Reviewerly/exHarmony huggingface dataset:
Building Trusted Startup Teams From LinkedIn Attributes: A Higher Order Probabilistic Analysis

Startups arguably contribute to the current business landscape by developing innovative products and services. The discovery of business partners and employees with a specific background which can be verified stands out repeatedly as a prime obstacle. LinkedIn is a popular platform where professional milestones, endorsements, recommendations, and skills are posted. A graph search algorithm with a BFS and a DFS strategy for seeking trusted candidates in LinkedIn is proposed. Both strategies rely on a metric for assessing the trustworthiness of an account according to LinkedIn attributes. Also, a stochastic vertex selection mechanism reminiscent of preferential attachment guides search. Both strategies were verified against a large segment of the vivid startup ecosystem of Patras, Hellas. A higher order probabilistic analysis suggests that BFS is more suitable. Findings also imply that emphasis should be given to local networking events, peer interaction, and to tasks allowing verifiable credit for the respective work.
RF-GAN: A Light and Reconfigurable Network for Unpaired Image-to-Image Translation

Generative adversarial networks (GANs) have been widely studied for unpaired image-to-image translation in recent years. On the other hand, state-of-the-art translation GANs are often constrained by large model sizes and inflexibility in translating across various domains. Inspired by the observation that the mappings between two domains are often approximately invertible, we design an innovative reconfigurable GAN (RF-GAN) that has a small size but is versatile in high-fidelity image translation either across two domains or among multiple domains. One unique feature of RF-GAN lies with its single generator which is reconfigurable and can perform bidirectional image translations by swapping its parameters. In addition, a multi-domain discriminator is designed which allows joint discrimination of original and translated samples in multiple domains. Experiments over eight unpaired image translation datasets (on various tasks such as object transfiguration, season transfer, and painters’ style transfer, etc.) show that RF-GAN reduces the model size by up to 75% as compared with state-of-the-art translation GANs but produces superior image translation performance with lower Fréchet Inception Distance consistently.
Gender Classification of Prepubescent Children via Eye Movements with Reading Stimuli

We present a new study of gender prediction using eye movements of prepubescent children aged 9--10. Despite previous research indicating that gender differences in eye movements are observed only in adults, we are able to predict gender with accuracies of up to 64%. Our method segments gaze point trajectories into saccades and fixations. It then computes a small number of features and classifies saccades and fixations separately using statistical methods. The used dataset contains non-dyslexic and dyslexic children. In mixed groups, the accuracy of our classifiers drops dramatically. To address this challenge, we construct a hierarchical classifier that makes use of dyslexia prediction to improve significantly the accuracy of gender prediction in mixed groups.
Intrusion Detection System Using K-Means Based on Cuckoo Search Optimization

Abstract Recently, data protection is very important with the technological and digital revolution, as a vast amount of data is generated from different networks. It was found that the Intrusion Detection System (IDS) is probably the best option because of its ability to differentiate between threats that occur inside or outside a public internet. Cluster analysis is a common method of data mining and is characterized as the grouping of similar data. One of the clustering algorithms for clustering numerical data is K-Means. The K-Means Algorithm features are simple to implement and large amounts of data can be handled efficiently. Natural optimization algorithms have recently been combined with clustering algorithms in order to reach the best global solution. Algorithm for optimization search in Cuckoo is a recent meta algorithm for heuristic optimization. The intelligent behavior of the cuckoo is based on this algorithm. Cuckoo Search Optimization (CSO) and the K-Means clustering algorithm are combined in this paper to achieve the optimal solution globally. Different data sets are evaluated and results are compared with those of the clustering algorithms based on optimization.
Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS

Correctly configuring a distributed database management system (DBMS) deployed in a cloud environment for maximizing performance poses many challenges to operators. Even if the entire configuration spectrum could be measured directly, which is often infeasible due to the multitude of parameters, single measurements are subject to random variations and need to be repeated multiple times. In this work, we propose Baloo, a framework for systematically measuring and modeling different performance-relevant configurations of distributed DBMS in cloud environments. Baloo dynamically estimates the required number of measurement configurations, as well as the number of required measurement repetitions per configuration based on a desired target accuracy. We evaluate Baloo based on a data set consisting of 900 DBMS configuration measurements conducted in our private cloud setup. Our evaluation shows that the highly configurable framework is able to achieve a prediction error of up to 12 %, while saving over 80 % of the measurement effort. We also publish all code and the acquired data set to foster future research.
Edge-enhanced analytics via latent space dimensionality reduction

