Reviewerly/exHarmony huggingface dataset:
Text classification problems via BERT embedding method and graph convolutional neural network

This paper presents a hybrid technique of combining the BERT embedding method and the graph convolutional neural network. This combination is then employed to solve the text classification problem. Initially, we apply the BERT embedding method to the whole corpus in order to transform all the texts into numerical vectors. Then, the graph convolutional neural network will be applied to these numerical vectors to classify these texts into their appropriate classes. Especially, in our approach, we need only a few labeled texts for the model training. For the illustration, in this paper, we use the BBC news and the IMDB movie reviews datasets to perform our experiments, showing that the performance of the graph convolutional neural network model is better than the performances of the combination of the BERT embedding method with other classical machine learning models.
QoE‐aware traffic monitoring based on user behavior in video streaming services

Summary In recent years, online video content has gained a lot of popularity and the exponential growth of video traffic continues in every area of the connected world. Thus, understanding the quality of experience (QoE) perceived by end‐users of video streaming services is important for both network operators and over the top providers since estimating end user's QoE has become one of the main points to meet user expectations. However, it is not trivial to ensure an adequate QoE since user experience is affected by various influencing factors (e.g., context factors, human factors, and system factors), and it is still challenging to identify the QoE key influencing factors. To address these challenges, we focused on improving QoE estimation and management strategies by exploiting valuable human and context information because the influence of social contextual information and user behavior on the perceptual quality is often neglected. In this article, we first proposed a classification of influence factors into four categories which are: system factors, human factors, context factors, and social‐behavioral factors. We developed a monitoring web application where video content is played to end‐users so that subjective and objective video metrics are collected. We built a new machine learning (ML) based model for QoE prediction. We used well‐known supervised ML algorithms like decision tree, k‐nearest neighbors, and support vector machine. Finally, we proposed a QoE management approach in the context of software defined network/multi‐access edge computing that implements the proposed QoE prediction model to optimize the video delivery transmission chain.
Visualizing Discord Servers

The last decade has seen the rise of global software community platforms, such as Slack, Gitter, and Discord. They allow developers to discuss implementation issues, report bugs, and, in general, interact with one another. Such real-time communication platforms are thus slowly complementing, if not replacing, more traditional communication channels, such as development mailing lists. Apart from simple text messaging and conference calls, they allow the sharing of any type of content, such as videos, images, and source code. This is turning such platforms into precious information sources when it comes to searching for documentation and understanding design and implementation choices. However, the velocity and volatility of the contents shared and discussed on such platforms, combined with their often informal structure, makes it difficult to grasp and differentiate the relevant pieces of information.We present a visual analytics approach, supported by a tool named DiscOrDance, which provides numerous custom views to support the understanding of Discord servers in terms of their structure, contents, and community. We illustrate DiscOrDance, using as running example the public Pharo development community Discord Server, which counts to date ∼180k messages shared among ∼2,900 developers, spanning 5 years of history. Based on our analyses, we distill and discuss interesting insights and lessons learned.
Weight Update Skipping: Reducing Training Time for Artificial Neural Networks

Artificial Neural Networks (ANNs) are known as state-of-the-art techniques in Machine Learning (ML) and have achieved outstanding results in data-intensive applications, such as recognition, classification, and segmentation. These networks mostly use deep layers of convolution and/or fully connected layers with many filters in each layer, demanding a large amount of data and tunable hyperparameters to achieve competitive accuracy. As a result, storage, communication, and computational costs of training (in particular time spent for training) become limiting factors to scale them up. In this paper, we propose a new training methodology for ANNs that exploits the observation of improvement of accuracy shows temporal variations which allow us to skip updating weights when the variation is minuscule. During such time windows, we keep updating bias which ensures the network still trains and avoids overfitting; however, we selectively skip updating weights (and their time-consuming computations). This training approach virtually achieves the same accuracy with considerably less computational cost and reduces the time spent on training. We developed two variations of the proposed training method for selectively updating weights, and call them as i) Weight Update Skipping (WUS), and ii) Weight Update Skipping with Learning Rate Scheduler (WUS+LR). We evaluate these two approaches by analyzing state-of-the-art models, including AlexNet, VGG-11, VGG-16, ResNet-18 on CIFAR datasets. We also use ImageNet dataset for AlexNet, VGG-16, and Resnet-18. On average, WUS and WUS+LR reduced the training time (compared to the baseline) by 54%, and 50% on CPU and 22%, and 21% on GPU, respectively for CIFAR-10; and 43% and 35% on CPU and 22%, and 21% on GPU, respectively for CIFAR-100; and finally 30% and 27% for ImageNet, respectively.
Getting grammars into shape for block-based editors

