Reviewerly/exHarmony huggingface dataset:
Understanding DevOps education with Grounded theory

DevOps stands for Development-Operations. It arises from the IT industry as a movement aligning development and operations teams. DevOps is broadly recognized as an IT standard, and there is high demand for DevOps practitioners in industry. Therefore, we studied whether undergraduates acquired adequate DevOps skills to fulfill the demand for DevOps practitioners in industry. We employed Grounded Theory (GT), a social science qualitative research methodology, to study DevOps education from academic and industrial perspectives. In academia, academics were not motivated to learn or adopt DevOps, and we did not find strong evidence of academics teaching DevOps. Academics need incentives to adopt DevOps, in order to stimulate interest in teaching DevOps. In industry, DevOps practitioners lack clearly defined roles and responsibilities, for the DevOps topic is diverse and growing too fast. Therefore, practitioners can only learn DevOps through hands-on working experience. As a result, academic institutions should provide fundamental DevOps education (in culture, procedure, and technology) to prepare students for their future DevOps advancement in industry. Based on our findings, we proposed five groups of future studies to advance DevOps education in academia.
Exploring Developer Preferences for Visualizing External Information Within Source Code Editors

Developers increasingly rely on external tools and services which causes development information to be scattered across different information silos. To access this information, developers need to access different applications, presentation files, and web services. This paper investigates mechanisms for incorporating external information sources into Integrated Development Environments using visual mechanisms to support common software development activities. Through a developer survey and a small experiment we find that developers prefer minimal representations for incorporating external information sources into their source code editors, and that they are able to use this information when performing their development tasks.
JAMPI: Efficient Matrix Multiplication in Spark Using Barrier Execution Mode

The new barrier mode in Apache Spark allows for embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage does not depend on any other tasks in the same stage, and hence it can be scheduled independently. However, several algorithms require more sophisticated inter-task communications, similar to the MPI paradigm. By combining distributed message passing (using asynchronous network IO), OpenJDK’s new auto-vectorization and Spark’s barrier execution mode, we can add non-map/reduce-based algorithms, such as Cannon’s distributed matrix multiplication to Spark. We document an efficient distributed matrix multiplication using Cannon’s algorithm, which significantly improves on the performance of the existing MLlib implementation. Used within a barrier task, the algorithm described herein results in an up to 24% performance increase on a 10,000 × 10,000 square matrix with a significantly lower memory footprint. Applications of efficient matrix multiplication include, among others, accelerating the training and implementation of deep convolutional neural network-based workloads, and thus such efficient algorithms can play a ground-breaking role in the faster and more efficient execution of even the most complicated machine learning tasks.
Is This What You Want? Looking for the Appropriate Digital Skills Set

Digital technology is the heart of the modern economy. Digital skills are then the reference point for many firms and workers. This means that employees should have skills to face change and at the same time have excellent technical preparation. Innovation and competitiveness of organizations are guided by today’s skills, the so-called twenty-first century digital skills (DS). In this paper we analyze the DS related to the digital transformation in an Italian firm operating in the manufacturing sector, observing that DS are not adequately distributed among the firm departments. In particular, the desiderata (job descriptions) seem often not aligned with the expectations of the managers (interviews).
Parallel Quantum Computation of Vibrational Dynamics

The vibrational dynamics in a linear triatomic molecule is emulated by a quantum information processing device operating in parallel. The quantum device is an ensemble of semiconducting quantum dot dimers addressed and probed by ultrafast laser pulses in the visible frequency range at room temperature. A realistic assessment of the inherent noise due to the inevitable size dispersion of colloidal quantum dots is taken into account and limits the time available for computation. At the short times considered only the electronic states of the quantum dots respond to the excitation. We show how up to 82 = 64 quantum logic variables can be realistically measured and used to process information. This is achieved by addressing the lowest and second excited electronic states of the quantum dots. With a narrower laser bandwidth (= longer pulse) only the lower band of excited states can be coherently addressed enabling 42 = 16 logic variables. Already this is sufficient to emulate both energy transfer between the two oscillators and coherent motions in the vibrating molecule.
Design of Wearable Wireless Body Area Network Monitoring System

Aiming at the disadvantage of poor real-time disease monitoring in traditional disease diagnosis and treatment mode, a wearable wireless body area network monitoring system is designed with low power consumption and high speed STM32 processor as the core. The system is composed of wireless sensor network, central monitoring module and mobile phone terminal. It has the functions of realtime acquisition, data storage, wireless transmission and remote monitoring for human vital signs. The testing results show that the system runs stably and the parameter meets the needs of the system setting. It can be used in remote medical system for realtime monitoring and early prevention.
The application of spatial domain in optimum initialization for clustering image data using particle swarm optimization

