Reviewerly/exHarmony huggingface dataset:
Assessing knowledge and skills gap for construction 4.0 in a developing economy

Most developing economies are plagued by a low supply of skilled human capital, which is a known driver of economic growth and development. The introduction of the fourth industrial revolution (4IR) concept to the construction industry referred to as Construction 4.0 has introduced new technologies that require an increase in knowledge and skills requirement for workers. This comes with a host of challenges for developing countries. To this end, the current study focuses on Nigeria, which is the largest economy in Africa and a fast developing country. The study therefore seeks to explore the knowledge and skills gap among professionals within the Nigerian construction industry. This was achieved via a structured survey research design to elicit information from professionals in the construction industry. A convenience sampling technique was used to select 136 respondents. Data collected from the questionnaire were analysed using mean score, Pearson chi‐square and t test of SPSS V23. The findings revealed a moderate knowledge level of Construction 4.0 technologies and a low level of training among professionals. Also, the result shows a high skills gap in human machine communication, data analytics and cyber security. The study recommends the promotion of human capital development to enhance the implementation of Construction 4.0. This can be achieved via a review and redesign of existing curricula in educational institutions and commitment on the part of management and professional bodies to training and re‐skilling of the workforce.
Simulation-Driven Platform for Edge-Based AAL Systems

The ever-growing aging of the population has emphasized the importance of in-home AAL (Ambient Assisted Living) services for monitoring and improving its well-being and health, especially in the context of care facilities (retirement villages, clinics, senior neighborhood, etc). The paper proposes a novel simulation-driven platform named E-ALPHA (Edge-based Assisted Living Platform for Home cAre) which supports both Edge and Cloud Computing paradigm to develop innovative AAL services in scenarios of different scales. E-ALPHA flexibly combines Edge, Cloud or Edge/Cloud deployments, supports different communication protocols, and fosters the interoperability with other IoT platforms. Moreover, the simulation-based design helps in preliminary assessing (i) the expected performance of the service to be deployed according to the infrastructural characteristics of each specific small, medium and large scenario; and (ii) the most appropriate applications/platform configuration for a real deployment (kind and number of involved devices, Edge- or Cloud-based deployment, required connectivity type, etc). In this direction, two different use cases modeled according to realistic input (coming from past experience involving real testbed) are shown in order to demonstrate the potentials of the proposed simulation-driven AAL platform.
Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma

Abstract Due to the complicated histopathological characteristics of renal neoplasms, traditional distinguishing of clear cell renal cell carcinoma (ccRCC) by naked eyes of experienced pathologist remains labor intensive and time consuming. Here, we extracted quantitative features of hematoxylin‐eosin‐stained images using CellProfiler and performed machine learning method to develop and verify a novel computational recognition of digital pathology for diagnosis and prognosis of ccRCC patients in the training, test and external validation cohort. The diagnostic model based on digital pathology could accurately distinguish ccRCC from normal renal tissues, with area under the curve (AUC) of 96.0%, 94.5% and 87.6% in the training, test and external validation cohorts, respectively. It could also accurately distinguish ccRCC from other pathological types of renal cancer, with AUC values of 97.0% and 81.4% in the Cancer Genome Atlas (TCGA) cohort and General cohort. We next developed and verified a computational recognition prognosis model with risk score. There was a significant difference in disease‐free survival comparing patients with high vs low risk score in training cohort (hazard ratio = 2.72, P &lt; .0001) and validation cohort (hazard ratio = 9.50, P = .0091). The integrated nomogram based on our computational recognition risk score and clinicopathologic factors demonstrated excellent survival prediction for ccRCC patients, with increased accuracy by 6.6% in patients from Shanghai General Hospital and by 2.5% in patients from TCGA cohort when compared to current tumor stages/grade systems. These results indicate the potential clinical use of our machine learning histopathological image signature in diagnosis and survival prediction of ccRCC.
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research

An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts. However, due to the rapid pace at which DL techniques have been adopted, it is difficult to distill the current successes, failures, and opportunities of the current research landscape. In an effort to bring clarity to this cross-cutting area of work, from its modern inception to the present, this article presents a systematic literature review of research at the intersection of SE &amp; DL. The review canvasses work appearing in the most prominent SE and DL conferences and journals and spans 128 papers across 23 unique SE tasks. We center our analysis around the components of learning , a set of principles that governs the application of machine learning techniques (ML) to a given problem domain, discussing several aspects of the surveyed work at a granular level. The end result of our analysis is a research roadmap that both delineates the foundations of DL techniques applied to SE research and highlights likely areas of fertile exploration for the future.
The application of noninvasive, restraint-free eye-tracking methods for use with nonhuman primates

