Miracle-dz/UniFAR-FTUs huggingface dataset:
An Epidemiological Approach for Mobile Ad-Hoc Networks Monitoring

—MANETS are vulnerable to many types of attacks. Moreover, many challenges arise in the MANET management, such as dynamic network topology, limited bandwidth, storage capacity, battery life and processing power. In order to ensure high network performance, an important function of network management is monitoring. It consists in observing the operational states of the connected mobile nodes and controlling the application quality of service and prevent attacks. Indeed, malicious participants may disrupt the system through altering the collected data, reporting false measurements, defining new management policies or flooding false alarms. In this paper, an epidemic model is developed to ensure an efficient MANET monitoring. It will be useful in various contexts, to provide for instance design parameters of the MANET such that the number of malicious nodes always remains under control. Theoretical modeling and analysis of various situations are then provided, and simulations results on real case scenario are proposed.
UNEMPLOYMENT INSURANCE AND RESERVATION WAGES

The present paper examines the reservation wages reported by a large sample of unemployed individuals in the United States in May 1976. The majority of unemployed individuals report reservation wages that are at least as high as the wage they were paid on their last job. Approximately one-fourth of all job seekers required a wage that was at least 10 percent higher than the wage on their previous job. Our econometric evidence shows that the level of unemployment benefits relative to previous wages has a powerful effect on the individual’s reservation wage. A 10 percent increase in the U.I. replacement ratio increases the reservation wage by about 4 percent for job losers who are not on layoff and by somewhat less for other unemployment groups. These estimates imply that reducing net unemployment insurance benefits (by lowering gross benefits or by taxing unemployment benefits) could significantly lower the average duration of unemployment and the relative number of long duration spells of unemployment. Because of the non-linear response of the unemployment duration to the reservation wage, reducing a high unemployment insurance ratio by 10 percentage points is likely to have a greater impact on unemployment than reducing a low unemployment insurance ratio by 10 percentage points.
Comprehensive Evaluation of Algorithms for Unrestricted Graph Alignment

The graph alignment problem calls for finding a matching be-tween the nodes of one graph and those of another graph, in a way that they correspond to each other by some fitness measure. Over the last years, several graph alignment algorithms have been proposed and evaluated on diverse datasets and quality measures. Typically, a newly proposed algorithm is compared to previously proposed ones on some specific datasets, types of noise, and quality measures where the new proposal achieves superiority over the previous ones. However, no systematic comparison of the proposed algorithms has been attempted on the same benchmarks. This paper fills this gap by conducting an extensive, thorough, and commensurable evaluation of state-of-the-art graph alignment algorithms. Our results highlight the value of overlooked solutions and an unprecedented effect of graph density on performance, hence call for further work.
Different approaches to compute Hawking Black Holes Decay

This paper discusses the comments presented in “Yes, Stephen Hawking Lied To Us All About How Black Holes Decay” published by Forbes. It shows that there are actually two contributions to, or two ways to characterize, Hawking radiations: the one from particles pairs created on the horizon of the black hole, with one particles falling behind the horizon, and the one from particle pairs created between the horizon and infinity, which have one particle reach infinity. We thought it is worth making sure all realize this, and doing so, correcting the article.
Mathematical and statistical models and methods in reliability : applications to medicine, finance, and quality control