Block-based environments are visual programming environments that allow users to program by interactively arranging visual jigsaw-like blocks. They have shown to be helpful in several domains but often require experienced developers for their creation. Previous research investigated the use of language workbenches to generate block-based editors based on grammars, but the generated block-based editors sometimes provided too many unnecessary blocks, leading to verbose environments and programs. To reduce the number of interactions, we propose a set of transformations to simplify the original grammar, yielding a reduction of the number of (useful) kinds of blocks available in the resulting editors. We show that our generated block-based editors are improved for a set of observed aesthetic criteria up to a certain complexity. As such, analyzing and simplifying grammars before generating block-based editors allows us to derive more compact and potentially more usable block-based editors, making reuse of existing grammars through automatic generation feasible.
Performance Evaluation of Asynchronous FaaS

Function as a Service (FaaS) is a novel but dynamically emerging field of cloud computing. The majority of the leading cloud service providers have their own FaaS platforms, however, the open source community has embraced this technology, therefore an increasing number of FaaS alternatives can be deployed for on-premise use-cases. FaaS systems support both synchronous and asynchronous function invocations. In this paper we examine the differences in performance and billing between the two invocation types in OpenFaaS, Kubeless, Fission and Knative by using a simple function chain containing echo functions and a more complex image and natural language processing chain, implemented in Python3. We also present our solution, the implementation of asynchronous function invocations by using the Redis key-value store. Finally, we show how asynchronous function invocations avoid the negative effects on the billing when functions are cold-started.
A Battery-Free Long-Range Wireless Smart Camera for Face Recognition

In this demo we present a battery-free smart camera that exploits aggressive power management and energy harvesting to achieve face recognition in an energy-neutral fashion. A novel hardware accelerator for Convolution Neural Networks is employed to speed up the inference of the Tiny Machine Learning algorithm. The recognized face, and not the entire image, is sent via LoRa in a sensor network-like scenario. Experimental results demonstrated the capability of the developed sensor node to start and work perpetually with only a small photovoltaic panel array.
The convergence of Blockchain and Machine Learning for Decentralized Trust Management in IoT Ecosystems

The EU data strategy postulates that by 2025 there will be a paradigm shift towards more decentralized intelligence and data processing at the edge. The convergence of a large number of nodes at the IoT edge along with multiple service providers and network operators exposes data owners and resource providers to potential threats. To address cloud-edge risks, trust-based decentralized management is needed. Blockchain technology has created an opportunity to decentralize IoT ecosystems, through its intrinsic properties and together with machine learning (ML) it can be used to provide a trusted backbone for managing IoT ecosystems to support automated and adaptive trust management. This paper presents a novel approach for crosslayer intelligent trust computation modelling leveraging ML and Blockchain for decentralized trust management in IoT ecosystems. The effectiveness of the proposed approach for flow-based trust assessment is demonstrated using the Hyperledger Framework and the Cooja-based simulation environment. Finally, an initial evaluation is presented to understand the performance in terms of scalability and trust convergence of the proposed model.
Medical Image Transmission Using Novel Crypto-Compression Scheme

The transmission of medical records over indiscrete and open networks has caused an increase in fraud involving stealing patients’ information, owing to a lack of security over these links. An individual’s medical documents represent confidential information that demands strict protocols and security, chiefly to protect the individual’s identity. Medical image protection is a technology intended to transmit digital data and medical images securely over public networks. This paper presents some background on the different methods used to provide authentication and protection in medical information security. This work develops a secure cryptography-based medical image reclamation algorithm based on a combination of techniques: discrete cosine transform, steganography, and watermarking. The novel algorithm takes patients’ information in the form of images and uses a discrete cosine transform method with artificial intelligence and watermarking to calculate peak signal-to-noise ratio values for the images. The proposed framework uses the underlying algorithms to perform encryption and decryption of images while retaining a high peak signal-to-noise ratio value. This value is hidden using a scrambling algorithm; therefore, a unique patient password is required to access the real image. The proposed technique is demonstrated to be robust and thus able to prevent stealing of data. The results of simulation experiments are presented, and the accuracy of the new method is demonstrated by comparisons with various previously validated algorithms.
Do practitioners intentionally self-fix Technical Debt and why?

