Reviewerly/exHarmony huggingface dataset:
Controlling Quadcopter Altitude using PID-Control System

This work presents the stabilization of a quadcopter by using PID controllers to regulate its four basic movements: roll, pitch, yaw angles, and altitude.The quadcopter was originally equipped with sensors and software to estimate and control the quadcopter's orientation, but did not estimate the current position.A GPS module, GPS antenna and a lidar have been added to measure the position in three dimensions.Filters have been implemented and developed to estimate the position, velocity and acceleration.The position controller maintains a constant position, while the trajectory controller maintains a constant velocity while travelling along a straight line.These position and trajectory controllers calculate the reference angles required to direct the thrust necessary to control the quadcopter's movement.The system also consists of IMU (Inertial Measurement Unit) which consists of accelerometer and gyro sensors to determine the system orientation and speed control of four motors to enable the quadcopter fly.Simulations analysis of quadcopter is carried out using MATLAB Simulink.
Synthesizing Programmatic Policies that Inductively Generalize

Deep reinforcement learning has successfully solved a number of challenging control tasks. However, learned policies typically have difficulty generalizing to novel environments. We propose an algorithm for learning programmatic state machine policies that can capture repeating behaviors. By doing so, they have the ability to generalize to instances requiring an arbitrary number of repetitions, a property we call inductive generalization. However, state machine policies are hard to learn since they consist of a combination of continuous and discrete structure. We propose a learning framework called adaptive teaching, which learns a state machine policy by imitating a teacher; in contrast to traditional imitation learning, our teacher adaptively updates itself based on the structure of the student. We show how our algorithm can be used to learn policies that inductively generalize to novel environments, whereas traditional neural network policies fail to do so.
New symmetric key cipher capable of digraph to single letter conversion utilizing binary system

In this paper, a new Playfair cipher built on bits level symmetric key cryptographic was proposed for the purpose of converting pairs of letters (digraphs) into single letters. The proposed algorithm is capable to overcome many of the shortcoming and vulnerabilities that exist in the current classical version of Playfair algorithm. The Playfair cipher is exceedingly complex than a classical substitution cipher, but still simple to hack using automated tactics. It is famous as a digraph cipher because two letters are exchanged by other two letters. This destroys any solo letter occurrence statistics, but the digraph statistics still unaffected (frequencies of two letters). Unluckily letter pairs have a flatter distribution than the one letter frequencies, so this intricacy matters for solving the code using pen and paper procedures. The suggested encryption process is conducted as follows; letters are first arranged in a spiral manner in Polybius square, afterwards, each pair will be replaced utilizing before-after technique if we are arranging pairs horizontally and down-up technique (vertically). The former process produces pairs of Plaintext that will be converted to binary bit stream then will be divided over blocks with stable sizes. Bits of these blocks are taken from pairs then fit them into square matrix of suitable order to put the concept of row-wise and revers row-wise matrix. Bits of this matrix are split into 2x2 square matrixes. The sub-matrixes are formed 8 bits. Here the XNOR operation is taken into consideration for bitwise operation to generate the keys for decryption and produce the cipher-text.
SimpleNLG-DE: Adapting SimpleNLG 4 to German

SimpleNLG is a popular open source surface realiser for the English language.For German, however, the availability of open source and non-domain specific realisers is sparse, partly due to the complexity of the German language.In this paper, we present SimpleNLG-DE, an adaption of SimpleNLG to German.We discuss which parts of the German language have been implemented and how we evaluated our implementation using the TIGER Corpus and newly created data-sets.
A Machine Learning Approach to Protect Electronic Devices from Damage Using the Concept of Outlier

Most of the appliances that we used in our everyday life are electronic devices, i.e. TV, Air Conditioner, Refrigerator, etc. Excessive voltage, current, temperature, etc. can harm the devices and in extreme cases, the devices can be completely damaged. We proposed a system to monitor the electrical behaviors of the devices in real-time. The system is trained with an unlabeled dataset and capable of identifying outliers. We have used a clustering technique, i.e. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to learn the label of a dataset and then apply machine learning algorithms, i.e. Support Vector Machine (SVM) and Decision Tree to predict the label of the new data. From the prediction, the system determines whether the device is operating in safe mode or not. In this work, we have achieved the accuracy of 98.61% in detecting outliers using SVM with `rbf' kernel. Hence, if the device operates beyond safe mode, we can shut down the device.
An ensemble technique for speech recognition in noisy environments

