Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtualization and Migration at the Network Edge: An Overview

As for the Cloud, essential features of Fog Computing are both virtualization and the capability to migrate virtual environments among nodes. In this paper, we thoroughly report both the state-of-the-art virtualization and migration techniques and their available implementations. In particular, we investigate the aptness of such technologies for a specific layer of the Fog hierarchy, namely the network edge. Indeed, this layer presents some characteristics that distinguish it from the Cloud so that a virtualization or migration technique that represents a good compromise for the Cloud might not be likewise suitable for the edge.
Sonographic Evaluation of Some Abdominal Organs in Sickle Cell Disease Patients in a Tertiary Health Institution in Northeastern Nigeria

Background: Sonographic evaluation of abdominal organs is an important work up in managing sickle cell disease (SCD) patients. This study aimed at evaluating abdominal organs by sonography among SCD patients. Methodology: A cross-sectional study was carried out in Maiduguri, Nigeria from April 2014 to July 2015. Two hundred and fifty-two patients participated in the study, with 126 controls and 126 SCD patients. There were 131 (52%) males, and 121 (48%) females, with age range 3–38 years. Measurements were done using ultrasound machine with probe frequencies ranging from 1–4MHz and 4–9MHz in supine, right and left oblique positions for optimum visualization of the abdominal organs. Results: Participants within the age range of 10–15 years had the highest frequency with 88 (34.9%), followed by those within the age group of 17–23 years with 86 (34.1%), and the least were those within the age group of 30 years and above with 8 (3.2%). Hepatomegaly was found in 53 (75.7%), and increased echogenicity of the liver was found in 50 (94.3%) of the SCD patients, P = 0.000. Gallstones were found in 45 (17.9%), gallbladder sludge was found in 51 (21.4%) and thickened gallbladder wall was found in 84 (33.3%) of the SCD patients, P = 0.000. Autosplenectomy was found in 45 (17.9%), splenomegaly in 63 (24.9%), and calcified spleen in 18 (8.7%) of the SCD patients, P = 0.000. Enlarged kidneys in SCD patients were found in 61 (98.4%) and 63 (98.4%) on the right and left kidneys, respectively. Shrunken kidneys were found in 5 (2.0%) and 4 (1.6%) of the SCD patients on the right and left kidneys, respectively, P = 0.000. Conclusion: Abdominal sonography in SCD patients revealed varied remarkable changes in the size, echotexture, intraluminal deposits and wall thickness in the liver, gallbladder, kidneys, and spleen.
IoT Data Integrity Verification for Cyber-Physical Systems Using Blockchain

Blockchain technologies can enable decentralized and trustful features for the Internet of Things (IoT). Although, existing blockchain based solutions to provide data integrity verification for semi-trusted data storages (e.g. cloud providers) cannot respect the time determinism required by Cyber-Physical Systems (CPS). Additionally, they cannot be applied to resource-constrained IoT devices. We propose an architecture that can take advantage of blockchain features to allow further integrity verification of data produced by IoT devices even in the realm of CPS. Our architecture is divided into three levels, each of them responsible for tasks compatible with their resources capabilities. The first level, composed of sensors, actuators, and gateways, introduces the concept of Proof-of-Trust (PoT), an energy-efficient, time-deterministic and secure communication based on the Trustful Space-Time Protocol (TSTP). Upper levels are responsible for data persistence and integrity verification in the Cloud. The work also comprises a performance evaluation of the critical path of data to demonstrate that the architecture respect time-bounded operations demanded by the sense-decide-actuate cycle of CPSs. The additional delay of 5.894us added by our architecture is negligible for a typical TSTP with IEEE 802.15. 4 radios which has communication latencies in the order of hundreds of milisseconds in each hop.
A Data-Driven Approach for Spatio-Temporal Crime Predictions in Smart Cities

The steadily increasing urbanization is causing significant economic and social transformations in urban areas and it will be posing several challenges in city management issues. In particular, given that the larger cities the higher crime rates, crime spiking is becoming one of the most important social problems in large urban areas. To handle with the increase in crimes, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends, finalized to an efficient deployment of police officers over the territory and more effective crime prevention. This paper presents an approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and reliably forecast crime trends in each region. The final result of the algorithm is a spatio-temporal crime forecasting model, composed of a set of crime dense regions and a set of associated crime predictors, each one representing a predictive model for forecasting the number of crimes that will happen in its specific region. The experimental evaluation, performed on real-world data collected in a big area of Chicago, shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.
Exemplar Encoder-Decoder for Neural Conversation Generation