The impact of Technical Debt (TD) on software maintenance and evolution is of great concern, but recent evidence shows that a considerable amount of TD is fixed by the same developers who introduced it; this is termed self-fixed TD. This characteristic of TD management can potentially impact team dynamics and practices in managing TD. However, the initial evidence is based on low-level source code analysis; this casts some doubt whether practitioners repay their own debt intentionally and under what circumstances. To address this gap, we conducted an online survey on 17 well-known Java and Python open-source software communities to investigate practitioners' intent and rationale for self-fixing technical debt. We also investigate the relationship between human-related factors (e.g., experience) and self-fixing. The results, derived from the responses of 181 participants, show that a majority addresses their own debt consciously and often. Moreover, those with a higher level of involvement (e.g., more experience in the project and number of contributions) tend to be more concerned about self-fixing TD. We also learned that the sense of responsibility is a common self-fixing driver and that decisions to fix TD are not superficial but consider balancing costs and benefits, among other factors. The findings in this paper can lead to improving TD prevention and management strategies.
Deep transfer learning for blended source identification in galaxy survey data

We present B LEND H UNTER , a proof-of-concept deep-transfer-learning-based approach for the automated and robust identification of blended sources in galaxy survey data. We take the VGG-16 network with pre-trained convolutional layers and train the fully connected layers on parametric models of COSMOS images. We test the efficacy of the transfer learning by taking the weights learned on the parametric models and using them to identify blends in more realistic Canada-France Imaging Survey (CFIS)-like images. We compare the performance of this method to SEP (a Python implementation of SE XTRACTOR ) as a function of noise levels and the separation between sources. We find that B LEND H UNTER outperforms SEP by ∼15% in terms of classification accuracy for close blends (&lt; 10 pixel separation between sources) regardless of the noise level used for training. Additionally, the method provides consistent results to SEP for distant blends (≥10 pixel separation between sources) provided the network is trained on data with noise that has a relatively close standard deviation to that of the target images. The code and data have been made publicly available to ensure the reproducibility of the results.
Deep Reinforcement Learning for Dynamic Workflow Scheduling in Cloud Environment

As a service-oriented parallel distributed computing paradigm, cloud computing can tackle large-scale computing problem by cloud resources. A challenge to optimize cloud resource utilization is more efficient scheduling users’ requests (workflows). However, most of algorithms assume that cloud resources’ performance is always fixed, which is impractical due to the uncertainty during the task execution. In this paper, workflow scheduling considering the performance variation of cloud resources is studied aiming to minimize the makespan, which is formulated as a Markov Decision Process. And, a dynamic workflow scheduling approach based on deep reinforcement learning (RLWS) is proposed. In this approach, a complete solution is as the input, and neural network parameters are learned by iteratively local re-scheduling to optimize the solution. Actor critic in deep reinforcement learning is designed to train the neural network parameters by self-learning procedure. Experiment results confirm that the proposed algorithm can efficiently shorten the makespan.
Privacy-Preserving Randomized Controlled Trials

Randomized Controlled Trials, when feasible, give the strongest and most trustworthy empirical measures of causal effects. They are the gold standard in many clinical, social, and behavioral fields of study. However, the most important settings often involve the most sensitive data, therefore cause privacy concerns. In this paper, we outline a way to deploy an end-to-end privacy-preserving protocol for learning causal effects from Randomized Controlled Trials (RCTs). We are particularly focused on the difficult and important case where one party determines which treatment an individual receives, and another party measures outcomes on individuals, and these parties do not want to leak any of their information to each other, but still want to collectively learn a true causal effect in the world. Moreover, we show how such a protocol can be scaled to 500 million rows of data and more than a billion gates. We also offer an open source deployment of this protocol.
User eXperience (UX) Evaluation for MR Cultural Applications: The CEMEC Holographic Showcases in European Museums

