Reviewerly/exHarmony huggingface dataset:
Fraud Detection Decision Support System for Indonesian Financial Institution

With the rise of technology, the channel of transaction increases, which increases the amount of transactions and opportunity for fraudsters to commit fraudulent acts. The current fraud detection system applied in financial institutions relies heavily on manual intervention, consuming a lot of time and prone to errors. Hence, a fraud detection decision support system will be created to aid financial institutions to detect suspicious activities quickly and requires minimum manual intervention. Classification rule will be set up depending on fraud scenario based on real-life use cases. Common scenarios will be adopted into learning models for the dataset to be processed. Transactional dataset will be prepared according to the predefined template and scenario. The result of the processing will be categorized based on the Risk Level which was derived from the Confidence Level that were obtained. Testing were conducted using 3 different datasets using a set of parameters. The result shows that the current algorithm detects all suspicious activity that were inserted into the financial dataset.
Aspect Category Detection Based on Attention Mechanism and Bi-Directional LSTM

Online reviews play an increasingly important role in users' purchase decisions. E-commerce websites provide massive user reviews, but it is hard for individuals to make full use of the information. Therefore, it is an urgent task to classify, analyze and summarize the massive comments. In this paper, a model based on attention mechanism and bi-directional long short-term memory (BLSTM) is used to identify the categories of these review objects for the classification of the reviews. The model first uses BLSTM to train the review in the form of word vectors; then according to the part-of-speech, the output vectors of the BLSTM are given corresponding weights. The weights as prior knowledge can guide the learning of attention mechanism to enhance the classification accuracy; finally, the attention mechanism is used to capture category-related important features which are used for category determination. Experiments on the SemEval data set show that our model outperforms the state-of-the-art methods on aspect category detection.
Force Detection and Control of Redundant Manipulator for Unknown Environment

With the continuous development of the industrial level, the application scenarios of redundant manipulators are becoming more and more complex. There are obstacles that can't be avoided in the task space. In order to complete the operation task, a contact-based obstacle avoidance method is needed to complete more complicated tasks. In order to realize this contact-type obstacle avoidance task, there is a need for a method capable of detecting the contact force between the links and the environment in an unknown environment and controlling the contact force. In this paper, a 7-DOF redundant manipulator is taken as the research object. Aiming at the situation that the redundant manipulator can not avoid obstacles in the unknown environment, the research on the detection and control method of the contact force between the link and the environment is carried out while guaranteeing the end-manipulation task.
Deep Adaptation Relation Networks for Across Domain Classification

Deep learning has achieved great success in visual recognition tasks which requires a great of annotated samples. However, annotated samples are not available in many situations. Domain adaptation improves the performance on an unlabeled target domain by utilizing the knowledge learned from a related source domain. How to match the domain distributions and transfer the source model to target ones when the labeled target samples cannot reflect the whole target domain distribution will be challenging tasks. In this paper, we provide a relation metric strategy which can learn a deep distance metric to compare a small set within episodes, so we can exploit the unlabeled data from target domain with a few-shot set. Furthermore, we use conditional distributions adaptation to close the difference between two domains by build pseudo labels. The comparative experiments demonstrate that the proposed network works better than previous methods on the standard domain adaptation benchmarks.
Analysis Of Community Interest In Using Passport Queue Application

This research was conducted to evaluate the benefits of implementing Information and Technology Systems in the Passport Queue Application by the Directorate General of Immigration. Evaluation is carried out to see the community's acceptance of the application and the effectiveness of the application in the process of making passports. The research is based on a set of questionnaires circulated to the public who will make passports at the Immigration office. Based on the data obtained, the author will measure the performance of the passport queue application using a combination of the Technology Acceptance Model (TAM) method and the Unified Theory of Acceptance and Use of Technology (UTAUT). Based on the overall results of testing of the questionnaire data, it can be concluded that the community feels the benefits and ease of use of Passport Queue application.
Named Entity Extraction in a Military Context

This paper gives an overview of the Named Entity (NE) Extraction experiments performed on the data sets of two cases: (1) Open-Source Intelligence, (2) Post-(Training-)Mission Analytics. In both cases the “tagging” of entities was done as part of wider analytics on the data sets, in a military context. In this context, recognition of military jargon, acronyms and terminology are important and the extraction results were tested for applicability in this area. KNIME Analytics Platform was used for its available models, algorithms and to reduce the effort needed for implementation. Techniques used include NE Model-based Tagging (machine learning) and NE Dictionary-based Tagging. The experiments showed us that the structure of the data set determines which approaches can be successful.
A Multi-protocol Payment System to Facilitate Financial Inclusion

