Reviewerly/exHarmony huggingface dataset:
Insurance Fraud: The Case in Turkey

Insurance frauds deeply affect insurance companies, policyholders, and the insurance industry as a whole. The cost of fraudulent damage affects the profitability of companies, and has negative effects on the society in terms of moral values. Increases in insurance costs can lead to increases in the premiums paid by policyholders, each family, and, ultimately, all of the insured. Recently, new legal regulations related to this issue have been performed in Turkey and higher institutions have been created. A regulation issued by the Under-secretariat of the Treasury, on June 1, 2011, defines insurance fraud as aggravated fraud. Insurance fraud in Turkey usually takes the form of intentional misrepresentations of facts to the insurance company to get the company to pay for something not actually covered by the policy. Studies examined the insurance industry in terms of the concept of financial crime, and inclusion of the concept of financial crime in insurance regulations was proposed since financial crimes have an important place in the current problems of the industry. In addition, it is seen that insurance frauds have changed over time as a result of studies.
Improving Arabic Stemmer: ISRI Stemmer

Stemmer is used in several types of applications such as Text Mining, Information Retrieval (IR), and Natural Language Processing (NLP). Stemmer is a step used to process text data. The main task in the stemmer is to return the word-formation to the basic word (root or stem). ISRI Stemmer is one of the Arabic stemmers contained in the NLTK package. This study improves the weakness of the ISRI stemmer in processing words consisting of two letters. From the results of the experiment, these improvements increased the stemmer yield by 7.3%.
Spoken Indian Language Classification using GMM supervectors and Artificial Neural Networks

Indian languages are phonetic in nature; phonetics is branch of linguistics which studies the structure of human language sound. Acoustic phonetic features associated with languages play an important role in spoken language identification. In this paper, Gaussian Mixture Model supervectors is used to capture acoustic phonetic variation in Indian languages. Mel frequency cepstral coefficient (MFCC) with delta coefficients is used to represent the language specific acoustic phonetic information of speech and artificial neural network ANN is used as a classifier for language identification. In the present work, we have conducted extensive experiments for three different datasets created from the news broadcast in different Indian languages from All India Radio. The performance of ANN classifier using GMM supervectors is evaluated on these three datasets.
Gender and Age Identification Through Romanized Urdu Dataset

Urdu ranks very high among languages used for communication in the Southern Asia. Even though with great following, it clearly lack computational support that is why it is written in Romanized Urdu script. There has been a lot of research done on the gender and age identification of author through written text but not ample have been done using Romanized Urdu dataset. In our research, we have proposed a model for the said purpose by identifying key parameter (defined attributes) of an author. These parameters were measured for both the genders and three categories of age. Weight assignment technique was used to plot graphs which help in computation of the desired results.
Information seeking behavior in academic assignments using smartphone among elementary school students

This article discusses information seeking behavior developed by elementary school students in completing academic assignments, their reasons for using smartphone to find information related to academic assignments, and students' opinions about the role of school libraries. All data presented in this article are the results of a study on 500 elementary school students in four major cities in East Java Province, Indonesia, namely Surabaya, Malang, Madiun and Malang. This study found that there has been a change in information seeking behavior among elementary students who no longer rely solely on information from textbooks in the libraries. The students have become part of the YouTube Generation that uses more social media (YouTube) viasmartphone. According to the elementary school students, YouTube is a social media platform that provides information that is easily understood, while Google offers unlimited information. Students' confidence in the library as a source of information began to fade. Students in the millennial era often perceived libraries as sources of information from school textbooks, not as sources of information that are able to provide broad and varied information. To maintain the existence of school libraries, the role of school librarians that is urgently developed in the future is that librarians must change the way of learning and the choice of information sources among YouTube generation by collaborating with teachers in developing digital and information literacy skill among students.
Adaptive swept volumes generation for human-robot coexistence using Gaussian Processes

Letting humans and robots share a common space for collaboration is considered a consolidated practice. The trajectories followed by the robot must be safe for the human mate, especially when the robot holds dangerous tools or parts. At the same time, the productivity must be preserved, without imposing too restrictive limitations on the robot's movements. This article proposes the use of Gaussian Processes to predict the motion of an operator in a robotic cell, with the aim of controlling the robot speed and avoid collisions. An adaptive approach is proposed and the model for the human motion is persistently re-updated. The resulting approach will be demonstrated to be less conservative than previous ones, while at the same time to preserve the safety of the operator. Real experiments have been conducted on the 7 d.o.f. ABB YuMi robot.
High-Throughput of SHA-256 Hash Function with Unfolding Transformation