With the Internet of Things technology, almost any remote sensing devices, wearables, and smart objects are equipped to transmit large volumes of data in continuous streams. In conventional cloud-centric analytics, all the raw data is transferred to a data centre and processed in real-time, near real-time, or in batches. However, this approach is usually not very responsive to real-time analytics due to the latency in transmission alongside network traffic, bandwidth and data transmission costs. To tackle this, edge-enhanced analytics ensures that raw data can be preprocessed at the edge and sent across the network channel in a more compact form. A specific category of deep learning model, autoencoder, can help to achieve this by transforming high-dimensional data into compact representation. We propose an edge-enhanced framework which deploys a deep autoencoder model on the network edge for data compression. After training of models in the cloud, the encoder part of the autoencoder is deployed on the edge for data reduction while the decoder remains on the cloud to reconstruct the data for an image classification task on the cloud. We applied supervised fine-tuning using the intrinsic dimensionality of the data to achieve an accuracy that surpasses the baseline cloud model. The solution was explored in the context of an image recognition problem using the MNIST and FASHION-MNIST datasets. The framework was validated on an event simulator to estimate the network savings of the proposed method in terms of bandwidth and latency. The edge-enhanced approach saves up to 74% bandwidth compared to the centralised analytics. In addition, real-time analytics is further improved by taking 25% less time to complete the task.
A probabilistic collaborative dictionary learning‐based approach for face recognition

Although Sparse Representation based Classifier (SRC), a non-parametric model, can obtain an interesting result for pattern recognition , a reasonable interpretation has been lacked for its classification mechanism. What is more, the training samples are used as off-the-shelf dictionary directly in SRC, which can make the feature hidden in the training samples hard be extracted. At the same time, the complexity of the algorithm is increased because of too many atoms of the dictionary. The authors first explains in detail the classification mechanism of SRC from the view of probabilistic collaborative subspace and offer the process to improve the stability of the algorithm using the joint probability in the case of the multi-subspace. Then, the authors introduce the dictionary learning (DL) and Fisher criterion into the model to further enhance the discrimination of the coding coefficient. In order to ensure the convexity of the discrimination term and further enhance the discrimination, the authors add the L21-norm term into the Fisher discrimination term and offer the proof for its convexity. Finally, the experimental result on a series of benchmark databases, such as AR, Extended Yale B, LFW3D-hassner, LFW3D-sdm and LFW3D-Dlib, show that PCDDL outperforms existing classical classification models.
Applying Machine Learning to Customized Smell Detection

Code smells are considered symptoms of poor implementation choices, which may hamper the software maintainability. Hence, code smells should be detected as early as possible to avoid software quality degradation. Unfortunately, detecting code smells is not a trivial task. Some preliminary studies investigated and concluded that machine learning (ML) techniques are a promising way to better support smell detection. However, these techniques are hard to be customized to promote an early and accurate detection of specific smell types. Yet, ML techniques usually require numerous code examples to be trained (composing a relevant dataset) in order to achieve satisfactory accuracy. Unfortunately, such a dependency on a large validated dataset is impractical and leads to late detection of code smells. Thus, a prevailing challenge is the early customized detection of code smells taking into account the typical limited training data. In this direction, this paper reports a study in which we collected code smells, from ten active projects, that were actually refactored by developers, differently from studies that rely on code smells inferred by researchers. These smells were used for evaluating the accuracy regarding early detection of code smells by using seven ML techniques. Once we take into account such smells that were considered as important by developers, the ML techniques are able to customize the detection in order to focus on smells observed as relevant in the investigated systems. The results showed that all the analyzed techniques are sensitive to the type of smell and obtained good results for the majority of them, especially JRip and Random Forest. We also observe that the ML techniques did not need a high number of examples to reach their best accuracy results. This finding implies that ML techniques can be successfully used for early detection of smells without depending on the curation of a large dataset.
Robust Keypoint Normalization Method for Korean Sign Language Translation using Transformer

In this paper, We propose a robust keypoint normalization method for sign language translation framework using Transformer. The proposed method normalizes human keypoints based on the length of the neck-shoulder bone. The normalized keypoints are utilized for the Transformer-based framework as an input sequence. In our extensive experiments, the proposed normalization method greatly improves the accuracy and robustness of sign language translation.
Sentiment-Aware Transformer Using Joint Training