In 2017, there were already 250 million unbanked people with a smartphone and internet access out of a total 1,7 billion, while only in 2018, the volume of migrant remittance inflows for low-and middle-income countries was $529 billion. Due to financial crisis leading to devaluation and hyperinflation, 12 countries (other than the USA) with 40,5 million population already adopted the U.S. dollar as their native currency. Alternatively, Bitcoin usage soared among people in developing countries who are looking for dependable ways to store and transfer value. Bitcoin, with its estimated 25 million global users and ten thousand globally distributed nodes, is by far the most widely used, best understood and battle-tested cryptocurrency in the world to reach this financially excluded demographic. Our goal in this paper is to demonstrate a technical solution that utilizes Bitcoin by making it (a) instantly transferable in a user-friendly way, (b) with minimal (preferably zero) transaction fees and (c) without sacrificing users' security (i.e. by not controlling their private keys). In our design, instant transactions are enabled by using 2-of-2 multisignature wallet addresses (split between the user and the custodial) and transaction fees are minimized by aggregating unspent transaction outputs (UTXOs). On top of these two pure Bitcoin-based improvements, a fast and low-cost Bitcoin sending mechanism is proposed by creating a "Bitcoin anchor" on Stellar platform, effectively using Stellar as a "Layer 2" solution. Lastly, mapping Bitcoin addresses to user-friendly identifiers (such as email addresses) has been evaluated to increase adoption. By combining these new techniques, we aim to overcome the limitations of Bitcoin and related solutions like off-chain (Lightning Networks) or federated side-chain (Blockstream Liquid) systems.
Fair Share Scheduler for OpenNebula

A small Cloud infrastructure for scientific computing likely operates in a saturated regime, which imposes to optimize the allocation of resources. Tenants typically pay a priori for a fraction of the overall resources. Within this business model, an advanced scheduling strategy is needed in order to optimize the data centre occupancy. FaSS, a Fair Share Scheduler service for OpenNebula, addresses this issue by satisfying resource requests according to an algorithm, which prioritizes tasks according to an initial weight and to the historical resource usage of the project. In this proceedings, we are going to describe the implementation of FaSS Version 1.0, released in March 2017 as a product of the INDIGO-DataCloud project. We are also going to discuss the results of FaSS functional and stress tests performed at the Cloud infrastructure of the INFN-Torino computing centre.
A New Method for Improving Computational Cost of Open Information Extraction Systems Using Log-Linear Model

A New Method for Improving Computational Cost of Open Information Extraction Systems Using Log-Linear Model
Predicting and Classifying Software Faults

In the field of software engineering, the detection of fault in the software has become a major topic to explore. With the help of data mining and machine learning approaches, this paper aims to denote whether a software is fault prone or not. In order to accomplish that this paper gives importance to compare between different machine learning approaches and by observing their performances we can conclude which models perform better to detect fault in the selected software modules. The dataset we have chosen to work on has imbalanced data. This paper research also worked with the imbalanced dataset and what results the imbalanced dataset gave when examined. The accuracy comparison, the performance of the different metrics can broadly help in software defect detection mechanism.
Semi-supervised Learning to Rank with Uncertain Data

Although, semi-supervised learning with a small amount of labeled data can be utilized to improve the effectiveness of learning to rank in information retrieval, the pseudo labels created by semi-supervised learning may not reliable. The uncertain data nearby the boundaries of relevant and irrelevant documents for a given query has a significant impact on the effectiveness of learning to rank. Therefore, how to utilize the uncertain data to bring benefit for semi-supervised learning to rank is an excellent challenge. In this paper, we propose a semi-supervised learning to rank algorithm, that builds a query-quality predictor by utilizing uncertain data. Specially, this approach selects the training queries following the empirical observation that the relevant documents of high quality training queries are highly coherent. This approach learns from the uncertain data to predict the retrieval performance gain of a given training query by making use of query features. Then the pseudo labels for learning to rank are aggregated iteratively by semi-supervised learning with the selected queries. Experimental results on the standard LETOR dataset show that our proposed approaches outperform the strong baselines.
Learning to Perform Visual Tasks from Human Demonstrations

The human visual system makes extensive use of saccadic eye movements to cope with decaying resolution towards the periphery of the visual field, and point the highest acuity region of the retina (i.e. the fovea) to regions of interest, while searching for objects in natural scenes. Experimental evidence has shown that, when searching for objects, humans exploit the advantage of a priori known relations between object classes (i.e. context) to prune the search space, which results in oculo-motor behaviours that are optimal both in terms of effectiveness (i.e. success rate) and efficiency (i.e. energy and time consumed) during search tasks execution. In this work we propose a biologically plausible system that learns from human demonstrations provided by eye tracking data to perform visual search tasks. The proposed framework leverages the recognition accuracy of state-of-the-art pre-trained CNNs with the sequential predictive power of RNNs, trained in an end-to-end manner to predict saliency maps (task-independent) and locate objects of interest (task-dependent).
A genetic approach to the maximum common subgraph problem

