Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Deduplication in a Distributed Primary Storage Infrastructure

A large amount of duplicate data typically exists across volumes of virtual machines in cloud computing infrastructures. Deduplication allows reclaiming these duplicates while improving the cost-effectiveness of large-scale multitenant infrastructures. However, traditional archival and backup deduplication systems impose prohibitive storage overhead for virtual machines hosting latency-sensitive applications. Primary deduplication systems reduce such penalty but rely on special cluster filesystems, centralized components, or restrictive workload assumptions. Also, some of these systems reduce storage overhead by confining deduplication to off-peak periods that may be scarce in a cloud environment. We present DEDIS, a dependable and fully decentralized system that performs cluster-wide off-line deduplication of virtual machines’ primary volumes. DEDIS works on top of any unsophisticated storage backend, centralized or distributed, as long as it exports a basic shared block device interface. Also, DEDIS does not rely on data locality assumptions and incorporates novel optimizations for reducing deduplication overhead and increasing its reliability. The evaluation of an open-source prototype shows that minimal I/O overhead is achievable even when deduplication and intensive storage I/O are executed simultaneously. Also, our design scales out and allows collocating DEDIS components and virtual machines in the same servers, thus, sparing the need of additional hardware.
Progress Towards the First Wireless Sensor Networks Consisting of Inkjet-Printed, Paper-Based RFID-Enabled Sensor Tags

This paper discusses the evolution towards the first integrated radio-frequency identification (RFID)-enabled wireless sensor network infrastructure using ultra-high frequency/radio frequency (UHF/RF) RFID-enabled sensor nodes and inkjet-printed electronics technologies on flexible and paper substrates for the first time ever. The first sections highlight the unique capabilities of inkjet printed electronics as well as the benefits of using paper as the ultra-low-cost, conformal and environmentally friendly substrate for the mass-scale ubiquitous implementation of the first RFID-enabled wireless sensing applications. Various inkjet-printed antenna configurations are presented for enhanced-range compact RFID-enabled sensing platforms in “rugged” environments up to 7 GHz, followed by the discussion of their 2-D integration with integrated circuit (IC) and sensors on paper. This integration is extended to a power-scavenging “smart-shoe” batteryless integrated RFID module on paper that could be used for autonomous wearable sensing applications with enhanced range. The paper concludes discussing the details for establishing for the first time an asynchronous wireless link between the aforementioned RFID-tags and a widely used commercial wireless sensor network (WSN) mote using a simplified protocol; a paramount step that could potentially create ubiquitous ultra-low-cost sensor networks and large-scale RFID implementations eliminating the need of expensive RFID reader infrastructure and linking RFIDs to the mature level of WSNs.
Applications of Genetic Programming to Building Decision Trees

Decision Trees generated with recursive local partitioning methods are widely used in classification problems. In most cases, sub-optimal yet efficient axis-parallel partitioning generates trees of sufficient quality. More complex partitioning is possible, yet it is less efficient and generally not cost effective. Genetic Programming methods have also been used to generate decision trees. In this case, which does not rely on the standard recursive partitioning but rather manipulates explicit trees, the major challenge is to maintain valid trees. This paper uses Constrained Genetic Programming – a method allowing automatic control of the evolved trees. The method is used to evolve axis-parallel, and then also more powerful trees, using the standard Iris dataset. The results show that this method can be used to easily evolve different kinds of trees, and that axis-parallel and also oblique trees offer sufficient quality for this dataset.
A SECURE DIFFIE-HELLMAN SCHEMES OVER ELLIPTIC CURVES

The protection of information technologies is very essential because information technologies play a major role in our information society, such protection includes data and system protection against many kinds of threats or attacks which may lead to lose of money, or lose of reputation and thus destroy businesses. The Diffie-Hellman public key cryptosystem over elliptic curves is often used in modern communications and system technologies; it is one of the firstly defined public key cryptosystem that enable secure communicating over public unsecure communication channels. This paper introduce a security Diffie-Hellman cryptosystem based on elliptic curves, it suggests the use of randomization in the encryption process to become immune against many attacks described in literature, this proposed security enhancement describe both the diffie-Hellman key exchange process and the Diffie-Hellman cryptosystem, this enhancement makes the Diffie-Hellman semantically secure, because an attacker will not be able to distinguish between two encryptions even if the attacker knows the corresponding plaintexts. Other important benefit is that the Diffie-Hellamn cryptosystem described here can easily be implemented and is very suitable on small and limited devices (e.g. smart cards) due the use of elliptic curves. This paper also briefly investigate some attacks on the Diffie-Hellman scheme and the suitable choice of DiffieHellman parameter to avoid such attacks.
A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory

We propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. Our model is an extension of the heterogeneous autoregressive model. Structural breaks occur through mixture distributions in state innovations of linear Gaussian state space models. Monte Carlo simulations evaluate the properties of the estimation procedures. Results show that the proposed model is viable and flexible for purposes of forecasting volatility. Model uncertainty is accounted for by employing Bayesian model averaging. Bayesian model averaging provides very competitive forecasts compared to any single model specification. It provides further improvements when we average over nonlinear specifications.
Identify the Antecedents of Distrust in a Website