Transformer-based models have emerged as a dominant position in recent sentiment analysis research due to their powerful ability to capture rich contextualized word information. However, the general-propose Transformer model lacks sentiment information for individual words, affecting sentiment classification performance, especially for small datasets. Also, previous lexicon-based methods fail to fully utilize sentiment lexicons. Base on the Transformer model and a sentiment lexicon, this paper proposes a novel Transformer-based model. The model is trained on a main task and an auxiliary task jointly and optimized using a hybrid loss function. Experiments show that the proposed model and training strategy leads to better performance than previous methods.
Incremental DC optimization algorithm for large-scale clusterwise linear regression

The objective function in the nonsmooth optimization model of the clusterwise linear regression (CLR) problem with the squared regression error is represented as a difference of two convex functions. Then using the difference of convex algorithm (DCA) approach the CLR problem is replaced by the sequence of smooth unconstrained optimization subproblems. A new algorithm based on the DCA and the incremental approach is designed to solve the CLR problem. We apply the Quasi-Newton method to solve the subproblems. The proposed algorithm is evaluated using several synthetic and real-world data sets for regression and compared with other algorithms for CLR. Results demonstrate that the DCA based algorithm is efficient for solving CLR problems with the large number of data points and in particular, outperforms other algorithms when the number of input variables is small.
MaxAFL: Maximizing Code Coverage with a Gradient-Based Optimization Technique

Evolutionary fuzzers generally work well with typical software programs because of their simple algorithm. However, there is a limitation that some paths with complex constraints cannot be tested even after long execution. Fuzzers based on concolic execution have emerged to address this issue. The concolic execution fuzzers also have limitations in scalability. Recently, the gradient-based fuzzers that use a gradient to mutate inputs have been introduced. Gradient-based fuzzers can be applied to real-world programs and achieve high code coverage. However, there is a problem that the existing gradient-based fuzzers require heavyweight analysis or sufficient learning time. In this paper, we propose a new type of gradient-based fuzzer, MaxAFL, to overcome the limitations of existing gradient-based fuzzers. Our approach constructs an objective function through fine-grained static analysis. After constructing a well-made objective function, we can apply the gradient-based optimization algorithm. We use a modified gradient-descent algorithm to minimize our objective function and propose some probabilistic techniques to escape local optimum. We introduce an adaptive objective function which aims to explore various paths in the program. We implemented MaxAFL based on the original AFL. MaxAFL achieved increase of code coverage per time compared with three other fuzzers in six open-source Linux binaries. We also measured cumulative code coverage per total execution, and MaxAFL outperformed the other fuzzers in this metric. Finally, MaxAFL can also find more bugs than the other fuzzers.
Drone-Based Car Counting via Density Map Learning

Car counting on drone-based images is a challenging task in computer vision. Most advanced methods for counting are based on density maps. Usually, density maps are first generated by convolving ground truth point maps with a Gaussian kernel for later model learning (generation). Then, the counting network learns to predict density maps from input images (estimation). Most studies focus on the estimation problem while overlooking the generation problem. In this paper, a training framework is proposed to generate density maps by learning and train generation and estimation subnetworks jointly. Experiments demonstrate that our method outperforms other density map-based methods and shows the best performance on drone-based car counting.
IRlab@IITV at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media Using SVM

This paper describes the IRlab@IIT-BHU system for the OffensEval 2020.We take the SVM with TF-IDF features to identify and categorize hate speech and offensive language in social media for two languages.In subtask A, we used a linear SVM classifier to detect abusive content in tweets, achieving a macro F 1 score of 0.779 and 0.718 for Arabic and Greek, respectively.
A Machine Learning-Based Framework for Mobile Forensics

With the development of smart mobile devices, many portable electronic equipment not only keep the connected for us but also can act as an office and amusement tool. Mobile devices and processor technology enable person to handle their works conveniently and many applications are also installed in the equipment for various purposes. In the meantime, mobile services may be employed by criminals to commit malicious activities, and the criminal acts is hard to track because the crime detail information would be investigated difficultly because of the diverse form files stored in the mobile equipment such as smartphones. In order to efficient investigation the various types of crime files that collect from mobile devices, we propose a framework that combine kernel k-means and multi SVM machine learning methods to analyze documents and images for forensics investigation.
Entropy difference and kernel-based oversampling technique for imbalanced data learning

