Miracle-dz/UniFAR-FTUs huggingface dataset:
Fact-aware Abstractive Text Summarization using a Pointer-Generator Network

In this work we use a pointer-generator network for abstractive text summarization. In addition to generating tokens from a fixed vocabulary, the sequence-to-sequence model is able to copy certain passages from the source text to the output. We identified two issues: Repeated tokens or phrases in the generated summary and fact fabrication. Based on [8], the former is addressed by modifying the attention mechanism and the loss function of the model. We try to avoid fact fabrication by also supplying the model with extracted fact descriptions from the article as suggested in [4]. We train the model on the 100′000 provided Wikipedia article and summary pairs. The generated summaries are in most cases relevant to the corresponding article, grammatically correct and fluent. 1 SYSTEM DESCRIPTION Figure 1 shows a schematic overview of our system. We discuss the main components in the following subsections. articles Figure 1: Fact aware pointer-generator network. 1.1 Preprocessing Using spaCy1 we lowercase and tokenize articles and summaries. On the tokenized corpus, we construct a vocabulary of the 60′000 most frequent tokens. 1https://spacy.io/ As in [8], to speed up training and testing, each article is truncated to 400 tokens. By comparison: The mean and the median number of tokens of the provided Wikipedia articles is 708.83 and 583.00, respectively. Thus, we believe that this restriction does not have a big impact on the results. Furthermore, most relevant information will arguably appear towards the beginning of the article. 1.2 Pointer-Generator Network The core of our system is the pointer-generator network, based on a sequence-to-sequence model with attention, proposed by See et al. [8]. Refer to Figure 2 for an illustration. Unlike in [8], we initialize our learnable word embeddings with fastText embeddings [3] which were pretrained on Wikipedia. Sequence-to-Sequnce Model with Attention. Let hi and st be the encoder and decoder LSTM states, respectively. The attention distribution at at decoder time step t is computed as in Bahdanau et al. [2]: et i = v T tanh(Whhi +Wsst + battn) (1) at = softmax(et ) (2) The model is given the opportunity to learn the weights v,Wh ,Ws and battn such that at can be used to decide where in the article ‘to look’ when generating the output token at time t . The attention distribution is used to calculate the context vector h∗ t , a weighted sum of the encoder states hi .
MWBS: An Efficient Many-to-Many Wireless Big Data Delivery Scheme

Wireless big data raises the demands on the networking schemes to support the efficient group data sharing over heterogeneous wireless technologies, which take many-to-many data delivery as the foundation. Information-centric networking (ICN) approach is a promising networking technology to support big data delivery, which has the potential to establish the harmony between networking and wireless big data sharing. However, the existing ICN schemes have not carefully addressed the many-to-many communications. To address this issue, we propose an efficient and secure many-to-many wireless big data delivery scheme (MWBS) to provide group-based data dissemination and retrieval with name-integrated forwarding. In MWBS, a bi-directional tree is securely constructed for each group through the procedures of group initiation, join, leave, publication, and multi-level inter-zone routing. Especially, Designated Forwarding and Cacheable Nodes (DFCNs) are introduced to act as the roots for the construction of such bi-directional trees. The implementation details of MWBS are provided for function verifications. To effectively deploy MWBS, we investigate the impacts to the MWBS performance from the number and locations of DFCNs, which show that the optimized number of DFCNs can reduce the total traffic cost and the DFCN close to users is preferred to be selected for a group. Finally, simulations are performed to evaluate the performance of MWBS, which show that MWBS can reduce the control packet overhead and the state storage overhead compared to the existing ICN schemes.
AWorkbench to Analyze X . 509 in Applications

Global networking and secure digital communication have become basic needs for society, economy and individuals of our time. Security in digital communication signi cantly depends on digital certi cates, which allow to prove authenticity and establish trust between communication partners. These certi cates can only provide any security properties, if relying applications correctly validate them. The correct validation of certi cates poses a complex task to overcome by implementing applications. For this purpose, this thesis analyzes certi cate validation of X.509, the most prominent certi cate standard, in applications. Outcome of this thesis is an automated workbench, that allows for systematic and reproducible testing of certi cate validation in applications. Therefore, the standardized validation process is analyzed and its complexity is elaborated. Based on this analysis, test cases are derived which describe the correct validation behaviour that must be provided by applications. This thesis develops a concept for systematically testing certi cate validation in applications and designs a exible and extensible system approach for the automated workbench. This workbench and the identi ed test cases are implemented and evaluated. The certi cate validation behaviour of a selected set of applications and libraries is analyzed by placing them into the automated workbench. The evaluation of the test results show that the applications in question predominantly conducted correct certi cate validation: However, one of the applications examined accepts certi cates that were issued for a di erent usage, while another library in default con guration accepts certi cates that were issued for a di erent certi cate subject. While the rst weakness breaks the concept of certi cate usage restrictions, the second weakness potentially misleads developers into severe Man-in-the-Middle vulnerabilities, which endanger all protection goals.
Molecular Characterization of Lettuce mosaic virus Isolates in Ankara Province