Finding the maximum common subgraph of a pair of given graphs is a well-known task in theoretical computer science and with considerable practical applications, for example, in the fields of bioinformatics, medicine, chemistry, electronic design and computer vision. This problem is particularly complex and therefore fast heuristics are required to calculate approximate solutions. This article deals with a simple yet effective genetic algorithm that finds quickly a solution, subject to possible geometric constraints.
Research on veneer correction system based on machine vision

In the process of laminated veneer lumber, it requires manual adjustment of the position of the veneer to accurately position it, which is inaccurate and inefficient. Aiming at these problems, a veneer correction algorithm on machine vision is designed and applied to the production process of laminated veneer lumber. First, the veneer image is subjected to a preprocessing operation. Second, the canny operator is used to detect edges, and the Hough transform is used to detect veneer straight lines. Finally, the deviation angle and displacement deviation of the veneer are calculated. The experimental results show that the veneer image processing and the correction algorithm on machine vision perform well in measuring the deviation and adjusting the position of the board.
Evaluation and Improvement of Load Balancing Using Proposed Cuckoo Search in CloudSim

Cloud computing offers many services by permitting end users the usage of infrastructure (like networks, servers, storage), platform (like operating systems & middleware services), & softwares (like online gaming) delivered by cloud providers (like Amazon, Google) at short cost. Furthermore, Cloud Computing permits its users to operate resources in on-demand manner. It is based on the pay as you go pricing model that causes cloud users to pay corresponding to user’s requirement only. But, demand of Cloud computing is increasing. So, Cloud Service Providers want to provide high Quality of Services (QoS) to cloud users but at same time they want to minimize cost. There are many challenges to achieve this aim. Optimum load balancing or load scheduling is one of technique which helps to achieve the Quality of Service requirements of cloud users which majorly includes Cloudlet Response Time, Cloudlet Makespan improvement. This paper consists of evaluation and improvement corresponding to these two parameters by utilizing and comparing Randomized scheduling, Round Robin scheduling, Shortest Job First Scheduling, Genetic Algorithm and last but most important Cuckoo Search With Levy Flight Algorithm scheduling results. Out of these five algorithms, genetic algorithm and cuckoo search are meta-heuristic where cuckoo search is considered to give global search due to levy flight and considered as most applicable in all application areas because of single parameter Pa. On the basis of comparisons a new Cuckoo Search has been proposed, implemented and showed better results in comparison to all above approaches. These experimental results have been attained with the help of CloudSim 3.0.3 toolkit.
Formalizing and Analyzing Security Ceremonies with Heterogeneous Devices in ANP and PDL

Security ceremonies extend cryptographic protocols with models of human users to allow us to take human behaviors into account when reasoning about security. Actor-network procedures (ANPs) are a well-known formal model of security ceremonies, and procedure derivation logic (PDL) allows us to reason logically about ANPs. In a security ceremony, different nodes may have different capabilities: computers can encrypt and decrypt messages, whereas humans cannot; a biometric device can capture biometric information, whereas a random number generator used in e-banking cannot; and so on. Furthermore, even if a node has the decryption capability, it must also know the encryption key to decrypt a message. ANPs do not support explicitly specifying node capabilities. In this paper, we extend ANPs to deal with heterogeneous devices by explicitly specifying the nodes’ capabilities. We also modify PDL to take into account the knowledge of participants at different points in time. All this allows us to reason about secrecy and authentication in ceremonies with different kinds of devices and human users.
Competence Classification of Twitter Users Using Support Vector Machine (SVM) Method

Twitter is one of the most popular social media in Indonesia. We freely share and write ideas, information or ideas into the available column (tweet). This convenience makes twitter users have different potential and behavior. In accordance with the experience of himself or his environment. Therefore, it is necessary to have the classification of twitter users to determine and obtain the competence of whether the tweet is credible and in accordance with the condition of the twitter user. The use of the Support Vector Machine method with sequential training optimization on the classification of Twitter tweet data competency, can be used to predict the level of credibility of twitter users. Based on the working principle of the method is to determine a straight line or the best hyperplane that separates two data classes. Then the results obtained in this study are in the form of classification accuracy in 5 categories in 5 scenarios of sharing training data and different testing data, and classification of competencies in each account tested.
Description and Recognition of Activity Patterns Using Sparse Vector Fields

