Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF GOOGLE APPS AND MIT APP INVENTOR ON ANDROID BASED REAL TIME ATTENDANCE REPORT APPLICATION

Attendance in educational institutions is an important thing that is always a common concern,attendance is used to measure student performance or seriousness in taking education. KlabatUniversity as an Educational institution, in this case at the Faculty of Computer Science.Trying to manage attendance as well as possible to get information that is fast, precise andaccurate. with this information about attendance, this institution can find out what things needto be improved and what things should be limited, or what decisions must be made forstudent success, which are related to attendance. The need for attendance reports at theComputer Faculty of Klabat University is urgently needed. Currently attendance report is stillusing paper, and the process for creating the report are by manual input one by one forgenerating the report. This research will create an application that runs on the Androidoperating system and this application will later be accessed with an Android-basedsmartphone with a student ID card barcode scan feature, as a marker of student attendance, sothe report can be generated automatically into the system. The research model or methodused is the Prototyping method. Prototyping design models are made faster so that the timeneeded for system development is shorter. This characteristic is very suitable with the needsof the existing system that only requires a short time in its development. The tools that will beused by researcher are MIT App Inventor to build an Android application. For the purposesof real time reports of student attendance, researcher will use Google apps as a data storagefacility and as a means for generating reports. The results of this study are expected to createan android-based mobile application that in real time can send student attendance data thatcan be accessed directly anytime and anywhere in the form of attendance report data. Forfuture development, researcher may suggest using other method than barcode scanning. RFIDor NFC tag supported for student ID Card can be used for attendance check.
Accounting Fraud and Prevention In Listed Companies In China

Accounting fraud would seriously damage the interests of investors and would bring severely damage to the development of capital market. Therefore, preventions should be strengthened. This paper attempts to systematically summarize the former research, analyzes the definition of accounting fraud, and the causes of accounting fraud in listed companies in China. And try to come up with a specifically solution for accounting fraud. Expecting to regulate the financial activities of listed companies in China and prevent the occurrence of accounting fraud.
Edge Security with an API Gateway

The API gateway is the most common pattern in securing APIs in a production deployment. In other words, it’s the entry point to your API deployment. There are many open source and proprietary products out there, which implement the API gateway pattern, which we commonly identify as API gateways. An API gateway is a policy enforcement point (PEP), which centrally enforces authentication, authorization, and throttling policies. Further we can use an API gateway to centrally gather all the analytics related to APIs and publish those to an analytics product for further analysis and presentation.
Problematic aspects of personal and professional adaptation of university students

We consider the current problems arising in the process of first year students professionalization. Based on contemporary educational standards and the basic provisions of the competency-based approach, we substantiate the necessity of special work with first year students at the stage of their entry into the profession in such areas as: understanding personal characteristics, development of skills to use their own resources and reserves; disclosure of the psychological con-text of choice and further development within the profession; prevention of the negative conse-quences of professionalization; the use of recreational tools to maintain psychological and social health; development of a positive life scenario, taking into account professional characteristics. We identify contradictions in accompanying the process of personal and professional adaptation of students in the considered areas. We substantiate the necessity of developing a special technology focused on resolving the identified contradictions.
Implementation of Nihilist Cipher Algorithm in Securing Text Data With Md5 Verification

Abstract The development of information technology that is increasingly racing today makes it easy for anyone to exchange data or information. On the other hand, there are problems with data security which can be disrupted by irresponsible parties such as wiretapping, destruction, data theft or other misuse. The application of cryptographic techniques is one solution that can be done to overcome these security problems. Cryptography is the science or art of maintaining data security by scrambling data or messages. In cryptography the term hash one-wayfunction is widely used to test the integrity of a file. In this study, cryptographic algorithms and hash functions used are Nihilist Cipher and MD5. Messages or files textthat will be sent are firstencrypted using thealgorithm Nihilist Cipher. Then the MD5 algorithm is used to get the hash value from the encrypted message. Furthermore, to get the original message back bydecryption Nihilist Cipher , the file verification process is first done to ensure that the file receivedhas not changed or is still original. The results show that thealgorithms Nihilist Cipher and MD5can be implemented properly so that data security can be increased because before decrypting the message, the message has to be verified first.
Using ELECTRA MLO multi-criteria decision making method in stepwise Benchmarking. Application in higher education