Lettuce (Lactuca sativa) is an important vegetable crop with increasing economic value in Ankara province of Turkey. Many viruses cause diseases and economic losses in lettuce production and among them Lettuce mosaic virus (LMV), is one of the most common virus infection. In this present research, 324 symptomatic and asymptomatic lettuce samples were collected from five districts of Ankara province (Beypazarı, Çubuk, Ayaş, Nallıhan and Centrum) in MayJuly, 2015 and all of the collected samples were subjected to serological test of double antibody sandwich-enzyme linked immunosorbent assay (DAS-ELISA) and among them, 25 samples were identified as solely infected with LMV. All of the selected isolates were confirmed by reverse transcriptionpolymerase chain reaction (RT-PCR) assay. Coat protein gene specific primer pairs were used and amplified products were 800 bp long. Amplified cDNA fragments of Beypazari, Cubuk, Ayas isolates were sequenced for molecular characterization and the detection of genetic diversity of LMV Ankara isolates. Sequence comparisons showed 89-99%, 93-100 % and 57100% sequence identity at nucleotide, amino acid level and Nterminus regions, respectively, of the CP gene of LMV isolates. The CP gene of the LMV Ankara isolates were clustered with Western European, Latin American, North African isolates.
Evaluación de estrategias de binarización en la clasificación de imágenes usando deep learning

Resumen—El reconocimiento de objetos pequeños en imágenes con redes neuronales convolucionales (CNNs) sigue siendo un reto, especialmente cuando estos objetos se manipulan con la mano de forma muy similar. En este trabajo proponemos dividir un problema multiclase del ámbito de la seguridad en un problema de binarización de clases para obtener un mejor resultado. Nuestro objetivo ha sido obtener el mejor modelo de aprendizaje que distinga entre 6 clases, pistolas, smartphone, billete, monedero, tarjeta y background. Concretamente, evaluamos las técnicas One-Versus-All (OVA), One-Versus-One (OVO) y Distance-based Relative Competence Weighting combination para OVO (DRCW-OVO) basadas en CNNs. El mejor rendimiento se obtiene usando DRCW-OVO con una precisión de 90,47 %, un recall del 90,93 % y un F1 del 90,59 %. Esto significó una mejora del 2,58 % en precisión, 1,48 % en recall y 2,13 % en F1 frente a un multiclasificador normal.
Applications and Mass Fabrication of Carbon Nanotubes

III Production 6 III.1 Aggregates versus Aligned . 6 III.2 Laser Ablation . 6 III.3 Arc Discharge . 7 III.4 Chemical Vapour Deposition . 7 III.5 Catalysts . 8 III.6 Fixed Bed Reactor . 8 III.7 Fluidized bed Reactor . 8 III.8 HiPCo . 8
Moral Cognition and Its Basis in Social Cognition and Social Regulation

Human beings live complex social lives, composed of various types of relationships across nested social hierarchies, all structured by rights, rules, and obligations. However, selfish goals persist, and keeping individuals' goals in line with community interests has become the primary challenge of modern morality. To meet this challenge, human societies have developed two major social-cultural tools: a vast network of rules, norms, and values (Sripada & Stich, 2006; Ullmann-Margalit, 1977) and complex social practices of norm enforcement, such as blame, praise, apology, and reconciliation (Semin & Manstead, 1983). This kind of social-cultural morality has to be taught, learned, and enforced by community members, even by the youngest among them (Gockeritz, Schmidt, & Tomasello, 2014). Acquiring norms likely benefits from early-appearing preferences for proso-
Recent changes in the export pricing behaviour of the German manufacturing sector