In this paper we present the Exemplar Encoder-Decoder network (EED), a novel conversation model that learns to utilize similar examples from training data to generate responses. Similar conversation examples (context-response pairs) from training data are retrieved using a traditional TF-IDF based retrieval model and the corresponding responses are used by our decoder to generate the ground truth response. The contribution of each retrieved response is weighed by the similarity of corresponding context with the input context. As a result, our model learns to assign higher similarity scores to those retrieved contexts whose responses are crucial for generating the final response. We present detailed experiments on two large data sets and we find that our method out-performs state of the art sequence to sequence generative models on several recently proposed evaluation metrics.
Luminescent Polynuclear Zn- and Cd-Ln Square-Like Nanoclusters With a Flexible Long-Chain Schiff Base Ligand

Two series of Zn-Ln and Cd-Ln nanoclusters [Ln4Zn8L2(OAc)20(OH)4] [Ln = Nd (1), Yb (2), and Sm (3)] and [Ln2Cd2L2(OAc)2(OH)2(OCH3)2] [Ln = Nd (4), Yb (5), and Sm (6)] were prepared using a long-chain Schiff base ligand with a flexible (CH2)2O(CH2)2O(CH2)2 chain. All these clusters show square-like structures. The Schiff base ligands show “linear” configurations in the structures of 1-6, and the metric dimensions of Zn-Ln and Cd-Ln clusters measure ~8 × 14 × 21 and 8 × 12 × 12 Å, respectively. The study of luminescence properties shows that the Zn/L and Cd/L chromophores can effectively transfer energy to the lanthanide ions, and 1-6 show visible and NIR emissions.
Existence of Variational Solutions in Noncylindrical Domains

We study gradient flows of integral functionals in noncylindrical bounded domains $E\subset\mathbb{R}^n\times[0,T)$. The systems of differential equations take the form $\partial_t u -{div} D_\xi f(x,u,Du) = -D_u f(x,u,Du)\ {on $E$}$, for an integrand $f(x,u,Du)$ that is convex and coercive with respect to the $W^{1,p}$-norm for $p>1$. We prove the existence of variational solutions on noncylindrical domains under the only assumption that $\mathcal{L}^{n+1}(\partial E)=0$, even for functionals that do not admit a growth condition from above. For nondecreasing domains, the solutions are unique and admit a time-derivative in $L^2(E)$. For domains that decrease the most with bounded speed and integrands that satisfy a $p$-growth condition, we prove that the constructed solutions are continuous in time with respect to the $L^2$-norm and solve the above system of differential equations in the weak sense. Under the additional assumption that the domain also increases the most at finite speed, we establish the u.
Comparisons of Deep Learning Algorithms for MNIST in Real-Time Environment

Recognizing handwritten digits was challenging task in a couple of years ago. Thanks to machine learning algorithms, today, the issue has solved but those algorithms require much time to train and to recognize digits. Thus, using one of those algorithms to an application that works in real-time, is complex. Notwithstanding use of a trained model, if the model uses deep neural networks it requires much more time to make a prediction and becomes more complicated as well as memory usage also increases. It leads real-time application to delay and to work slowly even using trained model. A memory usage is also essential as using smaller memory of trained models works considerable faster comparing to models with huge pre-processed memory. For this work, we implemented four models on the basis of unlike algorithms which are capsule network, deep residual learning model, convolutional neural network and multinomial logistic regression to recognize handwritten digits. These models have unlike structure and they have showed a great results on MNIST before so we aim to compare them in real-time environment. The dataset MNIST seems most suitable for this work since it is popular in the ﬁeld and basically used in many state-of-the-art algorithms beyond those models mentioned above. We purpose revealing most suitable algorithm to recognize handwritten digits in real-time environment. Also, we give comparisons of train and evaluation time, memory usage and other essential indexes of all four models.
Slim-DP: A Multi-Agent System for Communication-Efficient Distributed Deep Learning