Reliability Models, Methods, and Optimization. - Reliability of Semi-Markov Systems with Asymptotic Merging Phase Space. - Nonlinearly Perturbed Stochastic Processes and Systems. - On a Copula for Failure Times of System Elements. - On One Method of Reliability Coefficients Calculation for Objects in Non-Homogeneous Event Flows. - A New Approach to Maintenance Optimization by Modeling Intensity Control. - Longitudinal Latent Markov Processes Observable Through an Invariant Rasch Model. - Dynamics of Dependence Properties for Lifetimes Influenced by Unobservable Environmental Factors. - On Alternative of Choice for a Prophylaxis Problem. - Optimal Incomplete Maintenance for Systems with Discrete Time-to-Failure Distribution. - A Gini-Type Index for Aging/Rejuvenating Objects. - Redundancy Analysis for Multi-state System: Reliability and Financial Assessment. - On the Reliability Modeling of Hierarchical Systems. - Statistical Methods in Reliability. - Parametric Estimation of Redundant System Reliability From Censored Data. - Assessing Accuracy of Statistical Inferences by Resamplings. - Change Point Estimation in Regression Models with Fixed Design. - A Model for Field Failure Prediction Using Dynamic Environmental Data. - Efficient Regression Estimation Under General Censoring and Truncation. - On Generalized Tests of Fit for Multinomial Populations. - Modeling and Scaling of Categorical Data. - Nonparametric Estimation and Testing the Effect of Covariates in Accelerated Life Time Models Under Censoring. - Nonparametric Estimation of Time Trend for Repairable Systems Data. - Confidence Region for Distribution Function from Censored Data. - Empirical Estimate with Uniformly Minimal d-Risk for Bernoulli Trials Success Probability. - Estimation of Archival Lifetime Distribution for Writable Optical Disks from Accelerated Testings. - Applications. - Ages in Reliability and Bio Systems, Interpretations, Control, and Applications. - Shocks in Mixed Populations. - Bayesian Estimation of Degradation Model Defined by a Wiener Process. - Benefits of Threshold Regression: A Case-Study Comparison with Cox Proportional Hazards Regression. - Optimal Stopping and Reselling of European Options. - Bayesian Modeling of Health State Preferences. - Information Measures in Biostatistics and Reliability Engineering. - Reliability Computer Tools. - Software System for Simulation and Research of Probabilistic Regularities and Statistical Data Analysis in Reliability and Quality Control. - Inverse Gaussian Model and Its Applications in Reliability and Survival Analysis.
Programmable multi-dimensional table filters for line rate network functions

The ability to filter entries in the data plane from a set or table of resources (e.g., network paths, servers, switch ports) based on multi-dimensional policies over stateful resource-specific metrics (e.g., filter paths with utilization < 0.6 and latency < 3us) is critical for several key network functions, such as performance-aware routing, resource-aware load balancing, network diagnosis, security and firewall. However, current generation of programmable switches do not support table-wide stateful filtering at line rate. We present Thanos, which augments the existing programmable switch pipeline with support for programmable multi-dimensional filtering over a set of resources. Thanos seamlessly integrates with multi-terabit programmable switch pipelines at nominal chip area overhead. Our evaluation, based on an FPGA prototype and a simulator, shows that policies expressed in Thanos can improve the performance of key network functions by up to 1.7× compared to state-of-the-art.
The Nós Project: Opening routes for the Galician language in the field of language technologies

The development of language technologies (LTs) such as machine translation, text analytics, and dialogue systems is essential in the current digital society, culture and economy. These LTs, widely supported in languages in high demand worldwide, such as English, are also necessary for smaller and less economically powerful languages, as they are a driving force in the democratization of the communities that use them due to their great social and cultural impact. As an example, dialogue systems allow us to communicate with machines in our own language; machine translation increases access to contents in different languages, thus facilitating intercultural relations; and text-to-speech and speech-to-text systems broaden different categories of users’ access to technology. In the case of Galician (co-official language, together with Spanish, in the autonomous region of Galicia, located in northwestern Spain), incorporating the language into state-of-the-art AI applications can not only significantly favor its prestige (a decisive factor in language normalization), but also guarantee citizens’ language rights, reduce social inequality, and narrow the digital divide. This is the main motivation behind the Nós Project (Proxecto Nós), which aims to have a significant contribution to the development of LTs in Galician (currently considered a low-resource language) by providing openly licensed resources, tools, and demonstrators in the area of intelligent technologies.
Landmarks in Case-Based Reasoning: From Theory to Data

Widespread application of uninterpretable machine learning systems for sensitive purposes has spurred research into elucidating the decision making pro- cess of these systems. These efforts have their background in many different disci-plines, one of which is the feld of AI & law. In particular, recent works have ob- served that machine learning training data can be interpreted as legal cases. Under this interpretation the formalism developed to study case law, called the theory of precedential constraint, can be used to analyze the way in which machine learning systems draw on training data – or should draw on them – to make decisions. These works predominantly stay on the theoretical level, hence in the present work the formalism is evaluated on a real world dataset. Through this analysis we identify a signifcant new concept which we call landmark cases, and use it to characterize the types of datasets that are more or less suitable to be described by the theory.
Metrics for Sustainability in Data Centers

