Reviewerly/exHarmony huggingface dataset:
Pengaruh Nilai-Nilai Karakter dan Literasi Membaca dengan Model STAD (Student Team Achievement Divisions) terhadap Hasil Belajar

&lt;div align="center"&gt;&lt;table width="645" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="439"&gt;&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; The low character and reading of students in Indonesia, because there are cases of students persecuting teachers and the Indonesian PISA literacy test data is ranked 64th. Therefore, the learning process requires a cooperative learning model, namely the STAD model. Researchers integrate character values and literacy reading in RPP with the STAD model that is used to determine the effect on student learning outcomes. This study uses an experimental type and a &lt;em&gt;quasi-experimental design&lt;/em&gt;. There are 68 students in class V as samples, while tests, questionnaires, and observations are instruments. So, it can be obtained the value of t&lt;sub&gt;count &lt;/sub&gt;&amp;gt; t&lt;sub&gt;table &lt;/sub&gt;that is 3.047 is greater than 0.05 which can significantly influence between variables.&lt;/p&gt;&lt;p class="Abstract"&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;Rendahnya karakter dan membaca siswa di Indonesia dikarenakan terdapat kasus siswa menganiaya guru dan data PISA uji literasi Indonesia berada pada peringkat ke-64. Oleh karena itu, proses pembelajaran diperlukan model pembelajaran kooperatif yaitu model STAD. Peneliti mengintegrasikan nilai-nilai karakter dan literasi membaca dalam RPP dengan model STAD yang digunakan untuk mengetahui pengaruh terhadap hasil belajar siswa. Penelitian ini menggunakan jenis eksperimen dan desain &lt;em&gt;quasi eksperimen&lt;/em&gt;. Terdapat 68 siswa kelas V sebagai sampel, sedangkan tes, angket, dan observasi adalah instrumen. Sehingga, dapat diperoleh hasil nilai t&lt;sub&gt;hitung &lt;/sub&gt;&amp;gt; t&lt;sub&gt;tabel &lt;/sub&gt;yaitu 3,047 lebih besar dari 0,05 yang dapat berpengaruh secara signifikan antar variabel.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;
Factors Influencing Continuance Intention of Complaint Service: A Case Study of QLUE

The quality of information systems is the key to user satisfaction with an information system application. As a theoretical basis, this study uses the Information System Success Model to discuss the influence of the quality of information systems on the continued use of Information Systems applications. Qlue is used as a case study as one of the complaint service applications. This study uses a questionnaire with 299 data collected using Google Forms which are distributed through various social media. Then to test the hypothesis, we processed the data by Partial Least Square (PLS) using SmartPLS application. This study explains that the factors that influence the intention to continue using Qlue are satisfaction with system quality and information quality, including aesthetic design, immediacy, and customization.
Academic Information System Assessment of AKRB Yogyakarta Using UTAUT

Implementation of Academic Information System (AIS) at the Radya Binatama Communication Academy (AKRB) had some problems for some users. That problems are known from interviews with several AIS users. This caused a delay in data exchange with other divisions. This study aims to assess the implementation of AIS to the AKRB by using the Unified Theory of Acceptance and Use of Technology (UTAUT) method. UTAUT has four main constructs that affect user acceptance namely performance expectations, effort expectations, social influences, and facilitating conditions. The data were obtained from distributing questionnaires to all AIS users as many as 40 respondents. Then the data is processed using Structural Equation Modeling (SEM) techniques with the help of SmartPLS. The results of the analysis show that only construct facilitating conditions is valid with a t-statistic value of 2.733. While the other three constructs have values that are in the range of invalid values between -1.96 to 1.96 with the values of each construct being 1.891, 0.050, 1.440. It can be concluded that the application of SIA in AKRB has not been well received by all AIS users. Therefore, it is necessary to conduct an evaluation that represents the other three constructs.
A Novel Vote Counting System Based on Secure Blockchain

It has long been difficult to create a safe electronic voting system that provides the transparency and flexibility provided by electronic systems, while maintaining the fairness and privacy of present voting methods. Voting, especially during elections, is a technique where participants do not trust one another since the system might be attacked not just by an outsider but also by participants themselves (voters and organizers). The traditional methods of voting systems find it challenging to maintain the characteristics of an ideal voting system since there is a chance of tampering with results and disturbing the process itself. As a result, the effectiveness of the voting system is increased by translating the characteristics of an ideal voting system into digital space. It greatly lowers the expense of the elections and the work of the inspectors. In this essay, we'll use the open-source Blockchain technology to suggest a new electronic voting system's architecture. New chances to create new kinds of digital services are being provided by Blockchain. Numerous elements of our life have been altered by Blockchain technology, including the ability to save digital transactions via the Internet, confirm their legitimacy, license them, and provide the greatest level of security and encryption. This system offers a distributed architecture for storing the data, which distributes the data among many servers. In addition to maintaining voter identity outside of the vote count, this technology makes the voting process transparent.
Intelligent Traffic Light System Using YOLO

