Reviewerly/exHarmony huggingface dataset:
Online Live VM Migration Algorithms to Minimize Total Migration Time and Downtime

Virtual machine (VM) migration is a widely used technique in cloud computing systems to increase reliability. There are also many other reasons that a VM is migrated during its lifetime, such as reducing energy consumption, improving performance, maintenance, etc. During a live VM migration, the underlying VM continues being up until all or part of its data has been transmitted from source to destination. The remaining data are transmitted in an off-line manner by suspending the corresponding VM. The longer the off-line transmission time, the worse the performance of the respective VM. The above is because during the off-line data transmission, the VM service is down. Because a running VM's memory is subject to changes, already transmitted data pages may get dirtied and thus needing re-transmission. The decision of when suspending the VM is not a trivial task at all. The above is justified by the fact that when suspending the VM early we may result in transmitting off-line a significant amount of data degrading thus the VM's performance. On the other hand, a long waiting time to suspend the VM may result in re-transmitting a huge amount of dirty data, leading in that way to waste of resources. In this paper, we tackle the joint problem of minimizing both the total VM migration time (reflecting the resources spent during a migration) and the VM downtime (reflecting the performance degradation). The aforementioned objective functions are weighted according to the needs of the underlying cloud provider/user. To tackle the problem, we propose an online deterministic algorithm resulting in an strong competitive ratio, as well as a randomized online algorithm achieving significantly better results against the deterministic algorithm.
The quantified-self archive: documenting lives through self-tracking data

Purpose The purpose of this article is to examine the ways in which self-tracking data have meaning and value in and after the life of the creator, including how such data could become part of the larger historical record, curated in an institutional archive. In doing so, the article expands upon existing shared interests among researchers working in the areas of self-tracking, human–computer interaction and archival science. Design/methodology/approach A total of 18 people who had self-tracked for six months or more were recruited for the study. Participants completed a survey which gathered demographic data and characteristics vis-à-vis their self-tracking behavior. In-person semi-structured interviews were then conducted to ascertain the beliefs of the participants regarding the long-term use and value of personal quantified-self data. Findings The findings reveal the value that people place on self-tracking data, their thoughts on proper modes for accessing their archive once it moves from the private to the public space, and how to provide fidelity within the system such that their experiences are represented while also enabling meaning making on the part of subsequent users of the archive. Originality/value Today’s quantified-self data are generally embedded in systems that create a pipeline from the individual source to that of the corporate warehouse, bent on absorbing and extracting insight from a totality of big data. This article posits that new opportunities for knowing and for design can be revealed when a public interest rationale is appended to rich personalized collections of small data.
Using Feature Entropy to Guide Filter Pruning for Efficient Convolutional Networks

The rapid development of convolutional neural networks (CNNs) is usually accompanied by an increase in model volume and computational cost. In this paper, we propose an entropy-based filter pruning (EFP) method to learn more efficient CNNs. Different from many existing filter pruning approaches, our proposed method prunes unimportant filters based on the amount of information carried by their corresponding feature maps. We employ entropy to measure the information contained in the feature maps and design features selection module to formulate pruning strategies. Pruning and fine-tuning are iterated several times, yielding thin and more compact models with comparable accuracy. We empirically demonstrate the effectiveness of our method with many advanced CNNs on several benchmark datasets. Notably, for VGG-16 on CIFAR-10, our EFP method prunes 92.9% parameters and reduces 76% float-point-operations (FLOPs) without accuracy loss, which has advanced the state-of-the-art.
An Impossible Dialogue! Nominal Utterances and Populist Rhetoric in an Italian Twitter Corpus of Hate Speech against Immigrants

The paper proposes an investigation on the role of populist themes and rhetoric in an Italian Twitter corpus of hate speech against immigrants. The corpus had been annotated with four new layers of analysis: Nominal Utterances, that can be seen as consistent with populist rhetoric; In-out-group rhetoric, a very common populist strategy to polarize public opinion; Slogan-like nominal utterances, that may convey the call for severe illiberal policies against immigrants; News, to recognize the role of newspapers (headlines or reference to articles) in the Twitter political discourse on immigration featured by hate speech.
A note on constacyclic and skew constacyclic codes over the ring Zp[u, v]/hu 2 − u, v2 − v, uv − vui

