Reviewerly/exHarmony huggingface dataset:
Wavelet Analysis and Comparison from Coiflet Family on Image Compression

These days, image compression is an important task as it obtains less memory and easy. The Wavelet technique has the potential of providing high-quality compression. In this study, several wavelets from the coiflet family were applied in image compression to determine the best compression quality and fidelity of the wavelet. To achieve these goals, we analyze the Measurement of compression ratio, Mean Square Error (MSE), and peak signal-to-noise ratio (PSNR) to determine the `acceptability' of the compression. Our test with the `Lena' image found that the compression ratio based on all wavelets gave almost identical results, but the coif1 wavelet gave the smallest compression ratio. In terms of fidelity, coif5 gives the best results.
PUSH NOTIFICATION TECHNOLOGY AS A SYSTEM ANDROID BASED STUDENT PRESENCE

Knowing school academic information is important for parents of students to monitor their children's attendance during school hours, in this way parents will feel more comfortable when their children go to school and attend school hours. Many schools have not implemented a computerized student attendance system so that for attendance reports only monitoring is only the school, by using this presence system monitoring is not only the school but also involves the parents, guardians of students. With these students will be more disciplined. The application of this system based on android by utilizing push notification technology can be a solution in terms of notifying student presence information. This application is looking forward to providing student attendance information and sent to parents of parents via each smartphone automatically, with this it is hoped that it can increase parental supervision of their children. ) where this method produces a design that is acceptable to consumers and can be developed easily.
An Analysis of Utility for API Recommendation: Do the Matched Results Have the Same Efforts?

The current evaluation of API recommendation systems mainly focuses on correctness, which is calculated through matching results with ground-truth APIs. However, this measurement may be affected if there exist more than one APIs in a result. In practice, some APIs are used to implement basic functionalities (e.g., print and log generation). These APIs can be invoked everywhere, and they may contribute less than functionally related APIs to the given requirements in recommendation. To study the impacts of correct-but-useless APIs, we use utility to measure them. Our study is conducted on more than 5,000 matched results generated by two specification-based API recommendation techniques. The results show that the matched APIs are heavily overlapped, 10% APIs compose more than 80% matched results. The selected 10% APIs are all correct, but few of them are used to implement the required functionality. We further propose a heuristic approach to measure the utility and conduct an online evaluation with 15 developers. Their reports confirm that the matched results with higher utility score usually have more efforts on programming than the lower ones.
Learning 3D functionality representations

A central goal of computer graphics is to provide tools for designing and simulating real or imagined artifacts. An understanding of functionality is important in enabling such modeling tools. Given that the majority of man-made artifacts are designed to serve a certain function, the functionality of objects is often reflected by their geometry, the way that they are organized in an environment, and their interaction with other objects or agents. Thus, in recent years, a variety of methods in shape analysis have been developed to extract functional information about objects and scenes from these different types of cues.
Implementation of an Authentication System GTPass

This paper describes on implementation of an efficient personal authentication scheme called ‘GTPass’ (Graphic & Text based Password system). GTPass establishes a bulwark against shoulder-surfing attacks and is easy to use. GTPass introduces a novel method of integrating numerical and graphical password authentication techniques. The important feature of GTPass is the use of graphics and numbers together at the same time for a system. GTPass is easy to use and robust against shoulder-surfing attacks.
Authorship Attribution With Few Training Samples

This chapter discusses authorship attribution through a training sample. The focus on authorship attribution discussed in this chapter differs in two ways from the traditional authorship identification problem discussed in the earlier chapters of this book. Firstly, the traditional authorship attribution studies [63, 65] only work in the presence of large training samples from each candidate author, which are typically enough to build a classification model. With authorship attribution, the emphasis is on using a few training samples for each suspect. In some scenarios, no training samples may exist, and the suspects may be asked (usually through court orders) to produce a writing sample for investigation purposes. Secondly, in traditional authorship studies, the goal is to attribute a single anonymous document to its true author. In this chapter, we look at cases where we have more than one anonymous message that needs to be attributed to the true author(s). It is likely that the perpetrator may either create a ghost e-mail account or hack an existing account, and then use it for sending illegitimate messages in order to remain anonymous. To address the aforementioned shortfalls, the authorship attribution problem has been redefined as follows: given a collection of anonymous messages potentially written by a set of suspects {S1, ···, Sn}, a cybercrime investigator first wants to identify the major groups of messages based on stylometric features; intuitively, each message group is written by one suspect. Then s/he wants to identify the author of each anonymous message collection from the given candidate suspects. To address the newly defined authorship attribution problem, the stylometric pattern-based approach of AuthorMinerl (described previously in Sect. 5.4.1 ) is extended and called AuthorMinerSmall. When applying this approach, the stylometric features are first extracted from the given anonymous message collection Ω.
A No-reference Image Quality Assessment based on Reference Generating Network