Day-by-day traffic is increasing due to the technology limit in the traffic control. We made a critical survey and found the existing systems and their shortcomings. Currently time slot is fixed for each lane. Though the traffic is less in any lane the lights will glow up to a fixed time slot. As a result, vehicles in the other lanes having more traffic density have to wait for a long time. This problem can be solved by our proposed system that works depending up on the traffic density in each lane. A real-time traffic detection is extremely significant especially in the metropolitan cities. In this paper we are proposing a real-time traffic controlling system that is developed by using image processing, cloud, embedded board and You Only Look Once (YOLO) v4 algorithm. Our system computes the vehicle detection, vehicle count and classifies the vehicle. The vehicle count is utilized to calculate the traffic density. This YOLO v4 network maintains the precision, recall and F-score in the vehicle detection and traffic control. Our proposed system executes better performance than the existing systems.
Digital literacy: an empirical study for fashion design students in India

Purpose This study aims to conduct an exploratory research to find out the evolving constructs and variables of digital literacy, as seen by researchers since its inception. This research also includes an empirical study to identify and further analyze the digital literacy dimensions of university students studying fashion design program in Kolkata, India. Design/methodology/approach The exploratory study is based on a review of extant literature, whereas the empirical study is carried out through a self-assessment survey based on UNESCOs Digital Literacy Global Framework competences after validating their relevance with respect to the fashion and apparel industry. A total of 120 university students studying four years Bachelor of Design (Fashion Design) program were asked to rate their digital literacy competences on a five-point Likert scale, with a self-reported truth response against each statement. The results were analyzed using multivariate statistical tools. Findings Based on UNESCO competences, it came out that there are eight digital literacy dimensions. ANOVA further confirms that the dimensions requiring higher-order cognition, such as “software management competence” and “digital citizenship competence,” increase with progress in the graduate program. However, lower-order competence dimensions remained unchanged over time. Originality/value The research instrument used for this empirical study, its identified dimensions and the fact that higher-order competence dimensions are enhanced with progression in university education may be helpful for similar research in other fashion-related programs.
Search tools and scholarly citation practices in literary studies

Purpose The wide adoption of web-scale discovery tools calls into question the usefulness and viability of traditional subject indexes. This study examines this question of usefulness in the context of the discipline of literary studies. To what extent can researchers rely on the primary database devoted to language and literature study to discover relevant scholarship, and how does the database's performance compare to other common search tools? Design/methodology/approach The study uses a random sample of citations from articles published in the flagship journal, PMLA , to see how well the sources cited by literature scholars are covered in various search tools, including the MLA International Bibliography. Findings Of the search tools investigated, Google Scholar found the largest number of citations, even when limiting to literary scholarship. However, the eclecticism of citations suggests that scholars benefit from using a variety of search tools and methods. Originality/value Although other studies have looked at discoverability in certain subject areas, this one focuses on literary studies. An understanding of the relative coverage of different search tools can inform librarian practices and recommendations.
An Efficient Defending Mechanism Against Image Attacking on Medical Image Segmentation Models

Image attacking has been studied for a long time. However, in reality, the number of research on defending against the attacks on segmentation models is still limited especially for medical imaging. To fill this research gap, we propose a novel defending mechanism against adversarial attacks for the segmentation models. We focus on segmentation as robustness improvement on segmentation is much more challenging due to its dense nature, and segmentation is at the center of medical imaging tasks. In this paper, we are the first time to employ Transformer as a technique to protect the segmentation models from attacks. Our result on several medical well-known benchmark datasets shows that the proposed defending mechanism to enhance the segmentation models is effective with high scores and better compared to other strong methods.
The Use of Information Technology in the Educational Process during Martial Law

The aim of the article was to evaluate the effectiveness of emotional support in the introduction of information technology in higher educational institutions during martial law. The following methods were used: general theoretical; testing, analysis of products of activity, formative experiment, statistical methods. Motivational, cognitive, activity and reflective components of professional competence were identified in the course of theoretical analysis. The medium indicators of dominance of cognitive, activity and reflective components of professional competence of students during martial law were recorded. The motivational component was the least developed. Experimental impact is effective for the development of motivational and cognitive components, but requires further clarification in the development of activity and reflective components. The significance of the changes is confirmed by statistical verification of the data. Prospects for research on the problem involve expanding the number and duration of research on the use of educational information technology during martial law.
Fast physical random bit generation based on a chaotic optical injection system with multi-path optical feedback

