Reviewerly/exHarmony huggingface dataset:
Uncovering the Necessary Hard- and Soft-Skills to Get IT Personnel Jobs

With digitalization disrupting organizations every day, the necessary skills to get a job in modern societies are changing rapidly. Individuals and educational institutions seek knowledge of what industry demands in terms of hard- and soft-skills to plan their next step in having these skills in their personal or educational portfolio. To find out which hard- and soft-skills play a key role in finding IT personnel jobs, we employ a text mining approach on job advertisement data and generate a framework for IT personnel jobs. After doing some standard preprocessing steps, we use topic modeling to generate our results and map those with the IT personnel framework. Our results show that three competencies' fields are relevant for IT personnel jobs, and hard-and soft-skills play both a major role in those. Depending on which competencies field one wants to explore, we highlighted the most necessary skills that may play a decisive role when applying for a job in that position. Our findings contribute to the stream of literature equipping both educational institutions and individuals with recommendations of the skills to include and focus on regarding IT personnel.
BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images

Current state-of-the-art segmentation techniques for ocular images are critically dependent on large-scale annotated datasets, which are labor-intensive to gather and often raise privacy concerns. In this paper, we present a novel framework, called BiOcularGAN, capable of generating synthetic large-scale datasets of photorealistic (visible light and near-infrared) ocular images, together with corresponding segmentation labels to address these issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2 (DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic Mask Generator (SMG) component that produces semantic annotations by exploiting latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through extensive experiments across five diverse ocular datasets and analyze the effects of bi-modal data generation on image quality and the produced annotations. Our experimental results show that BiOcularGAN is able to produce high-quality matching bimodal images and annotations (with minimal manual intervention) that can be used to train highly competitive (deep) segmentation models (in a privacy aware-manner) that perform well across multiple real-world datasets. The source code for the BiOcularGAN framework is publicly available at https://github.com/dariant/BiOcularGAN.
STATNet: Spectral and Temporal features based Multi-Task Network for Audio Spoofing Detection

With the rise in mobile phone users and VoIP, voice has emerged as an easy and accessible biometric modality for identification or verification tasks. Given the increasing usage of voice biometrics, the security of these systems is also of paramount importance. Researchers have demon-strated that Automatic Speaker Verification (ASV) systems are prone to spoofing attacks like synthetic speech or fake speech, which can be used maliciously for a variety of tasks such as impersonation, fake news spreading, and opinion formation. This research proposes a deep convolution-based multi-task network which performs both spoof detection and source identification for synthetic speech. The pro-posed model is evaluated on three datasets ASVspoof2019 LA, FOR-Norm and In-the- Wild Audio Deepfake dataset. The results demonstrate the EER of 2.456%, 0.814%, and 0.199% on the ASVspoof2019 LA, FOR-Norm, and In-the-Wild Audio Deepfake datasets. In addition, we have also demonstrated results for cross-dataset evaluation and speech source identification.
A Sophisticated Framework for Document Forensics

Pretention of documents in today's era is a common problem for organizations as it has become so easier for fabricators to forge a document by using advanced imaging systems and softwares. Documents are very essential part of human society. These documents are images, contract deeds, identity cards, bank cheques, property papers etc. Documents also plays a very vital role in proceedings of law enforcement agencies as they are the only written roof of anything that existed or happened at some point of time or going to happen and also the proof of a person's identity. But there existed some sort of risk associated with these documents that are tempering of document, false document creation, and illegal editing. Due to the absence of a set and specific methodology and very wide genre of documents it often found tedious for the law enforcement agencies to adopt an appropriate technique to check the authenticity of the questioned document. In this paper, a sophisticated framework based on goal-driven approach with concept graphs is proposed. Further, attack tree is developed to present threats on document forensics. Attack scenario is also visualized by integrating computer systems and possible attacks to realize those threats. And anti-forensic model is also integrated to visualize the attack scenario in document related crime and obstruction that can be encountered while investigation process of the same respectively.
Automatic Image Caption Generation Using ResNet &amp; Torch Vision