In practice, owing to the lack of original information, no-reference image quality assessment (NR-IQA) that does not require a reference image is more attractive. However, the quality predicted by the full reference IQA that utilizes the full information of reference image is highly correlated with the subjective quality score and thus the prediction accuracy is higher than the NR-IQA. Hence to alleviate the accuracy difference and to imitate the FR-IQA performance, we proposed the NR-IQA method using a reference generating network, where the reference image is reconstructed, and relative associativity with the distorted image is calculated to find the final quality score. The proposed architecture is significantly simple and efficient. In addition, the comprehensive experimental result shows that the prediction accuracy is comparable with that of the state-of-the-art methods.
Radio Network Subscriber Authentication Method

The paper discusses a method related to radio communication, namely subscriber authentication, which can be used as a separate element when building authentication systems designed to identify subscribers in radio networks.
Implementing Data-Based Bank Loan Application via MS Access

The Data-based bank loan application is a system where potential borrowers can assess their approvals using MS access application. It's for the ease of access for both the bank and borrowers. It contains all the customers’ necessary details of the same when applying on-site. It will then be used for an easy way of automating loan approvals depending on bank requirements and formulations used to approve a loan application.
The Digital Random Signal Simulator

There is introduced a universal simple digital simulator of the random signal with the specified arbitrary two-dimensional probability distribution of its discrete values based on the Markov model. It allows us to generate an unlimited sequence of samples with the required probabilistic and correlation properties of adjacent random or pseudo-random values of the simulated random process. By applying the simplified version of the simulator, the independent signal samples obeying the specifying arbitrary one-dimensional probability distribution can be formed. The simulator design involves the technique of determining the parameters of the Markov model of the random signal values at the two time moments. The Markov model can be created based on either the specified two-dimensional probability density or the experimental sample of the simulated random process. There is presented the algorithm for signal sample generating that provides a high sampling frequency. Its hardware implementation by means of microprocessor devices or field-programmable gate arrays is also in focus. The computational algorithm procedure does not require complex mathematical transformations of the signal samples and is implemented using an inexpensive circuitry. Changing the statistical properties of the simulated random processes is achieved by either rebooting the storage device with the pre-formed data array or switching the memory pages in which the necessary arrays are stored. In order to demonstrate the performance of the simulator and its high efficiency, the properties of the simulator, its probabilistic and correlation characteristics are studied. It is shown that a high accuracy of coincidence is ensured between the two-dimensional probability distribution of the selected model and the histogram based on the generated sequence of samples of the random signal. #COMESYSO1120
Digital Logic Course Themed Around Arithmetic

Work in Progress-In-depth coverage of arithmetic operations in an introductory digital logic course equips students with the background knowledge necessary for high-level Computer and Electrical engineering and Computer Science courses. In this paper, we propose a new method of teaching digital logic based on including a module on fixed-point and floating-point arithmetic operations during the first four weeks of the course, and then theming the whole course around this module without sacrificing the recommended course outcomes. The paper shows that theming the whole course around arithmetic operations helps students to achieve the course outcomes, makes the course interesting, ties the course to real-life applications, and teaches students how to implement and compare alternative designs.
Optimal control protocols for single qubit quantum detectors

We identify optimal controls for a single qubit to detect the presence of a known signal over background noise. Using a combination of numeric and analytic techniques, we show that spin-locking is nearly optimal.
Prostate MRI Registration Using Siamese Metric Learning

