Reviewerly/exHarmony huggingface dataset:
Analysis, Design and Evaluation of a High-Performance Stochastic Multilayer Perceptron: from Mini-Batch Training to Inference

&lt;p&gt;The mini-batch technique is widely used in neural network training with conventional arithmetic for its efficiency; however, its feasibility and performance in SC MLPs have rarely been studied. This paper analyzes by theory and simulation the performance of the mini-batch technique in SC MLPs; the results show that it potentially has a larger benefit in MLPs using SC than the conventional version. Moreover, a pipelined SC MLP implementation is also pursued in this paper for performing the inference process. All findings and designs provided in this paper leverage the advantages of the mini-batch technique and SC implementation to design high-performance MLPs. &lt;/p&gt;
Investigating the sustainability of linguistics as a discipline in Mainland China based on the national research funding: A bibliometric analysis (2000-2020)

&lt;p&gt;National research funding regulates government research expenditures and thus greatly affects the sustainability of the academic disciplines. This study examined the sustainability of linguistics based on national research funding over the past 20 years (2000-2020). In particular, we used a bibliometric approach to examine the evolutionary trends in the number of topics, important factors, and major research themes in the field of linguistics based on the National Social Science Foundation (NSSFC). The main findings are three-fold and summarized as follows. First, a significant increase in awarded grants in linguistics was found over the examined periods. Second, influential factors such as regions, institutions, and academic ranks significantly affected the likelihood of receiving research grants. Third, eight major research themes were identified, along with their evolving trends. These identified research themes were similar to those in previous studies and focused on basic research. The results further indicate: (1) linguistics as an academic discipline in China has undergone sustainable growth over the examined periods; (2) close attention should be given to critical factors that affect the chance of receiving research grants; (3) research grants reflect government’s strategic plans to sustain the growth of academic disciplines; (4) research grants can work as a helpful tool for mining research information. Practically, while the present study has taken China as the representative case, the methods in use nevertheless have high ecological validity, thus being applicable to future studies concerning mining research themes from voluminous, diachronic text data. Besides, the current results can also serve as practical references for prospective researchers and applicants in their decision-making process.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt;
Adaptive Context Modeling for Arithmetic Coding Using Perceptron

&lt;p&gt;Arithmetic coding is used in most media compression methods. Context modeling is usually done through frequency counting and look-up tables (LUTs). For long-memory signals, probability modeling with large context sizes is often infeasible. Recently, neural networks have been used to model probabilities of large contexts in order to drive arithmetic coders. These neural networks have been trained offline. We introduce an online method for training a perceptron-based context-adaptive arithmetic coder on-the-fly, called adaptive perceptron coding, which continuously learns the context probabilities and quickly converges to the signal statistics.We test adaptive perceptron coding over a binary image database, with results always exceeding the performance of LUT-based methods for large context sizes and of recurrent neural networks. We also compare the method to a version requiring offline training, which leads to equally satisfactory results.&lt;/p&gt;
A Preliminary Case Study of Planning With Complex Transitions: Plotting

Plotting is a tile-matching puzzle video game published by Taito in 1989. Its objective is to reduce a given grid of coloured blocks down to a goal number or fewer. This is achieved by the avatar character repeatedly shooting the block it holds into the grid. Plotting is an example of a planning problem: given a model of the environment, a planning problem asks us to find a sequence of actions that can lead from an initial state of the environment to a given goal state while respecting some constraints. The key difficulty in modelling Plotting is in capturing the way the puzzle state changes after each shot. A single shot can affect multiple tiles directly, and the grid is affected by gravity so numerous other tiles can be affected indirectly. We present and evaluate a constraint model of the Plotting problem that captures this complexity. We also discuss the difficulties and inefficiencies of modelling Plotting in PDDL, the standard language used for input to specialised AI planners. We conclude by arguing that AI planning could benefit from a richer modelling language.
Lightweight Decoding Strategies for Increasing Specificity

Language models are known to produce vague and generic outputs. We propose two unsupervised decoding strategies based on either word-frequency or point-wise mutual information to increase the specificity of any model that outputs a probability distribution over its vocabulary at generation time. We test the strategies in a prompt completion task; with human evaluations, we find that both strategies increase the specificity of outputs with only modest decreases in sensibility. We also briefly present a summarization use case, where these strategies can produce more specific summaries.
Predictive machine learning for prescriptive applications: a coupled training-validating approach