Over the past 50 years there has been a strong interest in applying eye-tracking techniques to study a myriad of questions related to human and nonhuman primate psychological processes. Eye movements and fixations can provide qualitative and quantitative insights into cognitive processes of nonverbal populations such as nonhuman primates, clarifying the evolutionary, physiological, and representational underpinnings of human cognition. While early attempts at nonhuman primate eye tracking were relatively crude, later, more sophisticated and sensitive techniques required invasive protocols and the use of restraint. In the past decade, technology has advanced to a point where noninvasive eye-tracking techniques, developed for use with human participants, can be applied for use with nonhuman primates in a restraint-free manner. Here we review the corpus of recent studies (N=32) that take such an approach. Despite the growing interest in eye-tracking research, there is still little consensus on “best practices,” both in terms of deploying test protocols or reporting methods and results. Therefore, we look to advances made in the field of developmental psychology, as well as our own collective experiences using eye trackers with nonhuman primates, to highlight key elements that researchers should consider when designing noninvasive restraint-free eye-tracking research protocols for use with nonhuman primates. Beyond promoting best practices for research protocols, we also outline an ideal approach for reporting such research and highlight future directions for the field.
Unified Generative Adversarial Networks for Controllable Image-to-Image Translation

We propose a unified Generative Adversarial Network (GAN) for controllable image-to-image translation, i.e., transferring an image from a source to a target domain guided by controllable structures. In addition to conditioning on a reference image, we show how the model can generate images conditioned on controllable structures, e.g., class labels, object keypoints, human skeletons, and scene semantic maps. The proposed model consists of a single generator and a discriminator taking a conditional image and the target controllable structure as input. In this way, the conditional image can provide appearance information and the controllable structure can provide the structure information for generating the target result. Moreover, our model learns the image-to-image mapping through three novel losses, i.e., color loss, controllable structure guided cycle-consistency loss, and controllable structure guided self-content preserving loss. Also, we present the Fr´echet ResNet Distance (FRD) to evaluate the quality of the generated images. Experiments on two challenging image translation tasks, i.e., hand gesture-to-gesture translation and cross-view image translation, show that our model generates convincing results, and significantly outperforms other state-of-the-art methods on both tasks. Meanwhile, the proposed framework is a unified solution, thus it can be applied to solving other controllable structure guided image translation tasks such as landmark guided facial expression translation and keypoint guided person image generation. To the best of our knowledge, we are the first to make one GAN framework work on all such controllable structure guided image translation tasks. Code is available at https://github.com/Ha0Tang/GestureGAN.
A Framework for Evaluating Client Privacy Leakages in Federated Learning

Federated learning (FL) is an emerging distributed machine learning framework for collaborative model training with a network of clients (edge devices). FL offers default client privacy by allowing clients to keep their sensitive data on local devices and to only share local training parameter updates with the federated server. However, recent studies have shown that even sharing local parameter updates from a client to the federated server may be susceptible to gradient leakage attacks and intrude the client privacy regarding its training data. In this paper, we present a principled framework for evaluating and comparing different forms of client privacy leakage attacks. We first provide formal and experimental analysis to show how adversaries can reconstruct the private local training data by simply analyzing the shared parameter update from local training (e.g., local gradient or weight update vector). We then analyze how different hyperparameter configurations in federated learning and different settings of the attack algorithm may impact on both attack effectiveness and attack cost. Our framework also measures, evaluates, and analyzes the effectiveness of client privacy leakage attacks under different gradient compression ratios when using communication efficient FL protocols. Our experiments additionally include some preliminary mitigation strategies to highlight the importance of providing a systematic attack evaluation framework towards an in-depth understanding of the various forms of client privacy leakage threats in federated learning and developing theoretical foundations for attack mitigation.
Vertical Data Mining from Relational Data and Its Application to COVID-19 Data

Nowadays, big data are everywhere. These big data can be of different degrees of veracity (e.g., precise, imprecise and uncertain data). Many of them are open data and are stored in relational databases. Embedded in these big data are valuable information and knowledge, which can be discovered by data mining. Frequent pattern mining is a popular data mining task within the realm of data science. In this paper, we present data science techniques for vertical data mining—in particular, vertical mining of frequently occurring patterns—from these relational data. For illustration, we discuss applications of the vertical data mining for the discovery of knowledge and useful information from real-life epidemiological data about coronavirus disease 2019 (COVID-19) and economic data.
Siamese Local and Global Networks for Robust Face Tracking

