Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability , August 2016 Investigating Anonymous and Secure Fault-Tolerable Routing Protocols for Overlay Networks

Anonymity is important for both data requests and responses in IoT applications running on overlay networks. Overlay routing reveals information since identities are required to locate and obtain sensor data from nodes (i.e. IoT devices). An anonymous and secure fault-tolerable routing protocol is hence required to provide anonymity against adversaries and tolerance of node failures. In this paper, we discuss and compare the existing anonymous systems in terms of key management, storage cost and anonymity. To secure a communication session, confidentiality and integrity should be provided. Layered encryption algorithms are then recommended to secure messages flowing through an overlay network. To provide anonymity, it is ideal to have every node along the path only know its previous and next hops, so that an intermediate node can identify neither the initiator nor the responder. To guarantee fault tolerance, grouping nodes within a certain distance or with the same prefix is recommended. Thus, any node on the routing path can easily take over message forwarding if any of its group nodes is detected inactive or failed.
Methods with Preference from HTN Planning Instances

The hierarchical task network (HTN) planning technique is used in a growing number of real-world applications. However in many domains, such as the logistics domain, as there exist thousands of cases, it is difficult and time-consuming for humans to specify all HTN methods to cover all desirable plans. This suggests that it is important to learn HTN methods to accomplish the tasks via decomposition. The traditional HTN-method learning approaches require complete executable plans and annotated tasks, which are often difficult to acquire in real-world applications. In this paper, we propose a novel framework to learn HTN methods from HTN instances with incomplete method sets and without annotated tasks. Besides, previous approaches demand total orders on the subtasks in the methods while our approach is capable of learning methods with partial orders. To reduce the number of methods learned, we consider priorities on methods and compute the minimal set of methods based on prioritized preferences. By taking experiments on three wellknown planning domains, we demonstrate that our approach is effective, especially on solving new HTN problems.
FROM TRADITIONAL AGRICULTURE TO SMART AGRICULTURE : A REVIEW OF AGRICULTURE SECTOR IN DIMAPUR DISTRICT IN NAGALAND

Agriculture has been the backbone of the Indian economy for almost 5 decades and people living in rural areas and hinterland are mainly dependent on agriculture and its allied activities to earn their livelihoodsto which north east is no exception. This problem is compounded by seasonal variability in rainfall, shortage of water in rivers, flash floods, lack of traditional knowledge and lack of interest in youth. As technical advancements have failed to make inroads in agriculture sector in Nagaland, this sector is under huge pressure due to increase in population. Out of 40 per cent workers only 7 per cent are cultivators and 2 per cent agriculture labourers. Due to very less productivity in traditional farming many people are diversifying their agriculture towards more income-oriented vegetable cultivation and horticulture. Other than traditional income sources people are diversifying towards livestock, forestry and handicraft forfinancial security and stability. The present paper examines the impact of agriculture and allied activities on livelihoods of the people and suggests the possible interventions to make the livelihoods more resilient by shifting the people from traditional agriculture to smart agriculture.
Comparing Clang Abstract Syntax Trees using String Kernels

Abstract Syntax Trees (ASTs) are intermediate representations widely used by compiler frameworks. One of their strengths is that they can be used to determine the similarity among a collection of programs. In this paper we propose a novel comparison method that converts ASTs into weighted strings in order to get similarity matrices and quantify the level of correlation among codes. To evaluate the approach, we leveraged the corresponding strings derived from the Clang ASTs of a set of 100 source code examples written in C. Our kernel and two other string kernels from the literature were used to obtain similarity matrices among those examples. Next, we used Hierarchical Clustering to visualize the results. Our solution was able to identify different clusters conformed by examples that shared similar semantics. We demonstrated that the proposed strategy can be promisingly applied to similarity problems involving trees or strings.
Topic for a Bachelor/Master’s Thesis: An empirical comparison of active learning methods based on uncertainty sampling