We analyse recent changes in the export pricing patterns of the German manufacturing sector. Since the 1980s export prices had moved in lock step with domestic producer prices. However, we observe an astonishing divergence after around 2002: while the increase in domestic producer prices has strongly exceeded the increase in unit costs, export prices have been lagging behind unit costs, especially in export markets outside the euro area. We estimate a simple export pricing model econometrically for the period 1982:1-1990:12 and 1999:1-2009:12. We conclude that German exporters have fully passed through changes in the real exchange rate to export prices in the 1980s. By contrast, they followed a rather aggressive pricing-to-market strategy outside the euro area in the more recent period, but we do not find any evidence of pricing-to-market behaviour within the euro area for that period. We tentatively conclude that competitive pressures faced by the German exporting firms are rather strong outside the euro area, but relatively weak within the euro area, and even seem to be largely absent on the German domestic market.
Lens Physiological and Optical Alterations Precede the Appearance of Cataracts in Cx 46 fs 380 Mice

Citation: Minogue PJ, Gao J, Zoltoski RK, et al. Physiological and optical alterations precede the appearance of cataracts in Cx46fs380 mice. Invest Ophthalmol Vis Sci. 2017;58:4366– 4374. DOI:10.1167/iovs.17-21684 PURPOSE. Cx46fs380 mice model a human autosomal-dominant cataract caused by a mutant lens connexin46, Cx46. Lenses from Cx46fs380 mice develop cataracts that are first observed at ~2 months in homozygotes and at ‡4 months in heterozygotes. The present studies were conducted to determine whether Cx46fs380 mouse lenses exhibited abnormalities before there are detectable cataracts.
Why an Employee Leaves: Predicting using Data Mining Techniques

HR analyticss is the area of data analyticss helping the organization to understand its employees. Today companies face employee attrition as the major issue effecting the productive functioning of the organization. HR analyticss has enhanced the area of data analytics to an extent that organizations can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. Data mining is helping the HR department with methods to evaluate the historical data and predict the employee attrition, the baseline for this research. By far, employee attrition is predicted with the suggestions of the domain experts and the use of the classification methods by technical researchers. This research aims to investigate the extent to which ML techniques can help in predicting the employees who might leave, using the optimal hybrid ML models, where oversampling technique (SMOTE) & feature selection technique (SA) are integrated with the classication algorithms such as SVM & LR. The focus is towards the true positive accuracy predicted by the models. A comparison of these results was done between the features selected by the models in this research and the ones listed by the domain expert researchers in the past to see which one has the more reliable outcomes, concluding that the management expert should use the technical methods for their analysis in future to have reliable outcomes. This will help the HR system to adopt the right scenarios in real time & correctly predict the potential employees to leave & know why they do so.
DeepPhysio: Monitored Physiotherapeutic Exercise in the Comfort of your Own Home

This paper describes an action classification pipeline for detecting and evaluating correct execution of actions in video recorded by smartphone cameras; the use case is that of simplifying monitoring of how physiotherapeutic exercises are performed by patients in the comfort of their own home, reducing the need of physical presence of therapists. Our approach is based on applying DensePose to every frame of acquired video and subsequent sequence analysis by an LSTM network. We validate our proposed recognition approach on a subset of the NTU RGB+D dataset in order to determine the best classification pipeline for this application. We also describe a mobile, cross-platform application called DeepPhysio that is designed to allow at physiotherapy patients to obtain immediate feedback about the correctness of the physical exercises. Preliminary usability analysis shows that this type of application can be effective at monitoring physiotherapy exercises.
Document de travail du LEM / Discussion paper LEM 2017-22 version révisée Is strategic optimism good for long term policies ?

In this paper, I analyze the influence of strategic optimism on political myopia with respect to decisions on the budget composition. First, I show in a political economy model that the impact of optimism bias, defined as the difference between the incumbent ́s subjective and the objective probability of reelection, on the capital to current expenditure ratio is predicted to be negative i.e., strategic optimism increases short-term bias. This result is mitigated by strategic pessimism. Second, I test empirically this link by basing upon data from French departments and proxying optimism bias with budget forecast errors. The results of dynamic panel estimations confirm that optimistic revenue forecasts significantly tend to distort the budget composition in favor of current expenditures. This finding is shown to hold for different estimation approaches. JEL Classification: D72; D78; H68
Map Reduce Implementation for Malicious Websites Classification

Due to the rapid growth of the internet, malicious websites [1] have become the cornerstone for internet crime activities. There are lots of existing approaches to detect benign and malicious websites — some of them giving near 99% accuracy. However, effective and efficient detection of malicious websites has now seemed reasonable enough in terms of accuracy, but in terms of processing speed, it is still considered an enormous and costly task because of their qualities and complexities. In this project, We wanted to implement a classifier that would detect benign and malicious websites using network and application features that are available in a data-set from Kaggle, and we will do that using MapReduce to make the classification speeds faster than the traditional approaches. [2].
DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.
Parameter exploration of a neural model of state transition probabilities in model-based reinforcement learning

