Reviewerly/exHarmony huggingface dataset:
Opinion Mining Model System For Indonesian Non Profit Organization Using Multinomial Naive Bayes Algorithm

Instagram is one of the popular social media being used to publish information and gain awareness of Nonprofit organization (NPOs). In the process of sounding through social media, the engagement formed in the comment section also needs to be monitored to help NPOs assess the effectiveness of using Instagram as a tool to introduce their program. Moreover, to built engagement with their audience and extract knowledge of the potential donor and volunteer. Unfortunately, this knowledge is still difficult to obtain. One of the challenges in this field is how to find relationship between content features and NPO audience engagement. The purpose of this research is to study, analyze and design a model of Indonesian NPOs opinion mining from Instagram dataset, specifically for NPOs that concern with Indonesian child welfare. The proposed model also designed to obtain 10 categories to represent audience opinion, which are gratitude, appreciation, encouragement wishes, empathy, suggesting, sharing, need more information, behavioral intention, engagement, agreement, and disagreement. The proposed model adopts Binarized Multinomial Naϊve Bayes algorithm for categorizing caption's aspects and Normal Multinomial Naϊve Bayes for categorizing comment's aspect. From the captions and comments collected from four major Indonesian NPOs with children welfare programs for six months, 70% were used as training data and 30% were used as testing data. Based on the testing result, the proposed model proved successfully categorize the opinion, with average precision value 96.67% and recall 96.4% for categorizing caption aspects, and average precision value 83.3% and recall 80.3% for categorizing comment aspects.
A Compression-Based Design for Higher Throughput in a Lock-Free Hash Map

Lock-free implementation techniques are known to improve the overall throughput of concurrent data structures. A hash map is an important data structure used to organize information that must be accessed frequently. A key role of a hash map is the ability to balance workloads by dynamically adjusting its internal data structures in order to provide the fastest possible access to the information. This work extends a previous lock-free hash map design to also support lock-free compression. The main goal is to significantly reduce the depth of the internal hash levels within the hash map, in order to minimize cache misses and increase the overall throughput. To materialize our design, we redesigned the existent search, insert, remove and expand operations in order to maintain the lock-freedom property of the whole design. Experimental results show that lock-free compression effectively improves the search operation and, in doing so, it outperforms the previous design, which was already quite competitive when compared against the concurrent hash map design supported by Intel.
Cybersecurity Index for Undergraduate Computer Science Courses in the UK

The paper proposes a novel index to classify how well UK Computer Science courses articulate cybersecurity-related content through their course/module pages. The aim of this work is to raise awareness among British Universities to pay more attention to include and standardize cybersecurity content in computer science courses. Our results show that 80% of analyzed courses scored 1 or 2-stars on a 5-Stars scale. The results also suggest the need for a formal delivery of cybersecurity content from the first year of the courses and possibly in a collaborative manner with the British Computer Society (BCS). To emphasize cybersecurity education in mitigating security lapses, the analogy is: it is better if most people know how to use a match than to train many fire-fighters.
Adaptive body movement system for wearable IoT instruments based on matrix vector parameter estimation

Presently, the explosive growth of the portable Internet of Things (IoT) products presents a major challenge to the safety of wearable IoT devices by collecting significant amounts of sensitive information. In the area of protection and access control, the gyro sensor-based shift recognition is seen as a new technology that is emerging and achieved excellent performance at certain speeds. Therefore, a survey suggested that both illness and behaviour can impact gait habits, the gait criteria for clinical applications cannot be used by clinicians without awareness of the activities. Hence in this paper, the acceleration- gait cycle adaptive optimization technique (AGAO) hybridized with a non-linear model of gait recognition based on matrix–vector parameter estimation (MVP) has been proposed to modify the approach for producing a corresponding threshold that can effectively reduce cognitive-related problems. The detection of gait phases is taken into account in more accurate and difficult situations where the subject goes through mental tasks. Compared with adaptive gait cycle extraction (AGC) and linear discriminant analysis (LDA), AGAO -MVP reduces the duration of the algorithm by incorporating data from surface EMG sensors into the IoT system. Experimental results indicate that AGAO -MVP is more accurate in terms of performance accuracy and shows that cognitive task limits between the pose phase and the swing cycle become more dynamic.
Semantic Tree-Based 3D Scene Model Recognition