Various strategies for active learning have been proposed in the machine learning literature [1]. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are mostly of a probabilistic nature. Yet, various alternative measures of uncertainty have been proposed in the recent years [2].
A Tool for the Integration of Constraint Logic Programming in Spreadsheets

Spreadsheets have become widely used tools, but they are applied to increasingly complex problems, far beyond the kind of tasks for which they were originally conceived. This often results in large, hard to maintain sheets, with little guarantee about their correctness. Potential errors are due in part to unskilled users and also to the spreadsheet systems’ own limitations. This contribution presents a tool that aims at improving the usual working flow when filling in a spreadsheet. The tool integrates a constraint solver based on transformations of the cell content into equality and inequality systems over rationals. The transformed systems are then solved using constraint logic programs and the obtained solutions are presented to the user in an understandable way. One of the practical benefits of this solution based on constraint logic programming is backwards execution: our tool is able to find out the required input values to reach the desired outputs depending on aggregation formulae written in the cells. Also, the constraint model offers a simple and sound solution to the problem of circular references in cell formulas.
QRAT Polynomially Simulates ∀-Exp+Res

The proof system ∀-Exp+Res formally captures expansionbased solving of quantified Boolean formulas (QBFs) whereas the QRAT proof system captures QBF preprocessing. From previous work it is known that certain families of formulas have short proofs in QRAT but not in ∀-Exp+Res. However, it was not known if the two proof systems were incomparable (i.e., if there also existed QBFs with short ∀-Exp+Res proofs but without short QRAT proofs), or if QRAT polynomially simulates ∀-Exp+Res. We close this gap of the QBF-proof-complexity landscape by presenting a polynomial simulation of ∀-Exp+Res in QRAT. Our simulation shows how definition introduction combined with extendeduniversal reduction can mimic the concept of universal expansion.
Instrumented Posterior Lumbar Interbody Fusion in Grade I Degenerative Spondylolisthesis : Comparison With Soft Stabilization With an Artificial Intervertebral Ligament

Background The purpose of this retrospective study was to evaluate the efficacy of soft stabilization with an artificial intervertebral ligament after microdecompression for the treatment of grade I degenerative spondylolisthesis. Methods From a total of 54 patients with degenerative spondylolisthesis who were treated surgically from May 2000 to April 2003, 36 patients who showed grade I spondylolisthesis without evidence of concomitant disc herniation necessitating discectomy were enrolled in the study. After decompression, the patients had undergone either soft stabilization with an artificial intervertebral ligament (n = 17) or instrumented posterior lumbar interbody fusion (PLIF; n = 19). Results The average follow-up period was 24 months for the PLIF group and 16 months for the soft stabilization group. In the PLIF group, preoperative mean scores of 60% on the Oswestry Disability Index, 8.8 on the visual analog scale (VAS) for low-back pain, and 9.3 on the VAS for leg pain improved to 28%, 4.1, and 2.6, respectively, after surgery. Corresponding scores in the soft stabilization group were 55%, 8.4, and 8.9, improving to 25%, 4.1, and 2.2 after surgery. There were no significant differences between the 2 groups in any of these clinical parameters. Patients’ subjective improvement rates and satisfaction with the surgical procedure were higher in the soft stabilization group, but the differences were not significant. Mean operation time and mean blood loss were significantly lower in the soft stabilization group than in the PLIF group. In the soft stabilization group, there were 3 cases of progression of slippage in patients who had had preoperative slippage of more than 20%; there was 1 dural tear in the PLIF group. Conclusions Patients with grade I degenerative spondylolisthesis who received soft stabilization with an artificial intervertebral ligament after microdecompression had clinical outcomes similar to those of patients who received PLIF. Since soft stabilization can be done in a much less invasive way than fusion, if slippage is 20% or less, soft stabilization with an artificial ligament is a viable alternative to fusion for patients who are elderly or who have significant comorbidities that make a prolonged operation inadvisable. Level of Evidence This study was a retrospective comparative study with a very limited population (level III evidence).
Managing Aid Dependence : How African governments lost ownership and how they can regain it

