Reviewerly/exHarmony huggingface dataset:
Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets

A major challenges of deep learning (DL) is the necessity to collect huge amounts of training data. Often, the lack of a sufficiently large dataset discourages the use of DL in certain applications. Typically, acquiring the required amounts of data costs considerable time, material and effort. To mitigate this problem, the use of synthetic images combined with real data is a popular approach, widely adopted in the scientific community to effectively train various detectors. In this study, we examined the potential of synthetic data-based training in the field of intelligent transportation systems. Our focus is on camera-based traffic sign recognition applications for advanced driver assistance systems and autonomous driving. The proposed augmentation pipeline of synthetic datasets includes novel augmentation processes such as structured shadows and gaussian specular highlights. A well-known DL model was trained with different datasets to compare the performance of synthetic and real image-based trained models. Additionally, a new, detailed method to objectively compare these models is proposed. Synthetic images are generated using a semi-supervised errors-guide method which is also described. Our experiments showed that a synthetic image-based approach outperforms in most cases real image-based training when applied to cross-domain test datasets (+10% precision for GTSRB dataset) and consequently, the generalization of the model is improved decreasing the cost of acquiring images.
A Novel Code Stylometry-based Code Clone Detection Strategy

Program similarity-based detection can be used to discover potential code cloning issues. However, just based on the similarity of the program, it is difficult to accurately identify code clones with modified syntax and program similarity does not directly reflect the possibility of code plagiarism. In this paper, we propose a program similarity detection strategy with code stylometry matcher to solve this problem. Firstly, the code stylometry model based on the random forest classifier is used to identify a list of possible authors. If the code submitter is not in this list, we analyze the similarity between the submitted code and the program submitted by the author in the list to determine whether there is code cloning. In this way, we can effectively reduce the complexity of program similarity comparison. We propose a PBCS method based on parallel Bidirectional Long Short-Term(Bi-Lstm) Memory network for code similarity detection. This model effectively improves the accuracy of detection by supervising deep features extracted from AST and token. Experimental results show that the recall of the proposed method is 84%, which is improved compared to other methods.
An LSTM-Based Method for Detection and Classification of Sensor Anomalies

Most existing machine learning (ML) based solutions for anomaly detection in sensory data rely on carefully hand-crafted features. This approach has a fundamental limitation since it is often application-specific and requires considerable human effort from domain experts. Deep learning models have been demonstrated to have the ability to abstract relevant high-level features from raw data. Long short-term memory (LSTM) recurrent neural networks have proven effective in complex time-series prediction problems. In this paper, we propose an LSTM-based method for anomaly detection in sensory data. We systematically investigate its effectiveness on raw time-series of real medical sensors measurements and show that it achieves the same level of performance as traditional ML models operating on carefully designed feature vectors. The proposed method achieved micro, macro, and weighted precision, recall, and F1-score of over 0.99.
Real Time Twitter Sentiment Analysis using Natural Language Processing

Social media websites have emerged as one of the platforms to raise users' opinions and influence the way any business is commercialized.Opinion of people matters a lot to analyze how the propagation of information impacts the lives in a large-scale network like Twitter.Data analysis of the tweets determine the polarity and inclination of vast population towards specific topic, item or entity.These days, the applications of such analysis can be easily observed during public elections, movie promotions, brand endorsements and many other fields.In this project, we will go through making a program that analyzes the nature of tweets on a particular topic.The primary aim is to provide a method for analyzing polarity score in noisy twitter streams.This paper reports on the design of a data analysis, extracting vast number of tweets.Results classify user's perception via tweets into positive and negative.In this project, we will go through making a program that analyzes the nature of tweets on a particular topic.The user will be able to input a keyword(hashtag) and get the nature on it based on the latest tweets that contain the input keyword.Each tweet extracted classified based on its sentiment whether it is a positive or negative.Data were collected on movie reviews which were on IMDB Website.Naïve Bayes machine learning algorithm was used.The result from this model was tested using various testing metrics.Moreover, our model demonstrates strong performance on mining texts extracted directly from Twitter.
ANALISIS CURAH HUJAN BULANAN DI KOTA AMBON MENGGUNAKAN MODEL HETEROSKEDASTISITAS: SARIMA-GARCH

