Reviewerly/exHarmony huggingface dataset:
Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer

With increasing research interests in dialogue modeling, there is an emerging branch that formulates this task as next sentence selection, where given the partial dialogue context, the goal is to determine the most probable next sentence. To model natural language information, recurrent models have been applied to sequence modeling and shown promising results in various NLP tasks (Sutskever et al., 2014). Recently, the Transformer (Vaswani et al., 2017) has advanced modeling semantics for natural language sentences via attention, achieving improvement for sequence modeling. However, the Transformer focuses on modeling the intra-sentence attention but ignores inter-sentence information. In terms of dialogue modeling, the cross-sentence information is salient to understand dialogue content, so that the response selection can be better determined. Therefore, this paper proposes a novel attention mechanism based on multi-head attention, called highway attention, in order to allow the model to pass information through multiple sentences, and then builds a recurrent model based on the Transformer and the proposed highway attention. We call this model Highway Recurrent Transformer. This model focuses on not only intra-sentence dependency, but also inter-sentence dependency in the structure of dialogues. Experiments on the response selection task of the seventh Dialog System Technology Challenge (DSTC7) demonstrate that the proposed Highway Recurrent Transformer is capable of modeling both utterance-level and dialogue-level information for achieving better performance than the original Transformer in the single positive response scenario.
Spatio-Temporal Downscaling of Hourly Solar Irradiance Data Using Gaussian Copulas

This paper presents a novel method for downscaling hourly solar irradiance data to higher resolution in both space and time. The method is based on transforming any point in two-dimensional space and time to a position in a propagating cloud field, the internal spatial variability of which is modelled with a Gaussian copula. By relating the mean hourly clear-sky index to probability distributions for 15-s irradiance and exponential decorrelation rate, the required inputs to the copula model are reduced to cloud field velocity and hourly average clear-sky index. The model is applied to irradiance data from a sensor network and is shown to accurately downscale hourly data from one sensor to 15-s resolution and 17 dispersed locations on individual days, reproducing key statistical features of the empirical network data.
Illumination-Based Data Augmentation for Robust Background Subtraction

A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In this paper, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly, but also features semantic transformations of illumination which enhance the generalisation of the model. It successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. Such data allows us to effectively train an illumination-invariant deep learning model for BGS. Experimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place.
Classification of Employee Mental Health Disorder Treatment with K-Nearest Neighbor Algorithm

Mental health problems are increasingly crucial in the workplace. These issues affect employee productivity and accordingly it affects the company's prolificacy. To minimize mental health issues among employees, companies must identify factors related to employee's mental health. Therefore, a classification method to find out whether an employee requires mental health treatment or not is highly much-needed. This paper seeks to develop the application of features selection using chi-square to the performance of the K-Nearest Neighbor (KNN) algorithm in conducting classifications. The stages carried out were: (1) collecting data obtained from Open Sourcing Mental Illness (OSMI), (2) data preprocessing process (data cleaning, feature selection, data transformation), (3) implementing the KNN algorithm in classifying the data, and (4) evaluation process to determine algorithm performance outcome using a confusion matrix which generates precision, recall, and accuracy values. The classification using the KNN algorithm obtained 87.27% of accuracy, 84.21% of precision, and 66.7% of recall. Accordingly, the resulting performance is more effective than previous research. The 2.27% increase in accuracy compared to the research conducted by Shruti Appiah in conducting classifications using Naive Bayes and SVM resulted in an accuracy of 66%. To sum up, data on mental health treatment is applicable for classification using KNN with a high degree of accuracy.
Designing Ritual Artifacts for Technology-Mediated Relationship Transitions

Rituals are ubiquitous but not commonplace, help people to make sense of their life, and cultivate personal or social meaning. Although secularization and digitalization impact the occurrence of formal rituals, the need for marking life's transitions remains unchanged. New rituals emerge, such as marking relationship status by hanging love locks on bridges. Tangible technologies hold great potential for augmenting, changing, or enhancing ritual practices which often involve enactments and symbolic props. In this paper, we analyze individual stories of hanging love locks and derive six pointers for designing technology-mediated relationship transition rituals. We applied the pointers in the design of El Corazón, a tangible artifact for relationship transition rituals. The results of an evaluation with 20 sweethearts show that relationship rituals can be designed deliberately, that tangibles can shape ritual experiences and that technology-mediated rituals can provide people with new means of coping with relationship uncertainty.
Trajectory Planning for a Dual-Arm Planar Free-Floating Manipulator using RRTControl

