Reviewerly/exHarmony huggingface dataset:
K-Means and K-Medoids for Indonesian Text Summarization

Abstract The purpose of this study is to build automated summation tools, especially in grouping methods such as K-Means and K-Medoids. Finding the best method between the two algorithms, this study focuses on comparing the two methods to summarize thesis report documents. This system is divided into Filtering, Tokenization, TF-IDF, Cosine Similarity, and Clustering. Based on 50 test documents, the average accuracy rate is 51.16% for K-Means and 63.35% for K-Medoids. K-Means has a smaller accuracy value than K-Medoids. The accuracy of the resulting K-Means also depends on the size and center of the initial cluster chosen. So, as the next stage of development, research needs to be done that compares the results of the combination of initial size and center cluster values for K-Means and continue with several other classifications.
Improving Person Re-Identification by Combining Siamese Convolutional Neural Network and Re-Ranking Process

Person re-identification (re-ID) is an active task with several challenges such as variations of poses, view points, lighting and occlusion. When considering person re-ID as an image retrieval process, measuring the appearance similarity of a pairwise person images is the essential phase. Re-ranking process can improve its accuracy especially when it is based on an other similarity metric. In this paper, we propose a pipeline composed of two methods: A Siamese Convolutional Neural Network (S-CNN) and a k-reciprocal nearest neighbors (k-RNN) re-ranking algorithm. While most existing re-ranking methods ignore the importance of original distance in re-ranking, we jointly combine the S-CNN similarity measure and Jaccard distance to revise the initial ranked list. An experimental study is conducted on two benchmark person re-ID datasets (Market-1501 and Duke-MTMC-reID). The obtained results confirm the effectiveness of our method. A mAP improvement of 11.6% and 15.68% is obtained respectively for the two testing datasets.
ColloCaid: a tool to help academic English writers find the words they need

This short paper summarizes the development of ColloCaid (www.collocaid.uk), a text editor that supports writers with academic English collocations. After a brief introduction, the paper summarizes how the lexicographic database underlying ColloCaid was compiled, how text editor integration was achieved, and results from initial user studies. The paper concludes by outlining future developments.
Examining the Role of Video in Sports Media Viewing

As virtual reality becomes more prevalent in society, there has been a renewed focus on developing experiences for consumers to enjoy (Hartl and Berger, 2017). The current research project explores the relationship between presence, game attractiveness, suspense, and enjoyment in watching mediated sports content and exploring the differences between these constructs in 2D presentation versus 360 video presentation. Participants were asked to provide demographic information, watch a video presented either on a traditional monitor screen or in 360-degree video on the Oculus Rift and then complete surveys to assess their experience watching the clip. This data will be analyzed via confirmatory factor analysis to determine if presence, game attractiveness, suspense, and enjoyment are linked. Furthermore, 2D and 360 video conditions were also compared through MANOVA to determine if there are differences between conditions in regard to presence, suspense, and enjoyment.
Application of Deep Learning Approaches in Igneous Rock Hyperspectral Imaging

Hyperspectral imaging has been applied in remote sensing amongst other disciplines, success in these has triggered its extensive use. Hence, it comes as no surprise that we took advantage of this technology by conducting a study aimed at the spectral analysis of several igneous rocks, and to deduce the spectral signatures of each rock unit using neural networks. Through visual observations and comparisons of these spectral signatures, parameters such as band curvature(shape), tilt(position) and strength were used for lithological discrimination. Even with this said, there often exists similarities in rocks, which are rather difficult to differentiate by means of visual or graphical analysis. However, with numerous technologies making new waves in today’s era and artificial intelligence (AI) being at the forefront of these developments, it was best fitting to employ deep learning, often referred to as a subset of AI; to train/learn from these hyperspectral signatures with a goal aimed at classifying these rocks. Deep learning has networks such as the convolution neural network (CNN), which has algorithms that excel in feature representation from visual imagery; taking into account that the more data is fed into the training process and later used as a database for further training, the higher the future prediction accuracy. Gathered outcomes from the CNN show exceptionally high prediction accuracy capabilities of 96%; suggesting viable field and laboratory usage of these systems as a unit for mining and rock engineering applications.
Anomaly Detection in Multivariate Time Series Using Fuzzy AdaBoost and Dynamic Naive Bayesian Classifier