3D scene recognition is important for many applications including robotics, autonomous driving cars, augmented reality (AR), virtual reality (VR), 3D movie and game production. A lot of semantic information (i.e. objects, object parts and object groups) is existing in 3D scene models. To significantly improve 3D scene recognition accuracy, we incorporate such semantic information into the recognition process by building a semantic scene tree and propose a deep random field (DRF) model-based semantic 3D scene recognition approach. Experiments demonstrate that the semantic approach can effectively capture semantic information of 3D scene models, accurately measure their similarities, and therefore greatly enhance the recognition performance. Code, data and experimental results can be found on the project homepage.
Weak multi-targets track-before-detect algorithm based on recursive Hough transform

Aiming at the problem that the existing track-before-detect technology based on Hough transform(HT-Tbd) cannot realize the real-time detection of weak multi-target and some targets may be missing detection, a weak multi-target track-before-detect algorithm based on recursive Hough transform is proposed. For the obtained measurement data, firstly, the first <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> frames measurement data is subjected to non-coherent integration detection by using the HT-TBD technology to obtain the initial integration matrix and storage array. Then, the data of 2 ~ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> + 1 frames, 3 - n + 2 frames,… are sequentially subjected to recursive HT-TBD processing, By updating the accumulative matrix and storage array, the data of the previous moment are eliminated and the data of the next time are stored, realizing the recursive detection of each time. Finally, the track correlation is performed according to the detection result of each moment. The algorithm can effectively reduce the amount of calculation and improve the computational efficiency of the method while realizing effective detection of weak multi-targets and avoiding target missing detection.
WOLF

Applying machine learning techniques to solve real-world problems is a highly iterative process. The process from idea to code and then to experiment may require up to thousands of iterations to find the optimum set of hyper-parameters. Also, it is hard to find best machine learning techniques for a given dataset. The WOLF framework has been designed to simultaneously automate the process of selecting the best algorithm and searching for the optimum hyper-parameters. It can be useful to both who are novice in machine learning and just want to find best algorithm for their dataset, and also to those who are experts in the field and want to compare their new features or algorithm with state of the art techniques. By incorporating the WOLF framework in their designs, it is easier for novices to apply machine learning techniques on their dataset. With a wide range of evaluation metrics provided, WOLF also helps data scientists to develop better intuition towards machine learning techniques and speed up the process of algorithm development. Another main feature of the WOLF framework is that user can easily integrate new algorithms at any stage of the machine learning pipeline. In this paper, we present the WOLF architecture, and demonstrate how it could be used for standard machine learning datasets and for Android malware detection tasks. Experimental results show the flexibility and performance of WOLF.
Predicting Failures in 747–8 Aircraft Hydraulic Pump Systems

Civil aviation, be it for passengers or cargo, is a highly competitive market, airlines are therefore strongly driven to increase earnings and reduce costs. The maintenance of the aircraft fleet is one pivotal aspect of this. In the industry two types of events occur, scheduled and unscheduled maintenance. While normal scheduled maintenance is already expensive, unscheduled maintenance events are even more so. The potential for savings is paramount when unscheduled events can be reduced to a minimum. Additionally, the safety of the customers is a huge concern, which is why possible failures ought to be detected as soon as possible. Over the last years, the large amounts of data that became available over the last decade open then door to a new range of applications. It got possible to learn from the past to predict future events, detect abnormal changes or behaviors, based on newly generated data. In this work we describe the application of anomaly detection on aircraft data. The goal is to predict upcoming failures of the turbine's hydraulic pumps, having severe financial implications should they be replaced in a context of unscheduled maintenance. In this context, we describe how we addressed this challenging task, and how crucial expert knowledge is when approaching such difficult undertakings. With our dataset we studied multiple outlier detection methods, ST-DBSCAN has proven to be the best suited method for this use case. We show how we identified the correct data frames to apply the methodology, and evaluate its prediction performance on a real-world dataset from several aircrafts.
Slim-SSD: An Efficient Fast Object Detection Method

Object detection networks require large storage space and high computational cost, it is difficult to deploy deep neural networks on embedded devices with limited memory and computing resources in actual object detection tasks. In order to solve these challenges, we propose an efficient object detection framework by pruning channels of the feature extraction layer of the network. First, we apply the L1 regularization to the channel scale factor in the BN layer to obtain the object detection network with sparse structure. Then we trim the channel with less information to get the object detection framework. Based on this method, we obtained Slim-SSD with less trainable parameters and test time. Our experiments on benchmarks show that, on the basis of approximate accuracy with the original network, we have reduced the number of model parameters by 3x, and reduced the testing time by 2x. Our compression method helps to deploy complex object detection networks on resource-constrained embedded platform.
Tumbling to Succeed