The purpose of this paper reflects in a study of an optimal development path in the ELECTRE-based stepwise benchmarking context. In the paper, multi-criteria decision-making is first described as a tool for stepwise benchmarking, where the ELECTRE MLO ranking method is used. In order to make the problem of finding the optimal path easier and significantly reduce the number of the paths that have to be considered, we are proving the theorem showing that it is better to make gradual progress than “skip steps”. As an illustration of these considerations, the ELECTRE MLO method is applied to the benchmark teaching assistants of one faculty of Belgrade University, according to the marks given by their students. We are looking for an optimal development path by using our theorem that substantially reduces the number of cases. We are also checking that the paths with no steps skipped are superior to the paths in which steps are skipped, in accordance with the theoretical result we have obtained. Thus, we are demonstrating that one should first look up to the colleague who is a little better than him/her, and then gradually improve until he/she has reached the level of the individual given the best mark.
The Study of Mapping Strategies Between the Excitators of the Single-Reed Woodwind and the Bowed String

Mapping is one of the most important components in digital musical instruments. There have been many works on “longitudinal” mapping strategies from the input device to the sound synthesizer. Such mapping can be considered longitudinal because the mapping direction is in line with the information transfer direction. However, less research focuses on “transversal” mappings among input devices or sound synthesizers. In this paper, a transversal mapping strategy is explored between the excitators of bowed strings and single-reed woodwinds which aims to allow more natural use of a given controller to play the sound of another family of instruments. A three-layer mapping structure, namely the playing layer, the mathematical layer, and the physical layer, is built. The mappings in different layers are generated based on the analogy of the mathematical models of two excitators in the mathematical layer. As a result, in the playing layer, the bowing force and the bowing speed of the string instrument are mapped to the lip force and the mouth pressure of a single-reed woodwind, respectively. In the physical layer, the string velocity and the friction force at the bowed point are mapped to the acoustic pressure and the volume velocity in the mouthpiece, respectively. Finally, a Yamaha WX5 wind controller is used to drive the digital waveguide string model. Two different mapping strategies between the lip force and the bowing force are tested and the results are discussed.
Survey on Koha usage in India

Survey on Koha usage in India - IJLSIT- Print ISSN No: - 2582-1555 Online ISSN No:- 2456-9623 Article DOI No:- 10.18231/j.ijlsit.2019.022, IP Indian Journal of Library Science and Information Technology-IP Indian J Libr Sci Inf Technol
Doyoureadme? Temporal Trends in the Language Complexity of Financial Reporting

Regulators, practitioners, and researchers are expressing growing concern over the readability of financial disclosures. Several recent regulatory guidelines are aimed specifically at simplifying the language of financial reporting in order to ensure that the reports can be read and understood by the public at large. However, quantitative scientific evidence of the evolving linguistic complexity of finance is scarce. In this work we introduce various methods for measuring the linguistic complexity of financial texts. Some of these methods rely on advanced Natural Language Processing (NLP) techniques unavailable when earlier studies were conducted. We apply these methods to decades’ worth of texts from various domains. We have found that 10-K reports have grown substantially longer, more complex and less readable. In terms of education necessary to comprehend them, this increased complexity translates to additional 2.2 years of schooling over the course of 18 years. A similar pattern of using highly complex language is also evident in financial news. The latter finding suggests that financial reality is becoming increasingly difficult to describe and explain. In contrast, the language of other corpora, including general news and scientific publications, has become more readable over the same period of time.
Attention-Aware Joint Location Constraint Hashing for Multi-Label Image Retrieval

Learning based hashing has been widely used in approximate nearest neighbor search for image retrieval. However, most of the existing hashing methods are designed to learn only simplex feature similarity while ignored the location similarity among multiple objects, thus cannot work well on multi-label image retrieval tasks. In this paper, we propose a novel supervised hashing method which fusions the two kinds of similarities together. First, we leverage an adjacency matrix to record the relative location relationship among multiple objects. Second, by incorporating matrix discretization difference and image label difference, we re-define the pairwise image similarity in a more meticulous way. Third, to learn more distinguishable hash codes, we leverage an attention sub-network to identify the approximate regions of the objects in an image so that the extracted features can mainly focus on the foreground objects and ignore the background clutter. The loss function in our method consists of a multi-categories classification loss which is used to learn the attention sub-network and a hash loss with a scaled sigmoid function which is used to learn the efficient hash codes. Experiment results show that our proposed method is effective in preserving high-level similarities and outperforms the baseline methods in multi-label image retrieval.
A comprehensive analysis of the parameters in the creation and comparison of feature vectors in distributional semantic models for multiple languages