In this research we propose a new method for training predictive machine learning models for prescriptive applications. This approach, which we refer to as coupled validation, is based on tweaking the validation step in the standard training-validating-testing scheme. Specifically, the coupled method considers the prescription loss as the objective for hyper-parameter calibration. This method allows for intelligent introduction of bias in the prediction stage to improve decision making at the prescriptive stage, and is generally applicable to most machine learning methods, including recently proposed hybrid prediction-stochastic-optimization techniques, and can be easily implemented without model-specific mathematical modeling. Several experiments with synthetic and real data demonstrate promising results in reducing the prescription costs in both deterministic and stochastic models.
Zero-Shot Image Classification Using Coupled Dictionary Embedding

Zero-shot learning (ZSL) is a framework to classify images belonging to unseen classes based on solely semantic information about these unseen classes. In this paper, we propose a new ZSL algorithm using coupled dictionary learning. The core idea is that the visual features and the semantic attributes of an image can share the same sparse representation in an intermediate space. We use images from seen classes and semantic attributes from seen and unseen classes to learn two dictionaries that can represent sparsely the visual and semantic feature vectors of an image. In the ZSL testing stage and in the absence of labeled data, images from unseen classes can be mapped into the attribute space by finding the joint sparse representation using solely the visual data. The image is then classified in the attribute space given semantic descriptions of unseen classes. We also provide an attribute-aware formulation to tackle domain shift and hubness problems in ZSL. Extensive experiments are provided to demonstrate the superior performance of our approach against the state of the art ZSL algorithms on benchmark ZSL datasets.
On the automorphism groups of rank-4 primitive coherent configurations

The minimal degree of a permutation group $G$ is the minimum number of points not fixed by non-identity elements of $G$. Lower bounds on the minimal degree have strong structural consequences on $G$. Babai conjectured that if a primitive coherent configuration with $n$ vertices is not a Cameron scheme, then its automorphism group has minimal degree $\geq cn$ for some constant $c>0$. In 2014, Babai proved the desired lower bound on the minimal degree of the automorphism groups of strongly regular graphs, thus confirming the conjecture for primitive coherent configurations of rank 3. In this paper, we extend Babai's result to primitive coherent configurations of rank 4, confirming the conjecture in this special case. The proofs combine structural and spectral methods.
The VSP App Adoption by Royal Malaysia Police (RMP) in Law Enforcement

This study aimed at discovering the factors of VSP app adoption by RMP in law enforcement particulary towards crime prevention. Using qualitative methodology of case study design, significant discoveries were obtained through in-depth interviews with five VSP Enforcers from the Crime Prevention and Community Safety department or Jabatan Pencegahan Jenayah dan Keselamatan Komuniti (JPJKK) of Bukit Aman representing the Royal Malaysia Police. The NVivo 12.0 software was used in the process of open, axial and selective coding. The data were analzed by applying the thematic analysis. The result indicates that the RMP utilizes VSP application due to five salient factors that are; VSP acts as a prevention tool, identity protection, an espionage medium, comprehensible and last but not least, RMP utilizes VSP app in law enforcement in particular to prevent crime due to its functionality.
Developing Story Telling Performance Model in Improving Students’ Self Confidence

Extracurricular directs students to improve confidence and ability to speak English which is oriented toward student participation in various competitions such as speech, storytelling. The study aimed to develop material of storytelling performance model as a learning material of extracurricular particularly public speaking extracurricular. Since the extracurricular aims to improve students’ self-confidence to perform in public area through story telling. In this context, the quality of learning outcomes in English speaking extracurricular activities is as follows. (1) Mastery of knowledge; (2) Mastery of English-speaking skills; (3) Having confidence when speaking; and (4) Able to appear in speaking using English which is oriented towards public speaking through story telling performance. Research and development were conducted in this study which used ADDIE model. The study was conducted at SMA N 11 Medan and the subject of the study was 10th grade students. The instruments of the study were observation, questionnaire, and interview. Furthermore, the study was conducted in some stages such as, Analysis, Design, Develop. Implement, and Evaluation. The result of study showed that story telling material could improve students’ enthusiastic to perform in public speaking extracurricular at SMA N 11 Medan. Thus, it improved students’ confidence in telling the story. Additionally, storytelling material was categorized as an excellent material to improve students’ confidence through story telling validated by some experts.
Difficulty Controlled Piano Music Generation using GAN Approach