For odd prime $p$, this paper studies $(1+(p-2)u)$-constacyclic codes over the ring $R= \mathbb{Z}_{p} [u,v]/\langle u^2-u,v^2-v,uv-vu\rangle$. We show that the Gray images of $(1+(p-2)u)$-constacyclic codes over $R$ are cyclic and permutation equivalent to a quasi cyclic code over $\mathbb{Z}_{p}$. We derive the generators for $(1+(p-2)u)$-constacyclic and principally generated $(1+(p-2)u)$-constacyclic codes over $R$. Among others, we extend our results for skew $(1+(p-2)u)$-constacyclic codes over $R$ and exhibit the relation between skew $(1+(p-2)u)$-constacyclic codes with the other linear codes. Finally, as an application of our study, we compute several non trivial linear codes by using the Gray images of $(1+(p-2)u)$-constacyclic codes over this ring $R$.
A Cell-detection-based Table-structure Recognition Method

If tables are automatically recognized to extract the numerical values in them, digital documents containing such tables can be augmented with graphs generated using the recognized tables. In this paper, we propose a cell-detection-based table-structure recognition method for such automatic graph generation from tables. In detecting cells in a table, ruled lines are crucial but do not necessarily surround all cells. We therefore propose a method to detect cells by estimating implicit ruled lines, where necessary, to recognize the table structure. We demonstrate the effectiveness of the proposed method by experiments using the ICDAR 2013 table competition dataset.
Shifting Spaces

Choreography includes much improvisation and situated decision-making. These embodied abilities have inspired recent trends in HCI to design systems for nuanced experiences of movement [1]. We look to defamiliarization as one tactic that can enable new perspectives in the creative process during otherwise familiar experiences. We became interested in more deeply analyzing the interaction between mover and choreographic systems when we discovered limitations when attempting to choreograph collaboratively with a variety of existing systems. This led us to look more closely at how defamiliarization has been used in existing human-computer interaction projects, to understand provocative interaction in a different domain. We then applied the same analysis to choreographic technology projects to confirm the creative options enabled by this framework. This paper presents a framework for co-creative systems that proposes analytical components of: Disorientation, Open-Play, Closed-Exploration, and Balanced Creativity. These components focus on design for choreography yet they can be applied to many creative domains. To test our framework, we also present a variety of speculative designs that would engage with a choreographer in the sensory exploration, movement generation, and composition processes.
Wearable health devices in the workplace: The importance of habits to sustain the use

The use of wearable health devices, such as activity trackers or biosensors, offers great potential for managing one's health levels. Since they are affordable and widely available, organizations are also taking advantage of these systems (referred to as physiolytics), seeing an opportunity to handle some health-related challenges in the workplace. However, once physiolytics are implemented in the workspace, organizations face difficulties in sustaining employees' participation. This is a major problem because, to generate data that can contribute to personal health empowerment, physiolytics need to be used in a continuous manner. Habits are recognized as significant factors in determining sustained use of physiolytics, but there are no precise investigations on how organizations can have an impact on this matter. Because habits are highly contextdependent, organizations have the opportunity to create favorable conditions so that participating employees develop a habituation towards the use of physiolytics. Accordingly, in this paper, we conduct a narrative review to critically evaluate reported evidence and, then, formulate concrete propositions to support wearable health devices' habituation. We therefore aim to provide practitioners with upstream interventions to concretely help them in increasing the success of physiolytics' implementation as well as open the way for further investigations in this strand of research.
Deep Learning Technique for Improving Data Reception in Optical Camera Communication-Based V2I

Recently, as the existing lighting infrastructures are replaced by LED lighting, optical camera communication (OCC) technology in vehicle-infrastructure communication (V2I) systems have been actively researched. In this paper, we introduce a method to improve data packet reception rate of OCC-based V2I by using deep learning based region-of-interest (ROI) detector. In the V2I environment, traffic lights on the roadside are ROIs that perform as transmitters sending OCC data, and should be detected at the receiving camera. If ROI detectors utilize a deep learning model, data can be extracted stably during actual driving. From the experiment result, it was found that data packet reception rate using deep-learning based ROI detection technique outperforms that of conventional method based on image differentiation.
A Knowledge Selective Adversarial Network for Link Prediction in Knowledge Graph

Knowledge Graphs (KGs) contain rich semantic information and are of importance to many downstream tasks. In order to enhance practical utilization of KGs, KG completion task, which is also called link prediction, is a newly emerging hot research topic. During KG embedding model training, negative sampling is a fundamental method for obtaining negative samples. Inspired by an adversarial learning framework KBGAN, this paper proposes a new knowledge selective adversarial network, named as KSGAN, using a knowledge selector for high-quality negative sampling to benefit link prediction. The performances of our model KSGAN are evaluated on three standard knowledge completion datasets: FB15k-237, WN18 and WN18RR. The results show that KSGAN outperforms a list of baseline models on all the datasets, demonstrating the effectiveness of the proposed model.
Hardening Deep Neural Networks via Adversarial Model Cascades

Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an adversary to produce erroneous outputs. Works on securing neural networks against adversarial examples achieve high empirical robustness on simple datasets such as MNIST. However, these techniques are inadequate when empirically tested on complex data sets such as CIFAR-10 and SVHN. Further, existing techniques are designed to target specific attacks and fail to generalize across attacks. We propose Adversarial Model Cascades (AMC) as a way to tackle the above inadequacies. Our approach trains a cascade of models sequentially where each model is optimized to be robust towards a mixture of multiple attacks. Ultimately, it yields a single model which is secure against a wide range of attacks; namely FGSM, Elastic, Virtual Adversarial Perturbations and Madry. On an average, AMC increases the model's empirical robustness against various attacks simultaneously, by a significant margin (of 6.225% for MNIST, 5.075% for SVHN and 2.65% for CIFAR-10 ). At the same time, the model's performance on non-adversarial inputs is comparable to the state-of-the-art models.
Morphological characterization of breast tumors using conventional B-mode ultrasound images

This work aims to develop and test a vendor-independent computer-aided diagnosis (CAD) system that uses conventional B-mode ultrasound images to distinguish between benign and malignant breast tumors. Three morphological features were extracted from 323 breast tumor lesions including the perimeter, regularity variance, and circularity range ratio. Lesions were segmented using the active contour method via semi- andfully-automated algorithms. Then, the support vector machine classifier was used to identify breast lesions. Results of the CAD system exhibited accuracies of 95.98% and 95.67%using the semi- and fully-automated segmentation, respectively. Based on the preliminary results, this CAD system with such unique combination of geometrical features shall improve the diagnostic decisions and may reduce the need of unnecessary needle biopsies.
Pseudo-normal PET Synthesis with Generative Adversarial Networks for Localising Hypometabolism in Epilepsies

[ $${}^{18}$$ F]fluorodeoxyglucose (FDG) positron emission tomography (PET) aids in the localisation of the epileptogenic zone in patients with focal epilepsy, especially when magnetic resonance imaging (MRI) is normal or non-contributory. We propose a two-stage deep learning framework to support the clinical evaluation of patients with focal epilepsy by identifying candidate regions of hypometabolism in [18F]FDG PET scans. In the first stage, we train a generative adversarial network (GAN) to learn the mapping between healthy [18F]FDG PET and T1-weighted (T1w) MRI data. In the second stage, we synthesise pseudo-normal PET images from T1w MRI scans of patients with epilepsy to compare to the real PET scans. Comparing the estimated pseudo-PET images to the true PET scans in healthy control data, our GAN produced whole-brain mean absolute errors of $$0.053 \pm 0.015$$ , outperforming a U-Net ( $$0.058 \pm 0.021$$ ) and a high-resolution dilated convolutional neural network ( $$0.060 \pm 0.024$$ ; all images scaled 0–1). In a sample of 20 epilepsy patients, we created Z-statistic images (with thresholding at +2.33) by subtracting the patient’s true PET scans from their estimated pseudo-normal PET images to identify regions of hypometabolism. Excellent sensitivity for lobar location of abnormalities ( $$92.9 \pm 13.1\%$$ ) was observed for the seven cases with MR-visible epileptogenic lesions. For the 13 cases with non-contributory MR, a lower sensitivity of $$74.8 \pm 32.3\%$$ was observed. Our method performed better than a statistical parametric mapping analysis. Our results highlight the potential of deep learning-based pseudo-normal [18F]FDG PET synthesis to contribute to the management of epilepsy.
Augmented Reality in Food production traceability – use case

The contemporary food supply chain should adequately provide information that consumers and other concerned bodies need to know such as variety of the food attributes, country of origin, way of production etc. This paper discusses the use of Augmented Reality (AR) applications for the needs of food traceability. The aim of this paper is to provide an update on the contemporary knowledge and scientific development of mobile augmented reality (MAR) in food production and packaging systems, and to identify future needs for bringing the Augmented Reality (AR) to the food market. By showing the case of a developed mobile app based on Augmented Reality, this paper gives an example of the usage of MAR in food packaging.
The KEEN Universe

There is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embedding (KGE) models have been proposed that learn low dimensional representations while trying to maintain the structural properties of the KGs such as the similarity of nodes depending on their edges to other nodes. KGEs can be used to address tasks within KGs such as the prediction of novel links and the disambiguation of entities. They can also be used for downstream tasks like question answering and fact-checking. Overall, these tasks are relevant for the semantic web community. Despite their popularity, the reproducibility of KGE experiments and the transferability of proposed KGE models to research fields outside the machine learning community can be a major challenge. Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability. The KEEN Universe currently consists of the Python packages PyKEEN (Python KnowlEdge EmbeddiNgs), BioKEEN (Biological KnowlEdge EmbeddiNgs), and the KEEN Model Zoo for sharing trained KGE models with the community. Resource Type: Software Framework License: MIT License Permanent URL: https://figshare.com/articles/The_KEEN_Universe/7957445 .
A set of parameters for automatically annotating a Sentiment Arabic Corpus

