Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobility through Heterogeneous Networks in a 4 G Environment

The increase will of ubiquitous access of the users to the requested services points towards the integration of heterogeneous networks. In this sense, a user shall be able to access its services through different access technologies, such as WLAN, Wimax, UMTS and DVB technologies, from the same or different network operators, and to seamless move between different networks with active communications. In this paper we propose a mobility architecture able to support this users’ ubiquitous access and seamless movement, while simultaneously bringing a large flexibility to access network operators. This mobility architecture seamless integrates heterogeneous networks with different technologies, including broadcast ones, with different network types, such as MANETs and NEMOs, and able to interoperate with legacy architectures, such as 3GPP and Wimax.
SMEs , e-commerce and territorial development : the experience of a “ web district ”

This paper analyses the effects of the creation of an e-commerce common platform among small and medium-sized enterprises (SMEs) in the Italian region Valtellina on the territorial social capital and the integration among producers. After a description of the process of aggregation and the functioning of the web district, an analysis of the data on cross-selling is carried out to evaluate the potential of the project in terms of integration. Secondly, using information from in depth interviews, the dimension of social capital is taken into account, on the one hand to understand how social capital has favoured the development of the project, and, on the other, to discover if the project has contributed to strengthen the territorial social capital. As a result, territorial networks have emerged as an effective way for SMEs to overcome the entry barriers for the access to the e-commerce, while the establishment of a territorial web district allows the creation of “consumption linkages” given by the possibility of sharing clients. Furthermore, the paper shows how this project has generated a process of local development, driven both by local and non local actors, playing different roles. A major outcome has been the strengthening of bonding and bridging social capital and the creation of new external linkages potentially able to generate spillovers.
What happens when stochastic adaptive video streaming players share a bottleneck link?

------------------------------------------------------------------- ABSTRACT --------------------------------------------------------------- Competition among adaptive video streaming players severely diminishes user-QoE. When players compete at a bottleneck link many do not obtain adequate resources. This imbalance eventually causes ill effects such as screen flickering and video stalling. There have been many attempts in recent years to overcome some of these problems. This work focuses on such a situation. It evaluates current stochastic adaptive video players at a bottleneck link and when the number of players increases. Experimental setup includes the TAPAS player and emulated network conditions. The results show mDASH outperforms x-MDP, sdpDASH and the Conventional players.
On toric degenerations of flag varieties

Following the historical track in pursuing TT-equivariant flat toric degenerations of flag varieties and spherical varieties, we explain how powerful tools in algebraic geometry and representation theory, such as canonical bases, Newton–Okounkov bodies, PBW-filtrations and cluster algebras come to push the subject forward.
An Evaluation of Software Requirement Prioritization Techniques

Abstract — Requirements prioritization plays an important role in the requirement engineering process, particularly, with respect to critical tasks like requirements negotiation and software release planning. Selecting the right set of requirements for a product release largely depends on how successfully the requirement candidates are prioritized. There are different requirement prioritization techniques available which are some more elaborated than others. This paper takes a closer look at nine different techniques of requirement prioritization namely Analytical Hierarchy Process (AHP), Hierarchy AHP, Minimal Spanning Tree, Bubble Sort, Binary Search Tree (BST), Priority Group, Planning Game (PG), 100 points method and Planning Game combined with AHP (PGcAHP) and then put them into a controlled experiment, in order to find out the best one. The evaluation was done on the basis of some criteria like: ease of use, certainty, accuracy of result, method’s ability to scale up to many more requirements, required number of comparisons, and required time to make decision. Analysis of the data from the experiment indicates that the analytic hierarchy process to be a promising candidate, although it may be problematic to scale-up. However, the result clearly indicates that the Planning Game (PG) yields accurate result, is able to scale up, requires least amount of time, the easiest method to use and so on. For these reasons, finding of the experiment is, the Planning Game (PG) method is supposed to be the best method for prioritizing requirements.
Optimal Risk in Multiagent Blind Tournaments Extended Abstract

In multiagent blind tournaments, many agents compete at an individual game, unaware of the performance of the other agents. When all agents have completed their games, the agent with the best performance—for example, the highest score, or greatest distance, or fastest time—wins the tournament. In stochastic games, an obvious and time honoured strategy is to maximize expected performance. In tournaments with many agents, however, the top scores may be far above the expected score. As a result, maximizing expected score is not the same as maximizing the chance of winning the tournament. Rather, a “riskier” strategy, which increases the chance of obtaining a top score while possibly sacrificing some expected score, may offer a better chance of winning. In this paper, we study how an agent should optimally adapt its strategy based on the size of the tournament in which it is competing. Our solution involves first approximating the agent’s pool of opponents as a collection of known or estimated strategies. Second, score distributions are computed for those strategies, and the distributions are convolved to obtain a distribution for the maximum score of the agent’s opponents. Finally, a strategy that maximizes the chance of exceeding the opponents’ scores is computed. As a demonstration, we study optimal tournament-size adaptation in online Yahtzee tournaments involving as few as one and as many as ten thousand opponents. We find that strategies change dramatically over that range of tournament sizes, and that in large tournaments, an agent adopting the optimally risky strategy can nearly double its chance of winning.
OpenGamma Quantitative Research Multi-curves Frameworkwith Stochastic Spread : A Coherent Approach to STIR Futures and Their Options