Despite several calls from the community for improving the sustainability of computing, sufficient progress is yet to be made on one of the key prerequisites of sustainable computing---the ability to define and measure computing sustainability holistically. This position paper proposes metrics that aim to measure the end-to-end sustainability footprint in data centers. To enable useful sustainable computing efforts, these metrics can track the sustainability footprint at various granularities---from a single request to an entire data center. The proposed metrics can also broadly influence sustainable computing practices by incentivizing end-users and developers to participate in sustainable computing efforts in data centers.
Vision and Communication in Multi Agent Reinforcement Learning

Communication is an important element for improving performance in multi agent reinforcement learning. The study of Singh et al. (2019) proposed an algorithm called IC3Net which is capable of learning agents when to communicate in different scenarios. This research aims to confirm the results from Singh et al. (2019) and gain a better understanding about the relation between vision, communication and performance. This is done by installing the algorithm and replicating experiments from Singh et al. (2019). In addition, new experiments are conducted to investigate the influence of vision. These experiments make a comparison between trained models with poor and good vision. When replicating, the trained models show similar performance and communication. Furthermore, results show that models trained with better vision communicate less. Likewise, results show models with better vision achieve better performance. Interestingly, agents with sufficient vision choose to stop communication. These results confirm the results from Singh et al. (2019) and thus support their conclusion that IC3Net is able to learn when to communicate at scale. This research concludes that an increase in vision leads to less communication as agents are provided with another way of observing their environment. This also proves agents only communicate when necessary. Lastly, this research also concludes that an increase in vision results in an increase in performance, depending on the nature of the task.
THE EMERGENCE OF CRACK COCAINE AND THE RISE IN URBAN CRIME RATES

—Despite widespread popular accounts that link crack cocaine to inner-city decay, little systematic research has analyzed how the emergence of crack affected urban crime. We study this question using FBI crime rates for 27 metropolitan areas and two sources of information on when crack ﬁrst appeared in those cities. Using methods designed to control for unobserved differences among metropolitan areas, we ﬁnd that the introduction of crack had substantial effects on crime. In the absence of crack cocaine, the 1991 peak in urban crime rates would have been approximately 10% lower, remaining below the previous peak levels of the early 1980s.
Effect of ethanol extracts from Cnidoscolus aconitifolius leaves on foam cell formation and effect of leaf age and harvesting season on phytochemicals, antioxidant, cytotoxicity and nitric oxide production leaf age and harvesting season on

Effect of ethanol extracts from Cnidoscolus aconitifolius leaves on foam cell formation and effect of phytochemicals, antioxidant, cytotoxicity and nitric oxide production Cnidoscolus aconitifolius (Mill.) I.M. Johnst. or Chaya (Euphorbiaceae) leaf has been used as a food and a medicinal plant for treatment of hypertension. Foam cells are involved with an atherosclerosis that related with high level of LDL cholesterol and hypertension. Therefore, aims of this study were to investigate the effects of C . aconitifolius ethanolic extracts (CAE) on foam cell formation, nitric oxide (NO) and TNF- α . The differences in phytochemicals influencing from harvesting season and age stage of its leaves were also studied. Methods: Total phenolic and flavonoid contents were studied by using Folin-Ciocalteu and aluminum chloride reaction, respectively. Amount of kaempferol was analysed using HPLC. Antioxidative activity was determined by using DPPH and FRAP assay. In RAW264.7 cells, cytotoxicity, NO and TNF- α production were studied by using MTT assay, Griess reaction and ELISA, respectively. Foam cell formation was determined by using Oil Red O staining. Results: The difference in leaf age and harvesting season affected to its phytochemicals and antioxidative activity. Leaves that were harvested in dry season showed higher antioxidative activity, total phenolic and total flavonoid contents than those of leaves that were harvested in rainy season. Aging leaf extracts showed lower cytotoxicity than young and mature leaf extracts. All extracts exhibited inhibitory activity on NO production in LPS-induced RAW264.7 cells. Extracts that were harvested in dry season showed greater inhibitory activity on NO production than those of rainy season. Mature leaf extract harvested in dry season showed higher inhibitory effect on NO production (IC 50 of 66.16 ± 1.13 µg/mL) than those of the others. Co-treatment between mature leaf extract harvested in dry season and oxLDL could lower lipid accumulation and TNF- α production in foam cells than those of oxLDL treatment alone. These results indicate that CAE, especially mature leaf extract harvested in dry season, may reduce lipid accumulation in oxLDL-inducing foam cells via suppression of NO and TNF- α production. Conclusion: Mature leaf extract of C . aconitifolius that harvested in dry season had high antioxidants, low cytotoxicity, inhibitory activities on NO production, foam cell formation and TNF- α production. Therefore, C . aconitifolius leaf extract can be an alternative choice for health beneficial product development.
Condorcet solutions in frugal models of budget allocation