Based on the chaotic signal provided by a simple chaotic system, a random bit sequence with a rate of 640 Gb/s is generated through adopting the circulating exclusive-or (CXOR) post-processing method. Such a simple chaotic system is built via a slave semiconductor laser subject to optical injection of a chaotic signal originated from a master semiconductor laser under multi-path optical feedback. First, through inspecting the dependences of the time-delay-signature (TDS) and bandwidth of the chaotic signal on some key operation parameters, optimized parameters are determined for generating a high-quality chaotic signal with a large bandwidth and low TDS. Second, the high-quality chaotic signal is converted to an 8-bit digital signal by sampling with a digital oscilloscope at 80 GSa/s. Next, through adopting the CXOR post-processing method, a bit sequence with a rate of 640 Gb/s is obtained. Finally, the randomness is estimated by the National Institute of Standard Technology (NIST) Special Publication 800-22 statistical tests, and the results demonstrate that the obtained random bit sequence can pass all the NIST tests.
Shannon Meets Myerson: Information Extraction from a Strategic Sender

We study a setting where a receiver must design a questionnaire to recover a sequence of symbols known to strategic sender, whose utility may not be incentive compatible. We allow the receiver the possibility of selecting the alternatives presented in the questionnaire, and thereby linking decisions across the components of the sequence. We show that, despite the strategic sender and the noise in the channel, the receiver can recover exponentially many sequences, but also that exponentially many sequences are unrecoverable even by the best strategy. We define the growth rate of the number of recovered sequences as the information extraction capacity. A generalization of the Shannon capacity, it characterizes the optimal amount of communication resources required. We derive bounds leading to an exact evaluation of the information extraction capacity in many cases. Our results form the building blocks of a novel, noncooperative regime of communication involving a strategic sender.
A Study on Parallel Clustering Algorithms based on MapReduce

Since the advent of the global computerized market, the volume of digital information has grown exponentially, as has the demand for storing it. As the price of storage devices decreases, the necessity to analyze vast quantities of unstructured digital data to retain only essential information increases. MapReduce is a programming paradigm for producing and generating massive information indices. Using MapReduce to produce meaningful clusters from such a massive amount of raw data is an efficient way to manage such voluminous amounts of data. On the other hand, the existing industry standard for data clustering algorithms presents significant obstacles. The conventional clustering calculation efficiently handles a great deal of information from various sources, such as online media, business, and the web. Nevertheless, the sequential count in clustering approaches is time-intensive in these conventional calculations. The wide varieties of K-Means, including K-Harmonic Means, are sensitive to forming cluster centers in huge datasets. This work suggests a logical evaluation of such calculations. It offers a study of the various k-means clustering algorithms employed in MapReduce, as well as the study on the introduction and the open challenges of parallelism in MapReduce.
Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices

We propose an automatic method to estimate self-reported pain intensity based on facial landmarks extracted from videos. For each video sequence, we decompose the face into four different regions and pain intensity is measured by modeling the dynamics of facial movement using the landmarks of these regions. A formulation based on Gram matrices is used to represent the trajectory of facial landmarks on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. A curve fitting algorithm is then used to smooth the trajectories and a temporal alignment is performed to compute the similarity between the trajectories on the manifold. A Support Vector Regression classifier is then trained to encode the extracted trajectories into pain intensity levels consistent with the self-reported pain intensity measurement. Finally, a late fusion of the estimation for each region is performed to obtain the final predicted pain intensity level. The proposed approach is evaluated on two publicly available databases, the UNBCMcMaster Shoulder Pain Archive and the Biovid Heat Pain database. We compared our method to the state-of-the-art on both databases using different testing protocols, showing the competitiveness of the proposed approach.
Effects of Nitrogen and Phosphorus Nutrients on the Proliferation of Heterosigma Akashiwo under the Background of Mobile Big Data