&lt;p&gt;Musical learning is important whether for entertainment or as a profession. However, learning music depends on practice; finding a musical piece on the player’s level is challenging. This paper proposes a novel method for automated difficulty-controlled piano music generation using a Generative Adversarial Network (GAN) approach. Two applications are studied (1) Generation of a random musical piece given the required difficulty and (2) Generation of a musical piece similar (melodic similarity) to an input musical piece in the required difficulty. We use a Generative Adversarial Network for music generation and an autoencoder for encoding the melody of the input musical piece. The model is evaluated using multiple metrics for musicality, similarity, and difficulty. The results prove the effectiveness of the proposed approach and encourage future research in the same area.&lt;/p&gt;
Collecting, extracting and storing web research survey questionnaires data

Companies or institutions can use survey questionnaires to evaluate items or products, analyze their employees/customers’ satisfaction or collect any data they consider helpful. Furthermore, questionnaires can be used to collect data that can be used in research studies. Some problems in creating such questionnaires involve: deciding which questions to ask, how to ask them, and how to organize them. Many research communities, especially in the healthcare field, maintain repositories that are publicly accessible and include different questionnaires that help professionals and researchers analyze the results of questions, add new questions, or even point out nonsense questions. In this paper, we describe: (i) web crawler, which scans the Web searching for sites that possibly contain questionnaires; (ii) an extractor, which extracts the questionnaires from the list of pages collected by the crawler and saves them into a relational database; and (iii) the public dataset we have created to persist the questionnaires. The database created can then serve to analyze these data and/or as a centralized base of examples to prepare new questionnaires or reuse existing questions. The experiments we have conducted demonstrate that our crawler has achieved 94,47%, and the extractor has achieved a precision between 90% and 92%.
CandiDATA: an enhanced dataset for data analysis of elections in Brazil from 1945 to 2020

The Brazilian Superior Electoral Court (TSE) keeps data on elections that have taken place in Brazil since 1933. These data constitute an important collection serving as a reference for works in several research areas. However, this collection is not fully exploited due to some problems, such as missing and non-standard data, making analysis and integration with external databases difficult. Previous works built limited datasets and tools because of these problems as they only include data since the 1998 election, disregarding the election years from 1945 and 1996. This work discusses the steps to create CandiDATA – a standardized and enhanced dataset from TSE data, including a toolkit of webscrapping and data visualization. CandiDATA is available in open format and covers the election period between 1945 and 2020.
PFP Data Structures

Prefix-free parsing (PFP) was introduced by Boucher et al. (2019) as a preprocessing step to ease the computation of Burrows-Wheeler Transforms (BWTs) of genomic databases. Given a string $S$, it produces a dictionary $D$ and a parse $P$ of overlapping phrases such that $\mathrm{BWT} (S)$ can be computed from $D$ and $P$ in time and workspace bounded in terms of their combined size $|\mathrm{PFP} (S)|$. In practice $D$ and $P$ are significantly smaller than $S$ and computing $\mathrm{BWT} (S)$ from them is more efficient than computing it from $S$ directly, at least when $S$ consists of genomes from individuals of the same species. In this paper, we consider $\mathrm{PFP} (S)$ as a {\em data structure} and show how it can be augmented to support the following queries quickly, still in $O (|\mathrm{PFP} (S)|)$ space: longest common extension (LCE), suffix array (SA), longest common prefix (LCP) and BWT. Lastly, we provide experimental evidence that the PFP data structure can be efficiently constructed for very large repetitive datasets: it takes one hour and 54 GB peak memory for $1000$ variants of human chromosome 19, initially occupying roughly 56 GB.
Detecting Anomaly Event in Video Based on Generative Adversarial Network

Anomaly detection in videos is a challenging computer vision problem. Existing state-of-the-art video anomaly detection methods mainly focus on the structural design of deep neural networks to obtain performance improvements. Different from the main research trend, this paper focuses on combining ensemble learning and deep neural networks and proposes an approach based on ensemble generative adversarial network (GAN). In the proposed method, a set of generators and a set of discriminators are trained together, so each generator gets feedback from multiple discriminators and vice versa. Compared with a single GAN, the proposed ensemble GAN can better model the distribution of normal data to better detect anomalies. In the experiments, the performance of the proposed method is tested on two public datasets. The results show that ensemble learning significantly improves the performance of a single detection model, which outperforms some existing state-of-the-art methods.
An Wolf Hunting Approach for Optimization of Tasks Scheduling in Cloud Computing