Far-field activities represented as time series or trajectories can be summarized in compact representations of frequent patterns. Popular representations such as clustering or probabilistic modeling of trajectories often do not inform about both velocity and direction of motion, which are by definition visually and quantitatively embedded in vector fields. However, a common use of vector fields may dismiss information about forbidden areas, or regions with concurrent activity patterns. To address this problem we present a non-iterative layered vector field estimation process that yields sparse vector field abstractions of activity patterns from groups of trajectories. The key feature of our approach is the estimate of the probability density function (PDF) of targets positions: it automatically tunes the cost function parameter, and serves as weights in the sparse estimation problem. We also propose a trajectory labeling algorithm that labels trajectories according to their activity patterns using the vector field abstractions. Experiments in synthetic and real trajectory data show that the proposed estimation approach yields correctly sparse vector fields, which are similar to known generating vector fields, and 5–12% higher labeling accuracy on test trajectories when compared to other generative models. Outlier trajectories are also detected.
Feature Selection for DNN-HMM Based Mongolian Offline Handwriting Recognition

The purpose of this paper is to assess the performance of several popular features for handwriting on Mongolian offline handwriting recognition system (HWR). They have been classified into handcrafted and automatically learned features. The handcrafted features are distribution feature, concavity feature, local gradient histogram(LGH) feature and transforms feature. The automatically learned features are extracted by Restricted Boltzmann Machine (RBM) and autoencoder. In this paper, the handwriting recognition system is based on hybrid architectures of hidden Markov models (HMMs)-deep neural networks (DNN) which play state of art role on speech recognize (ASR) tasks. In order to performance comparison, several experiments based on different features extracted from MHW database were performed. The best system on word error rate is based on the LGH feature (5.90%), followed by the autoencoder feature (6.42%).
Scene Recognition in Short Video with Multi-Resolution CNNs

In order to solve the problems of scene recognition in short videos, this paper proposes a deep fusion network based on VGGNet. Firstly, VGGNet16 is used to learn global features, and VGGNet19 is used to learn images in details. After that the learning features are fused by means of weighted averaging; In the public dataset 2017-AI-Challenger-scene-classification, the result of top3 is 92.2% and the top3 of the Charades short video dataset has achieved 78.9%, which proves that the proposed method has a good performance in scene recognition.
Model clone detection and its role in emergent model pattern mining

Model-based software engineering approaches continue to gain traction in both industry and research. Accordingly the size, complexity, and prevalence of the models themselves are increasing. Model analysis and management thus becomes an essential task within any model-based process. One form of analysis that can support model-based approaches during the software engineering life cycle is pattern extraction, whereby tooling identifies emergent model patterns. These patterns can be used by analysts to ensure adherence to standards, software quality assurance, and library generation and optimization. In this chapter, we discuss the plausibility of using model clone detection as a form of emergent pattern mining for model-based systems. After a brief primer on the field of model clone detection and model pattern detection, we propose a conceptual framework, MCPM, centered on model clone detection that analysts can employ to detect emergent patterns in their models. In describing our framework concept, we illustrate our ideas using Simulink, and our Simulink model clone detector, Simone, as an example. However, we also consider other model clone detectors' potential within the MCPM framework. This includes how existing research and tooling can be applied to each step within the framework. We identify open challenges for researchers in realizing model clone detection as a model pattern mining tool, as well as the potential benefit that can be experienced by practitioners in the application of MCPM.
Poisson image deconvolution by promoting hyper-Laplacian prior with generalized lp/lq regularization

Poisson image deconvolution and denoising have received much attention in recent years. A key challenge of this problem is that the most effective prior might never exist. The purpose of our study is to investigate the characteristics of generalized <italic>l</italic><sub>p</sub> / <italic>l</italic><sub>q</sub> norm on the derivatives of nature images and to present an efficient method to promote hyper-Laplacian prior for the deconvolution of blurry and Poisson noisy images. We first analyze the mathematical characteristics of generalized <italic>l</italic><sub>p</sub> / <italic>l</italic><sub>q</sub> norm based on the Berkeley segmentation data set and implement deconvolution using generalized <italic>l</italic><sub>p</sub> / <italic>l</italic><sub>q</sub> norm for derivatives in high-frequency domain so as to enforce the preservation of strong edges. Then hyper-Laplacian prior is promoted for actual Poisson image deconvolution, which uses the estimated gradient information to penalize the small derivatives (often noise) and preserve large derivatives associated to image borders. Our preliminary experiments show that the proposed method can achieve higher image quality than state-of-the-art methods in terms of peak signal-to-noise ratio and noise sensitivity.