Reviewerly/exHarmony huggingface dataset:
Detecting Malicious Users in Online Dating Application

The flood of malicious users whose baleful behaviors seriously affect the social experience of others leads to the loss of common users. Currently, the detection and control of malicious users in mobile social applications mainly depend on users' reports, which is not timely. In this paper, we present the approaches to identify the malicious users according to information integrity, behavior characteristics and conversation content in early time. Considering statistical methods, we propose the Time Series (TIME) model based on the analysis of users' time behavior characteristics. And thinking from the theme of the users' session, the improved Latent Dirichlet Allocation (LDA) malicious topic model is designed which has improved the sensitivity of the model to detect malicious topics. To ensure the feasibility and accuracy of the models, we compare our models with the traditional machine learning methods of Decision Tree (DEC) and Support Vector Machine (SVM) in online dating application. The final evaluation results have demonstrated that the precision of our models has exceeded the traditional model over 8% in the identification of malicious users.
An SGD-based meta-learner with “growing” descent

Abstract The paper considers the problem of accelerating the convergence of stochastic gradient descent (SGD) in an automatic way. Previous research puts forward such algorithms as Adagrad, Adadelta, RMSprop, Adam and etc. to adapt both the updates and learning rates to the slope of a loss function. However, these adaptive methods do not share the same regret bound as the gradient descent method. Adagrad provably achieves the optimal regret bound on the assumption of convexity but accumulates the squared gradients in the denominator that dramatically shrinks the learning rate. This research is aimed at introducing a generalized logistic map directly into the SGD method in order to automatically set its parameters to the slope of the logistic loss function. The optimizer based on the population may be considered as a meta-learner that learns how to tune both the learning rate and gradient updates with respect to the rate of population growth. The present study yields the “growing” descent method and a series of computational experiments to point out the benefits of the proposed meta-learner.
Improving Deep Learning by Incorporating Semi-automatic Moving Object Annotation and Filtering for Vision-based Vehicle Detection

Deep learning has undergone tremendous advancements in computer vision studies. The training of deep learning neural networks depends on a considerable amount of ground truth datasets. However, labeling ground truth data is a labor-intensive task, particularly for large-volume video analytics applications such as video surveillance and vehicles detection for autonomous driving. This paper presents a rapid and accurate method for associative searching in big image data obtained from security monitoring systems. We developed a semi-automatic moving object annotation method for improving deep learning models. The proposed method comprises three stages, namely automatic foreground object extraction, object annotation in subsequent video frames, and dataset construction using human-in-the-loop quick selection. Furthermore, the proposed method expedites dataset collection and ground truth annotation processes. In contrast to data augmentation and data generative models, the proposed method produces a large amount of real data, which may facilitate training results and avoid adverse effects engendered by artifactual data. We applied the constructed annotation dataset to train a deep learning you-only-look-once (YOLO) model to perform vehicle detection on street intersection surveillance videos. Experimental results demonstrated that the accurate detection performance was improved from a mean average precision (mAP) of 83.99 to 88.03.
Analysis of flood inundation in North Sunter on the North Sunter Polder system performance

Abstract More than 40% area in Jakarta is below mean sea level which it causes inundation to occur every year in Jakarta. In order to solve this problem, the government developed Jakarta flood control management. Polder system is one of drainage system for areas that cannot drain water by gravity. There is one polder system located at North Jakarta named North Sunter Polder System. However, the inundation still occurs every year in that area. Based on the problem, the aim of this study is to analyse the cause of flood inundation in the area, it can be known by simulation using the HEC-HMS and HEC-RAS applications. Based on the simulation results it is known that the cause of flood inundation in the North Sunter Polder system is due to the less optimum pump operation in the polder system. So that to overcome the flood inundation can be done by increasing pump capacity and changing the pump operating elevation.
Mining Clickstream Patterns Using IDLists

To date, there remains a lack of works that focus on the problem of mining clickstream patterns. Although it is an alternative to use the general algorithms for sequential pattern mining to mine clickstreams, their performance may suffer and the resources needed are more than necessary. In this paper, we present a novel data structure, called index-IDList, that is suitable for clickstream pattern mining. Based on this data structure, we present a vertical format algorithm named CUI (Clickstream pattern mining Using Index-IDList). The experiments are carried out on four real-life clickstream databases and the results show that our proposed method is effective and efficient in terms of runtimes and memory consumption.
I know what you did last login