To afford the huge computational cost, large-scale deep neural networks (DNN) are usually trained on the distributed system, especially the widely-used parameter server architecture, consisting of a parameter server as well as multiple local workers with powerful GPU cards. During the training, local workers frequently pull the global model and push their computed gradients from/to the parameter server. Due to the limited bandwidth, such frequent communication will cause severe bottleneck for the training acceleration. As recent attempts to address this problem, quantization methods have been proposed to compress the gradients for efficient communication. However, such methods overlook the effects of compression on the model performance such that they either suffer from a low compression ratio or an accuracy drop. In this paper, to better address this problem, we investigate the distributed deep learning as a multi-agent system (MAS) problem. Specifically, 1) local workers and the parameter server are separate agents in the system; 2) the objective of these agents is to maximize the efficacy of the learned model through their cooperative interactions; 3) the strategy of the agents describes how they take actions, i.e. communicate their computed gradients or the global model; 4) rational agents always select the best-response strategy with the optimal utility. Inspired by this, we design a MAS approach for distributed training of DNN. In our method, the agents first estimate the utility (i.e., the benefit to help improve the model) of each action (i.e., transferring a subset of the gradients or the global model), and then take the best-response strategy based on their estimated utilities mixed with e-random exploration. We call our new method Slim-DP as it, being different from the standard data-parallelism, only communicates a subset of the gradient or the global model. Our experimental results demonstrate that our proposed Slim-DP can reduce more communication cost and achieve better speedup without loss of accuracy than the standard data parallelism and its quantization version.
Improving the Boilerpipe Algorithm for Boilerplate Removal in News Articles Using HTML Tree Structure

It is well-known that the lack of quality data is a major problem for information retrieval engines. Web articles are flooded with non-relevant data such as advertising and related links. More over, some of these ads are loaded in a randomized way every time you hit a page, so the HTML document will be different and hashing of the content will be not possible. Therefore, we need to extract the relevant text of documents. The automatic extraction of relevant text in on-line text (news articles, etc.) is not a trivial task. There are many algorithms for this purpose described in the literature. Boilerpipe is one of the most popular one sand its performance is one of the best. In this paper, we improve the precision of the Boilerpipe algorithm using the HTML tree for selection of the relevant content. We make the experiments for the news articles. We evaluated our approach by extracting news from English and Spanish websites and compared the results with other approaches. Our approach achieved better results than approaches from the state-of-the-art. We also present an analysis of our dataset confirming that the amount of relevant text is less than 40%.
A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets

Technological advancements in the World Wide Web and social networks in particular coupled with an increase in social media usage has led to a positive correlation between the exhibition of Suicidal ideation on websites such as Twitter and cases of suicide. This paper proposes a novel supervised approach for detecting suicidal ideation in content on Twitter. A set of features is proposed for training both linear and ensemble classifiers over a dataset of manually annotated tweets. The performance of the proposed methodology is compared against four baselines that utilize varying approaches to validate its utility. The results are finally summarized by reflecting on the effect of the inclusion of the proposed features one by one for suicidal ideation detection.
Multiplicative noise removal via combining total variation and wavelet frame

ABSTRACT Total variation (TV) regularization has been proved effective for cartoon images restoration however it produces staircase effects, and properly wavelet frames were confirmed to provide a more smoothing approximation to the original image. In this paper, a new model for multiplicative noise removal was proposed, which combines wavelet frame-based regularization and TV regularization. A modified proximal linearized alternating direction method is developed to solve the proposed model, considering that adding a new regularization term to the TV model would yield more parameters, which will result in computational difficulties. For the new model, the existence of solution and the convergence property of the proposed algorithm are proved. Numerical experiments have proved that the proposed model has a superior performance in terms of the peak signal-to-noise ratio and the relative error values for non-piecewise constant images when compared with some state-of-the-art multiplicative noise removal models.
Achievable region with impatient customers

We consider a queueing system with heterogeneous agents. One class of agents demand immediate service, would leave the system if not provided. The second class of customers have longer job requirements and can wait for their turn. We discuss the achievable region of such a two-class system, which is the region of all possible pairs of performance metrics. Blocking probability is the relevant performance for eager/impatient class while the expected sojourn time is appropriate for the tolerant class. We obtain the achievable region, considering static policies that do not depend upon the state of the second class, using a conjecture of a pseudo conservation law. This law relates the blocking probability of eager customers with the expected sojourn time of the tolerant customers, in a fluid limit for eager customers. We validate the pseudo conservation law using two example families of static schedulers, by deriving their performance. Along the way, we obtain smooth control (sharing) of resources between such heterogeneous classes (e.g., voice and data calls of a communication system). We also demonstrate that the dynamic achievable region (obtained using state dependent policies) is strictly bigger than the static region, by deriving the performance of an example dynamic policy.
An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems

Human-computer conversation systems have attracted much attention in Natural Language Processing. Conversation systems can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (namely a query ) in a large conversational repository and return a reply that best matches the query. Generative approaches synthesize new replies. Both ways have certain advantages but suffer from their own disadvantages. We propose a novel ensemble of retrieval-based and generation-based conversation system. The retrieved candidates, in addition to the original query, are fed to a reply generator via a neural network, so that the model is aware of more information. The generated reply together with the retrieved ones then participates in a re-ranking process to find the final reply to output. Experimental results show that such an ensemble system outperforms each single module by a large margin.
Automatic Clone Recommendation for Refactoring Based on the Present and the Past