Convolutional neural networks (CNNs) have achieved great success in several face-related tasks, such as face detection, alignment and recognition. As a fundamental problem in computer vision, face tracking plays a crucial role in various applications, such as video surveillance, human emotion detection and human-computer interaction. However, few CNN-based approaches are proposed for face (bounding box) tracking. In this paper, we propose a face tracking method based on Siamese CNNs, which takes advantages of powerful representations of hierarchical CNN features learned from massive face images. The proposed method captures discriminative face information at both local and global levels. At the local level, representations for attribute patches (i.e:, eyes, nose and mouth) are learned to distinguish a face from another one, which are robust to pose changes and occlusions. At the global level, representations for each whole face are learned, which take into account the spatial relationships among local patches and facial characters, such as skin color and nevus. In addition, we build a new largescale challenging face tracking dataset to evaluate face tracking methods and to facilitate the research forward in this field. Extensive experiments on the collected dataset demonstrate the effectiveness of our method in comparison to several state-of-theart visual tracking methods.
An Algebraic Formulation of the Division Property: Revisiting Degree Evaluations, Cube Attacks, and Key-Independent Sums

Since it was proposed in 2015 as a generalization of integral properties, the division property has evolved into a powerful tool for probing the structures of Boolean functions whose algebraic normal forms are not available. We capture the most essential elements for the detection of division properties from a pure algebraic perspective, proposing a technique named as monomial prediction, which can be employed to determine the presence or absence of a monomial in any product of the coordinate functions of a vectorial Boolean function $$\textit{\textbf{f}}$$ by counting the number of the so-called monomial trails across a sequence of simpler functions whose composition is $$\textit{\textbf{f}}$$ . Under the framework of the monomial prediction, we formally prove that most algorithms for detecting division properties in literature raise no false alarms but may miss. We also establish the equivalence between the monomial prediction and the three-subset bit-based division property without unknown subset presented at EUROCRYPT 2020, and show that these two techniques are perfectly accurate. The monomial prediction technique can be regarded as a purification of the definitions of the division properties without resorting to external multisets. This algebraic formulation gives more insights into division properties and inspires new search strategies. With the monomial prediction, we obtain the exact algebraic degrees of Trivium up to 834 rounds for the first time. In the context of cube attacks, we are able to explore a larger search space in limited time and recover the exact algebraic normal forms of complex superpolies with the help of a divide-and-conquer strategy. As a result, we identify more cubes with smaller dimensions, leading to improvements of some near-optimal attacks against 840-, 841- and 842-round Trivium.
PQC Acceleration Using GPUs: FrodoKEM, NewHope, and Kyber

In this article, we present the first GPU implementation for FrodoKEM-976, NewHope-1024, and Kyber-1024. These algorithms belong to three different classes of post-quantum algorithms: Learning with errors (LWE), Ring-LWE, and Module-LWE. We show the practical applicability of the algorithms in different scenarios using two different implementation approaches. Moreover, we achieve highly efficient realization of computationally expensive operations such as NTT (Number Theoretic Transform), matrix multiplication, and Keccak. Since, these are the most common operations in lattice-based cryptographic algorithms, the techniques presented in this article will likely benefit other similar algorithms. Using a NVIDIA QUADRO GV100 graphics card, we undertook a detailed experimental study. For NewHope and Kyber we were able to perform approximately 504K and 473K key exchanges per second, demonstrating a speedup of almost 53.1× and 51.05× compared to the reference C implementation. Compared to the optimized AVX2 versions we obtain speedups of 25.7× and 14.6×, respectively. Further, implementation of FrodoKEM resulted in a speedup of 50.6×, 44.2×, and 36.9× for KeyGen, Encaps and Decaps operations. Compared to its AVX2 counterpart, we achieved a speedup of about 7.3×, 4.7× and 4.9×, respectively. We also show that using multiple streams resulted in further speedup of about 28-38 percent.
Imbalance accuracy metric for model selection in multi-class imbalance classification problems

The overall accuracy, macro precision, macro recall, F-score and class balance accuracy, due to their simplicity and easy interpretation, have been among the most popular metrics to measure the performance of classifiers on multi-class problems. However, on imbalance datasets, some of these metrics can be unfairly influenced by heavier classes. Therefore, it is recommended that they are used as a group and not individually. This strategy can unnecessarily complicate the model selection and evaluation in imbalance datasets. In this paper, we introduce a new metric, imbalance accuracy metric (IAM), that can be used as a solo measure for model evaluation and selection. The IAM is built up on top of the existing metrics, is simple to use, and easy to interpret. This metric is meant to be used as a bottom-line measure aiming to eliminate the need for group metric computation and simplify the model selection.
Artificially Intelligent Electronic Money