Ontology visualization is an important component in the support of human-ontology interaction, as it amplifies cognition and offloads cognitive efforts to the human perceptual system. While a significant amount of research efforts has focused on designing and developing various visual layouts and improve performance of large-scale visualizations, the differences in user preferences and cognitive abilities have been largely overlooked. This provides an opportunity to investigate ways to potentially provide more personalized visual support in human-ontology interaction. To this end, this paper demonstrates successful predictions on an individual user's likelihood to succeed in a given task, based on this person's gaze data collected during interaction. Specifically, we show several statistically significant predictions against a baseline classifier when inferring users' success before a given task is actually completed. Moreover, we present results showing that accurate predictions of user success can be achieved early on during user interaction, such as after just a few minutes in some cases. These findings suggest there are ample opportunities throughout various stages of human-ontology interaction where the underlying visual system may adapt in real time to the user's visual needs to provide the most appropriate visualization with the overall goal of possibly increasing user success in a given task.
An Analysis of Heterogeneous Data with Extreme Learning via Unsupervised Multiple Kernels

Multiple sources without information about labels collect a large amount of data, often containing heterogeneous data, namely various types, structures as well as distributions. Such data can include Instagram, Twitter, and Facebook and YouTube, texts, images and videos. Advanced unsupervised learning methods (with multiple kernels) cabs are applied to extract information from such great unlabeled heterogeneous data MKL Traditional MKL algorithms are a good way to reveal information from multiple sources. Some efforts in managing difficult, heterogeneous distributed data, for example using the kernel, have currently been made to efficiently capture data from heterogeneous data. In recent times some unsupervised learning efforts have been made. In this paper, we illustrate the problem by heterogeneous knowledge and discuss various kernel and extreme learning approaches and their problems without supervision.
Rule-Based Composition Grammar Analysis and Applications

This paper proposes a visual method of melody writing based on music rules and the digital characteristics of Equal-temperament. Specifically, clock diagrams are used to visualize the equal ratio and cyclic relationship in Equal-temperament. After the visualization of all the keys used in the composition, this graphic method can be used to implement compose on any key. With the rule-based composition grammar, new melody can be written with chords as the basic unit through a visual "jump point" link. This grammar is only used for pitches in melody composition, and note duration will be studied in our future research. For those non-musicians, the rule-based composition grammar can be used to compose from simple to complex. The grammar analysis can be applied not only to melody, but also to chords, harmony, orchestration and so on.
Dietmar Offenhuber: Collaboration Via the Many Traces Our Data Sets Leave Behind

We asked Dietmar Offenhuber for an interview upon investigating his recent works, Staubmarke (Dust mark) and Ozone Tattoo, which together suggest an artist with an insightful eye for injecting particulate data engagingly into art pieces that spur awareness in an audience. We were impressed by his deep considerations on the potential for scientific and informatics data sets to offer the potential for, and foster, wider discussions and collaborations.
Font Search Across Various Languages Based on Multimodal Learning

We propose a framework that enables to search fonts by using keywords even in the case of minor language fonts without keywords. For example, English letters have a number of font types, and their font data are very well coordinated by being associated with several keywords. Therefore, we can efficiently use the keywords as queries. In contrast, fonts of minor languages are not usually associated with keywords. As a result, users experience difficulty in searching fonts for the best ones. In this paper, we present a methodology comprising two steps to search fonts without keywords: 1) multimodal search of English fonts with keywords; and 2) a search for Japanese fonts across the language. An effective search function was realized at each step. Moreover, in the user test, we verified the effectiveness of font search across various languages.
The variation of the posterior variance and Bayesian sample size determination

Abstract We consider Bayesian sample size determination using a criterion that utilizes the first two moments of the posterior variance. We study the resulting sample size in dependence on the chosen prior and explore the success rate for bounding the posterior variance below a prescribed limit under the true sampling distribution. Compared with sample size determination based on the average of the posterior variance the proposed criterion leads to an increase in sample size and significantly improved success rates. Generic asymptotic properties are proven, such as an asymptotic expression for the sample size and a sort of phase transition. Our study is illustrated using two real world datasets with Poisson and normally distributed data. Based on our results some recommendations are given.
New Mexico Fluorite: A Statewide Survey

Figure 1. Fluorite, 8.7 × 6.2 cm, Doublestrike prospects, Bound Ranch district, Grant County. Collected by Brian Huntsman, Ray DeMark specimen.Reproduced with permissionFigure 2. Fluorite, 14.1 × 1...
Model Transformation Approach According Model-Driven Architecture From BPMN to UML Up to IFML

