Reviewerly/exHarmony huggingface dataset:
Nonuniform language in technical writing: Detection and correction

Abstract Technical writing in professional environments, such as user manual authoring, requires the use of uniform language. Nonuniform language refers to sentences in a technical document that are intended to have the same meaning within a similar context, but use different words or writing style. Addressing this nonuniformity problem requires the performance of two tasks. The first task, which we named nonuniform language detection (NLD), is detecting such sentences. We propose an NLD method that utilizes different similarity algorithms at lexical, syntactic, semantic and pragmatic levels. Different features are extracted and integrated by applying a machine learning classification method. The second task, which we named nonuniform language correction (NLC), is deciding which sentence among the detected ones is more appropriate for that context. To address this problem, we propose an NLC method that combines contraction removal, near-synonym choice, and text readability comparison. We tested our methods using smartphone user manuals. We finally compared our methods against state-of-the-art methods in paraphrase detection (for NLD) and against expert annotators (for both NLD and NLC). The experiments demonstrate that the proposed methods achieve performance that matches expert annotators.
Recognize Facial Impression using Artificial Neural Network

This paper aims to design an artificial neural network to discover the impression by recognizing the expression of the human face. To achieve this goal, the artificial neural network was analyzed and to create patterns of the database containing a set of images with different expressions. The learning process of the network was also conducted through patterns training. The extent to which patterns of online training were recognized was compared to the true values of expressions. The grid was trained in 200 patterns and the anomalies were removed. Then re-learned the network again and analyzed the network performance by comparing the real expression with the expected expression and outputting the error for the network appearing. Impression recognition in the grid applied a three-layer back propagation model, with an average error of 0.321. The performance of the artificial neural network in the recognition of impressions was 80%
A Digital Infrastructure for Storing & Sharing Internet of Things, Wearables and App-Based Research Study Data.

In health and behavioural sciences, there has been a shift towards use of mobile, wearable and IoT based frequent granular data collection strategies to capture temporal patterns and environmental changes. There are a wide range of free and easy-to-use services that allow researchers to design and deploy intermittent assessments and surveys. However, there is a lack of tools suitable for frequent or real-time data collection. This paper outlines DROPS (Flinders Data Repository for Open Science) software infrastructure, that provides an easy-to-use web-based service for researchers to upload real-time research study data gathered from study participants devices into cloud storage as well as coordinate sharing of data with other researchers for secondary research. The infrastructure developed by Flinders University as part of a collaboration project with the Australian National Data Service, runs on a server, receives the data through an API interface, and stores in a no-SQL database. The solution eliminates the barriers of setting up back-end infrastructure to manage the reception and storage of research data assessed in real-time environments.
A General Data Renewal Model for Prediction Algorithms in Industrial Data Analytics

In industrial data analytics, one of the fundamental problems is to utilize the temporal correlation of the industrial data to make timely predictions in the production process, such as fault prediction and yield prediction. However, the traditional prediction models are fixed while the conditions of the machines have changed over time, thus making the errors of predictions increase with the lapse of time. In this paper, we propose a general data renewal model to deal with it. Combined with the similarity function and the loss function, the data renewal model estimates the time of updating the existing prediction model, and then updates it according to the evaluation function iteratively and adaptively. We have applied the data renewal model to two prediction algorithms. The experiments demonstrate that the data renewal model can effectively identify the changes of data, update and optimize the prediction model so as to improve the accuracy of prediction.
Two-degree-of-freedom manipulator path planning based on zeroing neural network

In this paper, the shortest path problem of manipulator path planning is transformed into a linear programming problem, and solved by zeroing neural network (ZNN). Firstly, the method of constructing the zeroing neural dynamics is given, and the ZNN model is constructed for shortest path problem of manipulator. Then, the Lyapunov method is utilized to prove the stability of the ZNN model. Finally, the ZNN model is applied to the path planning of manipulator to generate an optimal planning path. The simulation results show that the proposed method can effectively realize the optimal path planning of the manipulator.
An Improved SLIC Super-pixel Extraction Algorithm Based on MMTD