The process of registering intra-procedural prostate magnetic resonance images (MRI) with corresponding pre-procedural images improves the accuracy of certain surgeries, such as a prostate biopsy. Aligning the two images by means of rigid and elastic deformation may permit more precise use of the needle during the operation. However, gathering the necessary data and computing the ground truth is a problematic step. Currently, a single dataset is available and it is composed of only a few cases, making the training of standard deep convolutional neural networks difficult. To address this issue the moving image (intra-procedural) is randomly augmented producing different copies, and a convolutional siamese neural network tries to choose the best aligned copy with respect to the reference image (pre-procedural). The results of this research show that this method is superior to both a simple baseline obtained with standard image processing techniques and a deep CNN model. Furthermore, the best policy found for building the couple set for the siamese neural network reveals that a rule based on the mutual information that considers only the highest and the lowest value, representing similar and dissimilar cases, is the best option for training. The use of mutual information allows the model to be unsupervised, since the segmentation is no longer necessary. Finally, research on the size of the augmented set is conducted, showing that producing 18 different candidates is sufficient for a good performance.
Web-based picture dictionary of Dayak Ngaju language–English

This study aims to develop a learning application of Dayak Ngaju language - English that can be accessed by children of primary school age through web pages. The vocabularies used in the application were the basic vocabularies learned in elementary school as an introduction to the next step, which includes making simple sentences, both in local and foreign languages. The applied research model was used to adopt techniques in the Agile model using Extreme Programming which consists of planning, design, coding, and testing. For supporting the application development, the data were gathered through direct observation, interviews, and library research. The study was conducted at Sekolah Dasar Negeri Percobaan Palangka Raya, Jl. Damang Leman No. 37 Palangka Raya, Central Kalimantan. The final product of the study was an application of web-based Dayak Ngaju- English Language Dictionary for elementary school-age categories. The application displays several nouns related to parts of the body, things in the classroom, as well as food and drinks.
ρFEM: Efficient Backward-edge Protection Using Reversed Forward-edge Mappings

In this paper, we propose reversed forward-edge mapper (ρFEM), a Clang/LLVM compiler-based tool, to protect the backward edges of a program’s control flow graph (CFG) against runtime control-flow hijacking (e.g., code reuse attacks). It protects backward-edge transfers in C/C++ originating from virtual and non-virtual functions by first statically constructing a precise virtual table hierarchy, with which to form a precise forward-edge mapping between callees and non-virtual calltargets based on precise function signatures, and then checks each instrumented callee return against the previously computed set at runtime. We have evaluated ρFEM using the Chrome browser, NodeJS, Nginx, Memcached, and the SPEC CPU2017 benchmark. Our results show that ρFEM enforces less than 2.77 return targets per callee in geomean, even for applications heavily relying on backward edges. ρFEM’s runtime overhead is less than 1% in geomean for the SPEC CPU2017 benchmark and 3.44% in geomean for the Chrome browser.
Digital tools in corporate personnel training

Abstract This paper is devoted to the study of the implementation of information and communication technologies in corporate vocational training in modern organizations. The relevance of the study is due to the growing role of digitalization in modern society. The paper explores the main digital tools for professional training of personnel. The advantages and disadvantages of using digital tools in teaching are highlighted.
Elaborating test materials for digital assessment of BSc students’ learning outcomes in training area “Agroengineering”, profile "Electrical Equipment and Electrotechnology”

Abstract The paper considers modern specific features and requirements for engineering education. The authors show changes in the forms, methods and means of professional education. The main engineering activities are specified for training area "Agroengineering". The paper underlines the importance of the comprehensive development of future engineers’ thinking, in particular, theoretical thinking, and the ability of performing logical actions with scientific concepts, using mental operations of comparison, concretizing, generalizing and abstraction. To check the development level of theoretical and logical thinking, the authors developed an educational and diagnostic test on the "Electrical Materials" course to note the necessity and possibilities of using computer testing for the strategic, systematic and objective assessment of students’ knowledge, in terms of the development of their professional thinking and competencies. There is a description of the procedure of elaborating test materials, and the test results are presented. The authors analyze the digital test results at the stage of ongoing control and the number of mistakes that students made when performing tasks of different subtests and sections. Assumptions are made about the possible reasons for the poor outcomes; possible ways to improve the test procedure are offered.
Evaluating pattern restrictions for associative classifiers