&lt;span&gt;Automatic speech recognition (ASR) is a technology that allows a computer and mobile device to recognize and translate spoken language into text. ASR systems often produce poor accuracy for the noisy speech signal. Therefore, this research proposed an ensemble technique that does not rely on a single filter for perfect noise reduction but incorporates information from multiple noise reduction filters to improve the final ASR accuracy. The main factor of this technique is the generation of K-copies of the speech signal using three noise reduction filters. The speech features of these copies differ slightly in order to extract different texts from them when processed by the ASR system. Thus, the best among these texts can be elected as final ASR output. The ensemble technique was compared with three related current noise reduction techniques in terms of CER and WER. The test results were encouraging and showed a relatively decreased by 16.61% and 11.54% on CER and WER compared with the best current technique. ASR field will benefit from the contribution of this research to increase the recognition accuracy of a human speech in the presence of background noise.&lt;/span&gt;
Gazali, H.M Bitcoin Investment Behaviour: A Pilot Study

The evolution of blockchain technology that promotes decentralised peer to peer electronic cash system named as Bitcoin is becoming trending across the globe. Although the Bitcoin price is extremely volatile, the popularity of bitcoin is seemingly unambiguous. The Bitcoin trending has garnered serious attention from the public, media and academia. Hitherto, no research has yet systematically investigated the factors influencing the intention to invest in Bitcoin. Thus, the objective of this study is to identify the factors that influence the intention to invest in Bitcoin. An online survey was distributed via the email to the target respondents, those who have basic knowledge of Bitcoin investment. After three weeks of the data collection period for the pilot study, 45 samples were gathered to be tested further. Using the SPSS programme, the data were analysed through the reliability test and factor analysis. The results of the study indicate that the items are valid and reliable. The exploratory factor analysis also discloses that the sampling is satisfactory. Being limited preliminary analysis, this study lacks the empirical evidence to confirm the relationships between the studied variables. Therefore, in future an adequate and bigger sample is needed to establish the empirical support on the associations of the studied variables
Hybrid Method for Sentiment Analysis Using Homogeneous Ensemble Classifier

We analyzed the sentiment of a TripAdvisor's review as one of the leading tourist review webs. In TripAdvisor, the sentiment of travelers to the attraction they reviewed is described by a star rating with a numerical scale that ranks from 1 (Terrible) to 5 (Excellent). Travelers usually use this as their polarity. However, there is often inconsistency between their comments and their polarity score such that it cannot be representative of all comments. We used hybrid method to be an alternative solution for this problem which combines two original method, i.e. the lexical-based method and the machine learning-based method. The SenticNet in the lexical-based method is used to determine the representative label of each review. As for the machine learning-based method, we tried to apply several homogeneous ensemble classifiers, such as Bagged Decision Trees, Logistic Model Tree, and Bagged Multi-layer Perceptron algorithms, to determine the sentiment of either existing or new review data with some selected features. The experimental results show that the Homogeneous Ensemble Random Forest algorithm provides the highest accuracy, i.e., 98.1267%. Finally, we obtained the positive review percentages for each attraction in Bali as an alternative for travelers to get information about the sentiment of some attractions other than the unaffected subjective star rating. Some attractions have slightly different between sentiments of hybrid-based methods proposed and star rating, while others have a significant difference.
Combined global and local semantic feature–based image retrieval analysis with interactive feedback

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.
An automatic skills standardization method based on subject expert knowledge extraction and semantic matching