Between 2005 and 2007, the Global Economic Governance Programme carried out research on the factors accounting for the bargaining power in aid negotiations of governments in eight African countries: Ghana, Mali, Mozambique, Rwanda, Tanzania, Zambia, Ethiopia and Botswana. The case studies assessed the degree of control governments are able to secure over their development policies. Through the country cases the project sought to understand complex aid relationships from the viewpoints of recipient governments, investigating what strategies African states have adopted to advance their objectives in aid negotiations and how successful their efforts have been. The case studies developed detailed descriptions of the institutions and processes that make up contemporary donor-recipient relations. They concentrated on the past and present economic, ideological, political and institutional contexts of aid negotiations, and how these conditions shape the balance of negotiating capital between governments and donors. They then used specific cases of aid negotiations to move beyond this general picture. The research is published as The Politics of Aid: African Strategies for Dealing with Donors, (ed.) Lindsay Whitfield, Oxford University Press, forthcoming 2008. Managing Aid Dependence: How African governments lost ownership and how they can regain it Lindsay Whitfield and Gervase Maipose
Porcine CSRP 3 : polymorphism and association analyses with meat quality traits and comparative analyses with CSRP 1 and CSRP 2

CRP3 is the muscle-specific form of the cysteine and glycine-rich protein family and plays an important role in myofiber differentiation. Here we isolated and characterized its coding gene CSRP3 from porcine muscle. Phylogenic analyses demonstrated that CSRP3 diverged first and is distinguished from two other members, CSRP1 and CSRP2. CSRP3 mRNA was up-regulated during the development of porcine embryonic skeletal muscle, indicating its potential importance in muscle growth. Genetic variant analyses detected multiple variations in an approximately 400 bp region covering exon 4 and its downstream intron, and two haplotypes were identified by sequencing. One of synonymous substitutions C1924T was used for linkage and association analyses. It was revealed that the substitution of C1924T had significant associations with firmness (P \ 0.01), Lab Loin pH, Off Flavor Score and Water Holding Capacity (P \ 0.05), and a suggestive effect (P \ 0.1) on Flavor Score and Average Glycolytic Potential in a Berkshire 9 Yorkshire F2 population. The association analyses results agreed with the gene’s localization to a QTL region for meat quality traits on porcine chromosome 2p14-17 demonstrated by both linkage mapping and RH mapping. These results provide fundamental evidence for CSRP3 as a functional candidate gene affecting pig meat quality.
Recent Progress of Deterministic Models for Complex Networks

The deterministic models for complex networks play an indispensable role in the field of network modelling.This paper reviews the recent progress of deterministic models such as random graphs,small-world networks,scale-free networks,and weighted networks,with focus on construction and properties of these models.Then,some new directions of deterministic models for complex networks are explored.
FACTORS ASSOCIATED WITH SEVERE FIBROSIS IN CHRONIC HEPATITIS C

The paper presents an epidemiological, biological, virological and histological study of patients suffering from chronic viral hepatitis C with severe fibrosis whose aim was to identify the main etiological factors of the disease. The main characteristics for the group were advanced age, alcohol consumption, severe cytolytic syndrome, high viral load values, severe necroinflammatory lesions, alcohol induced histological lesions, biliary pathways pathology – leading to a pathological entity that is very severe, with a strong potential towards cirrhosis and hepatocellular carcinoma.
The Road to Structural Convergence in the Eurozone . Industrial Policies in a future Eurozone Federal State