This paper presents a novel method to detect anomaly using Fuzzy AdaBoost and Dynamic Naive Bayesian classifier. Dynamic Naive Bayesian Classifier (DNBC) is an extension of Hidden Markov Models (HMM) that is used here to model multivariate observation sequences typically generated from multiple sensors associated to monitoring processes. The Fuzzy AdaBoost (FAB) method is used for ensembling multiple DNBCs to classify an instance as an anomaly. FAB needs Footprint of Uncertainty (FOU) of error that is further used to Figure out the misclassification error and update weights of the data samples required during the boosting process. Here, we introduce an approach to initialize the intervals of FOU using the statistical assets of data that belongs to the normal class. The efficacy of the proposed method is demonstrated through a case study on a stuck pipe problem that occurs during oil well drilling process.
Segmentation‐based MAP despeckling of medical ultrasound images in shearlet domain based on normal inverse Gaussian distribution

Speckle noise is an undesirable phenomenon that exhibits granular pattern which reduces the diagnostic capability of clinical ultrasound (US) images. In this study, a new speckle denoising method based on modelling of detail band shearlet coefficients of log-transformed US images is presented. In each detail shearlet subband, coefficients corresponding to signal and speckle noise are modelled as normal inverse Gaussian and Gaussian priors, respectively. These coefficients, based on image local statistics are segmented into different regions of heterogeneity viz. homogeneous, heterogeneous and strongly heterogeneous regions, respectively, so as to control over smoothing of denoised images. Then, using the prior distributions maximum a posteriori (MAP) estimation is performed over all regions of detail bands except those regions that represent strongly heterogeneous coefficients. For better performance, an adaptive weight function is also used in the MAP expression which reduces the loss of feature information. Experimentation is done on noise-free synthetic kidney and foetus US images and a set of 60 real US images. The results are presented for objective and subjective quality assessment of the proposed method and five other methods for speckle denoising. The potential of the proposed method in comparison to other methods can easily be ascertained from the obtained denoising results.
Efficient Explicit Constructions of Multipartite Secret Sharing Schemes

Multipartite secret sharing schemes are those having a multipartite access structure, in which the set of participants is divided into several parts and all participants in the same part play an equivalent role. Secret sharing schemes for multipartite access structures have received considerable attention due to the fact that multipartite secret sharing can be seen as a natural and useful generalization of threshold secret sharing. This work deals with efficient and explicit constructions of ideal multipartite secret sharing schemes, while most of the known constructions are either inefficient or randomized. Most ideal multipartite secret sharing schemes in the literature can be classified as either hierarchical or compartmented. The main results are the constructions for ideal hierarchical access structures, a family that contains every ideal hierarchical access structure as a particular case such as the disjunctive hierarchical threshold access structure and the conjunctive hierarchical threshold access structure, and the constructions for compartmented access structures with upper bounds and compartmented access structures with lower bounds, two families of compartmented access structures. On the basis of the relationship between multipartite secret sharing schemes, polymatroids, and matroids, the problem of how to construct a scheme realizing a multipartite access structure can be transformed to the problem of how to find a representation of a matroid from a presentation of its associated polymatroid. In this paper, we give efficient algorithms to find representations of the matroids associated to the three families of multipartite access structures. More precisely, based on know results about integer polymatroids, for each of the three families of access structures, we give an efficient method to find a representation of the integer polymatroid over some finite field, and then over some finite extension of that field, we give an efficient method to find a presentation of the matroid associated to the integer polymatroid. Finally, we construct ideal linear schemes realizing the three families of multipartite access structures by efficient methods.
Development and approbation of the methodology for determining the individual negotiating style

The article presents the results of development and approbation of the method for determining the negotiation style of the personality. The methodology is based on the idea of the presence of integrative and distributive styles of negotiation. Among the distinctive features of the integrative style areorientation on collective problem solving, achieving win-win solutions, establishing a productive interaction. The distributive style is characterized by the orientation of the subject on maximizing of his own profit, often to the detriment of another, the lack of focus on long-term collaboration, the perception of the partner as a method of reaching his own goals. The approbation has been conducted on a sample of 776 people. A three-component factor structure, including such scales as “Distributive style”, “Integrative style/partner oriented” and “Integrative style /situation oriented”, has been allocated. The data obtained during the psychometric evaluation of the method indicate its consistency, reliability and validity, which allows to make a conclusion on the effectiveness of the method and the possibility to use it in the studies of the peculiarities of the negotiation process.
Joint Embedding Learning of Educational Knowledge Graphs