Non-linear characteritics in rainfall allow volatility clustering. This condition occurs in Ambon City with seasonal rainfall patterns. The aims of this research are to find the best model and to forecast monthly rainfall in Ambon City using heteroscedasticity model. This research examines secondary data from BMKG for monthly rainfall data in Ambon City from January 2005 – December 2018. The data is divided into two parts. First part, is called in-sample data, consist of data form January 2005 – December 2017. Second part, is called out-sample data, consist data from Januari 2018 – December 2018. The research used SARIMA–GARCH to model the data. The results are the is the best model and the residual model satisfied assumptions of normality, white noise, and there is no ARCH effect. The MAPE value in simulation using in-sample data is 0.73%. On the other side, the MAPE value of forecast results is 30%.
How to Tame Your Data: Data Augmentation for Dialog State Tracking

Dialog State Tracking (DST) is a problem space in which the effective vocabulary is practically limitless.For example, the domain of possible movie titles or restaurant names is bound only by the limits of language.As such, DST systems often encounter out-ofvocabulary words at inference time that were never encountered during training.To combat this issue, we present a targeted data augmentation process, by which a practitioner observes the types of errors made on held-out evaluation data, and then modifies the training data with additional corpora to increase the vocabulary size at training time.Using this with a RoBERTa-based Transformer architecture, we achieve state-of-the-art results in comparison to systems that only mask trouble slots with special tokens.Additionally, we present a datarepresentation scheme for seamlessly retargeting DST architectures to new domains.
Risk Factors Discovery for Cancer Survivability Analysis Using Graph-Rule Mining

Mining and understanding patients’ disease-development pattern is a major healthcare need. A huge number of research studies have focused on medical resource allocation, survivability prediction, risk management of diagnosis, etc. In this article, we are specifically interested in discovering risk factors for patients with high probability of developing cancers. We propose a systematic and data-driven algorithm and build around the idea of association rule mining. More precisely, the rule-mining method is firstly applied on the target dataset to unpack the underlying relationship of cancer-risk factors, via generating a set of candidate rules. Later, this set is represented as a rule graph, where informative rules are identified and selected with the aim of enhancing the result interpretability. Compared to hundreds of rules generated from the standard rule-mining approach, the proposed algorithm benefits from a concise rule subset, without losing the information from the original rule set. The proposed algorithm is then evaluated using one of the largest cancer data resources. We found that our method outperforms existing approaches in terms of identifying informative rules and requires affordable computational time. Additionally, relevant information from the selected rules can also be used to inform health providers and authorities for cancer-risk management.
Evaluasi Usability Sistem Aplikasi Mobile JKN Menggunakan Use Questionnaire

This study aims to find out how superior and important is the application of information technology to produce an information system that is useful for answering user needs, by evaluating and analyzing information systems that have been embedded on the smartphone (smartphone) of the user who uses. Where the intended application is Mobile JKN (BPJS Health Application). The analysis was carried out to recognize the strengths and weaknesses of the system used by the user by measuring the usability of the system using the Use Questionnaire, in which there are several aspects of usability measurement according to IDO, namely efficiency, effectiveness and satisfaction, where these points become a reference for evaluating and analyzing an information system.The results of this study will show that there are several advantages or strengths of the system that can support the development and progress of this application, but the results of this study also show the weaknesses and weaknesses of the system, so that the existing system needs to be improved or improved towards a better direction
Continuous Fuzzy Transform as Integral Operator

The fuzzy transform (F-transform) is ubiquitous in different research fields and applications, such as image and data compression, data mining, knowledge discovery, and the analysis of linguistic expressions. As a generalization of the F-transform, in this article, we introduce the continuous F-transform and its inverse, as an integral operator induced by a kernel function. Through the relation between membership functions and integral kernels, we show that the main properties (e.g., continuity and symmetry) of the membership functions are inherited by the continuous F-transform. Then, the relation between the continuous F-transform and integral operators is used to introduce a data-driven F-transform, which encodes intrinsic information (e.g., structure, geometry, and sampling density) about the input data. In this way, we avoid coarse fuzzy partitions, which group data into large clusters that do not adapt to their local behavior, or a too dense fuzzy partition, which generally has cells that are not covered by the data, thus being redundant and resulting in a higher computational cost. To this end, the data-driven membership functions are defined by properly filtering the spectrum of the Laplace–Beltrami operator associated with the input data. Finally, we introduce the space of continuous F-transforms, which is useful for the comparison of different continuous F-transforms and for their efficient computation.
Classification-Based Fraud Detection for Payment Marketing and Promotion