When many clones are detected in software programs, not all clones are equally important to developers. To help developers refactor code and improve software quality, various tools were built to recommend clone-removal refactorings based on the past and the present information, such as the cohesion degree of individual clones or the co-evolution relations of clone peers. The existence of these tools inspired us to build an approach that considers as many factors as possible to more accurately recommend clones. This paper introduces CREC, a learning-based approach that recommends clones by extracting features from the current status and past history of software projects. Given a set of software repositories, CREC first automatically extracts the clone groups historically refactored (R-clones) and those not refactored (NR-clones) to construct the training set. CREC extracts 34 features to characterize the content and evolution behaviors of individual clones, as well as the spatial, syntactical, and co-change relations of clone peers. With these features, CREC trains a classifier that recommends clones for refactoring. We designed the largest feature set thus far for clone recommendation, and performed an evaluation on six large projects. The results show that our approach suggested refactorings with 83% and 76% F-scores in the within-project and cross-project settings. CREC significantly outperforms a state-of-the-art similar approach on our data set, with the latter one achieving 70% and 50% F-scores. We also compared the effectiveness of different factors and different learning algorithms.
Cross-Domain Sentiment Classification with Target Domain Specific Information

The task of adopting a model with good performance to a target domain that is different from the source domain used for training has received considerable attention in sentiment analysis. Most existing approaches mainly focus on learning representations that are domain-invariant in both the source and target domains. Few of them pay attention to domain-specific information, which should also be informative. In this work, we propose a method to simultaneously extract domain specific and invariant representations and train a classifier on each of the representation, respectively. And we introduce a few target domain labeled data for learning domain-specific information. To effectively utilize the target domain labeled data, we train the domain invariant representation based classifier with both the source and target domain labeled data and train the domain-specific representation based classifier with only the target domain labeled data. These two classifiers then boost each other in a co-training style. Extensive sentiment analysis experiments demonstrated that the proposed method could achieve better performance than state-of-the-art methods.
Iron status and dietary iron intake in vegetarians.

BACKGROUND Iron is one of the nutrients that require special consideration in a plant-based diet. The widespread belief is that meat is the best source of iron and a vegetarian diet increases the risk of its deficiency. This conviction has been the subject of analysis in a growing number of scientific reports. OBJECTIVES The aim of this study was to assess the iron intake and iron metabolism in vegetarians and vegans compared to a control group. MATERIAL AND METHODS A total of 55 vegetarians and 36 healthy volunteers were studied. The following parameters were measured in serum: iron, ferritin, transferrin, transferrin receptor, and hepcidin-25, using the enzyme-linked immunosorbent assay (ELISA) method. The dietary iron intake was assessed by a 24-h dietary recall. RESULTS The mean daily intake (DI) of iron was significantly higher in the female vegan group compared to the control group. Iron, hepcidin-25, ferritin and transferrin receptor in serum remained within their normal ranges. The ferritin concentration was significantly decreased and that of transferrin significantly higher in both female groups and in the male vegan group. CONCLUSIONS The obtained results show that the studied parameters, excluding transferrin, remained within normal ranges. However, the ferritin concentration was significantly decreased in the female vegetarian group and also in both vegan groups. This may indicate low iron storage.
Visual pursuit of one’s own face in disorders of consciousness: a quantitative analysis

ABSTRACT Background: Eye behaviour is important to distinguish minimally conscious state (MCS) from vegetative state (VS). Objective: To search for conditions most suitable to characterize patients in MCS and in VS on quantitative assessment of visual tracking. Design: This is a cross-sectional study. Participants: In total, 20 patients in VS, 13 in MCS plus and 11 in MCS minus participated in this study. Setting: Neurorehabilitation Unit. Methods: Evaluation of eye behaviour was performed by infrared system; stimuli were represented by a red circle, a picture of a patient’s own face and a picture of an unfamiliar face, slowly moving on a personal computer (PC) monitor. Visual tracking on the horizontal and vertical axes was compared. Main outcome measures: The main outcome measures were proportion of on-target fixations and mean fixation duration. Results: The proportion of on-target fixations differed as a function of the stimulus in patients in MCS plus but not in other groups. Own face and unfamiliar face elicited a similar proportion of on-target fixations. Tracking along the horizontal axis was more accurate than that along the vertical axis in patients in both MCS plus and MCS minus. Fixation duration did not differ among the three groups. Conclusions: Horizontal visual tracking of salient stimuli seems particularly suitable for eliciting on-target fixations. Quantitative assessment of visual tracking can complement clinical evaluation for reducing diagnostic uncertainty between patients in MCS or VS.