Due to the increasingly important role of space manipulators in orbital operations and the difficulties encountered in trajectory planning as a consequence of the dynamic coupling between the robot arm and the robot base, this paper proposes to use the RRTControl algorithm, an adaptation of the RRT algorithm used when the system is subject to differential constraints, for trajectory planning of a Dual-Arm Planar Free-Floating Manipulator (FFSM) in joint space with static obstacle avoidance. The FFSM was modeled using the Dynamically Equivalent Manipulator approach, and The Open Motion Planning Library (OMPL) was used as the basis for system implementation. The results of simulations demonstrate that the RRTControl algorithm was able to solve the problem of trajectory planning for the FFSM with and without an obstacle.
Oracle Complexity Separation in Convex Optimization

Many convex optimization problems have structured objective functions written as a sum of functions with different oracle types (e.g., full gradient, coordinate derivative, stochastic gradient) and different arithmetic operations complexity of these oracles. In the strongly convex case, these functions also have different condition numbers that eventually define the iteration complexity of first-order methods and the number of oracle calls required to achieve a given accuracy. Motivated by the desire to call more expensive oracles fewer times, we consider the problem of minimizing the sum of two functions and propose a generic algorithmic framework to separate oracle complexities for each function. The latter means that the oracle for each function is called the number of times that coincide with the oracle complexity for the case when the second function is absent. Our general accelerated framework covers the setting of (strongly) convex objectives, the setting when both parts are given through full coordinate oracle, as well as when one of them is given by coordinate derivative oracle or has the finite-sum structure and is available through stochastic gradient oracle. In the latter two cases, we obtain accelerated random coordinate descent and accelerated variance reduced methods with oracle complexity separation.
BnBeeEpi: An Approach of Epistasis Mining Based on Artificial Bee Colony Algorithm Optimizing Bayesian Network

Mining epistatic gene locus which influence complex disease has great research significance. Bayesian network (BN) has been widely used in many researches of epistasis mining. However, Bayesian network methods have disadvantages of being easily trapped into local optimum, low learning efficiency and not being able to handle large-scale network. In this work, we propose an epistasis mining approach based on artificial bee colony algorithm optimizing Bayesian network (BnBeeEpi). We apply artificial bee colony algorithm into the heuristic search strategy of Bayesian network, and then use two kinds of BN scoring functions (BIC and MIT) to calculate the network fitness value to avoid overfitting and reduce false positive rate. Moreover, we introduce decomposable BIC scoring to solve the large-scale network learning problem. Finally, we compare BnBeeEpi with current popular epistasis mining algorithms by using both simulated and real datasets. Experiment results show that omb-Fast has very short running time with its accuracy is as good as other methods, and BnBeeEpi has better F1-score and lower false positive rate compared to others. Availability and implementation: codes and visualization platform are available at: http://106.14.132.202/.
String Matching based Plagiarism Detection for Document in Bahasa Indonesia

One method to prevent plagiarism is to rely on plagiarism checker application. However, prevention of plagiarism to date in Indonesia still relies on the plagisarism checker held by institutions/organizations outside Indonesia for example Turnitin, Dupli Checker, Copyleaks, PaperRater, gramarly, etc. Plagiarism checker applications currently use methods that are applied for English. Development of plagiarism checker application for Bahasa requires support from multiple disciplines. Bahasa has a very unique pattern and different from English. String matching is an approach for plagiarism detection in computer sience. String matching approach is a “character by character” matching method. This approach could also use hashing block of the character and then use n-Gram to match the hashing block. But before n-Gram, file or ducument need a preprocesing such as tokenization, stopword removal, stemming, dan hashing with Rabin-Karp algorithm. The plagiarism detection system with a string matching approach generates a percentage of the similarity of the document by calculating the N-Gram result with Dice's Similarity Coefficient. For text extraction on a system built with tokenization, stopword removal, and stemming. The text then randomized using the Rabin-Karp method.
DeepCT: A novel deep complex-valued network with learnable transform for video saliency prediction