We explore the effects of parameters in our novel model of model-based reinforcement learning. In this model, spiking neurons are used to represent state-action pairs, learn state transition probabilities, and compute the resulting Q-values needed for action selection. All other aspects of model-based reinforcement learning are computed normally, without neurons. We test our model on a two-stage decision task, and compare its behaviour to ideal model-based behaviour. While some of these parameters have expected effects, such as increasing the learning rate and the number of neurons, we find that the model is surprisingly sensitive to variations in the distribution of neural tuning curves and the length of the time interval between state transitions.
Path planning and energy flow control of wireless power transfer for sensor nodes in wireless sensor networks

Abstract: In wireless sensor networks (WSNs), limited-capacity batteries are generally used for powering sensor nodes, where unbalanced electricity is the primary cause of premature death of WSNs. In this paper, wireless power transfer networks are used to power batteries in WSNs to avoid the problem of limited lifetime of traditional limited-capacity batteries. Furthermore, path planning based on the Dijkstra algorithm, which aims at minimizing the overall energy consumption in the network to improve system efficiency, is discussed in detail. Finally, three power transfer models with low contention level (LCL) topology for energy flow control are experimentally validated from the optimal path.
Private Money , Bank Operations , and Government Regulations

We study competition between inside and outside money in economies with trading frictions and Þnancial intermediation. We show that, claims on banks circulate if the redemption rate is low. When the quantity of Þat money is scarce, coexistence of inside and outside money dominates equilibria with a unique medium of exchange. If outside money is ample, banks choose to redeem claims in outside money, which increases welfare. Under binding reserve requirements, tightening monetary policy leads to credit rationing. Our results support recent trends toward lower reserve requirements. However, we also identify situations where restrictions on note issue are beneÞcial.
Morphometric characteristics of coconut crabs ( Birgus latro Linnaeus , 1767 ) in North Moluccas Province , Indonesia

Coconut crab (Birgus latro) is a fishery resource of crustacean class that lives on land with the largest size up to 4 kg. This research was conducted to reveal morphometric characteristics of the coconut crabs populations living in Daeo (Morotai Island), Laigoma (South Halmahera District) and Fitako (North Halmahera District), of North Moluccas Province. The morphometric characteristics measured included the claw length i.e. the ductyl length (DacL), propodus length (PL), carpus length (CL), and merus length (ML) on the first to the fourth walking leg on the left and right claws and carapace characters consisting of chest length, cephalotorax length with rostrum, cephalotorax length without rostrum and head length as well as body weight. The data analysis was conducted using SPSS program version 16 and Minitab version 18. The results showed that the average left claw characters have sizes larger than the right ones. The compared characters were not significantly different within or among the populations (p > 0.05) except for the character of propodus length of the fourth walking leg in Daeo population but significantly different in Fitako population (p < 0.05). However, the character cannot become the characteristic of the population phenotype because based on the cluster analysis, all characters of the males and females of coconut crabs are mixed together and based on the discriminant analysis, there is an occurrence of the sharing component of the morphometric character values compared.
Partnership for Advanced Computing in Europe Improving the scalability and applicability of Elmer finite element software for industrial problems

Recent developments of Elmer finite element solver are presented. The applicability of the code to industrial problems has been improved by introducing features for handling rotational boundary conditions with mortar finite elements. The scalability of the code has been improved by making the code thread-safe and by multithreading some critical sections of the code. The developments are described and some scalability results are presented.
Heavy metal contamination in surface soil dust at selected fuel filling stations in Accra , Ghana

Environmental studies have revealed the significant contributions of vehicular exhaust and nonexhaust emissions to high pollution levels in urban dwellings. The levels and sources of heavy metal contaminations at selected fuel stations in Accra have been investigated in this work. Soil dust samples collected from these fuel stations were analyzed for their elemental concentrations using Energy Dispersive X-ray Florescence (EDXRF). Fifteen (15) elements of interest were identified: Si, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, Sr, Zr and Pb. Contamination factors (CF), Pollution load index and Principal Component Analysis (PCA) were respectively used to verify the levels and sources of contamination of soil dust. Results obtained for the CF showed moderate contamination for V, Cr and Cu while Zn, and Pb were considerably contaminated. Principal Component Analysis identified four sources and their contributions to the elemental contents in the soil dust. Natural crust, vehicular exhaust emission, tyre wear and brake wear were the four sources identified.