Purpose This paper aims to propose an approach to automatically annotate a large corpus in Arabic dialect. This corpus is used in order to analyse sentiments of Arabic users on social medias. It focuses on the Algerian dialect, which is a sub-dialect of Maghrebi Arabic. Although Algerian is spoken by roughly 40 million speakers, few studies address the automated processing in general and the sentiment analysis in specific for Algerian. Design/methodology/approach The approach is based on the construction and use of a sentiment lexicon to automatically annotate a large corpus of Algerian text that is extracted from Facebook. Using this approach allow to significantly increase the size of the training corpus without calling the manual annotation. The annotated corpus is then vectorized using document embedding (doc2vec), which is an extension of word embeddings (word2vec). For sentiments classification, the authors used different classifiers such as support vector machines (SVM), Naive Bayes (NB) and logistic regression (LR). Findings The results suggest that NB and SVM classifiers generally led to the best results and MLP generally had the worst results. Further, the threshold that the authors use in selecting messages for the training set had a noticeable impact on recall and precision, with a threshold of 0.6 producing the best results. Using PV-DBOW led to slightly higher results than using PV-DM. Combining PV-DBOW and PV-DM representations led to slightly lower results than using PV-DBOW alone. The best results were obtained by the NB classifier with F1 up to 86.9 per cent. Originality/value The principal originality of this paper is to determine the right parameters for automatically annotating an Algerian dialect corpus. This annotation is based on a sentiment lexicon that was also constructed automatically.
Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks

Nowadays, it is clear that the old mathematical models are incomplete because of the large size of image data set. For this reason, the Deep Learning models introduced in the field of image processing meet this need in the software field. In this study, Convolutional Neural Network (CNN) model from the Deep Learning Algorithms and the Optimization Algorithms used in Deep Learning have been applied to international image data sets. Optimization algorithms were applied to both datasets respectively, the results were analyzed and compared. The success rate was approximately 96.21% in the Caltech 101 data set, while it was observed to be approximately 10% in the Cifar-100 data set.
Peculiarity of the bit rot and link rot phenomena

Purpose Almost two decades after diagnosing the link rot phenomenon, and in spite of the universal character of the bit rot phenomenon, the need for reminding about these is still valid. This is because it appears that digital device users have become accustomed to them. The bit rot and link rot phenomena describe the inability to access specific contents or the inability to use specific digital resources. This paper aims to answer the question as to whether it is possible to eliminate the link rot and bit rot phenomena from the internet space. Design/methodology/approach The link rot and bit rot phenomena were characterised based on a review of various source materials, including the academic literature and internet materials. Findings The phenomena known as bit rot and link rot have already been well researched and described. Research has revealed the reasons for their occurrence, and also offered recommendations on how to minimise their occurrence. In the current digital ecosystem, however, it is not possible to completely eliminate the link rot and bit rot phenomena. Research limitations/implications One can forecast that the link rot phenomenon will intensify and the internet resources will be even more unstable, because more and more new, highest-level domains which are purchased are emerging. Originality/value The study compares the phenomena of bit rot and link rot, even though they are usually considered separately. Moreover, it presents the terms of “digital bumps”, which cause “digital tsunami”.
Releasing Earnings Distributions using Differential Privacy

The U.S. Census Bureau recently released data on earnings percentiles of graduates from post-secondary institutions. This paper describes and evaluates the disclosure avoidance system developed for these statistics. We propose a differentially private algorithm for releasing these data based on standard differentially private building blocks, by constructing a histogram of earnings and the application of the Laplace mechanism to recover a differentially-private CDF of earnings. We demonstrate that our algorithm can release earnings distributions with low error, and our algorithm out-performs prior work based on the concept of smooth sensitivity from Nissim et al. (2007).
Website Transformation of a Latin American Airline: Effects of Cultural Aspects and User Experience on Business Performance

This paper describes a real case of redesigning a Chilean airline's website, considering cultural elements, customer behavior, and general user experience. We analyzed usability issues and problems in the information architecture, such as a benchmarking for best practices in the current area. We considered Hofstede's cultural recommendations, and previous work, to propose the final design. Different experiments and more than 40 direct participants worked in the current proposal. After a year of the implementation, and more than 4,000 transactions, the company obtained significant improvements and a better position on the market.