Account security to protect user accounts against sensitive data breaches is a major mission for online service providers. Therefore, they exert tremendous effort in securing account authentication. Although threats from complete outsiders, such as account hijacking for monetization, still occur, recent studies have shed light on threats to privacy from insiders. This paper sheds light on the latter threats. Specifically, we present the first comprehensive study of an attack from insiders that identifies the existence of a target's account by using the target's email address and insecure login-related messages displayed. Such a threat may violate intimates' or acquaintances' privacy because the kinds of service accounts a user has implies his/her personal preferences or situation. We conducted surveys regarding user expectations and behaviors on online services and a measurement study of the login-related messages on online services that are considered sensitive. We found that over 80% of participants answered that there are sensitive services and that almost all services were vulnerable to our attack. Moreover, about half the participants who have sensitive services are insecurely registered on them and thus could be potential victims. Finally, we make recommendations on the basis of our findings for online service providers to improve login-related messages and for users to take appropriate defensive actions.
Leveraging Queueing Theory and OS Profiling to Reduce Application Latency

Request latency is a critical metric in determining the usability of online services, such as web applications and databases. Most existing approaches to improve application latency start by detecting bottlenecks in the application deployment; this typically entails determining stages of processing where the application spends most of its time. Inspired by queueing theory, we present an alternative approach to detect and mitigate bottlenecks âĂrŞ using variability of processing time as a guiding principle when designing applications.
TI-Stan: Model Comparison Using Thermodynamic Integration and HMC

We present a novel implementation of the adaptively annealed thermodynamic integration technique using Hamiltonian Monte Carlo (HMC). Thermodynamic integration with importance sampling and adaptive annealing is an especially useful method for estimating model evidence for problems that use physics-based mathematical models. Because it is based on importance sampling, this method requires an efficient way to refresh the ensemble of samples. Existing successful implementations use binary slice sampling on the Hilbert curve to accomplish this task. This implementation works well if the model has few parameters or if it can be broken into separate parts with identical parameter priors that can be refreshed separately. However, for models that are not separable and have many parameters, a different method for refreshing the samples is needed. HMC, in the form of the MC-Stan package, is effective for jointly refreshing the ensemble under a high-dimensional model. MC-Stan uses automatic differentiation to compute the gradients of the likelihood that HMC requires in about the same amount of time as it computes the likelihood function itself, easing the programming burden compared to implementations of HMC that require explicitly specified gradient functions. We present a description of the overall TI-Stan procedure and results for representative example problems.
A Proposed Natural Language Processing Preprocessing Procedures for Enhancing Arabic Text Summarization

The techniques of pre-processing for text summarization process are utilized to get better text summarization performance. Previous researches have low performance concerning summarization of Arabic text graph-based procedure. This could be retrieved to: (1) Arabic is acomplicated language. (2) Dependence of the results in a graph-based procedure mainly on the weight between sentences. This paper discusses Arabic language techniques of pre-processing for text summarization process, with four major steps. These are tokenization, normalization, stopword removal and structural processing. Tokenization is to divide the text into its basic units. The text is separated into paragraphs, sentences, and words. Then, normalization processes take place with the following steps: removing diacritics, punctuations and duplicated white spaces, also then unifying character shapes. Stop-words are then deleted relying upon a pre-characterized list. In the final stage, morphological analyses and words stemming are done as a part of structural processing. In the basic procedure, words are labeled with their situation in sentences. This is important to get the number of nouns in the sentence sand the document to use as feature in Page Rank and minimum spanning tree weighing. Finally, Khoja stemmer return the best result in stemming while Alkhalil morphological analyzer returns the best results. The category of science and technology from EASC corpus is used as dataset to discuss the results of stop words removal, it returns better results than keeping stop words in the sentences.
Assistance Strategies for Robotized Laparoscopy

Robotizing laparoscopic surgery not only allows achieving better accuracy to operate when a scale factor is applied between master and slave or thanks to the use of tools with 3 DoF, which cannot be used in conventional manual surgery, but also due to additional informatic support. Relying on computer assistance different strategies that facilitate the task of the surgeon can be incorporated, either in the form of autonomous navigation or cooperative guidance, providing sensory or visual feedback, or introducing certain limitations of movements. This paper describes different ways of assistance aimed at improving the work capacity of the surgeon and achieving more safety for the patient, and the results obtained with the prototype developed at UPC.
BESMER: An Approach for Bad Smells Summarization in Systems Models