Nowadays, many payment service providers use the discounts and other marketing strategies to promote their products. This also raises the issue of people who deliberately take advantage of such promotions to reap financial benefits. These people are known as ‘scalper parties’ or ‘econnoisseurs’ which can constitute an underground industry. In this paper, we show how to use machine learning to assist in identifying abnormal scalper transactions. Moreover, we introduce the basic methods of Decision Tree and Boosting Tree, and show how these classification methods can be applied in the detection of abnormal transactions. In addition, we introduce a graph computing method, which implicitly describes the characteristics of people and merchants through node correlation, in order to mine deep features. Because of the volume of large data, we carried out reasonable block calculation, and succeeded in reducing a large amount of data to a series of segments, thereby decreasing the computational resources and memory requirements. Compared with other work on abnormal transaction detection, we pay more attention to creating and using the portraits of merchants or individuals to assist in decision-making. After data analysis and model building, we find that focusing on only one transaction or one day does not yield a comprehensive number of characteristics, and many characteristics can be obtained by examining the transactions of a person or a merchant over a period of time. Furthermore, a large number of characteristics can be obtained from transactions in a period of time. After GBDT (Gradient Boosting Decision Tree) based classification prediction and analysis, we can conclude that there is a clear distinction between abnormal trading shops and conventional shops, facilitating the clustering of abnormal merchants. By filtering transaction data from multiple dimensions, multiple sub-graphs can be obtained. After hierarchical clustering, the abnormal trading group is mined and classified according to its features. Finally, we build a scoring model and apply it to the big data platform of one of China’s largest payment service providers to help enterprises identify abnormal trading groups and specific marketing strategies.
A Discrete-Time State Estimation for Nonlinear Systems With Noises

This paper proposes a discrete-time observer to estimate states of continuous-time nonlinear systems with presences of noises. Most conventional discrete-time state observers are designed using discrete-time models, which are derived by applying the forward-difference method to continuous-time models without considering their poor accuracy. This poor accuracy of the discretization may affect the accuracy of the observer-based on it and lead to a misevaluation. The proposed observer is based on an application of the extended Kalman filter (EKF) for a discrete-time model derived by a discretization method called continualized discretization. The proposed observer was applied to estimate the states of the Lorenz and van der Pol oscillators, which have complex dynamics such as limit cycle and chaos. The simulation results showed that the proposed observer gives better performances in estimating the real state and retaining the system dynamics of the original continuous-time model than the conventional method.
SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration

Dialogue systems in open domain have achieved great success due to the easily obtained single-turn corpus and the development of deep learning, but the multi-turn scenario is still a challenge because of the frequent coreference and information omission. In this paper, we investigate the incomplete utterance restoration which has brought general improvement over multi-turn dialogue systems in recent studies. Meanwhile, inspired by the autoregression for text generation and the sequence labeling for text editing, we propose a novel semi autoregressive generator (SARG) with the high efficiency and flexibility. Moreover, experiments on Restoration-200k show that our proposed model significantly outperforms the state-of-the-art models in terms of quality and inference speed.
Monitoring and Prediction of SLA for IoT based Cloud

Internet of Things (IoT) and cloud computing are the expertise captivating the technology. The most astonishing thing is their interdependence. IoT deals with the production of an additional amount of information that requires transmission of data, storage, and huge infrastructural processing power, posing a solemn delinquent. This is where cloud computing fits into the scenario. Cloud computing can be treated as the utility factor nowadays and can be used by pay as you go manner. As a cloud is a multi-tenant approach, and the resources will be used by multiple users. The cloud resources are required to be monitored, maintained, and configured and set-up as per the need of the end-users. This paper describes the mechanisms for monitoring by using the concept of reinforcement learning and prediction of the cloud resources, which forms the critical parts of cloud expertise in support of controlling and evolution of the IT resources and has been implemented using LSTM. The resource management system coordinates the IT resources among the cloud provider and the end users; accordingly, multiple instances can be created and managed as per the demand and availability of the support in terms of resources. The proper utilization of the resources will generate revenues to the provider and also increases the trust factor of the provider of cloud services. For experimental analysis, four parameters have been used i.e. CPU utilization, disk read/write throughput and memory utilization. The scope of this research paper is to manage the Cloud Computing resources during the peak time and avoid the conditions of the over and under-provisioning proactively.
REDI: Towards knowledge graph-powered scholarly information management and research networking