The main key in MDA is the model's transformation. There are two transformation kinds into MDA: CIM to PIM and PIM to PSM. Most researchers focused on transformation from PIM to PSM because there are several points in common between these two levels. But transforming CIM to PIM is rarely discussed in research subjects because they are two different levels. This chapter presents a methodology to master model transformation from CIM to PIM up to PSM respecting MDA. The methodology is founded on creating a good CIM level, through well-chosen rules, to facilitate transformation to the PIM level. However, the authors establish a rich PIM level, respecting the three classic modeling views: functional, dynamic, and static. The method conforms to MDA by considering the business dimension in the CIM level, through BPMN which is the OMG standard for modeling business process. Nevertheless, they used UML models into the PIM level because UML is recommended by MDA in this level. Then, they use IFML the OMG standard for representing web interface model in PSM level.
A Clustering Backed Deep Learning Approach for Document Layout Analysis

Large organizations generate documents and records on a daily basis, often to such an extent that processing them manually becomes unduly time consuming. Because of this, automated processing systems for documents are desirable, as they would reduce the time spent handling them. Unfortunately, documents are often not designed to be machine-readable, so parsing them is a difficult problem. Image segmentation techniques and deep-learning architectures have been proposed as a solution to this, but have difficulty retaining accuracy when page layouts are especially dense. This leads to the possibilities of data being duplicated, lost, or inaccurate during retrieval. We propose a way of refining these segmentations, using a clustering based approach that can be easily combined with existing rules based refinements. We show that on a financial document corpus of 2675 pages, when using DBSCAN, this method is capable of significantly increasing the accuracy of existing deep-learning methods for image segmentation. This improves the reliability of the results in the context of automatic document analysis.
Identifying Hidden Sentiment in Text Using Deep Neural Network

Deep learning has emerged as de-facto standard in computer vision. Due to its wide spread success and popularity with image data almost every problem from other domain is now trying to finding its solution with deep learning and the case is no different for text analytics and language modeling in NLP. In sentiment analysis, identifying users' hidden sentiment in text review is a challenging task. Though there are many rule based and statistical machine learning based approaches for sentiment analysis. Most of these machine learning approaches are probabilistic based model and treats the problem as document classification problem based on the word count and their polarity often missing sentiment. With the introduction of the distributed word vector representation embedding models like word2vec, GloVe and FastText, capturing the meaning of the neighboring words for a give target word in the sentence, NLP expectation with deep learning in sentiment analysis has increased a lot. In this paper we used deep neural network based model to perform sentiment analysis on the IMDB movie review dataset. We compare deep learning based approach for sentiment analysis with other traditional machine learning approaches to evaluate the performance of the model. With experiment result we found that the deep learning based NLP model outperformed the traditional machine learning approaches in several ways.
Data-Driven Representations for Testing Independence: A Connection with Mutual Information Estimation

From the design of a data-driven partition, this paper addresses the problem of testing independence between two multidimensional random variables from i.i.d. samples. The empirical log-likelihood statistics is adopted with the objective of approximating the sufficient statistics of a test against independence that knows the two distributions (the oracle test). It is shown that approximating the sufficient statistics of the oracle test (asymptotically) offers a connection with the problem of estimating mutual information. Applying these ideas in the context of a data-dependent tree-structured partition (TSP), we derive concrete sufficient conditions on the parameters of the TSP scheme to obtain a strongly consistent test of independence distribution-free over the family of joint probabilities equipped with densities.
A Framework for Disclosing DoD Artificial Intelligence-Based Cybersecurity Product Information

The United States Department of Defense (DoD) is developing artificial intelligence-based cybersecurity products. However, they do not currently have a method for discussing these products with the public and other agencies. In this paper, I explore private corporation disclosures of similar products to create a format for the DoD to adopt, while accounting for the sensitivity of DoD projects. By discussing AI projects more openly, citizen trust in the government could be improved and facilitate efficient private-public partnerships.
Robust Stability Control for a Class of Uncertain Quantum Systems Through Direct and Indirect Couplings

In this paper, a class of uncertain quantum systems with uncertainties in the interaction Hamiltonian is connected with a coherent controller through direct and indirect couplings. The aim of the paper is to design a robust coherent stability controller to make the closed-loop system robustly mean square stable. A sufficient and necessary condition is presented to build a connection between robust control design with uncertain parameters and scaled control design without uncertain parameters. This condition is also considered for a special class of linear passive quantum systems. A numerical procedure is proposed to obtain explicit expression for parameters of the desired coherent controller using linear matrix inequality, multi-step optimization methods and physical realizability conditions.