The development of the multi-curves framework has mainly concentrated on swaps and related products. By opposition, this contribution focuses on STIR futures and their options. They are analysed in a stochastic multiplicative spread multi-curves framework which allows a simultaneous modelling of the Ibor rates and of the cash-account required for futures with continuous margining. The framework proposes a coherent pricing of cap/floor, futures and options on futures. In the final part of the note, we also propose the pricing formula for a financial product which does not exists yet, betting that it will be proposed soon.
A Rapid PCI NoC Prototyping Platform with VCI Interfaces Methodology

System-on-chip (SoC) designs provide integrated solutions to challenging design problems in the telecommunications, multimedia, and consumer electronics domains. Much of the progress in these fields hinges on the designers ability to conceive complex electronic engines under strong time-to market pressure. Success will rely on using appropriate design and process technologies, as well as on the ability to interconnect existing components including processors, controllers, and memory array reliably, in plug and play fashion. This paper presents a prototyping methodology and flexible hardware platform developed to facilitate rapid prototyping of such communication systems.
STATIC LOADS EVALUAT ION IN A FLEXIBLE AIRCRAFT USING HIGH FIDELITY FLUID-STRUCTURE ITERATION TOOL ( E 2-FSI )

This article presents a Fluid Structure Iteration method applied as a study case to a recent conventional transport aircraft with aspect ratio of 12. It evaluates the nonlinear structure effect on the static limit loads calculation. It uses the tool called E2-FSI, which stands for Nonlinear High-fidelity Static Fluid-structure Iteration, developed for high flexibility static aeroelastic evaluations. It uses Reynolds Average Navier Stokes Computational Fluid Dynamics combined with detailed Finite Elements Method in linear and nonlinear structural analyses. A discussion about the use and applicability of high fidelity static Fluid Structure Iteration for static loads calculation is presented as part of the conclusion, for both current and future conventional transport aircrafts.
Detection of Comparability Subgraphs from Large Networks

Real world large scale networks can be represented as graphs. This approach plays a key role in analysis in the domains of social networks [1] and bioinformatics [2], among others. Analyzing these networks is computationally complex and expensive, especially in terms of memory and time complexity. A popular technique subverting time and computation expense for analyzing networks is extracting substructures, which preserves more important information and less noise [12]. In this work, we use special a special substructure called comparability, which preserves transitive orientation. Our motive is to extract a maximal comparability subgraph since no algorithm exists. Our algorithm is able to find a maximal comparability subgraph from both undirected and directed graphs. Finding a clique of given size is a NP-complete problem, so we must implement some additional constraints to maximize time efficiency. If the given input graph is chordal, then extraction of the clique of size n becomes a problem that is solvable in polynomial time. So we have written an algorithm to find the clique of given size, and implemented the algorithm to find a maximal chordal subgraph. Since we worked on two different special subgraphs, we compared our results to investigate whether the given graph is chordal or comparability in nature. In our research, we have proposed a parallel sampling method for efficient network analysis.
Exploiting Leaky Chrome Extension API 6 . 857 Final Project

Given the popular use and reliance on Chrome extensions, it is important to consider their security, especially because these extensions often have access to user data and permissions to modify the browser itself. Extensions can have permissions to sensitive user data, and extension API calls with the browser can be leaky, potentially exposing this data to malicious websites. Consequently, we examined the security threat of malicious websites stealing user data through benign Google Chrome extensions. To demonstrate this, we created a proof-of-concept website and extension that display an attack to read user cookies, as well as expand on how this attack could be manipulated in other ways to do more harm. Based on our results and discussion, we provide recommendations to users, developers, and Chrome to mitigate these security concerns, as well as provide a final assessment on the security of Chrome extensions for our attack vector.
Modelling migration flows in the Mekong River Delta region of Vietnam : an augmented gravity approach