The job market is rapidly changing. Artificial Intelligence and automation technologies are reshaping the career market. Everyday, new jobs appear and new skills are added to the scope of existing job profiles. At the same time, some skills that once were assumed to be "must-haves" for particular jobs are no longer requested and some jobs are even becoming obsolete. The speed of changes as well as the increasing complexity of the job market introduce a key new challenge: there is no clear definition for a particular job in terms of skills and scope and consequently, people holding the same job title cannot be assumed to be actually doing the same thing. In addition, applicants find difficult to develop career paths, as the mapping of skills to particular jobs are fuzzier than ever before. In this article, we present a novel approach to homogenize the job definition, gathering first subject matter expertise using semantic expansion techniques on collaborative wikies, applying a word embeddings supported method to mine the skills from existing job posts and finally executing a semantic matching algorithm to converge to a consistent skills mapping. In order to show how our method performs, we apply it to one of the most popular, yet heterogeneous modern jobs, the data scientist and discuss the results obtained for the English speaking market.
Game-Theoretic Model of the Optimal Distribution of Labor Resources

The article presents a model of the optimal distribution of labor resources. The developed game-theoretic model of a static optimal-purpose problem is described as a game in normal form. In the game, many workers and many enterprises are given, and the situation is a substitution. Each substitution is one of the possible assignments of workers to enterprises. An assessment criterion has been introduced to select an employee or enterprise. The number of the assessment criterion is called the utility for the employee from being assigned to the enterprise (degree of satisfaction of the interests of the player), and for the enterprise - the utility for the enterprise from appointing an employee to him (degree of satisfaction of the interests of the player). From the numbers of the evaluation criterion, the utility matrices are written and the matrix of winnings of the players in the game is constructed. The matrix is used to construct a compromise set in the game and find a compromise gain, which is the guaranteed gain of the least satisfied player. An algorithm for constructing a compromise set is presented in steps. For the algorithm, its temporal estimate and complexity class are found. Thus, the article presents a solution to the static problem of the optimal distribution of labor resources based on the principle of optimality of a compromise set.
FuzzE: Fuzzy Fairness Evaluation of Offensive Language Classifiers on African-American English

Hate speech and offensive language are rampant on social media. Machine learning has provided a way to moderate foul language at scale. However, much of the current research focuses on overall performance. Models may perform poorly on text written in a minority dialectal language. For instance, a hate speech classifier may produce more false positives on tweets written in African-American Vernacular English (AAVE). To measure these problems, we need text written in both AAVE and Standard American English (SAE). Unfortunately, it is challenging to curate data for all linguistic styles in a timely manner—especially when we are constrained to specific problems, social media platforms, or by limited resources. In this paper, we answer the question, “How can we evaluate the performance of classifiers across minority dialectal languages when they are not present within a particular dataset?” Specifically, we propose an automated fairness fuzzing tool called FuzzE to quantify the fairness of text classifiers applied to AAVE text using a dataset that only contains text written in SAE. Overall, we find that the fairness estimates returned by our technique moderately correlates with the use of real ground-truth AAVE text. Warning: Offensive language is displayed in this manuscript.
Big Data Analytics in Government: Improving Decision Making for R&amp;D Investment in Korean SMEs

To expand the field of governmental applications of Big Data analytics, this study presents a case of data-driven decision-making using information on research and development (R&amp;D) projects in Korea. The Korean government has continuously expanded the proportion of its R&amp;D investment in small and medium-size enterprises to improve the commercialization performance of national R&amp;D projects. However, the government has struggled with the so-called “Korea R&amp;D Paradox”, which refers to how performance has lagged despite the high level of investment in R&amp;D. Using data from 48,309 national R&amp;D projects carried out by enterprises from 2013 to 2017, we perform a cluster analysis and decision tree analysis to derive the determinants of their commercialization performance. This study provides government entities with insights into how they might adjust their approach to Big Data analytics to improve the efficiency of R&amp;D investment in small- and medium-sized enterprises.
The Impact of Data Challenges on Intent Detection and Slot Filling for the Home Assistant Scenario

Natural Language Understanding (NLU) is currently a very high-interest domain to both academia and the commercial environment, due in the largest part to the recent increased popularity of conversational systems. In this paper we focus on the home assistant application context and identify a set of language and data-related challenges that can occur in such a scenario, such as: distribution shift, missing information and class imbalance. We systematically generate datasets in the Romanian language that model these data complexities and further investigate how well two of the most prominent tools - Wit.ai and Rasa NLU - solve the tasks of intent detection and slot filling, given the considered data complexities. We perform a thorough analysis of the errors produced by both tools, and provide the most probable justification for their occurrence. We found that both tools focus extensively on the verb for identifying intents, and that antonyms, class-imbalance and certain small variations in formulation greatly impact intent and slot identification. This opens new research directions to directly address these shortcomings.
Reference-frame-independent quantum key distribution with an untrusted source*