The past decade has witnessed the success of transformed domain methods for image saliency prediction. However, it is intractable to develop a transformed domain method for video saliency prediction, due to the limited choices on spatio-temporal transforms. In this paper, we propose learning the transform from training data, rather than the predefined transform in the existing methods. Specifically, we develop a novel deep Complex-valued network with learnable Transform (DeepCT) for video saliency prediction. The architecture of DeepCT includes the Complex-valued Transform Module (CTM), inverse CTM (iCTM) and Complex-valued Stacked Convolutional Long Short-Term Memory network (CS-ConvLSTM). In the CTM and iCTM, multi-scale pyramid structures are introduced, as we find that transforms at multiple receptive scales can improve the accuracy of saliency prediction. To make the CTM and iCTM "invertible", we further propose the cycle consistency loss in training DeepCT, which is composed of frame reconstruction loss and complex feature reconstruction loss. Additionally, the CS-ConvLSTM is developed to learn the temporal saliency transition across video frames. Finally, the experimental results show that our DeepCT method outperforms other 13 state-of-the-art methods for video saliency prediction.
Robust geodesic based outlier detection for class imbalance problem

Outlier detection is very useful in many applications, such as fraud detection and network intrusion detection. However, some existing methods often generate incorrect identification results due to the imbalanced distribution of data points. In this paper, we present a robust geodesic-based outlier detection algorithm which simultaneously considers both global disconnectivity score and local real degree as measures of outlierness. We first construct the global disconnectivity score to incorporate suitable global characteristics of data, then we provide the local real degree to effectively consider the local characteristics of points. Thus, we can identify local outliers with higher overall connectivity but in a smaller cluster with fewer points. Experimental results obtained for a number of synthetic and real-world data sets demonstrate the effectiveness and robustness of our method. In particular, we estimate an increase in average area under curve (AUC) on ten datasets of approximately 15%, with smaller RMSD than any of the competing methods.
Machine Learning for Sentiment Analysis: A Survey

The scope of this research fits in sentiment analysis. This latter is becoming more and more an active field of research where to extract people’s opinion concerning political, economic and social issues. The objective of sentiment classification is to classify opinions of users as positive, negative or neutral from textual information alone. For that, purpose researchers used data mining classification techniques such as naïve Bayes classifier and the Neural Networks. The sentiment analysis area is confronted to several problems that distinguish it from traditional thematic research, since the sentiment is expressed in a very varied and very subtle ways. In the last few years, several researches focused on sentiment analysis in order to study attitudes, opinions, and emotions. In this paper we present an analytical and comparative study of different researches conducted on sentiment analysis in social networks using machine Learning. This study analyzes in more detail the preprocessing steps which are very important in sentiment analysis process success and are the most difficult especially in the case where the comments are written in not structured language.
Research on Local Path Planning of Mobile Robot Based on Artificial Potential Field Method

In this paper, based on the traditional artificial potential field method, the local minimum point oscillation and the gravitational force appearing on the path planning are improved. The improvement measures and model scheme are proposed. The target point and the mobile robot are added in real time based on the original repulsion potential field model. The weighting factor of the position. In the process of improving the repulsive field model, the vector superposition method is adopted in this paper. The effect analysis and parameter debugging of the repulsive field model under different conditions are carried out. The actual environment map simulation by Matlab shows that the improved method can effectively solve the traditional artificial potential field. The problem is that the target point is unreachable and the local minimum point is oscillated due to excessive repulsive force and too small gravitational force.
Context Aware Laplacian Mechanism for Local Information Privacy

In this paper, we consider the problem of designing additive noise mechanisms for data release subject to a local information privacy constraint. While there has been significant prior work on devising additive noise mechanisms for differential privacy (such as Laplacian and Gaussian mechanisms), for the notion of information privacy, which accounts for prior-knowledge about the data, there are no such general purpose additive noise mechanisms. To this end, we devise a prior-aware Laplacian noise mechanism, which satisfies local information privacy. We show that adding context awareness (i.e., via the knowledge of prior of the data) improves the tradeoff between utility and privacy when compared to context-unaware mechanisms.
Identifying Crashing Fault Residence Based on Cross Project Model

Analyzing the crash reports recorded upon software crashes is a critical activity for software quality assurance. Predicting whether or not the fault causing the crash (crashing fault for short) resides in the stack traces of crash reports can speed-up the program debugging process and determine the priority of the debugging efforts. Previous work mostly collected label information from bug-fixing logs, and extracted crash features from stack traces and source code to train classification models for the Identification of Crashing Fault Residence (ICFR) of newly-submitted crashes. However, labeled data are not always fully available in real applications. Hence the classifier training is not always feasible. In this work, we make the first attempt to develop a cross project ICFR model to address the data scarcity problem. This is achieved by transferring the knowledge from external projects to the current project via utilizing a state-of-the-art Balanced Distribution Adaptation (BDA) based transfer learning method. BDA not only combines both marginal distribution and conditional distribution across projects but also assigns adaptive weights to the two distributions for better adjusting specific cross project pair. The experiments on 7 software projects show that BDA is superior to 9 baseline methods in terms of 6 indicators overall.
Automated Drug Suggestion Using Machine Learning