Measuring the semantic similarity and relatedness of words is important for many natural language processing tasks. Although distributional semantic models designed for this task have many different parameters, such as vector similarity measures, weighting schemes and dimensionality reduction techniques, there is no truly comprehensive study simultaneously evaluating these parameters while also analysing the differences in the findings for multiple languages.We would like to address this gap with our systematic study by searching for the best configuration in the creation and comparison of feature vectors in distributional semantic models for English, Spanish and Hungarian separately, and then comparing our findings across these languages.During our extensive analysis we test a large number of possible settings for all parameters, with more than a thousand novel variants in case of some of them. As a result of this we were able to find such configurations that significantly outperform conventional configurations and achieve state-of-the-art results.
Deep Learning Based Topics Detection

Detecting topics from textual data streams is an interesting task in social networks studies. Traditional techniques have certain limitations when processing social network data such as tweets and online conversations, because of the large amount of data and noises. Deep learning appears to be a viable approach for harvesting and extracting valuable knowledge from complex systems. Therefore, we suggest using deep autoencoder model with Kmeans++ algorithm and work with the reconstructed data that contains less noise to detect the eventual topics within it. We evaluate the proposed model on two public datasets of annotated topics. Then, we compare our results to three well known methods. According to the results, our deep learning based method for detecting topics from social networks data outperforms all the three methods, and was able to detect perfectly the right topics in unsupervised way.
Bank Card Number Identification Based on Template Matching Method

In order to solve the identification problem of bank card number, an effective identification algorithm for bank card number based on template matching is proposed. The binary image is obtained by preprocessing the bank card image. According to the binary matrix feature of bank card number and based on template matching. The matrix of character image to be recognized is superimposed on the template matrix. In addition, the unmatched pixel statistics of the superimposed image are performed to obtain the matching factor. The minimum matching factor is regarded as the optimum matching. The results show that the proposed method is simple and effective, and with strong anti-interference performance and high identification rate. Moreover, the identification accuracy of bank card number is about 96%.
Partial Image Encryption Using Cellular Automata

In this work is proposed a partial image encryption method based on the synchronization of the cellular automaton rule 90. The security analysis proves that this cryptosystem is resistant to different tests and attacks such as the Chosen/Knownplain image attack and bit-replacement. This algorithm is a variant from another encryption algorithm named ESCA, in this case we modified the ESCA system to a partial encryption version reducing the latency time up to a 50%. This version has two changes a)it only encrypts the three most significant bits, and b) the bits of the secret key are rotated instead of calculating a key for each block of plaintext. These changes allow to reduce the latency time and according to the tests do not compromise the security. This proposal could be a feasible and secure option for real-time applications.
Suspicious Activity Detection Using Live Video Analysis

Video analytics is a field of computer science which involves processing of videos and live video streams to gather data for obtaining domain-specific information. The traditional closed-circuit television (CCTV) system requires humans to monitor the CCTV camera streams for 24/7 which is inefficient and costly. Additionally, the existing systems work in a post-offense mechanism, that is the video streams are checked only after a crime has already taken place. Expert video-surveillance tools can be used to automate this process and provide real-time solutions for different risk situations and helping the police officers or the intended group of people. This paper proposes an intelligent system, which utilizes an expert video-surveillance tool for monitoring of potentially suspicious and criminal activities in shopping malls. The system uses methods such as object detection, movement tracking and activity monitoring to robustly and efficiently track the location of objects and the subjects to identify questionable actions and activities. Along with this, an interface is developed to notify the concerned authority in real time. Thus, the system works as an assistant to the security personnel. The system uses various custom objects and person detection models to identify and establish a relationship between them.
Geometrical identification of invisible defects in structural elements basing on digital image correlation data

Abstract The paper addresses an experimental-calculational technique intended to determine geometrical parameters of invisible crack-like defects in thin-wall structural elements. Object deformation response to low trial loading is used as baseline data for assessment. The response is recorded using the digital image correlation method. Further analysis consists in minimization of differences between experimental findings and serial model calculations. The description of the approach is carried out on the example of a test specimen in the form of cylindrical shell (tube section) with a non-through crack simulator on the inner surface. The expected displacement fields for different particular cases are calculated using the finite element method.
Financial Development and Income Inequality in the Selected Southern African Development Community Countries