Bad smells are surface indications of potential problems with source code quality and have been investigated deeply within the purview of object-oriented programming. However, there has not been much research conducted to understand bad smells within the context of systems models. Moreover, the majority of bad smells in existing literature have been suggested by experienced developers and researchers who may view "smells" differently from inexperienced developers. To this end, we propose BESMER, our project that categorizes bad smells in systems models by mining discussion forum posts of end-users of a specific systems modeling tool. Specifically, this paper describes how our three-level discovery mechanism based on machine learning techniques assisted us in finding bad smells from the LabVIEW online discussion forum. Our experimental results not only confirm that end-users also encounter the bad smells proposed by experts, but also reveal new bad smells for LabVIEW models. We also present some implications discovered from the examination of user posts and list areas of future work based on our current findings. As far as we know, this is the first paper to investigate bad smells from an end-user's perspective within the context of systems models.
Evaluation of Trustworthy Scrum Employment for Agile Software Development based on the Views of Software Developers

Software development processes, in general, focus on organizing the order of the phases to develop the intended software product within time and cost constraints. However, the vulnerabilities of the software projects released to the market indicate the need of a security oriented software development process along with providing the time and cost criteria. Moreover, to our best knowledge, there exist some studies stating that the security activities in conventional software development contribute to the effectiveness in providing the security. However, if the agile software development processes are reviewed, it can be seen that principles of agile and security do not match at some points. At this point, the model of Trustworthy Scrum (TS) comes to prominence by enabling the security activities to cooperate with the agile methods and to work in Scrum framework. In this study, a statistical analysis related to the feasibility of the Trustworthy Scrum model was conducted in order to analyze this mismatch based on scientific evidence.
Visual product recommendation using neural aggregation network and context gating

Abstract In this paper we focus on the problem of user interests’ classification in visual product recommender systems. We propose the two-stage procedure. At first, the visual features are learned by fine-tuning the convolutional neural network, e.g., MobileNet. At the second stage, we use such learnable pooling techniques as neural aggregation network and context gating in order to compute a weighted average of image features. As a result we can capture the relationships between the products images purchased by the same user. We provide an experimental study with the Amazon product dataset. It was shown that our approach achieves a F1-score of 0.90 for 15 recommendations, which is much higher when compared to 0.66 F1-measure classification of traditional averaging of the feature vector.
Fractional-Order Extreme Learning Machine With Mittag-Leffler Distribution

Abstract Extreme Learning Machine (ELM) has a powerful capability to approximate the regression and classification problems for a lot of data. ELM does not need to learn parameters in hidden neurons, which enables ELM to learn a thousand times faster than conventional popular learning algorithms. Since the parameters in the hidden layers are randomly generated, what is the optimal randomness? Lévy distribution, a heavy-tailed distribution, has been shown to be the optimal randomness in an unknown environment for finding some targets. Thus, Lévy distribution is used to generate the parameters in the hidden layers (more likely to reach the optimal parameters) and better computational results are then derived. Since Lévy distribution is a special case of Mittag-Leffler distribution, in this paper, the Mittag-Leffler distribution is used in order to get better performance. We show the procedure of generating the Mittag-Leffler distribution and then the training algorithm using Mittag-Leffler distribution is given. The experimental result shows that the Mittag-Leffler distribution performs similarly as the Lévy distribution, both can reach better performance than the conventional method. Some detailed discussions are finally presented to explain the experimental results.
A Modelling and Formalisation Tool for Use Case Design in Social Autonomous Robotics

The modelling of use cases in Social Robotics and its formalisation in a control architecture is a complex and time consuming task, which can be seen as a knowledge engineering process. This manuscript focuses on the description of a novel tool created to simplify the process of modelling these interactions through the definition of such use cases as nominal behaviours with exogenous events. The control architecture of the robotic platform used is based on Automated Planning, allowing robots to deal with stochastic environments. The tool comprises two main objectives: to ease the knowledge engineering process to model social interactions and to generate Planning Domain Description Language code (PDDL) as a formalisation that can be injected into a control architecture. We model different use cases and test the PDDL formalisation by using the control architecture. The test carried out shows evidence of good results, encouraging further research of this type of tools to provide autonomy and facility to experts and non-experts in the field.
Comparison of Cryptographic Algorithms GOST and RSA