This article aims at modeling inter-provincial migration flows between provinces of the Mekong River Delta (MRD) region and 3 major urban cities in Vietnam. The key feature of the model is that it departs from the time proofed gravity model, which is expected to verify whether hypothesis on determinants of migration suggested by the literature hold or not in the case of the MRD region. The result of estimations indicates that migration flows between the MRD provinces and 3 major urban cities vary with the square root of the product of province populations and the ratio of income at destination over income at source, but inversely relate with distance. In addition, the forecast shows that the MRD region remains an important out-flow region with out-flows from provinces increasing by 0.4 million in the next five years, among Ca Mau, Kien Giang, Dong Thap and An Giang will see the largest increases in out flows.
A ZERO GRADIENT CALORIMETER FOR THE MEASUREMENT OF ANOMALOUS HEAT FROM THE ELECTROLYSIS OF DEUTERATED METALS

A null balance calorimeter has been designed for the measurement of anomalous heat in e 1 ectrol yt ic cell s conta i ni ng deuterium and palladium. Early measurements indicate an accuracy of less than 1% of the total energy processed through the calorimeter. Anomalous heat has been observed at the 4% level for palladium cathodes, or 4w per cc. Measurements have been made of cathode material resistance change and cathode gas absorption during electrolysis. Cell voltage variations over time have been correlated with cell gas evolution. Attempts have been made to correlate radiation with cell activity.
THE IMPACT OF UNDERFLOOR HEATING IN A HISTORICAL CHURCH : A CASE STUDY

The aim of this project is to study the thermal conditions of a fairly typical elderly Romanian stone church, by creating its appropriate model, using a CFD modelling program. At the beginning and for many years, there was not any heating system in these buildings so the indoor environment was basically defined by the outdoor conditions. Wide walls, small windows, the huge indoor air volume and the restricted natural ventilation, caused the indoor climate to be more invariable compared to the outdoor climate. As a result of large heat and moisture capacity of indoor air, conditions were very different inside and outside the building. During summer, indoor conditions were colder than outdoors but during winter indoor air climate was warmer. Concluding, it is difficult to design a heating system for a church, since it has to compensate the thermal comfort and energy needs while it is respectful with preservation and aesthetics.
SIGIR ’ 17 , August 0711 , 2017 , Shinjuku , Tokyo , Japan

Product search is an important part of online shopping. In contrast to many search tasks, the objectives of product search are not conned to retrieving relevant products. Instead, it focuses on nding items that satisfy the needs of individuals and lead to a user purchase. e unique characteristics of product search make search personalization essential for both customers and e-shopping companies. Purchase behavior is highly personal in online shopping and users oen provide rich feedback about their decisions (e.g. product reviews). However, the severe mismatch found in the language of queries, products and users make traditional retrieval models based on bag-of-words assumptions less suitable for personalization in product search. In this paper, we propose a hierarchical embedding model to learn semantic representations for entities (i.e. words, products, users and queries) from dierent levels with their associated language data. Our contributions are three-fold: (1) our work is one of the initial studies on personalized product search; (2) our hierarchical embedding model is the rst latent space model that jointly learns distributed representations for queries, products and users with a deep neural network; (3) each component of our network is designed as a generative model so that the whole structure is explainable and extendable. Following the methodology of previous studies, we constructed personalized product search benchmarks with Amazon product data. Experiments show that our hierarchical embedding model signicantly outperforms existing product search baselines on multiple benchmark datasets.
Parallel Calculation to find Minimum Spanning Tree

This paper reviews the benefits of Parallelizing the Minimum spanning tree algorithm using Message Passing Interface. In this work, we perform parallel computing for the Kruskal’s algorithm for finding the minimum spanning tree with an adjacency list as a data structure. Overall, the results showed that by running the algorithm in a distributed manner we achieve efficiency improvement in its running time in comparison with standard case.
THE IMPACT OF THE ENTRY OF FOREIGN MNES IN SERVICE SECTORS ON THE PRODUCTIVITY OF DOMESTIC MANUFACTURING FIRMS . EVIDENCE ON VERTICAL SPILLOVERS IN ITALY

The issue of horizontal and vertical spillovers by foreign MNEs on domestic manufacturing firm has been largely investigated in recent years. While the literature has mainly focused on horizontal (i.e. intraindustry) spillovers or vertical (inter-industry) spillovers between manufacturing firms, the role of vertical spillovers stemming from MNEs in service sectors has been so far mainly neglected. Nonetheless, foreign direct investments in service sectors are acquiring growing relevance. This paper is one of the first attempts to size the relevance of this phenomenon. Using a database on 77964 manufacturing firms located in Italy, we estimate their Total Factor Productivity, and show that the entry of foreign firms in five different service sectors positively affect the productivity of manufacturing firms. We observe that the latter differently benefit from MNEs’ spillovers depending on the technological level of the industry. Finally, we find that spillovers are highly localized, but that results depend on the type of services considered.
Spectral Classification of a Set of Hyperspectral Images using the Convolutional Neural Network, in a Single Training