We study a voting model with incomplete information in which the evaluation of social welfare must be based on information about agents’ top choices plus general qualitative background conditions on preferences. The former is elicited individually, while the latter is not. We apply this ‘frugal aggregation’ model to multi-dimensional budget allocation problems, relying on the specific assumptions of convexity and separability of preferences. We propose a solution concept of ex-ante Condorcet winners which is widely and flexibly applicable and naturally incorporates the epistemic assumptions of particular frugal aggregation models. We show that for the case of convex preferences, the ex-ante Condorcet approach naturally leads to a refinement of the Tukey median. By contrast, in the case of separably convex preferences, the same approach leads to different solution, the 1-median, i.e. the minimization of the sum of the L1-distances to the agents’ tops. An algorithmic characterization renders the latter solution analytically tractable and efficiently computable.
DNA analysis by Southern blotting and linkage RFLP analysis (5,6). A second approach is based on mRNA analysis by amplification of HPRT cDNA. Samples

INTRODUCTION Lesch-Nyhan syndrome (LNS) is a severe X-linked disease typically due to the absence of the ubiquitous enzyme hypoxanthine-guanine-phosphoribosyltransferase (HPRT). The clinical phenotype of male patients is characterized by hyperuricemia, learning difficulties, self-injurious behavior (SIB), choreoathetosis, and spasticity; the female carriers are usually healthy. So far, only 5 female carriers have presented a classic LNS phenotype, probably owing to a nonrandom inactivation of the X chromosome carrying the normal allele (1). HPRT gene consists of 9 exons spanning approximately 44 kb at the Xq26 region and coding for a 219-aa protein that converts hypoxanthine into inosinic acid and guanine into guanylic acid. In the absence of HPRT, hypoxanthine is degraded into xanthine and uric acid in liver by xanthine oxidase (2). The first step of LNS diagnosis is the biochemical analysis of residual HPRT activity in patients’ erythrocytes; specific mutations are then discovered by molecular diagnosis by PCR analysis and direct sequencing of HPRT gene (3). Diagnosis of a healthy carrier is difficult because HPRT enzyme activity in blood cells is similar in carriers and healthy people; genetic tests can help reveal mutations only when both alleles are amplified by PCR (from genomic DNA or cDNA), while gross deletions involving the ends of cDNA are not detectable by PCR (4). So far, several approaches have been used to diagnose healthy carriers bearing large deletions. The first is based on DNA analysis by Southern blotting and linkage RFLP analysis (5,6). A second approach is based on mRNA analysis by amplification of HPRT cDNA. Samples from female carriers give rise to 2 PCR products and both of them can be sequenced (7). These methods are insufficient to identify the deletion of the whole HPRT gene or deletions including its extremities. A third approach is based on a protein study such as the biochemical test of HPRT activity on hair bulbs (8); this biochemical/statistical approach was completely abandoned after the discovery of false-negative results (9). The last approach is based on a cell-growth test in a selective medium. O’Neill (10) developed a test in which only HPRTdeficient lymphocytes could grow in 6thioguanine–enriched medium; the sensitivity and specificity of this test is about 99%. The method allows identification of carriers even if the mutation present in the patient’s family is unknown, and it provides highly accurate results despite being technically and analytically demanding. Analysis requires a fresh blood sample and has to be performed by highly experienced personnel in diagnostic laboratories with the specific equipment for the cell γ-irradiation. Here we introduce a new method to identify carriers with large deletion in Real-Time PCR and Linkage Studies to Identify Carriers Presenting HPRT Deleted Gene
Analyze Cyber Attacks in Cloud Cryptography and Short Comings in Blockchain Cryptocurrency