As an efficient model for knowledge organization, the knowledge graph has been widely adopted in several fields, e.g., biomedicine, sociology, and education. And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks. In general, knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph. And conventional embedding learning models rely on structural relationships among entities and relations. However, in educational knowledge graphs, structural relationships are not the focus. Instead, rich literals of the graphs are more valuable. In this paper, we focus on this problem and propose a novel model for embedding learning of educational knowledge graphs. Our model considers both structural and literal information and jointly learns embedding representations. Three experimental graphs were constructed based on an educational knowledge graph which has been applied in real-world teaching. We conducted two experiments on the three graphs and other common benchmark graphs. The experimental results proved the effectiveness of our model and its superiority over other baselines when processing educational knowledge graphs.
Dynamic Gallery for Real-Time Multi-Target Multi-Camera Tracking

For multi-target multi-camera recognition tasks, tracking of objects of interest is one of the essential yet challenging issues due to the fact that the task requires re-identifying identical targets across distinct views. Multi-target multi-camera tracking (MTMCT) applications span a wide range of variety (e.g. crowd behavior analysis, anomaly individual tracking and sport player tracking), so how to make the system perform real-time tracking becomes a crucial research issue. In this paper, we propose an online hierarchical algorithm for extreme clustering based MTMCT framework. The system can automatically create a dynamic gallery with real-time fashion by collecting appearance information of multi-object tracking in single-camera view. We evaluate the effectiveness and efficiency of our framework, and compare the state-of-the-art methods on MOT16 as well as DukeMTMC for single and multiple camera tracking. The high-frame-rate performance and promising tracking results confirm our system can be used in realworld applications.
Text Sectioning based on Stylometric Distances

This article continues the stylometric study started in a previous one; we focus on stylometric distances between text segments and the use of these distances in text sectioning based on maximizing the distances between text parts. We refine the method previously introduced and improve on the results. Applications include the automation of stylistic analysis of texts, with implication on text summarization, historical analysis, and authorship analysis.
Service-Oriented Architecture for E-Marketplace Model Based on Multi-Platform Distributed System

Abstract E-Commerce has become a solution in online transactions. Based on data from the Central Bureau of Statistics of Indonesia, in 2016, there were 26.2 million users and continued to grow by 17% from 2006 to 2016. On the other hand, the development of e-marketplace has also grown significantly in Indonesia. There are 100 trillion transaction value in 2018. Currently, e-commerce actors are still unable to compete with existing e-marketplace. This is due to limited stock and types of products sold by e-commerce. One of the challenges faced by e-commerce managers is increasing the number of customers and transactions. The purpose of this study produced an architecture for data integration between e-commerce to the e-marketplace. The multi-platform e-marketplace model was developed that was integrated and could distribute products from several e-commerce. Service oriented architecture was used as a method in integrating and distributing products from e-commerce to e-marketplace. Based on the results of testing with 90 request can be concluded that the process of data distribution from e-commerce to e-marketplace can be done in a fast time with average loaded time of 0,72 seconds and the size of data accessed is relatively small with packed average size of 5,68 KB. The conclusion of this study remains the use of service-oriented architecture can be applied for e-marketplace that integrated with several e-commerce for supporting some feature like online payment and courier.
Predicting Student Interests Against Laptop Specifications Through Application of Data Mining Using C4.5 Algorithms

Abstract The purpose of this research was to provide information about the prediction of student interest in the laptop specifications through the application of data mining using the C4.5 algorithm. The method used in this research is a survey method by conducting interview. This research was conducted by conducting interviews with several students. The results of this research was a decision tree that describes the laptop specifications that is most in demand by students, so students who want to have a laptop can easily determine laptop specifications based on the number of enthusiasts on certain laptop specifications. The technique used in the application of data mining in this research is a classification technique. The conclusion in this research is that by implementing data mining, students don’t need to look for various sources to find laptop specifications that are needed by students in meeting their college needs in long time.
Detect Abnormal Behaviours in Ethereum Smart Contracts Using Attack Vectors

Blockchain has gradually been popularized by its transparency, fairness, and democracy. This technology has opened the door to the development of Ethereum, a blockchain platform with smart contracts that can hold and automatically transfer tokens. Like a legacy computer program, smart contracts are vulnerable to security bugs. In recent years, many successful attacks on Ethereum network have been recorded, cost victims millions of dollars. In this paper, we classify attack vectors of Ethereum smart contracts, then propose some behaviour-based methods to detect them. To realize the ideas, we implement Abbe, a tool that can not only discover known attacks but also detect zero-day vulnerabilities.
Hiding Audio in Images: A Deep Learning Approach