Hash Function in cryptography algorithms is used to encrypt the message by giving the appropriate output based on the structure of the hash function itself. This algorithm is important for security applications such as Keyed-Hash Message Authentication Code (HMAC), digital signature, and others. There are different types of hash functions such as MD5, SHA-1, RIPEMD-160, SHA-256, SHA-224, SHA-384, SHA-512, and others. In this paper, the unfolding transformation method was proposed to improve the throughput of the SHA-256 hash function. Three types of SHA-256 hash function were designed namely SHA-256 design, SHA-256 design inner pipelining with unfolding factor 2, and SHA-256 design inner pipelining with unfolding factor 4. The designs were written in Verilog code and the output simulations were verified using ModelSim. The simulation results showed that the proposed SHA-256 inner pipelining unfolding with factor 4 provided the highest throughput which is 4196.30 Mbps, and factor 2 was superior in terms of maximum frequency and was better than the conventional SHA-256 design. Type of Paper: other. Keywords: Cryptography algorithm; FPGA; SHA-256 Hash Function; Unfolding transformation, Verilog
Path Planning of An Autonomous Mobile Robot With Multiobjective Functions

Path planning contributes a significant role in a mobile robot. The accuracy of path depends upon mapping and limitation of an indoor environment. Various methodology are now in applications like A* Algorithms, D* Algorithm (Heuristic Approach), Dijkstra's Algorithm (Deterministic methodology), Cell Decomposition Technique. This paper considers the path planning strategy with different target point. Global path planning method always considers the static obstacles in fixed environment where the indoor environment matrix of 50×50 with static obstacles has taken for simulation. In this paper, we propose a multi-objective method for solving the path planning problem using A* algorithm. It is a multi objective algorithm and handles three particular objectives(multiple destinations, minimum distance and safety).Consider two initial distance point on the grid, that construct a minimum distance path by using A* algorithm. At the destination node the minimum path has been calculated by adding all the minimum distance of two consecutive-nodes. For a safe simulation of path planning also virtual cell are consider around the static obstacles, so for the collision free or safe path has been created with shortest distance at multiple nodes or target.
Towards a Systematic Understanding of Blockchain Governance in Proposal Voting: A Dash Case Study

The transparent and immutable nature of the blockchain provides incentives for organizations wishing to create and implement an open, decentralized governance structure. As members exercise their voting rights, a fault tolerant record accumulates on the blockchain that can be analyzed to diagnose and intercept potential threats to the governing body. To date, there has not been a systematic study of on-chain governance with respect to voting. In this paper, we provide an analysis of blockchain governance through a case study of the first cryptocurrency to adopt on-chain voting, Dash. Our analysis introduces the key characteristics of blockchain governance, steps through a data driven exploration of Dash's on-chain voting system enabled only by the transparent nature of the blockchain and highlights exploitable attack vectors and vulnerabilities for the subversion of Dash's on-chain voting system via a novel network analysis methodology before concluding with guidelines for other organizations looking to implement similar blockchain governance solutions while maintaining integrity in their operations.
Learning Character Recognition with Graph-Based Privileged Information

This paper proposes a pre-training method for neural network-based character recognizers to reduce the required amount of training data, and thus the human labeling effort. The proposed method transfers knowledge about the similarities between graph representations of characters to the recognizer by training to predict the graph edit distance. We show that convolutional neural networks trained with this method outperform traditional supervised learning if only ten or less labeled images per class are available. Furthermore, we show that our approach performs up to 33% better than a graph edit distance based recognition approach, even if only one labeled image per class is available.
Estimating similarity between differently compiled procedures using neural networks

Dual licensing is a form of licensing where the same software product can be used under two different licenses. One license is usually free but introduces major restrictions on the usage of the software product, while the other allows a greater degree of freedom in using it, but at a specified monetary price. Although this form of licensing has a lot of benefits, one of its drawbacks is the increased possibility of license violation and copyright infringement. This paper deals with the problem of detecting a certain procedure from the source code in a set of disassembled binary procedures. The problem is made more difficult by not knowing which compiler was used to produce the binaries. The paper proposes an approach that falls into the category of Siamese neural networks and uses a combination of LSTM and DNN architectures. The proposed approach is evaluated independently on a dataset, as well against some of the currently available solutions.
Bottom-Up Unsupervised Word Discovery via Acoustic Units