Image captioning is a task through which a textual description can be generated that illustrated the action performed in the image. It is one of the most complicated research areas where only the machine learning approach can intervene. In the area of image captioning, a system should be intelligent enough to understand the semantic knowledge to recognize the object present in the image and the situation that evolves with it. In the proposed work an image captioning system has been generated using ResNet along with CNN and RNN. CNN is used as an encoder and RNN is used as a decoder. The system is able to infer the situation precisely for MSCOCO benchmark. The model has been trained with ResNet152 which effectively utilizes the layers and minimizes the computational time. ResNet skips the convolutional layers that solved the gradient exploding problem, that is why it is also known as skip connection. System perceived better Bilingual Evaluation Understudy (BLEU), METEOR, CIDEr, and Rouge score as compared to the previously implemented model. BLEU score has been evaluated with four parameters as B1, B2, B3 and B4 i.e., 0.57, 0.404, 0.279, 0.191 respectively. METEOR, CIDEr and Rouge have been depicted as 0.195, 0.396 and 0.6 respectively. Model has been better utilized to train the samples by reducing the size of the image and enhancing the brightness with pillow. System also uses the Torch Vision library to enhance the model for better predicting the situation.
An Algorithm of Set-Based Differential Evolution for Discrete Optimization Problem

This paper presents an algorithm of set-based differential evolution for discrete optimization problem. Differential evolution has been hitherto studied for continuous optimization problem. Extending approaches in continuous space to these in discrete space with set-based representation schemes, differential evolution can adopt to discrete optimization problem. A candidate solution is defined by a crisp set and all arithmetic operations in mutation are redefined by new operators. The mutation operator of our algorithm adds two different solutions selected randomly to the current solution and our algorithm constructs the solution probabilistically. In order to evaluate the validity of our algorithm, we examine numerical experiments compared to existing algorithms.
A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network

Recently, digital images have been considered the primary key for many applications, such as forensics, medical diagnosis, and social networks. Image forgery detection is considered one of the most complex digital image applications. More profoundly, image splicing was investigated as one of the common types of image forgery. As a result, we proposed a convolutional neural network (CNN) model for detecting splicing forged images in real-time and with high accuracy, with a small number of parameters as compared with the recently published approaches. The presented model is a lightweight model with only four convolutional layers and four max-pooling layers, which is suitable for most environments that have limitations in their resources. A detailed comparison was conducted between the proposed model and the other investigated models. The sensitivity and specificity of the proposed model over CASIA 1.0, CASIA 2.0, and CUISDE datasets are determined. The proposed model achieved an accuracy of 99.1% in detecting forgery on the CASIA 1.0 dataset, 99.3% in detecting forgery on the CASIA 2.0 dataset, and 100% in detecting forgery on the CUISDE dataset. The proposed model achieved high accuracy, with a small number of parameters. Therefore, specialists can use the proposed approach as an automated tool for real-time forged image detection.
Assessment of the digital competencies of university instructors through use of the machine learning method

The explosion of COVID-19 has brought new challenges to the education industry, especially higher education. Digital competency is becoming an essential competency for higher education instructors, and how to assess instructors' digital competency is attracting increasing attention in higher education. However, most studies have used self-report questionnaires or manual reviews to assess digital competencies, which are time-consuming and potentially biased, and there is a current need for valid and effective assessment methods. To address this issue, this study uses machine learning to analyze syllabi to assess the extent to which university instructors have incorporated digital competency into their courses. The results show that not only is the proposed method feasible, but the results of the assessment using machine learning are highly consistent with those of the human assessment. This approach contributes to the assessment of digital competency in higher education institutions and provides evidence that can be used as a reference for future research on the development of digital competency in higher education institutions.
The rapid growth of cryptocurrencies: How profitable is trading in digital money?

Abstract There has been a tremendous growth in cryptocurrencies, which has challenged policy makers around the globe. We obtain millisecond data of some of the most frequently traded cryptocurrencies – bitcoin, ethereum, ripple, litecoin and dash – and two cryptocurrency indices – CRIX and CCI30 – to examine their profitability. Our profitability findings suggest that cryptocurrency traders generate significant profits after considering reasonable transaction costs. We also observe that cryptocurrency market participants can expand and sustain the levels of profitability levels in the subsequent trading activity. Our robustness checks with more recent post‐Covid data are consistent with the initial profitability findings, although we observe lower levels of profits for the two indices and weaker profit persistency for all digital assets.
Characterizing nonclassical correlations of tensorizing states in a bilocal scenario

In the present paper, we attempt to address the question of "can tensorizing states have quantum advantages?". To answer this question, we exploit the notion of measurement-induced nonlocality (MIN) and advocate a fidelity based nonbilocal measure to capture the nonlocal effects of tensorizing states due to locally invariant von Neumann projective measurements. We show that the properties of the fidelity based nonbilocal measure are retrieved from that of MIN. Analytically, we evaluate the nonbilocal measure for any arbitrary pure state. The upper bounds of the nonbilocal measure based on fidelity are also obtained in terms of eigenvalues of correlation matrix. As an illustration, we have computed the nonbilocality for some popular input states.
Local approximation of operators