Reference frame independent quantum key distribution (RFI-QKD) allows two legitimate parties to share the common secret keys with the drift of reference frames. In order to reduce the actual requirements of RFI-QKD protocol on light source and make it more suitable for practical applications, this paper gives a specific description of RFI-QKD protocol with an untrusted source and analyzes the practical security of this protocol based on the two-way “plug and play” structure commonly used in practical systems. In addition, we also investigate the performance of RFI-QKD with an untrusted source considering statistical fluctuations based on Chernoff bound. Using simulations, we compare the secret key rate of RFI-QKD with an untrusted source to RFI-QKD with trusted source. The results show that the performance of RFI-QKD with an untrusted source is similar to that of RFI-QKD with trusted source, and the finite data size clearly effects the performance of our protocol.
An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image Steganography

Content-adaptive steganography intends to hide data in the complex texture content of the image. Recently, some secure steganography methods have been proposed to identify the textural complexity of an image. However, most of the techniques do not take into account the information of pixel variation around the central pixel in all possible directions and therefore they are unable to accurately analyse the texture complexity. This work offers a quality-enhanced and secure method of content-adaptive image steganography. The proposed method is divided into three sequential steps: image segmentation, pixel complexity identification, and data embedding. An input cover image is initially divided into small local regions and the pixel-complexity is identified based on the proposed Complex Block Prior (CBP) criterion. In a local block, a high pass filter (HPF) bank is applied and eight residual responses are obtained. Following the CBP criterion, a complexity level out of nine levels is assigned to an individualized pixel block. The pixels are then arranged in the priority of complexity from highest to lowest. Data embedding for the corresponding complexity level then takes place using the proposed adaptive embedding algorithm. Experimental results verify the preservation of visual quality of stego images produced by the proposed method. Three image datasets: Standard test images, BOWS2 and BOSS-base are used for the experimentation and comparison with prior state-of-art methods. Highest values of the IQ (image quality) parameters e.g., SSIM and WPSNR show the effectiveness of the proposed method.
Mining Daily Work Report Data for Detecting Patterns of Construction Sequences

Sequencing construction activities in highway projects is a complex planning process which requires not only considerable knowledge and practical experience of the planner/scheduler about various relevant aspects, such as the activities themselves, construction and procurement processes, and construction methods, but also input from other key members of the project regarding specific constraints and requirements.Moreover, sequencing is an iterative process; the sequence developed in the planning phase is likely to change in the construction phase.Therefore, learning from as-built schedules of past completed projects is needed to improve the planning and scheduling processes for future projects.In current practices, most state Departments of Transportation (DOTs) still mainly rely on schedulers' experience for schedule development.A data-driven systematic approach is still lacking, although the highway agencies have been spending a significant amount of money, time, and effort to collect various digital data during the construction process.This study aims to leverage historical digital daily work report data available in the DOTs' database to detect patterns of construction sequences in highway projects.Daily work report data collected from a state DOT were used to conduct a case study that developed a Sequential Pattern Mining algorithm to extract frequent sequential relationships among the activities for one major type of highway projects.
Distributional property testing in a quantum world

A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. In particular, we give fast quantum algorithms for testing closeness between unknown distributions, testing independence between two distributions, and estimating the Shannon / von Neumann entropy of distributions. The distributions can be either classical or quantum, however our quantum algorithms require coherent quantum access to a process preparing the samples. Our results build on the recent technique of quantum singular value transformation, combined with more standard tricks such as divide-and-conquer. The presented approach is a natural fit for distributional property testing both in the classical and the quantum case, demonstrating the first speed-ups for testing properties of density operators that can be accessed coherently rather than only via sampling; for classical distributions our algorithms significantly improve the precision dependence of some earlier results.