Abstract The GOST and RSA algorithms are one of the cryptographic algorithms. This journal has several objectives to compare the GOST and RSA algorithms, and determine which algorithms are good to be used. The method applied in this journal is the result of internationally indexed journal reviews and this journal helps in determining which algorithm is correct in the second variable. This research explains and concludes journals that have been published internationally. As conclusions that provide choices for readers, by giving the advantages and disadvantages of each algoritm. This research has the effect of making the reader choose which algorithm is suitable as the best recommendation from the available journals.
Software Models for Source Code Maintainability: A Systematic Literature Review

In this study, we collected evidence from empirical studies published over two decades-from 1995 to 2018-to answer the following general research question: "What is the current existing empirical evidence concerning the use of software modeling to support source code maintenance?" We carried out a systematic literature review on research published in relevant journals, as well as conference and workshop proceedings, to investigate various aspects of our general research question. Although there is a common belief that software models are beneficial for source code maintainability, this aspect has been scarcely investigated in the literature and only a few empirical studies have been conducted in an industrial context. Moreover, most of the used models are UML. As so, our study shows that there is a small body of research related to this field.
Floating-Point FIR-Based Convolution Suitable for Discrete Wavelet Transform Implementation on FPGA

In this paper, the FPGA implementation of the convolution block suitable for Discrete Wavelet Transform (DWT) decomposition is presented. The transposed form Finite Impulse Response (FIR) structure is employed for performing the convolution process. The design is generic in nature to fit for different wavelet types and symmetric to expand for filters of multiple taps. The proposed convolution block is integrated to design a DWT decomposition system as an application. the proposed architecture is implemented using IEEE -754 single precision floating-point representation offering higher precision and better accuracy than scaled integer wavelet coefficients. The whole system is implemented on Virtex 5 FPGA achieving 243.6 MHz clock frequency.
Contrastive Feature Learning and Class-Weighted Loss for Facial Action Unit Detection

Facial action unit detection (FAUD) is aimed to detect subtle facial motions, known as action units (AUs), induced by the traction or towing of facial muscles. In recent years, FAUD has become an attractive task since these slight changes on face reveal some cues of emotions which can then be utilized to infer people’s affective. Many prior works are developed based on physical features of AUs, such as regional occurrence and temporal continuity, but few of them concern the negative impact of individual differences, causing an unrobust result in variant subjects and environments. To deal with the problem, a contrastive feature learning method is proposed to make a convolutional neural network (CNN) learn to extract the contrastive feature which is the difference between the features of a neutral face image and an AU-occurred face image. In this way, the individual information will be mitigated so that it becomes easier to detect AUs according to the features. A great disparity between the number of positive and negative samples, named as data imbalance, makes a serious interference in most detection problems, including FAUD. A lower occurrence rate of an AU leads a more severe data imbalance problem, which results in a biased result for the AU detection. Therefore, the class-weighted loss is proposed to change the weight-ratio between the positive and negative samples so as to encourage the learning of the positive samples and ease that of negative samples. Two widely used databases, BP4D and DISFA, are adopted to be the benchmark of the performance testing. In the experiment, it shows that the proposed method performs well both in accuracy and speed comparing with the state-of-the-art approaches.
Design and implementation of SVM OTPC searching based on Shared Dot Product Matrix

In this paper, we proposed a FPGA implementation architecture for SVM classifier. The architecture is based on the proposed Shared Dot Product Matrix (SDPM) method which computes and stores the dot product of all training data before SVM searching process. We implemented the proposed method by software simulation and hardware implementation. The software simulation of SDPM method achieves twice the speed of LIBSVM, which is one of the most popular SVM implementation libraries. This acceleration mainly results from the reduction of repeat Kernel function calculation. Then the hardware software collaboration architecture for SDPM is also proposed in this paper. Results show that the proposed architecture achieves approximately 30 times faster searching speed compared with LIBSVM.
Classification and Temporal Localization of Robbery Events in CCTV Videos through Multi-Stream Deep Networks

Robbery is an open social problem. Towards tackling this problem, we in this paper propose multi-stream deep networks for the classification as well as temporal localization of robbery events in CCTV videos. In our multi-stream architecture, each stream is comprised of a pre-trained 3D ConvNet in combination with LSTM which is followed by softmax. In particular, we investigate three streams based on three different types of input: (a) RGB data, (b) optical flows, and (c) foreground masks. Each stream is trained independently, and the final scores are averaged for predictions.To test the approach, we compile a robbery dataset from YouTube, which contains 124 untrimmed CCTV videos. Empirical comparison with several state-of-the-art methods demonstrate the promise of our multi-stream model in both the classification as well as temporal localization tasks.