Electronic money or e-Cash is becoming increasingly popular as the preferred strategy for making purchases, both on and offline. Several unique attributes of e-Cash are appealing to customers, including the convenience of always having “cash-on-hand” without the need to periodically visit the ATM, the ability to perform peer-to-peer transactions without an intermediary, and the peace of mind associated in conducting those transactions privately. Equally important is that paper money provides customers with an anonymous method of payment, which is highly valued by many individuals. Although anonymity is implicit with fiat money, it is a difficult property to preserve within e-Cash schemes. In this article, we investigate several artificial intelligence (AI) approaches for improving performance and privacy within a previously proposed e-Cash scheme called PUF-Cash.PUF-Cash utilizes physical unclonable functions for authentication and encryption operations between Alice, the Bank, and multiple trusted third parties. The AI methods select a subset of the TTPs and distribute withdrawal amounts to maximize the performance and privacy associated with Alice's e-Cash tokens. Simulation results show the effectiveness of the various AI approaches using a large test-bed architecture.
Automated machine learning for predictive quality in production

Applications that leverage the benefits of applying machine learning (ML) in production have been successfully realized. A fundamental hurdle to scale ML-based projects is the necessity of expertise from manufacturing and data science. One possible solution lies in automating the ML pipeline: integration, preparation, modeling and model deployment. This paper shows the possibilities and limits of applying AutoML in production, including a benchmarking of available systems. Furthermore, AutoML is compared to manual implementation in a predictive quality use case: AutoML still requires programming knowledge and is outperformed by manual implementation - but sufficient results are available in a shorter timespan.
Mining local periodic patterns in a discrete sequence

Periodic frequent patterns are sets of events or items that periodically appear in a sequence of events or transactions. Many algorithms have been designed to identify periodic frequent patterns in data. However, most assume that the periodic behavior of a pattern does not change much over time. To address this limitation, this paper proposes to discover a novel type of periodic patterns in a sequence of events or transactions, called Local Periodic Patterns (LPPs) which are patterns (sets of events) that have a periodic behavior in some non predefined time-intervals. A pattern is said to be a local periodic pattern if it appears regularly and continuously in some time-interval(s). Two novel measures are proposed to assess the periodicity and frequency of patterns in time-intervals. The maxSoPer (maximal period of spillovers) measure allows detecting time-intervals of variable lengths where a pattern is continuously periodic, while the minDur (minimal duration) measure ensures that those time-intervals have a minimum duration. To discover all LPPs, the paper presents three efficient algorithms. An experimental evaluation on real datasets shows that the proposed algorithms are efficient and can provide useful patterns that cannot be found using traditional periodic pattern mining algorithms.
Development of an Intelligent Tutoring System Using Bayesian Networks and Fuzzy Logic for a Higher Student Academic Performance

In this experimental study, an intelligent tutoring system called the fuzzy Bayesian intelligent tutoring system (FB-ITS), is developed by using artificial intelligence methods based on fuzzy logic and the Bayesian network technique to adaptively support students in learning environments. The effectiveness of the FB-ITS was evaluated by comparing it with two other versions of an Intelligent Tutoring System (ITS), fuzzy ITS and Bayesian ITS, separately. Moreover, it was evaluated by comparing it with an existing traditional e-learning system. In order to evaluate whether the academic performance of the students in different learning groups differs or not, analysis of covariance (ANCOVA) was used based on the students’ pre-test and post-test scores. The study was conducted with 120 undergraduate university students. Results showed that students who studied using FB-ITS had significantly higher academic performance on average compared to other students who studied with the other systems. Regarding the time taken to perform the post-test, the results indicated that students who used the FB-ITS needed less time on average compared to students who used the traditional e-learning system. From the results, it could be concluded that the new system contributed in terms of the speed of performing the final exam and high academic success.
How Blockchain, Virtual Reality, and Augmented Reality are Converging, and Why

Nowadays, breakthrough technologies, such as virtual reality (VR), augmented reality (AR), and Blockchain, have definitively attracted the attention of a huge number of investors worldwide. Although, at first glance, Blockchain (traditionally used for financial services) seems to have little to none to share with VR and AR (originally adopted for entertainment), in the last few years several use cases started to appear showing effective ways to integrate these technologies. In this article, an overview of opportunities investigated by current solutions combining VR, AR, and Blockchain will be discussed, showing how they allowed both companies and academic researchers cope with issues affecting traditional services and products in a rather heterogenous set of application domains. Opportunities that could foster the convergence of these technologies and boost them further are also discussed.