Super-pixel can improve the computational efficiency of image segmentation algorithm, which is a description of image with more visual significance. The research on super-pixel extraction algorithm has always been a hot spot of current research. SLIC is a common super-pixel extraction algorithm. This method can generate compact and nearly uniform super-pixels. It has a high comprehensive evaluation in the aspects of computing speed, object contour preservation and super-pixel shape. As a result it has been widely used. However, when extracting SLIC (Simple Linear Iterative Clustering) super-pixel blocks, the determination of weight m needs to be specified manually, and the same weight value for different images leads to the problem of unsatisfactory segmentation effect. In order to solve this problem, this paper introduces the medium mathematics used to deal with the fuzzy phenomenon into the super-pixel extraction, and proposes a super-pixel extraction algorithm based on MMTD (Measuring of Medium Truth Scale) and applies it to image segmentation. Firstly, the similarity between Lab distance and coordinate distance is obtained based on MMTD. Then, the weight m of SLIC super-pixel extraction algorithm is adaptively determined by iteration method. Finally, the isolated points are corrected by connected components. The experimental results show that this algorithm has better super-pixel extraction effect than the SLIC algorithm, which can effectively improve the quality of image segmentation in the later stage.
Sistem Rekomendasi Pemilihan Smartphone Android Dengan Dana Terbatas Menggunakan Modified Simple Additive Weighting (M-SAW)

With the development of communication technology today, the smartphone is one of the choices on communication tools that many users have now. Even now, smartphones have various types and prices that reach user needs, so that with limited funds, they can adjust it to their needs. By creating a smartphone selection recommendation systems, it can make it easier for people in determining smartphone purchases based on available funds, with features, brands, and advantages that are already sophisticated today. The recommendation system is made using a combined method called Modified Simple Additive Weighting (M-SAW), where Simple Additive Weighting (SAW) is used for weighting and normalization process, meanwhile Weighted Product (WP) is used for fixing the weight of criteria, determinating vector S, and ranking process. The result for best recommendation using this method is Oppo F5 Youth with score 0,444. With this recommendation results, makes the user make their decision for smartphone recommendation using this system.
Computational Analysis of French Reborrowing Process for English Loanwords

Analyzing semantic change of loanwords over time between different languages has been a longstanding sociolinguistic problem. However, few efforts have been put to computational approaches to analyzing the semantics of loanwords. In this paper, we present a new computational method for detecting and tracking the semantic change of loanwords between two languages, specifically for the reborrowing process of loanwords. We trained our model on pre-trained historical bilingual English-French word embeddings aligned with MUSE and proposed two quantitative measures of detecting loanwords reborrowing. The first measure analyzes cosine similarity of one word from two languages in the same year, and it is sensitive to cultural shifts and lexicon reborrowing. The second measure analyzes Pearson correlation from the tendency of the cosine similarity and thus predicts the pattern of these reborrowing loanwords. We show that our model can detect reborrowing loanwords that have been discovered in literature.
SISTEM PENDUKUNG KEPUTUSAN PENILAIAN KINERJA DOKTER MENGGUNAKAN METODE WEIGHT PRODUCT (WP) BERBASIS WEB

The doctor is someone who because of his knowledge tries to heal people who are sick. One effort to spur doctors' performance is by conducting a performance evaluation. At present the job evaluation system at Royal Prima Medan Hospital is still done conventionally and the results of the assessment are subjective. Based on this, the author designed a Decision Support System for Evaluating Doctor Performance using WEB-based Weighted Product (WP). There are five criteria in evaluating doctor's performance including assessment of patient care, interpersonal and communication skills, writing and completeness of medical records, professionalism / work behavior. The research was conducted by looking for the weight values of the criteria and then processing the ranking from the doctor's judgment.&#x0D; Keywords: Decision Support System, Weight Product, WEB
The Internet of Things for Crowd Panic Detection

Crowd behavior detection is important for the smart cities applications such as people gathering for different events.However, it is a challenging problem due to the internal states of the crowd itself and the surrounding environment.This paper proposes a novel crowd behavior detection framework based on a number of parameters.We first exploit a computer vision approach based on scale invariant feature transform (SIFT) to classify the crowd behavior either into panic or normalness.We then consider a number of other parameters from the surroundings namely crowd coherency, social interaction, motion information, randomness in crowd speed, internal chaos level, crowd condition, crowd temporal history, and crowd vibration status along with time stamp.Subsequently, these parameters are fed to deep learning model during training stage and the behavior of the crowd is detected during the testing stage.The experimental results show that proposed method renders significant performance in terms of crowd behavior detection.
Clustering Tourist Destinations Based on Number of Visitors Using the K-Mean Method

The office door is the main access to enter the office in a company.Currently the office door generally still uses the key to be able to unlock the office door that has weak security.The authors will make the office door that integrated by a computer system that can unlock the office door using the registered faces in the system.The series of these tools use the camera to take a picture of the face and then sent it to the raspberry pi as the brain or the process tool to verification face, which will turn the face image into data information and make the decision to take action.After that solenoid will open after receiving the appropriate data from the raspberry pi.This tool becomes essential in helping employees to unlock the office door, so no need difficult to find the key to unlock the office door.
Threshold plasticity of hybrid Si-VO2 microring resonators