Clustering algorithms are affected by the initial seeds, therefore any improvement of the initialization process can improve the final clustering results. There exist several initialization algorithms that most of them are focused on using the distance and density based metrics defined in the feature space. However image space has a great potential to be used as the search space for initial seeds. In this research, developing clustering initialization using spatial information (image space) and spectral information (feature space) with the help of particle swarm optimization has been examined. Standard deviation and homogeneity of pixels in the image space in addition to distance and density of points in the feature space have been utilized in the objective function of the particle swarm optimization. Two different search spaces (feature and image spaces) and 26 objective functions have been applied to a simulated image and two real satellite multi-spectral images. Comparing the results of 26 cases with four prevailing initialization methods, demonstrated that searching for initial seeds in the image space using PSO with a full objective function (using four spectral–spatial criteria) can produce better results than the other tested cases. Using this case for k-means clustering initialization, led to about 20% improvement in overall accuracy relative to the clustering results with commonly used initialization algorithms.
Performance Analysis of Multilayer Perceptron in Profiling Side-Channel Analysis

In profiling side-channel analysis, machine learning-based analysis nowadays offers the most powerful performance. This holds especially for techniques stemming from the neural network family: multilayer perceptron and convolutional neural networks. Convolutional neural networks are often favored as results suggest better performance, especially in scenarios where targets are protected with countermeasures. Multilayer perceptron receives significantly less attention, and researchers seem less interested in this method, narrowing the results in the literature to comparisons with convolutional neural networks. On the other hand, a multilayer perceptron has a much simpler structure, enabling easier hyperparameter tuning and, hopefully, contributing to the explainability of this neural network inner working.We investigate the behavior of a multilayer perceptron in the context of the side-channel analysis of AES. By exploring the sensitivity of multilayer perceptron hyperparameters over the attack’s performance, we aim to provide a better understanding of successful hyperparameters tuning and, ultimately, this algorithm’s performance. Our results show that MLP (with a proper hyperparameter tuning) can easily break implementations with a random delay or masking countermeasures. This work aims to reiterate the power of simpler neural network techniques in the profiled SCA.
Story Semantic Relationships from Multimodal Cognitions

We consider the problem of building semantic relationship of unseen entities from free-form multi-modal sources. This intelligent agent understands semantic properties by creating (1) logical segments from sources, (2) finds interacting objects, (3) infers their interaction actions using (4) extracted textual, auditory, visual, and tonal information. The conversational dialogue discourses are automatically mapped to interacting co-located objects, and fused with their Kinetic action embeddings at each scene of occurrence. This generates a combined probability distribution representation for interacting entities spanning over every semantic relation class. Using these probabilities, we create knowledge graphs capable of answering semantic queries and infer missing properties in a given context.
Proofs of Ownership on Encrypted Cloud Data via Intel SGX

To deal with surging volume of outsourced data, cloud storage providers (CSPs) today prefer to use deduplication, in which if multiple copies of a file across cloud users are found, only one unique copy will be stored. A broadly used deduplication technique is client-side deduplication, in which the client will first check with the cloud server whether a file has been stored or not by sending a short checksum and, if the file was stored, the client will not upload the file again, and the cloud server simply adds the client to the owner list of the file. This can significantly save both storage and bandwidth, but introduces a new attack vector that, if a malicious client obtains a checksum of a victim file, it can simply claim ownership of the file. Proofs of ownership (PoWs) were thus investigated to allow the cloud server to check whether a client really possesses the file. Traditional PoWs rely on an assumption that the cloud server is fully trusted and has access to the original file content. In practice, however, the cloud server is not fully trusted and, data owners may store their encrypted data in the cloud, hindering execution of the traditional PoWs.
Designing a MOOC to prepare faculty members to teach on virtual learning environments in the time of COVID-19

The emergency caused by COVID-19 exposed significant challenges for Higher Education Institutions. Beyond the connectivity defiance and the technological infrastructure required to give continuity to the learning processes, professors' digital skills development has been an essential part in addressing these challenges. This work shows how the use of a MOOC can help professors to migrate from the classroom environment to the virtual one. The paper explores the phases and methodologies implemented in the MOOC designing. Results revealed that 90% of the participants demonstrated superior skills in the management of tele-education tools, 70% of them were capable to scheduled assignments and 60% designed create online exams properly after the training.
Adaptive Importance Sampling Unscented Kalman Filter With Kernel Regression for SAR Image Super-Resolution