The growing healthcare industry generates a large amount of data on patient health conditions, demographic plans, and drugs required for such conditions. These attract the attention of the medical professionals and the data scientists alike. In this paper, we propose a drug recommendation assistant built using machine learning techniques and natural language processing, which draws its accuracy from several major datasets. The proposed system makes it possible to manifest the contrasting effects, reviews, ratings, and then recommend the most "effective" drug for a given individual. The results of the predictive analysis were that from 2005–2015, between the ages 55 and 80, the death rates of the top deadliest diseases in the U.S. all increased drastically. Based on the current trends, with some level of accuracy, it is possible to predict the next top five medical conditions (Birth Control, Depression, Pain, Anxiety, Acne) which will be prevalent in the near future and the top five drugs for used to treat them.
Performance Evaluation of Spark SQL for Batch Processing

Now-a-days, large amount of data is being generated at various organizations. In many organizations, there is an inefficiency of handling Big Data with higher volumes, velocity, and variety. Though data is a huge resource, organizing Big Data is a huge challenge in present days. Currently, number of companies adopted different types of NoSQL databases like Cassandra, MongoDB, HBase, etc., which can handle number of requests at a time. To process the Big Data, Apache Spark, one of the most powerful processing engines, has a number of benefits. The main programming notion in Apache Spark is Resilient Distributed Datasets (RDDs), which handles only procedural processing. However, the most regular data processing paradigms are relational queries which cannot be handled by RDD. To overcome this, there is a need to use several higher-level libraries on Apache Spark. Spark SQL is one of the novel components in Apache Spark Framework that integrates relational processing through Apache Spark’s functional programming API. It allows Apache Spark programmers to use the benefits of relational processing. It also provides an integration of relational processing and procedural processing using a declarative Data Frame API. Hence, in this study, Spark SQL Data Frames are experimented to enhance the processing of weather data stored in Cassandra database. Further, the study has proved that the Spark SQL Data Frames have outperformed performance than Spark Core RDD which we have experimented earlier.
Optimizing Resource Availability in Composable Data Center Infrastructures

To meet service level agreement (SLA) requirements, the majority of enterprise IT infrastructure is typically overprovisioned, underutilized, non-compliant and lacking in required agility resulting in significant inefficiencies. As enterprises introduce and migrate to next-generation applications designed to be horizontally scalable, they require infrastructure that can manage the duality of legacy and next generation application requirements. To address this, composable data center infrastructure disaggregates and refactors compute, storage, network and other infrastructure resources in to shared resources pools that can be “composed” and allocated on-demand. In this paper, we model a theorical problem of resource allocation in a composable data center infrastructure as a bounded multidimensional knapsack and then apply multi-objective optimization algorithms, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Generalized Differential Evolution (GDE3), to allocate resources efficiently. The main goal is to maximize resource availability for the application owner, while meeting minimum requirements (in terms of CPU, memory, network, and storage) within budget constraints. We consider two different scenarios to analyze heterogeneity and variability aspects when allocating resources on composable data center infrastructure.
Diagnosis classification of dengue fever based on Neural Networks and Genetic algorithms

To classify the diagnosis of dengue fever is not easy, an accurate accuracy is needed for doctors and health workers in making a decision related to the diagnosis of dengue fever. In the classification of diagnosis of dengue fever, the Neural Network is used as the model applied. To implement the Neural Network there are several parameters that we must determine such as learning rate and momentum, the problem is the absence of standard guidelines in determining the parameters to be used in this method whom the experimental method is used. For this reason, a method that can solve the problem is needed, with the parameters obtained can be optimized. The solution that can be applied with applying the Genetic Algorithm (GA) on the Neural Network, in order to optimize the learning rate parameter value and momentum. The results obtained are apparently the application of optimization techniques with Genetic Algorithms which can make it easier to find parameter values optimally and can increase the accuracy value in the Neural Network algorithm, thus the model obtained can be used for doctors and health workers in determining the classification of dengue fever diagnosis.