Within the EU CEMEC project framework, a novel approach for using holographic showcases in museums has been conceived and experimented upon in different venues in the context of an itinerant exhibition dealing with Early Medieval European collections. The purpose of this holographic showcase, the so-called “box of stories”, is to improve the link and interaction between real and virtual contents in the museum’s context, making the exhibited object “alive” in the visitors’ perception. An Avar sword and a Byzantine treasure have been used as the main case studies, and they have been experienced in the museums of several European regions by audiences with different cultural backgrounds. This has been a great opportunity to carry out user experience (UX) evaluations in order to collect feedback (from about 600 museum visitors) regarding the attractiveness of such a mixed reality (MR) system, its usability, the comprehension of the contents, the efficacy of the logistics and environmental conditions, as well as the educational impact. The results of such inquiries helped the CNR ISPC team to identify the most meaningful User eXperience Analytics (UXA) able to support the work of UX evaluators and UX designers to assess the efficacy of digital cultural products. Indeed, this manuscript presents UXA and tries to draft a concrete and effective evaluation model for future digital projects for museum contexts.
Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification

In a frequency hopping spread spectrum (FHSS) network, the hopping pattern plays an important role in user authentication at the physical layer. However, recently, it has been possible to trace the hopping pattern through a blind estimation method for frequency hopping (FH) signals. If the hopping pattern can be reproduced, the attacker can imitate the FH signal and send the fake data to the FHSS system. To prevent this situation, a non-replicable authentication system that targets the physical layer of an FHSS network is required. In this study, a radio frequency fingerprinting-based emitter identification method targeting FH signals was proposed. A signal fingerprint (SF) was extracted and transformed into a spectrogram representing the time–frequency behavior of the SF. This spectrogram was trained on a deep inception network-based classifier, and an ensemble approach utilizing the multimodality of the SFs was applied. A detection algorithm was applied to the output vectors of the ensemble classifier for attacker detection. The results showed that the SF spectrogram can be effectively utilized to identify the emitter with 97% accuracy, and the output vectors of the classifier can be effectively utilized to detect the attacker with an area under the receiver operating characteristic curve of 0.99.
Learning Graphs From Smooth and Graph-Stationary Signals With Hidden Variables

Network-topology inference from (vertex) signal observations is a prominent problem across data-science and engineering disciplines. Most existing schemes assume that observations from all nodes are available, but in many practical environments, only a subset of nodes is accessible. A natural (and sometimes effective) approach is to disregard the role of unobserved nodes, but this ignores latent network effects, deteriorating the quality of the estimated graph. Differently, this paper investigates the problem of inferring the topology of a network from nodal observations while taking into account <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the presence of hidden (latent) variables</i> . Our schemes assume the number of observed nodes is considerably larger than the number of hidden variables and build on recent graph signal processing models to relate the signals and the underlying graph. Specifically, we go beyond classical correlation and partial correlation approaches and assume that the signals are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">smooth</i> and/or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stationary</i> in the sought graph. The assumptions are codified into different constrained optimization problems, with the presence of hidden variables being explicitly taken into account. Since the resulting problems are ill-conditioned and non-convex, the block matrix structure of the proposed formulations is leveraged and suitable convex-regularized relaxations are presented. Numerical experiments over synthetic and real-world datasets showcase the performance of the developed methods and compare them with existing alternatives.
Validating the Integrity of Audit Logs Against Execution Repartitioning Attacks

Provenance-based causal analysis of audit logs has proven to be an invaluable method of investigating system intrusions. However, it also suffers from dependency explosion, whereby long-running processes accumulate many dependencies that are hard to unravel. Execution unit partitioning addresses this by segmenting dependencies into units of work, such as isolating the events that processed a single HTTP request. Unfortunately, we discover that current designs have a semantic gap problem due to how system calls and application log messages are used to infer complex internal program states. We demonstrate how attackers can modify existing code exploits to control event partitioning, breaking links in the attack and framing innocent users. We also show how our techniques circumvent existing program and log integrity defenses.
Towards a Reinforcement Learning-based Exploratory Search for Mashup Tag Recommendation

The rapid increase in the number of online mashups requires better management and organization to facilitate mashup discovery, selection and recommendation. Tagging is one of the widely known and efficient ways to better manage and organize web services. Existing tagging methods are typically manual and tedious processes. On the other hand, current automatic tagging methods are either not very effective in describing their associated mashups or omit vital keywords that would enrich the set of candidates for tag selection. This paper presents a reinforcement learning (RL) method to automatically recommend tags for mashups. Our proposed method carries out effective exploratory actions to automatically extract suitable combinations of tags for mashups, through word vector similarities. Using our proposed method in a RL setup, we carry out experiments in an online mashup platform and evaluate our method with a real-world dataset from ProgrammableWeb <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Our method shows improved performance compared with state-of-the-art baselines.