Hyperspectral imagery has seen a great evolution in recent years. Consequently, several fields (medical, agriculture, geosciences) need to make the automatic classification of these hyperspectral images with a high rate and in an acceptable time. The state-of-the-art presents several classification algorithms based on the Convolutional Neural Network (CNN) and each algorithm is training on a part of an image and then performs the prediction on the rest. This article proposes a new Fast Spectral classification algorithm based on CNN, and which allows to build a composite image from multiple hyperspectral images, then trains the model only once on the composite image. After training, the model can predict each image separately. To test the validity of the proposed algorithm, two free hyperspectral images are taken, and the training time obtained by the proposed model on the composite image is better than the time obtained from the model of the state-of-the-art.
MAXIMUMMARGIN DISCRIMINANT PROJECTIONS FOR FACIAL EXPRESSION RECOGNITION

We present a novel dimensionality reduction method which aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated and separated with maximum margin. The proposed method brings certain advantages, both to data embedding and classification. It improves classification performance, reduces the required training time of the SVM classifier, since it is trained over the projected low dimensional samples and also data outliers and the overall data samples distribution inside classes do not affect its performance. The proposed method has been applied for facial expression recognition in CohnKanade database verifying its superiority in this task, against other state-of-the-art dimensionality reduction techniques.
Error as a Limit to Inquiry for Finite Agents : Challenges for the Posthuman Scientist

Finite agents must build rule-governed processes of some kind in order to extend the reach of inquiry beyond their limitations in a non-arbitrary manner. The clearest and most pervasive example of a rule-governed process that can be deployed in inquiry is a piece of scientific software. In general, the error distribution of all but the smallest or most trivial software systems cannot be characterized using conventional statistical inference theory, even if those systems are not subject to the halting problem. In this paper we examine the implications of this fact for the conditions governing inquiry generally. Scientific inquiry involves trade-offs. We show how increasing use of software (or any other rule-governed procedure for that matter) leads to a decreased ability to control for error in inquiry. We regard this as a fundamental constraint for any finite agent.
POMDP Planning with Regularization

The partially observable Markov decision process (POMDP) provides a principled general framework for planning under uncertainty, but solving POMDPs optimally is computationally intractable, due to the “curse of dimensionality” and the “curse of history”. To overcome these challenges, we introduce the Determinized Sparse Partially Observable Tree (DESPOT), a sparse approximation of the standard belief tree, for online planning under uncertainty. A DESPOT focuses online planning on a set of randomly sampled scenarios and compactly captures the “execution” of all policies under these scenarios. We show that the best policy obtained from a DESPOT is near-optimal, with a regret bound that depends on the representation size of the optimal policy. Leveraging this result, we give an anytime online planning algorithm, which searches a DESPOT for a policy that optimizes a regularized objective function. Regularization balances the estimated value of a policy under the sampled scenarios and the policy size, thus avoiding overfitting. The algorithm demonstrates strong experimental results, compared with some of the best online POMDP algorithms available. It has also been incorporated into an autonomous driving system for real-time vehicle control. The source code for the algorithm is available online.
Automatic Hard Disk Drive Slider Bar Auditing with Spatial Light Modulator based Imaging System

In Hard Disk Drive (HDD) slider fabrication process, the slider bar auditing is required to verify if the slider bars in the tray are sorted correctly as indicated by the serial numbers printed on sliders. In this paper, we present a machine vision system for automated reading of the serial numbers. Since the sizes of slider bars are very small, an imaging system (CCD camera) with high magnification lens is usually exploited to acquire the slide bar images. For such high magnification vision system, an autofocus module is indispensable. Unlike conventional autofocus modules which perform using mechanical zoom lens, we develop an autofocus module based on Spatial Light Modulator (SLM) where the SLM will act as phase mask filter for focus adjustment. The key contribution of our work is that we perform non-mechanical autofocus approach by adjusting the pixel-based phase mask pattern sending through the SLM. The main advantage of our system is that there is no macromechanical movement part involved in Z-axis focus adjustment. We propose a machine vision algorithm that consists of 3 major steps including coarse localization of slider bar, Autofocus and optical character recognition (OCR). To our best knowledge, our developed system is the first system that uses the SLM based autofocus in machine vision for HDD slider manufacturing. From the experiment, our system can accomplish the task with very high accuracy. By using our system, we can improve the machine vision applications by replacing conventional mechanical zoom lens based autofocus modules that generally cause machine vibrations and increasing maintenance costs related to mechanical movement issues.