Unsupervised term discovery is the task of identifying and grouping reoccurring word-like patterns from the untranscribed audio data. It facilitates unsupervised acoustic model training in zero resource setting where no or minimal transcribed speech is available. In this paper, we investigate two-step bottom-up approaches for unsupervised discovery of word-like units. The first step discovers phone-like acoustic units from data and the second step combines the basic acoustic blocks to identify word-like units. We investigated Embedded Segmental K-means and Nested Hierarchical Pitman-Yor (PYR) model as bottom-up strategies. ESK-Means iteratively selects boundaries from an initial set to arrive at the word boundaries. The final performance critically depends on the quality of the initial boundaries. We used a segmentation method that discovers boundaries much closer to actual boundaries. PYR model has been used for word segmentation from space removed text data, and here we use it for word discovery from unsupervised acoustic units. The term discovery performance is evaluated on the Zero Resource 2017 challenge dataset, which consists of around 70 hours of unlabelled data. Our systems outperformed the baseline systems on all the languages without language-specific parameter tuning. We performed comprehensive experiments of the system parameters on the system performance.
Image Processing and Vehicles – Using FPGA to Reduce Latency of Time Critical Tasks

When automating complex vision-controlled objects such as self-driving cars, large amounts of data need to be processed accurately, quickly and reliably at video frame rates. In this paper we propose the use of an Intel Cyclone V FPGA to process image data in a parallel form, building a safe real-time system. We discuss the hardware used to build the physical prototype and the control algorithms to build the control architecture that controls the prototype.
Edge-guided Hierarchically Nested Network for Real-time Semantic Segmentation

Semantic segmentation requires both large region context information and rich spatial information. The former can be achieved by a deep-enough network structure. However, most spatial information lost in repeated down-sample operation in deep Convolutional Neural Networks cannot be easily recovered. In this paper, we propose a novel edge-guided hierarchically nested network to improve both speed and accuracy of real-time semantic segmentation. We introduce Edge Guidance Unit which aims to guide the decoder network to exploit high-resolution clues, recover and refine spatial information with low additional computational cost. We also introduce a well-designed Fusion Block to combine different resolution features along with edge guidance. In experiments we demonstrate that our network stabilizes the small object region while achieving a result of 72.7% mean IoU at 40 FPS on CityScapes test dataset.
A Hybrid Memetic Algorithm for Simultaneously Selecting Features and Instances in Big Industrial IoT Data for Predictive Maintenance

In Industry 4.0, various types of IoT sensors which are installed on machines to collect data for predictive maintenance. As the collected data increases, there are more missing values and noisy data. Related studies have already proposed various methods to solve the problems in big data. Among them, most studies focused on either feature selection or instance selection for data preprocessing before training forecast models. Metaheuristic algorithm is one of the mainstream methods in data preprocessing. However, most of these studies rarely considered feature and instance selection simultaneously. In addition, they seldom focused on noisy data. Therefore, this work combines the UCI datasets with noisy data to simulate the real situation. Memetic algorithm (MA) has excellent performance in machine learning of data selection, and variable neighborhood search (VNS) was also proved to be widely applied to the systematic change of local search algorithms. This work proposes a hybrid MA and VNS to find a new subset that maximizes the accuracy of the classifier while preserving the minimum amount of data by feature and instance selection simultaneously. Experimental results show that the proposed method can efficiently reduce the amount of data and the ratio of noisy data. By comparison with other metaheuristic algorithms, the proposed method has good performance by an excellent balance between exploration and exploitation.
State assessment method for transformer under DC bias based on gray cloud model

In order to evaluate the influence of DC bias on the operating state of transformer, a state assessment method for transformer based on gray cloud model is proposed. Firstly, the method determines the maximum and minimum values, the distortion rate and the DC component of the magnetizing current as the evaluation indicators, and uses a modified analytical hierarchy process to clarify the weight of each indicator in the evaluation. Meanwhile, considering that single unqualified indicator being covered by the qualified indicators possibly, the variable weight theory is used to increase weight of unqualified indicators dynamically. Finally, the gray cloud model is used to calculate the index clustering coefficient to determine what operating state the transformer is in. The simulation model is used to obtain the magnetizing current under the influence of different DC bias. Using above data to evaluate the operating state of transformer, the effectiveness of method is verified.
Formal Cross-Domain Ontologization of Human Knowledge