Many applications, such as system identification, classification of time series, direct and inverse problems in partial differential equations, and uncertainty quantification lead to the question of approximation of a non-linear operator between metric spaces X and Y. We study the problem of determining the degree of approximation of such operators on a compact subset KX⊂X using a finite amount of information. If F:KX→KY, a well established strategy to approximate F(F) for some F∈KX is to encode F (respectively, F(F)) in terms of a finite number d (respectively m) of real numbers. Together with appropriate reconstruction algorithms (decoders), the problem reduces to the approximation of m functions on a compact subset of a high dimensional Euclidean space Rd, equivalently, the unit sphere Sd embedded in Rd+1. The problem is challenging because d, m, as well as the complexity of the approximation on Sd are all large, and it is necessary to estimate the accuracy keeping track of the inter-dependence of all the approximations involved. In this paper, we establish constructive methods to do this efficiently; i.e., with the constants involved in the estimates on the approximation on Sd being O(d1/6). We study different smoothness classes for the operators, and also propose a method for approximation of F(F) using only information in a small neighborhood of F, resulting in an effective reduction in the number of parameters involved. To further mitigate the problem of large number of parameters, we propose prefabricated networks, resulting in a substantially smaller number of effective parameters. The problem is studied in both deterministic and probabilistic settings.
Face Tracking Strategy of 7-DOF Manipulator Using Optimization Algorithm Based on Velocity Analysis

Patients' facial expressions can reflect their condition, but expressions cannot be captured like ECG or EEG monitor. Long-term monitoring will increase the workload of nurses. To tackle this issue, we develop a face tracking strategy using a 7-DOF robot manipulator with a camera mounted on the end-effector. First, we analyze the relationship between the manipulator's workspace and optimal tracking pose and design an optimization function to initialize the head position. By comparing the speed of the head and manipulator, knowledge of the maximum tracking limit of the head and the manipulator in the tracking process is given. Moreover, a configuration-based optimization algorithm is proposed to minimize the error with theoretical optimal pose by converting radian to arc length. We calculate the optimal configuration of the camera with the feedback of the head configuration while minimizing the difference between theoretical optimal pose. Simulation experimental results verified that the proposed face tracking strategy outperforms the visual servoing control strategy only based on the pseudo-inverse of the Jacobian matrix.
Machine and Deep Learning (ML/DL) Algorithms, Frameworks, and Libraries

Each primary, secondary, tertiary, quaternary, and the quinary sector has huge or very huge incremental data from large-scale, small-scale industries, medium industries, or cottage industries. The data associated with each of them are very crucial from every point of view. The complex problems are increasing day by day in real-time execution which can be addressed using current trends of technology like machine learning and deep learning. Machine learning is a subset of artificial intelligence. ML is functioning for image &amp; speech recognition, mail filtering, Facebook tagging mechanism, and many others. Deep learning is an advanced technology that is a subset of machine learning with the capacity to learn more intelligently on a large set of data. Deep learning works with multiple hidden layers to produce the predicted outcomes. Deep learning algorithms include convolutional neural networks, recurrent neural networks, long short-term memory networks, stacked auto-encoders, deep boltzmann machines &amp;, etc.
Attention-Based Active Learning Framework for Segmentation of Breast Cancer in Mammograms

Breast cancer is one of most serious malignant tumors that affect women’s health. To carry out the early screening of breast cancer, mammography provides breast cancer images for doctors’ efficient diagnosis. However, breast cancer lumps can vary in size and shape, bringing difficulties for the accurate recognition of both humans and machines. Moreover, the annotation of such images requires expert medical knowledge, which increases the cost of collecting datasets to boost the performance of deep learning methods. To alleviate these problems, we propose an attention-based active learning framework for breast cancer segmentation in mammograms; the framework consists of a basic breast cancer segmentation model, an attention-based sampling scheme and an active learning strategy for labelling. The basic segmentation model performs multi-scale feature fusion and enhancement on the basis of UNet, thus improving the distinguishing representation capability of the extracted features for further segmentation. Afterwards, the proposed attention-based sampling scheme assigns different weights for unlabeled breast cancer images by evaluating their uncertainty with the basic segmentation model. Finally, the active learning strategy selects unlabeled images with the highest weights for manual labeling, thus boosting the performance of the basic segmentation model via retraining with new labeled samples. Testing on four datasets, experimental results show that the proposed framework could greatly improve segmentation accuracy by about 15% compared with an existing method, while largely decreasing the cost of data annotation.
The Potential for Rooftop Photovoltaic Systems in Nepal