: Technological improvements have happened in companies digitalizing various elements of their actions. The threat landscape of cyber-attacks is rapidly changing. The possible consequence of such attacks is unknown because there is a lack of useful metrics, tools and frameworks to explain and evaluate the harm industries suffer from cyber-attacks. Cyber-attacks are not new to IoT, but as IoT will be strongly mixed in our days and communities, it is becoming important to step up and take cyber security severely. Hence, there is a real need to secure IoT, resulting in a need to comprehensively recognize the warnings and attacks on IoT infrastructure. The paper's main objective is to investigate the impact of the cyber-attacks in the common cyber framework and analyze the blockchain crypto-currency architecture to evaluate the weaknesses and safety representation. The security of cryptocurrency heavily relies on the incentive-compatible Proof of Authority (PoA) based on distributed consensus protocol; a consensus algorithm type based on the reputation of trusted parties in a blockchain network. This paper introduces Proof of Authority (PoA) based distributed consensus protocol and a novel blockchain-based decentralized federation model that embodies quality verification for cloud providers who lease computing resources from each other.
-ONLINE MATERIAL- Fat-Secreted Ceramides Regulate Vascular Redox State and Influence Outcomes in Patients with Cardiovascular Disease Brief title: Adipose C16:0-ceramide modulates vascular redox state

Nadia Akawi, PhD*a, b, Antonio Checa, PhD*c, Alexios S. Antonopoulos, MD, PhDa, Ioannis Akoumianakis, MD, PhDa, Evangelia Daskalaki, PhDc, Christos P. Kotanidis, MD, MSca, Hidekazu Kondo, MD, PhDa, Kirsten Lee, MDa, Dilan Yesilyurt, MSca, Ileana Badi, PhDa, Murray Polkinghorne, MD, MSca, Naveed Akbar, PhDa, Julie Lundgren, PhDd, e, Surawee Chuaiphichai, PhDa, Robin Choudhury, MD, PhDa, Stefan Neubauer, MD, PhDa, Keith M. Channon, MD, PhDa, Signe S. Torekov, MD, PhDd, e+, Craig E. Wheelock, PhDc+, Charalambos Antoniades, MD, PhDa+
Fuzzy Inference Based Pre-impact Fall Detection System Using Dynamic Threshold

Problems of falling are particularly vital safety in seniors. For these reasons, increasing a useful fall prevention approach is necessary to relieve the infliction of falls. This study focuses on a dynamic threshold model for real-time pre-impact fall detection that enables the falls to be identified before the body crashes to the ground. The velocity of head and chest position and the center of gravity of the subject body used for the feature combination classified by fuzzy inference for pre-impact fall detection. It only needs subjects to wear some tiny coin-size sensors that combine with the Kinect sensor, vision-based, without recording any data due to privacy issue. The dynamic threshold-based model with stereotypes suitable for an individual one, is applied for real-time fall and non-fall classification for the longest lead time of pre-impact fall detection. Moreover, the various kinds of integration of single, multiple, and triple Kinect combined with and without the wearable device are evaluated. The 14 rules of Sugeno fuzzy set defining the falling posture, movement transitions, and comparison of the different combination of devices are inferred first, whereas the final decision is produced through thinking and trigger on such fuzzy sets. The experimentation result found that the highest lead-time of pre-impact fall detection is 549.83 ms. However, the integration method that combined multiple Kinect with the wearable device can reduce camera overlapping and obscurity with the highest accuracy about 98.09 percent. Vice versa, the method using only multiple Kinect without the wearable device provide lower accuracy than 93.00 percent.