Associative classification is a pattern recognition approach that integrates classification and association rule discovery to build accurate classification models. These models are formed by a collection of contrast patterns that fulfill some restrictions. In this paper, we introduce an experimental comparison of the impact of using different restrictions in the classification accuracy. To the best of our knowledge, this is the first time that such analysis is performed, deriving some interesting findings about how restrictions impact on the classification results. Contrasting these results with previously published papers, we found that their conclusions could be unintentionally biased by the restrictions they used. We found, for example, that the jumping restriction could severely damage the pattern quality in the presence of dataset noise. We also found that the minimal support restriction has a different effect in the accuracy of two associative classifiers, therefore deciding which one is the best depends on the support value. This paper opens some interesting lines of research, mainly in the creation of new restrictions and new pattern types by joining different restrictions.
Automatic Language Identification using Suprasegmental Feature and Supervised Topic Model

Language identification is quite challenging when it comes to discriminating between closely related dialects of the same language. The fundamental issue is to explore the discriminative cue and effective representation. In this paper, the multi-dimensional language cues are used to distinguish languages, which includes the phonotactic and prosodic information and can be found in the unsupervised setting. Moreover, a novel supervised topic model is proposed to represent and learn the difference of languages. We built the system of language identification and reported the test results on the NIST LRE07 datasets and the Chinese dialect spoken corpus. Compared with other state-of-the-art methods, the experiment results show that the proposed method provides competitive performance and helps to capture robust discriminative information for short duration language identification.
E-Learning Development as Interactive System with Scrum Methodology

Along with the rapid development and product development so that all parts particularly in the field of education are required to fast paced to meet the needs of businesses that are capricious and unpredictable. With the needs of the business change then it is used software development Scrum methodology, the Scrum can accommodate the problems, although complex and needs changing. With a fixed focus on its main purpose is to produce products with high value and quality. In the field of education, especially in the scope of a university that need to be anticipated include student difficulty in finding learning material as well as the lecturers still find trouble in the form of a rating system that is still using a way that has not been computerized which resulted in many errors in the processing of student grades. Depart from that, to evaluate a wide variety of the development of the educational entities that the system needed always changing so it encourages researchers to use the method of Scrum as the appropriate method. Results to be achieved in this research is a software E-Learning system model and framework that can be applied to universities, especially the University of 17 Agustus 1945 Surabaya in the process of the success of teaching and learning in the classroom and outside the classroom so that E-Learning can facilitate the activities of students and faculty, in addition to a study of this framework can be a tool for the development of other systems.
Postcolonial and Indigenous Authorship

Involved as they necessarily are in the social and economic contexts of literary publishing, authors can cater to the “dictates” of the market or set out to subvert these dictates. As Graham Huggan has argued with regard to Arundhati Roy, for instance, an author – in this case, an Indian or “postcolonial” author – may seemingly cater to the expectations of a Western audience by providing an “exoticist” description of a far-away country, but she may at the same time weave into her text a questioning of the politics of exoticism.1 At the same time, the reception of a given literary text may be informed not only by the politics of the literary marketplace or a mass audience, but also by the academic reception of this text. Here, too, authorship and scholarly criticism have often worked in tandem: literary authors have taken up, responded to, or resisted certain “turns” in criticism. Much has been made of this “symbiotic” relationship between literary writing and academic analysis, and it has often remained unclear whether writing precedes new developments in literary criticism or whether academic discussion in turn inspires new conventions of writing.
Criminal Information Mining

In the previous chapters, the different aspects of the authorship analysis problem were discussed. This chapter will propose a framework for extracting criminal information from the textual content of suspicious online messages. Archives of online messages, including chat logs, e-mails, web forums, and blogs, often contain an enormous amount of forensically relevant information about potential suspects and their illegitimate activities. Such information is usually found in either the header or body of an online document. The IP addresses, hostnames, sender and recipient addresses contained in the e-mail header, the user ID used in chats, and the screen names used in web-based communication help reveal information at the user or application level. For instance, information extracted from a suspicious e-mail corpus helps us to learn who the senders and recipients are, how often they communicate, and how many types of communities/cliques there are in a dataset. Such information also gives us an insight into the inter and intra-community patterns of communication. A clique or a community is a group of users who have an online communication link between them. Header content or user-level information is easy to extract and straightforward to use for the purposes of investigation.