In this work, we propose an end-to-end trainable model of Generative Adversarial Networks (GAN) which is engineered to hide audio data in images. Due to the non-stationary property of audio signals and lack of powerful tools, audio hiding in images was not explored well. We devised a deep generative model that consists of an auto-encoder as generator along with one discriminator that are trained to embed the message while, an exclusive extractor network with an audio discriminator is trained fundamentally to extract the hidden message from the encoded host signal. The encoded image is subjected to few common attacks and it is established that the message signal can not be hindered making the proposed method robust towards blurring, rotation, noise, and cropping. The one remarkable feature of our method is that it can be trained to recover against various attacks and hence can also be used for watermarking.
Identifying Physician Fraud in Healthcare with Open Data

Health care fraud is a serious problem that impacts every patient and consumer. This fraudulent behavior causes excessive financial losses every year and causes significant patient harm. Healthcare fraud includes health insurance fraud, fraudulent billing of insurers for services not provided, and exaggeration of medical services, etc. To identify healthcare fraud thus becomes an urgent task to avoid the abuse and waste of public funds. Existing methods in this research field usually use classified data from governments, which greatly compromises the generalizability and scope of application. This paper introduces a methodology to use publicly available data sources to identify potentially fraudulent behavior among physicians. The research involved data pairing of multiple datasets, selection of useful features, comparisons of classification models, and analysis of useful predictors. Our performance evaluation results clearly demonstrate the efficacy of the proposed method.
Computing with Words in Natural Language Processing

Today’s natural language processing system is based on a system of Aristotle’s binary logic and in this case, the applied methods usually cannot take the semantics into account in processing a language. The main issue of the paper is the problem of processing. Fuzzy logic and its extension computing with words has an ability to process the data in more abstract level without taking into consideration the semantics of sentences, texts, materials, etc. in all aspects. In this paper, we tried to use an approach to analyze a set of terms given in a natural language.
Towards Development of an Autonomous Robotic System for Beard Shaving Assistance for Disabled People

Neurological disorders, among the several other factors, are the causes of chronic disabilities where patients suffering from such disabilities usually remain unable to perform basic daily live activities. Tasks around the head are some of these activities which include shaving, brushing, eating and drinking. The current trend towards this area is the development of active assistive robotic systems that enable disabled people to achieve these tasks without human assistance. Rather than the active assistive robotic systems developed for tasks around the head, this paper presents our work towards the development of an autonomous robotic assistance system for the beard shaving task. A Kinect RGB-D sensor is utilized to detect the patient's face, recognize the beard regions based on selected geometrical features and subsequently plan optimal trajectories for fulfilling the intended shaving task. Once the pose of the patient face changes, the system still has the capability to online replanning the optimal trajectories taking into consideration the occlusion. A UR-10 six DOF manipulator, with an attached electrical beard trimmer, is simulated to evaluate the feasibility of the proposed approach. Simulation results demonstrated the capability of the proposed system to achieve beard shaving autonomously that could be extended in the future for fulfilling other tasks around the head.
Deep Learning Cookbook: Recipes for your AI Infrastructure and Applications

Deep Learning (DL) has gained wide adoption and different DL models have been deployed for an expanding number of applications. It is being used both for inference at the edge and for training in datacenters. Applications include image recognition, video analytics, pattern recognition in networking traffic, and many others. Different applications rely on different neural network models, and it has proven difficult to predict resource requirements for different models and applications. This leads to the nonsystematic and suboptimal selection of computational resources for DL applications resulting in overpaying for underutilized infrastructure or, even worse, the deployment of models on underpowered hardware and missed service level objectives. In this paper we present the DL Cookbook, a toolset that a) helps with benchmarking models on different hardware, b) guides the use of DL and c) provides reference designs. Automated benchmarking collects performance data for different DL workloads (training and inference with different models) on various hardware and software configurations. A web-based tool guides a choice of optimal hardware and software configuration via analysis of collected performance data and applying performance models. And finally, it offers reference hardware/software stacks for particular classes of deep learning workloads. This way the DL Cookbook helps both customers and hardware vendors match optimal DL models to the available hardware and vice versa, in case of acquisition, specify required hardware to models in question. Finally, DL Cookbook helps with reproducibility of results.