Financial development is widely regarded as another conduit through which income inequality can be reduced. The study empirically examines the relationship between financial development and income inequality in selected Southern African Development Community (SADC) countries, employing the Generalised Method of Moments technique for the period 1980 to 2016. Based on the inequality-decreasing hypothesis, a model which links financial sector development and inequality was estimated. Empirical results revealed that financial sector development overall does have an impact on income inequality in the selected SADC countries. An interesting observation from the empirical results is that the actual dimension of financial development plays a significant role in determining the relationship between financial development and income inequality in the SADC region. The impact of financial depth on income inequality is not obvious in the study, depending on the variable used. On the relationship between financial system stability and income inequality, results reveal that a stable financial system is beneficial to the poor. Financial efficiency does not appear to have a significant role in reducing income inequality in the selected SADC countries. The findings imply that a specific approach to financial sector development rather than a blanket approach is desirable.
DoG Based Local Ternary Pattern for Image Retrieval

In this paper, a novel pattern-based feature is proposed for the purpose of image retrieval. It extracts edge information of images using difference of Gaussian (DoG) filter. The choice of DoG filter depends upon the quality of available image and the information content, i.e, the value of blurriness (standard deviation) can be controlled and hence the edges can be highlighted more accurately. From the filtered image, texture statistics are captured using local ternary pattern (LTP) extracted from four different orientation images to form DoGLTP. The different orientations used in this work enable us to capture angle information usually ignored by many pattern-based features. In the proposed feature, color information is included through Hue and Saturation histograms. This makes the proposed feature more robust and the retrieval results highlight its superior performance in comparison to existing techniques on two popular databases: Corel 1000 and Corel 10k.
Efficient cover image selection based on spatial block analysis and DCT embedding

Abstract In steganography, the cover medium is widely treated as a mere container for the embedded information, even though it affects the stego-image quality, security, and robustness. In addition, there is no consensus on the characteristics of a suitable cover image. In this work, we introduce and practically prove the most suitable cover image (MSCI) framework to automatically select a cover image for a given secret image. This paper proposes choosing the most suitable cover from a set of images based on two steps. First, a set of cover images is filtered based on relative entropy and a histogram in order to identify the most suitable candidates. Second, the local block pixel intensity features of the candidates are analyzed to select the most suitable cover image. Furthermore, cover image local blocks were optimized, using rotation and flipping, during the embedding process to further improve stego-image representation. The proposed framework demonstrated high visual image quality when compared with existing solutions. Steganalysis tests indicated that the proposed solution for cover selection provided an increased resistance to modern steganalyzers with up to 30% lowered detection rate, which improved security.
Pricing efficiency and market efficiency of two bitcoin funds

This study explores whether pricing inefficiency, or imperfect tracking in the market value of shares of a bitcoin fund of the fund’s net asset value (NAV), makes a significant impact on the fund’s market efficiency relative to the retail bitcoin market. Two bitcoin funds whose shares are traded at the exchanges which impose more stringent criteria for transparency compared to cryptocurrency exchanges are considered. The fund whose shares have been trading without significant premium or a discount relative to its NAV is found as weak-form efficient. The fund, whose shares have been trading at a significant premium over its NAV is found inefficient due to the presence of persistent and strong positive autocorrelation in its returns. Trading of shares in the inefficiently priced fund appears to be even more emotion-driven than the already volatile and emotional trading of bitcoin and exhibits a strong herding behaviour.
Annotation-free Automatic Examination Essay Feedback Generation

Many of the demands of scale appear at odds with quality, such as in the case of online MOOC degree programs. This paper presents research that focuses on reducing this dichotomy. Examination essays are important learning tools in for-credit courses. Psychology studies show they drive deeper learning strategies and invoke higher levels of cognitive processing in students. High-quality feedback is another critical aspect of students’ learning in advanced education. Key components of high-quality feedback are how expeditiously feedback is presented and how unambiguous it is. Examination essays are time-consuming for educators to grade, and at scale this may create an incentive for superficial feedback that may also reach students in a less than ideal turnaround time. This imposes clear scalability constraints and a potential decrease in course quality in online for-credit MOOC programs. We propose an annotation-free artificial intelligence-based approach for the automatic generation of examination essay rubrics and its subsequent utilization as part of high-quality feedback to students. It utilizes a combination of the natural language processing techniques TextRank and semantic similarity for automatic rubric generation and concept map creation for feedback presentation. The feedback is immediately available upon essay submission and offers the student a conceptual analysis of the submitted essay against the assignment’s learning objectives.