Academic data management has become an increasingly challenging task as research evolves over time. Essential tasks such as information retrieval and research networking have turned into extremely difficult operations due to an ever-growing number of researchers and scientific articles. Numerous initiatives have emerged in the IT environments to address this issue, especially focused on web technologies. Although those approaches have individually provided solutions for diverse problems, they still can not offer integrated knowledge bases nor flexibility to exploit adequately this information. In this article, we present REDI, a Linked Data-powered framework for academic knowledge management and research networking, which introduces a new perspective of integration. REDI combines information from multiple sources into a consolidated knowledge base through state-of-the-art procedures and leverages semantic web standards to represent the information. Moreover, REDI takes advantage of such knowledge for data visualisation and analysis, which ultimately improves and simplifies many activities including research networking.
Adaptive Detection Tracking System for Autonomous UAV Maritime Patrolling

Nowadays, Unmanned Aerial Vehicles (UAVs) are considered reliable systems, suitable for several autonomous applications, especially for target detection and tracking. Although significant developments were achieved in object detection systems over the last decades using the deep learning technique known as Convolutional Neural Networks (CNN), there are still research gaps in this area. In this paper, we present a new object detection-tracking algorithm that can be used on low power consuming processing boards. In particular, we analysed a specific application scenario in which a UAV patrols coastlines and autonomously classifies different kind of marine objects. Current state of the art solutions propose centralised architectures or flying systems with human in the loop, making the whole system poorly efficient and not scalable. On the contrary, applying a Deep Learning detection system that runs on commercial Graphics Processing Units (GPUs) makes UAVs potentially more efficient than humans (especially for dull tasks like coastline patrolling) and the whole system becomes easily scalable because each UAV can fly independently and the Ground Control Station does not represent a bottleneck. To deal with this task, a database consisting of more than 115000 images was created to train and test several CNN architectures. Furthermore, an adaptive detection-tracking algorithm was introduced to make the whole system faster by optimizing the balancing between detecting new objects and tracking existing targets. The proposed solution is based on the measure of the tracking confidence and the frame similarity, by means of the Structural SIMilarity (SSIM) index, computed both globally and locally. Finally, the developed algorithms were tested on a realistic scenario by means of a UAV test-bed.
Making Explainable Friend Recommendations Based on Concept Similarity Measurements via a Knowledge Graph

Studying the similarities between the concepts in a knowledge graph can be useful in making friend recommendations on various microblogging platforms. Most existing approaches that only focus on accurate friend recommendation and can not give a reasoning explaining. In addition, existing similarity measurements are too costly and ineffective to be used in practical applications. To solve these problems, we purposed the shortest path-guide reasoning path, we perform explicit reasoning with knowledge for decision making so that the friend recommendation are supported by an interpretable causal inference. Then we designed a novel Weighted Euclidean-Shortest Path (WESP) method for measuring concept similarity in a knowledge graph and applied it to friend recommendations on a microblogging platform. First we took the shortest path as an example to measure concepts similarity. Although it was easy to use the shortest path to measure the similarity between concept pairs, the results of the measurements via the shortest path were affected by local structural imbalance in the knowledge graph. The imbalance had a significant impact on measuring concepts similarity; the more balanced the local structure, the greater similarity between the concept pairs. Then, we applied the WESP method to friend recommendations on the microblogging platform. We use the optimization similarity measurement (OSM) model that calculated the similarity between corresponding concept pairs. Our experimental results showed that the OSM method achieved better performance than the baseline methods in making friend recommendations.
Developing a Loss Prediction-based Asynchronous Stochastic Gradient Descent Algorithm for Distributed Training of Deep Neural Networks

Training Deep Neural Network is a computation-intensive and time-consuming task. Asynchronous Stochastic Gradient Descent (ASGD) is an effective solution to accelerate the training process since it enables the network to be trained in a distributed fashion, but with a main issue of the delayed gradient update. A recent notable work called DC-ASGD improves the performance of ASGD by compensating the delay using a cheap approximation of the Hessian matrix. DC-ASGD works well with a short delay; however, the performance drops considerably with an increasing delay between the workers and the server. In real-life large-scale distributed training, such gradient delay experienced by the worker is usually high and volatile. In this paper, we propose a novel algorithm called LC-ASGD to compensate for the delay, basing on Loss Prediction. It effectively extends the tolerable delay duration for the compensation mechanism. Specifically, LC-ASGD utilizes additional models that reside in the parameter server and predict the loss to compensate for the delay, basing on historical losses collected from each worker. The algorithm is evaluated on the popular networks and benchmark datasets. The experimental results show that our LC-ASGD significantly improves over existing methods, especially when the networks are trained with a large number of workers.