Class imbalance is often a problem in various real-world datasets, where one class contains a small number of data and the other contains a large number of data. It is notably difficult to develop an effective model using traditional data mining and machine learning algorithms without using data preprocessing techniques to balance the dataset. Oversampling is often used as a pretreatment method for imbalanced datasets. Specifically, synthetic oversampling techniques focus on balancing the number of training instances between the majority class and the minority class by generating extra artificial minority class instances. However, the current oversampling techniques simply consider the imbalance of quantity and pay no attention to whether the distribution is balanced or not. Therefore, this paper proposes an entropy difference and kernel-based SMOTE (EDKS) which considers the imbalance degree of dataset from distribution by entropy difference and overcomes the limitation of SMOTE for nonlinear problems by oversampling in the feature space of support vector machine classifier. First, the EDKS method maps the input data into a feature space to increase the separability of the data. Then EDKS calculates the entropy difference in kernel space, determines the majority class and minority class, and finds the sparse regions in the minority class. Moreover, the proposed method balances the data distribution by synthesizing new instances and evaluating its retention capability. Our algorithm can effectively distinguish those datasets with the same imbalance ratio but different distribution. The experimental study evaluates and compares the performance of our method against state-of-the-art algorithms, and then demonstrates that the proposed approach is competitive with the state-of-art algorithms on multiple benchmark imbalanced datasets.
КОМУНІКАТИВНІ ЗАСОБИ ПСИХОЛОГІЧНОГО СУПРОВОДУ ПРОФЕСІЙНОГО САМОЗДІЙСНЕННЯ ВИКЛАДАЧА

The abstract presents the results revealing with performed regression analysis of the determinants of university lecturers’ professional self-fulfilment. The research has shown that communicative factors are placed among the clearest predictors of achievement by university lecturers of high self-fulfilment in their profession. It is found out that the most significant factor in achieving of high level professional self-fulfilment (general, internal and external) by these specialists is the «creativity in professional communications» predictor. The main directions, communicative means and forms of self-fulfilment psychological support are outlined on the result basis.
Question Answering based University Chatbot using Sequence to Sequence Model

Educational chatbots have great potential to help students, teachers and education staff. They provide useful information in educational sectors for inquirers. Neural chatbots are more scalable and popular than earlier ruled-based chatbots. Recurrent Neural Network based Sequence to Sequence (Seq2Seq) model can be used to create chatbots. Seq2Seq is adapted for good conversational model for sequences especially in question answering systems. In this paper, we explore the ways of communication through neural network chatbot by using the Sequence to Sequence model with Attention Mechanism based on RNN encoder decoder model. This chatbot is intended to be used in university education sector for frequently asked questions about the university and its related information. It is the first Myanmar Language University Chatbot using neural network model and gets 0.41 BLEU score.
Mixture‐based clustering for count data using approximated Fisher Scoring and Minorization–Maximization approaches

Abstract The multinomial distribution has been widely used to model count data. To increase clustering efficiency, we use an approximation to the Fisher scoring algorithm, which is more robust regarding the choice of initial parameter values. Then, we use a novel approach to estimate the optimal number of components, based on minimum message length criterion. Moreover, we consider a generalization of the multinomial model obtained by introducing the Dirichlet as prior, yielding the Dirichlet Compound Multinomial (DCM). Even though DCM can address the burstiness phenomenon of count data, the presence of Gamma function in its density function usually leads to undesired complications. In this article, we use two alternative representations of DCM distribution to perform clustering based on finite mixture models, where the mixture parameters are estimated using the minorization–maximization framework. To evaluate and compare the performance of our proposed models, we have considered three challenging real‐world applications that involve high‐dimensional count vectors, namely, sentiment analysis, facial expression recognition, and human action recognition. The results show that the proposed algorithms increase the clustering efficiency of their respective models remarkably, and the best results are achieved by the second parametrization of DCM, which can accommodate over‐dispersed count data.
Integrated DLT and non-DLT system design for central bank digital currency

Central Bank Digital Currency (CBDC) is gaining popularity due to its potential benefit. However, experiments to explore the Distributed Ledger Technology (DLT) based CBDC is mostly done in the wholesale area, not in the retail area which has more impact on the public. This paper proposes a CBDC design where the digital currency or token that is distributed in the wholesale DLT network, can be widely accessed by the public through accounts provided by commercial banks. The proposed design maintains reliability as well as user-friendliness and scalability. This paper describes the architecture, infrastructure, and business process of the proposed design.