wajah Untuk Aplikasi Absensi dengan Metode Viola Jones dan Algoritam LBPH

The human face can be used to assess because of its uniqueness based on certain parameters. To perform facial recognition, the first thing that needs to be done is face detection. The author uses the Viola-Jones method to detect faces. The Viola-Jones method is known to have high speed and accuracy because it combines several concepts (Haar Features, Integral Image, AdaBoost, and Cascade Classifier) into the main method for handling objects. The principle of camera face recognition itself is that the captured face object will be processed and compared with all face images in the existing data set so that the identity of the face is known. One of the applications of face recognition is to do attendance with individual faces. The attendance process does not need physical contact interactions between humans and devices such as the fingerprint system so that during the current COVID-19 pandemic, the spread of the virus can reduce. In this research, a system that can be checked and a person's face is used as a leverage medium for arrival and return attendance using the Viola-Jones method and the LBPH algorithm. The language used is python with the OpenCV library. The PHP language is used for the user interface so that users perform attendance via a browser with the MySQL database to store attendance data. The result of the research is that using the Viola-Jones method and the LBPH algorithm faces are identified and the data is stored in the database used for data attendance. Distance and slope affect the results of face recognition. The distance is too close about 30 cm from the camera, the face cannot be detected. Instead of face position is too far approximately 200 cm, the face can still be detected but could not be identified. For a face tilt level of about 20o from perpendicular, it can still be recognized, but at a tilt degree of about 30o up or to the right, faces cannot be detected.