In the era of information explosion, mobile data is developing rapidly, and many fields are facing the challenges of data collection, analysis and operation. Heterosigma akashiwo is a kind of wide temperature and wide salt algae, belonging to the class Echinococcus and the genus heterobenthos. It is widely distributed in the coastal waters of the world. It has formed harmful red tides for many times in many countries, causing a large number of fish deaths, causing great economic losses and serious damage to aquatic resources. If toxic red tides break out frequently, they will pose a great threat to public health. Seawater eutrophication is the main environmental inducement for the formation of red tide by Heterosigma akashiwo. In particular, the content of nutrients in seawater significantly affects the reproduction of Heterosigma akashiwo and the excessive proliferation of plankton such as algae. At the same time, a large number of planktons will devour the dissolved oxygen in the water, thus affecting the photosynthesis of the water body, resulting in the deterioration of the water quality and the damage to the functions of ecology and water resources. As for the effects of nitrogen, phosphorus, iron and different vitamins on the growth of Heterosigma akashiwo, this paper takes Heterosigma akashiwo as the research object, and focuses on the analysis of the effects of ammonium chloride, urea and sodium dihydrogen phosphate on the proliferation of Heterosigma akashiwo. The results showed that the higher or lower the concentration of nitrogen and phosphorus, the slower the proliferation rate of Heterosigma akashiwo; Heterosigma akashiwo had the maximum proliferation rate under the nitrogen concentration of 300–500 μmol/L; At the concentration of 10∼15 μmol/L phosphorus, its growth rate is the fastest and its proliferation level is the highest. The selected nitrogen and phosphorus have a very significant impact on the proliferation of microalgae.
The Computational Complexity of Some Explainable Clustering Problems

We study the computational complexity of some explainable clustering problems in the framework proposed by [Dasgupta et al., ICML 2020], where explainability is achieved via axis-aligned decision trees. We consider the $k$-means, $k$-medians, $k$-centers and the spacing cost functions. We prove that the first three are hard to optimize while the latter can be optimized in polynomial time.
Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets

We propose a novel 2-stage sub 8-bit quantization aware training algorithm for all components of a 250K parameter feedforward, streaming, state-free keyword spotting model. For the first stage, we adapt a recently proposed quantization technique using a non-linear transformation with $$\tanh (.)$$ on dense layer weights. In the second stage, we use linear quantization methods on the rest of the network, including other parameters (bias, gain, batchnorm), inputs, and activations. We conduct large scale experiments, training on 26,000 h of de-identified production, far-field and near-field audio data (evaluating on 4,000 h of data). We organize our results in two embedded chipset settings: a) with commodity ARM NEON instruction set and 8-bit containers, we present accuracy, CPU, and memory results using sub 8-bit weights (4, 5, 8-bit) and 8-bit quantization of rest of the network; b) with off-the-shelf neural network accelerators, for a range of weight bit widths (1 and 5-bit), while presenting accuracy results, we project reduction in memory utilization. In both configurations, our results show that the proposed algorithm can achieve: a) parity with a full floating point model’s operating point on a detection error tradeoff (DET) curve in terms of false detection rate (FDR) at false rejection rate (FRR); b) significant reduction in compute and memory, yielding up to 3 times improvement in CPU consumption and more than 4 times improvement in memory consumption.
Quick and (Maybe Not So) Easy Detection of Anorexia in Social Media: To Explainability and Beyond

This book chapter overviews our participation to the first sub-task of eRisk 2019 shared task, which was focused on the task of early detection of anorexia in Reddit posts. Our approach was to utilize several attention-based neural sub-models to extract features and predict class probabilities, which were later used as input features to a Support Vector Machine (SVM) making the final classification. Our submission, named CLaC achieved F1 and latency-weighted F1 scores of 0.7073 and 0.6908 respectively, allowing it to rank first in terms of these metrics, and achieved competitive results based on other evaluation metrics. This book chapter also extends our participation to eRisk 2019, and includes the additional experiments that were performed on the model in order to evaluate it in terms of explainability.
Android Media Development Based on Augmented Reality in Photosynthetic Material for PGSD UN PGRI Kediri Students

This study aims to produce educational media based on Augmented Reality (AR) on the photosynthesis material used in PGSD students at Nusantara PGRI Kediri University who are taking the basic concepts of Science (IPA) 2. The type of development research model used in this study is the Borg Gall model and Lee Owen with the stages of research and data collection, planning, product development, design validation, design revision. However this research is only limited to design revision after expert validation, this is due to limited research time. The instrument used in this research activity was a validity questionnaire (media expert, material expert, and linguist). Data analysis used in this research activity is descriptive qualitative data analysis and qualitative descriptive. The results of the research obtained from the media expert validator were 73 or 91%, the score of the results of the validation of the Photosynthesis material expert was 37 or 92%, and the score of the results of the validation of the linguists was 38 or 95%. This means that the development of AR-based educational media on photosynthetic material is valid, so that it can be used for learning media on photosynthetic material