We theoretically simulate the threshold plasticity of a high-Q-factor silicon-on-insulator microring resonator integrated with VO 2 . The proposed structure can perform excitatory and inhibitory learning by tuning the initial working condition.
FLOUTING MAXIM AND HEDGING MAXIM IN MULTICULTURAL STUDENTS INTERACTION

IKIP Budi Utomo as a private campus in the city of Malang has a composition of students from various tribes and cultures, making it a campus with a multi-cultural society. On this campus there are five tribes that dominate, they are: Javanese, Madurese, Dayaks, Sumba Flores, and Ambon. In multicultural societies, there are often obstacles, which in turn cause conflicts, in communication. A convention is needed between the speaker and the hearers so that communication becomes effective and efficient, and is able to minimize misunderstandings. In this case the concept called the principle of cooperation (Cooperative Principles) which is intended as a rule of communication in order to run effectively and efficiently becomes relevant to be studied. The design of this study is descriptive qualitative in which data sources obtained from dialogs that arise in daily interactions both between multicultural students and students and lecturers inside and outside the class at IKIP Budi Utomo Malang. In line with the results of the research analysis, it can be concluded that, of the 476 utterances recorded as raw data from this study, there were 45 cases of maxims flouts and 12 cases of maxims hedges, which formed a certain pattern in each multicultural students.
IMAGE FORENSICS TOOL WITH STEGANOGRAPHY DETECTION

The problem context that inspired and motivated this project idea is that as the quote says a picture or image speaks a thousand words. An Image is forensically rich media it contains a lot of metadata you can extract for any Digital Forensics investigation and it can answer the 3 ws. which is what (what device is used to capture the picture or Image), where (The location where the picture or image was capture) and when (the exact time and date when the image was capture). The current issues are that most of the current image forensics tools is their output is too complex to understand, for students starting out their studies into digital forensics its quite difficult for them to comprehend some details of their output. The tool will also detect if the image has been tempered with if any hidden messages or items is stored inside using steganography. The project is an Image Forensics Tool with Steganography Detection, which can aid in a digital forensics investigation where by the investigator is required to get metadata out of any Digital image.
Modified Alignment Technique Using Matched Important Features

In image processing the exact alignment of the two images takes great and wide attention, and achieving the perfect results is very important because of the importance of that process in many scientific applications. In this work, a strategy has been presented that blends pixel-based alignment techniques with those that focus on the important features or characteristics of the image, and the work on this technique has been tested and proven to be effective in terms of high accuracy and fast delivery. The working principle of the proposed method is divided into four non-overlapping phases: the important features extraction, the discovery of the matching features, the creation of the perfect conversion coefficients for the image, the implementation of the alignment process The extraction step of the features is considered important or essential ,which greatly improves the alignmentprocedure. The fundamental step is the extraction of the main key points in each image with the use of a SIFT detector. The matching features between the two images are then searched using a method based on the principle of grouping adjacent pixels for each important feature. The alignment process uses the Iterative Closest Point algorithm to perform a rigid transform with the use of the matched key points. The results show that the alignment process using the suggested method has yielded quite efficient results and it is highly important finding the interesting features and the strong correspondence point.
A Ranking-Based Hashing Algorithm Based on the Distributed Spark Platform

With the rapid development of modern society, generated data has increased exponentially. Finding required data from this huge data pool is an urgent problem that needs to be solved. Hashing technology is widely used in similarity searches of large-scale data. Among them, the ranking-based hashing algorithm has been widely studied due to its accuracy and speed regarding the search results. At present, most ranking-based hashing algorithms construct loss functions by comparing the rank consistency of data in Euclidean and Hamming spaces. However, most of them have high time complexity and long training times, meaning they cannot meet requirements. In order to solve these problems, this paper introduces a distributed Spark framework and implements the ranking-based hashing algorithm in a parallel environment on multiple machines. The experimental results show that the Spark-RLSH (Ranking Listwise Supervision Hashing) can greatly reduce the training time and improve the training efficiency compared with other ranking-based hashing algorithms.
Extensive reading to improve students’ writing of explanation text

This research aims at finding out whether or not the use of extensive reading is effective for teaching writing explanation text to the eleventh grade at Islamic University in Semarang. Quasi experimental research was applied as the method. The researchers took two groups as the sample, they were XI MIPA 6 as the experimental class and XI MIPA 2 as the control class. The total subject of this research was 65. This research used SPSS in analysing the data of writing test through several activities. They were pre-test before treatments, and post-test after treatments. The result of the research shows the significance difference in the writing ability of explanation text between eleventh-graders who were taught by using extensive reading and those were not taught by using it. It can be seen in the result of mean score in the post-test of experimental class which is 64.4118 and control class which is 48.5484 and as a result Sig. (2-tailed) is 0.00 &lt; 0.05. Therefore, the null hyothesis of this research is rejected. It means that the use of extensive reading had significantly improved the eleventh-graders’ writing ability of the explanation text in English teaching and learning process of the experimental class at Islamic School in Semarang in the academic year 2018/2019.
Indium in polymetallic mineralisation at the Gierczyn mine, Karkonosze-Izera Massif, Poland: results of EPMA and LA-ICP-MS investigations