Abstract Cloud computing is the most emerging technology in the era that provides scalable, distributed, elastic computer resources for the end-user using the internet.However, the Cloud Computing Networks(CCNs) fails in providing many Quality of Services(QoS) such as reliability, load balancing, resource allocation and task scheduling, security, etc out of which the most challenging task in cloud computing is task scheduling, which directly affects the operational cost and resource usage of the system.Hence,this paper proposes an advanced technique named the Wolf Hunting Approach (WHA)for further improvement of an optimal solution for searching with a multi-objective optimization technique. The implementation of WHA involves simulation based experiment and shows that WHA has better accuracy and convergence speed in seeking for optimal task scheduling. In comparison to the current meta-heuristic algorithms, it provides superior performance in utilization of system resources in the presence of large-scale and small-scale tasks. The approach is well illustrated with suitable examples
Developing English Reading Descriptive Text Materials Based on Scientific Approach for Grade X Students of Computer and Network Engineering Study Program at SMKS Harapan Al-Washliyah Sigambal

The objective of this study was to develop materials of reading descriptive text as supporting learning for grade X computer and network engineering students at SMKS Harapan Al- Washliyah Sigambal. The research was conducted by using the simplication of Research and Development theory by Borg and Gall (2003) which simplified by Puslitjaknov (2008). The data was taken through observation, questionnaires, and interview. The materials were validated by an English lecturer and an English teacher by using validation sheets which is adapted from BSNP. The data were collected by using descriptive analysis and the materials were arranged based on Scientific Approach. The study’ results revealed that: (1) the materials used do not support the students study program because the topics of the materials are still considered general, (2) relevant reading materials are needed by students in vocational school especially in computer and network engineering study program, (3) the developed reading materials based on Scientific Approach have been validated with the score 3.52 from the first expert and 3.65 from the second expert which is in the range of 3.25 &lt; ≤̅ 4.00 and categorized as “Very Good”. The reading descriptive text materials have been developed in accordance to the students’ needs based on Scientific Approach.
Perceptions of English and Literature Department Students on Lecturer’s Feedback for Critical Book Review (CBR)

This study aims to analyze Perceptions of English and Literature Department Students perceptions on lecturer’s feedback for Critical Book Review (CBR). A descriptive qualitative design was used in this study. The data sources are all students outside the English Education study program who take 5 courses, namely Curriculum and Textbook Analysis, Translation in Language Teaching, English Culture in ELT, Technology in ELT and Management of English Based Institution. The data are the results of questionnaires given to students outside the Study Program who are conducting the student exchange program (Permata). Research shows that students feel helped by the feedback provided by the lecturer in the form of corrections, criticisms, suggestions, and providing solutions either orally or in writing. The reason why lecturers provide feedback is to measure students' abilities in doing CBR assignments.
Implementation of Content-Based Instruction Approach in Teaching English for Maritime at Vocational High School Pelayaran Buana Bahari

This study aims to describe the implementation of the English for Maritime teaching method using the Content-Based Instruction approach at Vocational High School Pelayaran Buana Bahari, Medan. The subjects of this study were 30 students with a total of 10 students per class from grade ten, grade eleven, and grade twelfth. The data collected in the study was taken using a questionnaire containing questions about the implementation of the Content-Based Learning Approach in Teaching English for Maritime at Vocational High School Pelayaran Buana Bahari, Medan. The study was conducted once with data collection carried out for three days. From this study it can be concluded that the maritime English teaching method using the Content-Based Instruction Approach can improve students' competence in understanding English for Maritime because the teaching approach through this method is carried out in a structured and systematic manner so that the aim is to equip students with mastery of English for the benefit of marine communication can be achieved.
Digitizing Lehrwerkanalyse Materials with the Flip PDF Professional Application

The development of digital book using the Flip PDF Professional application for Lehrwerkanalyse course is motivated by the lack of interactive teaching materials for students. The aims of this research are: (1) to develop a digital book for Lehrwerkanalyse course through the Flip PDF Professional application; (2) to determine the feasibility of digital books based on expert judgment and student perceptions. This research is a research and development (R&amp;D) using the ADDIE development model. This research was tested on students of German Language Education Study Program, Universitas Negeri Malang. The stages of this research are: 1) Analysis, 2) Design, 3) Development, 4) Implementation, and 5) Evaluation. The data collection method used a questionnaire to obtain assessment data from students on the product. The result of the product is an OFFEN digital book contains 8 materials presented in the form of text, video, audio, supporting images, mini quizzes, and essay assignments for the Lehrwerkanalyse course. The results of the expert assessment using the Gregory validity formula, the feasibility is at a score of 0.96 (very high). The implementation of digital book for students results in that these digital books have an attractive appearance, easier to understand the material, can be used as independent teaching materials, and is easy to access. Based on these two assessments, the OFFEN digital book is suitable to use as independent teaching material.