Resolution enhancement of Earth’s images from synthetic aperture radars (SARs), used for applications that require scene interpretations and detailed analysis, fails due to the presence of inherent speckle noise. An inexpensive alternative solution to the problem is to use super-resolution (SR) algorithms that deal with speckle. A novel approach to augment kernel regression into the Adaptive Importance Sampling Unscented Kalman Filter (AISUKF) framework for SAR image SR has been presented in this letter. We have experimented with three different nonlinear kernel regressions, namely, arc-cosine kernel, radial basis function kernel, and steering kernel (SK) regressions. Empirical results suggest that AISUKF with SK regression is more appropriate for the abovementioned SR problem resulting in a better denoised and more detail-preserved output.
Time Series Anomaly Detection with Variational Autoencoder Using Mahalanobis Distance

Two themes have dominated the research on anomaly detection in time series data, one related to explorations of deep architectures for the task, and the other, equally important, the creation of large benchmark datasets. In line with the current trends, we have proposed several deep learning architectures based on Variational Autoencoders that have been evaluated for detecting cyber-attacks on water distribution system on the BATADAL challenge task and dataset. The second research aim of this study was to examine the impact of using Mahalanobis distance as a reconstruction error on the performance of the proposed models.
Smarter Smart Contracts: Efficient Consent Management in Health Data Sharing

The healthcare industry faces serious problems in data fragmentation and insufficient data sharing between patients, healthcare service providers and medical researchers. At the same time, patients' privacy must be protected, and patients should have authority over who can access their data. Researchers have proposed blockchain-based solutions to health data sharing, using blockchain for consent management. However, the implementation of the smart contracts that underpin these solutions has not been studied in detail. In this paper, we develop a blockchain-based framework for consent management in interorganizational health data sharing. We study the design of smart contracts that support the operation of our framework and evaluate its efficiency based on the execution costs on Ethereum. Our design improves on those previously proposed, lowering the computational costs of the framework significantly. This allows the framework to operate at scale and is more feasible for widespread adoption. Additionally, we introduce a novel contract that supports searching for patients in the framework that match certain criteria. This feature would be useful to medical researchers looking to obtain patient data.
Risk and Architecture Factors in Digital Exposure Notification

To effectively trace the infection spread in a pandemic, a large number of manual contact tracers are required to reach out to all possible contacts of infected users. Exposure notification, a.k.a. digital contact tracing, can supplement manual contact tracing to ease the burden on manual tracers and to digitally obtain accurate contact information. We study how risk emerges in security, privacy, architecture, and technology aspects of exposure notification systems. We provide potential overhead in using Bluetooth-based systems and discuss the architectural support required for other types of systems, and we wrap up with a discussion on architecture aspects to support these solutions.
Arc-Type and Tilt-Type: Pen-based Immersive Text Input for Room-Scale VR

As immersive, room-scale virtual and augmented reality become more utilized in productive workflows, the need for fast, equally-immersive text input grows. Traditional keyboard interaction in these room-scale environments is limiting because of its predominantly-seated usage and the necessity for visual indicators of the hands and keys potentially breaking immersion. Pen-based VR/AR interaction presents an alternative immersive text input modality with high throughput. In this paper, we present two novel interfaces designed for a pen-shaped stylus that do not require the positioning of the controller within a region of space, but rather detect input from rotation and on-board buttons. Initial results show that compared with Controller Pointing text entry techniques, Tilt-Type was slower but produced fewer errors and was less physically demanding. Additional studies are needed to validate the results.
Multimodel Sentiment Analysis with Self-attention

Sentiment analysis is an important technique to analyze and evaluate users’ opinions (e.g., feedbacks for online services). The widely use of online social media allows users to express their opinions in a more flexible way (e.g., from text to video). Multimodal sentiment analysis is therefore needed. Existing multimodal sentiment analysis solutions do not consider the coordination between different multimodal signals and connection between utterances. As a result, the contribution of nonverbal behavior to sentiment analysis is not well understood. To address this issue, we propose an attention framework based on multi-head self-attention to predict utterance-level sentiment concerning the context information. By fusing three modal representations with attention mechanism, our model can learn the contributing features amongst them. We evaluate our proposed approach using the CMU-MOSI dataset. The experimental results show that our model outperforms existing solutions.
The Social Engineer