Cassiterite and base-metal mineralisation from the Gierczyn mine on the Karkonosze-Izera Massif, Poland has been studied using electron microprobe and LA-ICP-MS (Laser Ablation Inductively Coupled Plasma Mass Spectrometry) techniques in order to determine the content of indium in the ore minerals. The main carriers of this element are sphalerite (up to 433 µg/g of In), chalcopyrite (up to 117 µg/g of In) and cassiterite (up to 0.02 wt.% In). Trace element contents of sulphides, especially Ga in chalcopyrite intergrown with sphalerite in comparison with chalcopyrite without sphalerite, may indicate metamorphic re-equlibration and suggests that the ore minerals might recrystallized and underwent metamorphism in conditions of greenschist/amphibolite facies. Although the deposits in the Stara Kamienica Schist Belt are not of economic value, they can contribute to better understanding of the relations between trace element partitioning and the formation of ore minerals.
Risk Aversion Operator for Addressing Maximization Bias in Q-Learning

In Q-learning, the reduced chance of converging to the optimal policy is partly caused by the estimated bias of action values. The estimation of action values usually leads to biases like the overestimation and underestimation thus it hurts the current policy. The values produced by the maximization operator are overestimated, which is well known as the maximization bias. For correcting the bias, the values are reduced towards the underestimation by the double estimators operator. However, according to the proposed analysis, the performances of the two operators (the maximization operator and the double estimators operator) rely on the undetermined dynamic of environment in which the estimated bias results from not only the difference between the current policy and optimal policy, but also the sampling error of reward. The sampling error which is increased by the operators leads to the risk of converging to the non-optimal policy. In order to reduce the risk, this paper proposes a flexible operator which takes account of the most visited action value instead of the greedy value, named Risk Aversion operator which is inspired by the humans’ response to the uncertainty. Based on this operator, the Risk Aversion Q-learning is proposed; the boundary of action values and the convergence are proven. In three demonstration tasks whose optimal policy is known, the proposed algorithm increases the chance of converging to the optimal policy.
Sound model of an orchestral kettledrum considering viscoelastic effects

The modeling of the mechanical systems that describe musical instruments has long been a topic of interest for acoustic physicists. The orchestral kettledrum, distinguished by a broad spectrum of sounds, has been the subject of multiple investigations related to the goal of generating a realistic digital sound synthesis. In the present work, we apply the Green function method to estimate modal frequencies considering an air-loaded viscoelastic membrane. We propose a method that includes viscoelasticity to accurately predict the sound spectrum of the modeled kettledrum. Results are compared to real sound recordings of an orchestral kettledrum obtained in controlled conditions. The calculated modal frequencies are found to coincide well with the real values with an absolute mean error of 1.25±0.76 Hz and 1.87±1.83 Hz for the A2 and B2♭ tuned drumheads respectively. The spectral envelope of the synthesized sound spectrum coincides well with the Fourier transform of the real sound. The viscoelastic term was found to generally reduce the amplitude of the sound spectrum and in certain cases, better approximate the modal frequencies and decay times. The modal synthesis method used here is numerically lightweight and can be adapted to be used in real-time applications with low computational resources. To reproduce our experiment, the recorded kettledrum sounds and Python source code of the model are freely available.
Short Poem Generation (SPG): A Performance Evaluation of Hidden Markov Model based on Readability Index and Turing Test

We developed a Hidden Markov Model (HMM) that automatically generates short poem.The HMM was trained using the forward-backward algorithm also known as Baum Welch algorithm.The training process was exhausted by a hundreds of iterations through recursion method.Then we used the Viterbi algorithm to decode all the best possible hidden states to predict the next word, and from the previous predicted word, it will generate another word, then another word until it reaches the desire word length that was set in the program.Afterwards, the model was evaluated using several kinds of readability metrics index which measure the reading difficulty and comprehensiveness of the generated poem.Then, we performed a Turing Test, which participated by 75 college students, who are well versed in poetry.They determined if the generated poems was created by a human or a machine.Based from the evaluation results, the highest readability score index of the generated short poem is in the grade 16 th level.While 69.2% of the participants in the Turing Test, agreed that most of the machine generated poems were likely created by some well-known poets and writers.