Nepal possesses a good solar resource, and there has been increasing interest in the use of photovoltaic systems. About 1.1 million solar home systems, rated at nearly 30 MWp, have been installed across Nepal. With the introduction of net metering by the Nepal Electricity Authority, an increase in rooftop photovoltaics (RPV) is expected. However, to inform any policy developments around increased electricity generation, and the uptake of RPV, there is a need to quantify the potential of such systems (a situation mirrored in many developing countries). To this end, this study utilized a hierarchical geospatial technique based on open-source data to estimate the potential output from RPV in several cities in Nepal (Kathmandu, Pokhara, Butwal, Nepalgunj, and Biratnagar). It was found that the potential theoretical output of RPV ranged from 637 GWh per annum, in Kathmandu, to 50 GWh per annum in Butwal. Moreover, the total RPV potential from urban households of Nepal was estimated to be in the order 6.5 TWh per annum. As such, the findings of this paper can be used to make informed policy decisions about the future of Nepal’s energy mix.
Multiview Deep Forest for Overall Survival Prediction in Cancer

Overall survival (OS) in cancer is crucial for cancer treatment. Many machine learning methods have been applied to predict OS, but there are still the challenges of dealing with multiview data and overfitting. To overcome these problems, we propose a multiview deep forest (MVDF) in this paper. MVDF can learn the features of each view and fuse them with integrated learning and multiple kernel learning. Then, a gradient boost forest based on the information bottleneck theory is proposed to reduce redundant information and avoid overfitting. In addition, a pruning strategy for a cascaded forest is used to limit the impact of outlier data. Comprehensive experiments have been carried out on a data set from West China Hospital of Sichuan University and two public data sets. Results have demonstrated that our method outperforms the compared methods in predicting overall survival.
Hyperparameter Black-Box Optimization to Improve the Automatic Classification of Support Tickets

User requests to a customer service, also known as tickets, are essentially short texts in natural language. They should be grouped by topic to be answered efficiently. The effectiveness increases if this semantic categorization becomes automatic. We pursue this goal by using text mining to extract the features from the tickets, and classification to perform the categorization. This is however a difficult multi-class problem, and the classification algorithm needs a suitable hyperparameter configuration to produce a practically useful categorization. As recently highlighted by several researchers, the selection of these hyperparameters is often the crucial aspect. Therefore, we propose to view the hyperparameter choice as a higher-level optimization problem where the hyperparameters are the decision variables and the objective is the predictive performance of the classifier. However, an explicit analytical model of this problem cannot be defined. Therefore, we propose to solve it as a black-box model by means of derivative-free optimization techniques. We conduct experiments on a relevant application: the categorization of the requests received by the Contact Center of the Italian National Statistics Institute (Istat). Results show that the proposed approach is able to effectively categorize the requests, and that its performance is increased by the proposed hyperparameter optimization.
Continuous-variable (3,3)-threshold quantum secret sharing based on one-sided device-independent security

In this paper, we investigate a feasible scheme for continuous-variable (3,3)-threshold quantum secret sharing (QSS) protocol, which owns unconditional security against arbitrary attacks by an eavesdropper and/or dishonest participants. Our scheme is based on a one-sided device-independent approach. It treats a dealer as a trusted party while treating all participants as untrusted. We demonstrate that the (3,3)-threshold QSS protocol with unconditional security against eavesdropping and/or dishonest participants can be fulfilled by an analog of van Loock-Braunstein (vLB) entangled states, which uses only two single-mode squeezed vacuum states and linear optics. Such a vLB-like entangled state may find its potential applications in multipartite QSS with unconditional security.
On the data privacy practices of Android OEMs

In this paper we present the first in-depth measurement study looking at the data privacy practices of the proprietary variants of the Android OS produced by Samsung, Xiaomi, Huawei and Realme. We address two questions: how are identifiers used in network connections and what types of data are transmitted. To answer these, we decrypt and decode the network traffic transmitted by a range of Android handsets. We find that all of the OEMs make undue use of long-lived hardware identifiers such as the hardware serial number, handset IMEI and so fail to follow best privacy practice. Hardware identifiers are also linked to the handset user's real identity when they sign in to an OEM account on the handset. All of the OEMs collect the list of apps installed in a handset. This is a privacy concern since the list of installed apps can be used to profile user traits and preferences. All of the OEMs collect analytics/telemetry data, raising obvious privacy concerns.