In this paper, we focus on and try to measure structural productive asymmetries between peripheral and central economies in the eurozone. We assess the economic effects of such asymmetries according to a post-Keynesian structuralist approach, and try to get some implications as to the design of future industrial policies at EU level. We stress that future EU industrial policies should dispose of much larger funds with respect to the present setting, and specifically target productive development in the eurozone periphery. At a European-wide level, we argue that sectoral policies must be rediscovered with respect to recent past experience, and that public EU procurements for domestically produced may be extremely useful for achieving both short-run expansionary goals, and long-run improvements in productivity dynamics.
Training dataset construction for anomaly detection in face anti-spooﬁng

Anomaly detection, which is approaching the problem of face anti-spooﬁng as a one-class classiﬁcation problem, is emerging as an increasingly popular alternative to the traditional approach of training binary classiﬁers on specialized anti-spooﬁng databases which contain both client and imposter samples. In this paper, we discuss the training protocols in the existing work on anomaly detection for face anti-spooﬁng, and note that they use images exclusively from specialized anti-spooﬁng databases, even though only common images of real faces are needed. In a proof-of-concept experiment, we demonstrate the potential beneﬁts of adding in the anomaly detection training sets images from general face recognition, rather than specialised face anti-spooﬁng, databases, or images from the in-the-wild images. We train a convolutional autoencoder on real faces and compare the reconstruction error against a threshold to classify a face image as either client or imposter. Our results show that the inclusion in the training set of in-the-wild images increases the discriminating power of the classiﬁer on an unseen database, as evidenced by an increase in the value of the Area Under the Curve.
Web clustering based on hybrid probabilistic latent semantic analysis model

In E-commerce,in order to know more about the inherent characteristics of user access and make better marketing strategies,a Web clustering algorithm based on Hybrid Probabilistic Latent Semantic Analysis(H-PLSA) model was proposed in this paper.The Probabilistic Latent Semantic Analysis(PLSA) models were established respectively on user browsing data,page information and enhanced user transaction data by using PLSA technology.Using log-likelihood function,three PLSA models were merged to get the user clustering H-PLSA model and the page clustering H-PLSA model.Similarity calculation was based on the conditional probability among latent themes and user,page as well as site in the clustering analysis.The k-medoids algorithm based on distance was adopted in this clustering algorithm.The H-PLSA model was designed and constructed in this article,and the Web clustering algorithm was verified on this H-PLSA model.Then it is proved that the algorithm is effective.
Pension reforms and welfare gains from eliminating business cycle in Poland

In the paper we study consequences of the changes in the pension system in Poland for the welfare gains from eliminating business cycle fluctuations. Using an overlapping generation model with aggregate fluctuations and exogenous idiosyncratic labour market risk we quantitatively show that raising the retirement age significantly reduces, whereas lowering the replacement rate from the public part of the pension system increases the business cycle costs. The impact of these changes is different as far as consumers with different skill level and wealth are concerned.
Cost-Effective Positioning based on WiFi-Probes : A Quantitative Study Deriving the Position of a Smartphone using the Signal Strength of WiFi-Probes

In the modern society, almost everyone has a smartphone. These devices tend to almost always use WiFi-networking. For the device to identify nearby WiFi access points it has to send out WiFi probing broadcasts. Nearby access points respond to these broadcasts in order to let the device know that they are within reach. This technique is called active scanning. This paper aims to answer if it is possible to use the signal strength of these broadcasts to localize the device transmitting them. We are interested in the possibility of creating this kind of system and the accuracy that it would be able to provide. This is a quantitative study where we produce our results based on experiments, measurements and observations. The experiments are set in a large square shaped area. A sensor was placed at each corner of the area that the smartphone will be tracked within. The smartphone will be sending WiFi probing broadcasts that will be monitored and measured by the sensors. The strength of the broadcast signal will be converted into the relative distance between the devices position and the sensors. These four distances, collected from each of the sensors, will further be converted into a position within the area by using trilateration. To measure the accuracy of the system, the true position of the device will be compared against the calculated position from the system using only the signal strength. Further, a deviation in the distance between the two locations will be calculated. The experiments resulted in a positioning system that was able to estimate positions within an 80 x 80m area. Fourteen location positions were taken which resulted in a mean deviation of 16.6 meters from the true location and a root mean squared error of 19.5 meters. We concluded that more readings within the same position gave a significant increase in accuracy, to the expense of time. Using single measurements would be more practical, but would not produce reliable positions.
A phylogeny of the Rutaceae and a biogeographic study of its subfamily Aurantioideae