We introduce a new software solution to the problem of labour- and time-consuming linguistic crossdomain ontological modelling. TSGraph contributes to the interaction of a cognitive linguist with an automatically collected terminological database. The program builds ontology of several knowledge domains and highlights their overlapping in terms of semantic relations of ontological areas. The relations are parsed from Datamuse API, “a word-finding query engine for developers” [1]. TSGraph involves a number of technologies including Java 8, Spring Framework, Maven, and MySql. To build the graph we use the graph visualization library arbor.js which is a library of java script language. The current version of TSGraph includes terminology of three domains, namely Computer Security, Biology, and Chemistry. The terminological database is collected by means of a supervised machine learning technology, implemented with TSBuilder, which is our previous development. For visual clustering, the terms have attraction points highlighted in different colours and labeled according to the knowledge domains they represent. Links within and between clusters are highlighted in relevant colours. Pop-up windows provide information on term’s semantic relations, specifically semantically related term and the knowledge domain it belongs to. The app will be useful to linguists to determine crossdomain synonymy. Further efforts are required to add morphological, syntactic, and other types of relations between terms and to extend the number of domains. These are the features, which are in high demand by researchers in the field of cognitive terminology.
Stereovision‐based initial pose estimation relative to non‐cooperative space target

Initial relative parameters are of great importance as inputs of the subsequent filter process based on the stereovision system. The relative dynamic model is approximated to estimate the initial relative state. Firstly, the error distribution property of feature point captured using cameras can be approximately regarded to be zero-mean Gaussian distribution when the measurement error of pixel is assumed to be the same. Furthermore, the cofactor matrix can be derived to describe the error distribution property. Built on what is mentioned above, the weighted total least square is adopted to compute the relative angular velocity. To analyse the estimation precision of the algorithm, the Cramer-Rao low bound is also exploited to calculate the theoretical precision trend with the number of the feature point. The relative position estimation uses the particle swarm optimisation algorithm to seek optimal solution due to the fact that the corresponding coefficient matrix is not full rank. Finally, three groups of simulation experiments are conducted to verify its effectiveness. The experiment results show that this algorithm can be utilised to estimate the initial relative state.
Employment Recommendation Algorithm Based on Ensemble Learning

The user's filtering costs and time costs were increased because of a large number of search results in the context of big data technology. Therefore, personalized recommendation technology is needed to improve the utilization ratio of information resources, which carries out accurate and efficient information filtering. This paper focuses on the employment recommendation algorithm hoping to maximize the true intention of job applicants in the recommended job list. As an important tool in machine learning, ensemble learning can improve the prediction accuracy and adaptability effectively in job recommendation algorithms. The main work of this paper is to carry out some research on the algorithm concept related to recommendation algorithm and gradient promotion decision tree algorithm based on recommendation algorithm, and build an employment recommendation algorithm. The employment recommendation algorithm is evaluated by the TOPN list experiment.
Clustering Based Algorithmic Design for Cab Recommender System (CRS)

An efficient Cab Recommender System (CRS) assists the cab drivers with the shortest distance for the next passenger location. For this, it becomes imperative for a CRS to generate clusters for Geolocations. Clustering of Geolocations faces major challenges like noise, identification of meaningful clusters, semantic locations, etc. Therefore, the objectives of this research paper are fourfolds. Firstly, to extensively review the literature for Geolocations and identify the existent clustering techniques. Secondly, to propose an algorithm for generating clusters for Geolocations. Thirdly, to implement and test the proposed algorithm on standard dataset pertaining to different clustering techniques and finally, to analyze and compare the results of the proposed algorithm for effective clustering of Geolocations.
The Balanced Loss Curriculum Learning

Curriculum Learning (CL) selects the training samples from the easy to difficult to boost the classification results.Most existing variates of CL measure the difficulty level of an example in an intuitive way, i.e., the loss value between the prediction and the ground truth.This way ignores the different distances to every class boundary, which is implied in the prediction vectors.In this paper, we propose a novel CL framework, named Balance Loss Curriculum Learning(BLCL), to reveal the comprehensive difficulty level of an example and improve the curriculum process based on the deep architectures.We follow the teacherstudent learning module and train the teacher & student networks simultaneously.The predictions learned by the teacher network indicates the boundary information and is set as the difficulty measurement to guide the student network.The student network focuses on the easy samples according to the boundary information by decreasing the loss from difficult examples as well as increasing the loss from easy examples.Through extensive and rigor experiments, we demonstrate the effectiveness and generality of our framework.