As system infrastructures are becoming more secure against technical attacks, it is more difficult for attackers to overcome them with technical means. Social engineering instead exploits the human factor of information security and can have a significant impact on organizations. The lack of awareness about social engineering favors the successful realization of social engineering attacks, as employees do not recognize them as such early enough, resulting in high costs for the affected company. Current training approaches and awareness courses are limited in their versatility and create little motivation for employees to deal with the topic. The high immersion of virtual reality can improve learning in this context. We created The Social Engineer, an immersive educational game in virtual reality, to raise awareness and to sensitize players about social engineering. The player impersonates a penetration tester and conducts security audits in a virtually simulated company. The game consists of a detailed game world containing three distinct missions that require the player to apply different social engineering attack methods. Our concept enables the game to be highly extensible and flexible regarding different playable scenarios and settings. The Social Engineer can potentially benefit companies as an immersive self-training tool for their employees, support security experts in teaching social engineering awareness as part of a comprehensive training course, and entertain interested individuals by leveraging fun and innovative gameplay mechanics.
How to Securely Collaborate on Data: Decentralized Threshold HE and Secure Key Update

Threshold homomorphic encryption (Threshold HE) schemes are modified homomorphic encryption schemes to be suitable for privacy-preserving data integration and analysis. In actual usage of it, one should take it care into consideration who manages secret keys. In Eurocrypt 2012, Asharov et al. proposed decentralized (n, n)-threshold HE schemes in bottom-up approach for which all n parties must allow by doing a partial decryption to decrypt successfully a ciphertext. To support more general threshold structure for HE, Boneh et al. presented (t, n)-threshold HE schemes using secret sharing schemes in top-down approach with a central key dealer. In this article, decentralized (t, n)-threshold HE schemes in bottom-up approach will be constructed. The decentralized (n, n)-threshold HE scheme is fisrt modified to reduce the error contained in the common evaluation key which affects to the entire parameter size. Then by applying (t, n)-threshold secret sharing scheme, (n, n)-threshold HE scheme is converted to (t, n)-threshold HE scheme. Moreover, proactive secret sharing scheme is applied to update secret key share of the constructed (t, n)-threshold HE scheme whenever needed.
Handwriting Recognition of Hiragana Characters using Convolutional Neural Network

Hiragana is one of the basic types of letters used in Japanese writing. This research proposes the method of recognizing Hiragana's writing characters using the Convolutional Neural Network (CNN) method. At the preprocessing stage, the segmentation process is carried out using the thresholding method to segment, followed by the process of noise removal, resize, and cropping for image normalization. In the CNN training process, maxpooling methods and danse functions are used for the fully connected process. Whereas in the testing phase the accuracy of using the Adam Optimizer tool. By using 1000 image datasets consisting of 50 characters, each with 50 samples, and with a composition of 950 training data and 50 testing data, the accuracy is 95%. This proves that the CNN method has a good performance for Hiragana character recognition.
Healthcare Framework for Smarter Cities with bio-sensory data

In recent years, innovations that occurred in technology caused an evolution in computational and communicational areas. These advancements allowed us to apply some capabilities of smartphones, smart devices, and wearable devices as well. Sensory data, produced by smart devices are essential for implementing a cognitive, scalable, autonomous, and extensible Smart City architecture. Based on it, we propose an architecture which could extend Smart City solutions in a way that recognizes new data sources and applies data categorization rules without introducing any side effects for maintenance while protecting data privacy definition that complies with the GDPR and providing an anonymized data repository that is publicly available for the client consumption to further produce useful, value-added data based on analytics. Considering the evolution of smart devices, it becomes a necessity to provide an architecture definition such as microservice orchestration that allows an easier adaption of new data types and domain requirements to keep the ever-maintainable state of the Smart City architecture in a technology-agnostic fashion. Our paper shows a small fragment of this field on a focus on eHealth.
BlackMirror: Preventing Wallhacks in 3D Online FPS Games

Online gaming, with a reported 152 billion US dollar market, is immensely popular today. One of the critical issues in multiplayer online games is cheating, in which a player uses an illegal methodology to create an advantage beyond honest game play. For example, wallhacks, the main focus of this work, animate enemy objects on a cheating player's screen, despite being actually hidden behind walls (or other occluding objects). Since such cheats discourage honest players and cause game companies to lose revenue, gaming companies deploy mitigation solutions alongside game applications on the player's machine. However, their solutions are fundamentally flawed since they are deployed on a machine where the attacker has absolute control.