The Rutaceae classification is complex and has undergone several changes. In addition to morphological studies, phylogenetic inference using molecular data has also led to classification changes. Thus far only chloroplast data and ITS have been used, sometimes combined with morphology to infer the phylogeny. This study adds information from a low copy nuclear gene to test the existing phylogenetic hypothesis using a species tree framework. A biogeographic study was also performed on the Aurantioideae subfamily. A pilot study looked at the choice of genes, followed by testing and evaluation of several methods for extraction of Rutaceae DNA. Thereafter, a new method for efficient separation of alleles and paralogues was examined. The sequences obtained were analysed for recombination, positive selection and hybridisation. Trees for three loci (chloroplast, nuclear HYB and MDH) were made using MrBayes and BEAST, and a species tree was constructed with *BEAST. The *BEAST species tree is used as a template for a biogeographic study with the Lagrange geographic range likelihood analysis. A Bayesian biogeographic study is also performed using a Bayesian discrete biogeographical mode (an addition to BEAST). The results are then compared with previous studies, corroborating some and rejecting others. Sammanfattning Rutace-familjens struktur är komplex och föränderlig. Förutom morfologiska studier har även fylogenetiska studier använts för att få ordning i den. Hittills har man endast tittat på kloroplastgener och ribosom-DNA, i enstaka fall i kombination med morfologiska karaktärer. Den här studien tillför nukleär-DNA från lågkopiegener samt en biogeografisk studie av dess underfamilj, Aurantioideae. En förstudie undersöker hur användbara generna som använts är. Detta följs av en undersökning av flera metoder för extraktion av DNA. Därefter testas en ny metod för att skilja på alleler och paraloga gener. De gensekvenser som tas fram analyseras för rekombination, riktad utveckling samt hybridisering. Fylogenetiska träd för de olika generna konstrueras med MrBayes och BEAST. *BEAST används för att göra ett gemensamt artträd. Detta artträdet blir sedan stommen i en studie där programmet Lagrange beräknar sannolikheten för hur den geografiska utbredningen har utvecklats. En bayesisk biogeografisk undersökning utförs också med BEAST. Resultaten jämförs till sist med tidigare forskning, där en del studier blir styrkta medan andra blir avvisade.
PrivateSQL: A Differentially Private SQL Query Engine

Differential privacy is considered a de facto standard for private data analysis. However, the definition and much of the supporting literature applies to flat tables. While there exist variants of the definition and specialized algorithms for specific types of relational data (e.g. graphs), there isn't a general privacy definition for multi-relational schemas with constraints, and no system that permits accurate differentially private answering of SQL queries while imposing a fixed privacy budget across all queries posed by the analyst. This work presents PrivateSQL, a first-of-its-kind end-to-end differentially private relational database system. PrivateSQL allows an analyst to query data stored in a standard database management system using a rich class of SQL counting queries. PrivateSQL adopts a novel generalization of differential privacy to multi-relational data that takes into account constraints in the schema like foreign keys, and allows the data owner to flexibly specify entities in the schema that need privacy. PrivateSQL ensures a fixed privacy loss across all the queries posed by the analyst by answering queries on private synopses generated from several views over the base relation that are tuned to have low error on a representative query workload. We experimentally evaluate PrivateSQL on a real-world dataset and a workload of more than 3, 600 queries. We show that for 50% of the queries PrivateSQL offers at least 1, 000x better error rates than solutions adapted from prior work.