It has been widely accepted that distrust is qualitatively different from trust. Yet scholarly knowledge about the antecedents of distrust is scarce, while the antecedents of trust have received extensive attention. Furthermore, little empirical research has explored how website factors impact on the formation of distrust in a user. Drawing upon the review of distrust literature and competence-motive perception, we propose a new theoretical framework to explain how website factors impact on perceptions of the attributes of a given website, which in turn form user distrust. In this research, distrust is triggered by website evaluations in two areas, namely malevolence and incompetence. These two attributes are determined by three website factors: lack of structural assurance, interface design, and lack of third-party recognition. The proposed research model of distrust formation in the online context is verified by an online survey with 283 valid responses. We offer theoretical and practical implications for our findings.
Semantic positioning via structured sparsity models

Semantic positioning is a new paradigm emerging with the Internet-of-Things (IoT) technology and its application to context-aware services in smart-spaces. Specifically, it refers to the problem of detecting user actions and locations based on prior characterization of the space and sensed data. Differently from classic positioning, input data are measurements of the interaction between human and sensors and location information is not a vector of coordinates but a a point in a topological map. In this paper, we tackle this challenge with a mere passive monitoring system in order to preserve user privacy, handle device heterogeneity, energy efficiency and utilizing low-complexity sensors that are able to capture events generated by human actions. We develop a structured sparsity model based on the notion of discrete Radon transforms on homogeneous space in order to construct mappings from events to actions and from actions to semantic locations. We propose algorithms for human activity detection and semantic positioning. Specifically, the Least Absolute Residual and Shrinkage Operator (LARSO) for human action detection, and a mixed-norm optimization to perform semantic positioning. Simulation results are shown to validate the proposed model and compare different algorithms.
Limited Randomness LT Codes

LT codes are asymptotically optimal rateless erasure codes with highly efficient encoding and decoding algorithms. In the original analysis of these codes, it was assumed that for each encoding symbol, the neighbors used to generate that encoding symbol are chosen uniformly at random. Practical implementations of LT codes cannot afford this amount of randomness, because all random bits must be communicated to the decoding party. Instead, they use a linear congruential generator to reduce the randomness used per encoding symbol to a seed consisting of two random numbers. We show that such limited randomness LT codes perform almost as well as the fully random version. Thus even limited randomness LT codes are asymptotically optimal.
Anticipating Information Needs Based on Check-in Activity

In this work we address the development of a smart personal assistant that is capable of anticipating a user's information needs based on a novel type of context: the person's activity inferred from her check-in records on a location-based social network. Our main contribution is a method that translates a check-in activity into an information need, which is in turn addressed with an appropriate information card. This task is challenging because of the large number of possible activities and related information needs, which need to be addressed in a mobile dashboard that is limited in size. Our approach considers each possible activity that might follow after the last (and already finished) activity, and selects the top information cards such that they maximize the likelihood of satisfying the user's information needs for all possible future scenarios. The proposed models also incorporate knowledge about the temporal dynamics of information needs. Using a combination of historical check-in data and manual assessments collected via crowdsourcing, we show experimentally the effectiveness of our approach.
Adaptive MIMO time-varying channel equalization using particle filtering

This paper addresses the problem of semi-blind multi-input multi-output (MIMO) equalization of non-Gaussian time-varying channels by employing particle filter methods. Based on the Middleton Class A noise model, we derive a state-space model that characterizes the behavior of the channel in time. The channel estimation and tracking are performed using particle filter, and a decision feedback equalizer derived using MMSE criterion is used to perform the equalization. Simulation result shows the validity of the proposed algorithm.
Level lines based disocclusion

Object recognition, robotic vision, occluding noise removal or photograph design require the ability to perform disocclusion. We call disocclusion the recovery of hidden parts of objects in a digital image by interpolation from the vicinity of the occluded area. It is shown in this paper how disocclusion can be performed by means of a level lines structure, which offers a reliable, complete and contrast-invariant representation of an image, in contrast to edges. Level lines based disocclusion yields a solution that may have strong discontinuities, which is not possible with PDE-based interpolation. Moreover, the proposed method is fully compatible with Kanizsa's (1996) theory of "amodal completion".
Automatic extraction of semantic concepts in medical images

A novel automatic system for extracting the semantic descriptions of medical image content and concept in text form is presented. We first extract and analyze image features and the features are mapped to semantic descriptions by fuzzy functions. Based on these semantic descriptions, our system facilitates knowledge base construction using a machine learning scheme. The result would be useful for other researchers in medical image retrieval area, who can take advantage of both text-based queries and image queries.
New Analysis of BAN-Yahalom Protocol by Strand Spaces and its Improvement

There're some security flaws in the improvement of Yahalom protocol by BAN. This paper makes a detailed analysis on such flaws from a new angle induced by strand spaces. First, the algebraic model of BAN-Yahalom protocol is established with the help of strand spaces. Subsequently, attackers' abilities are defined in an accurate math form. And then new improvement to this protocol is proposed in order to enhance the defensive ability of this protocol against potential attackers, which will make it more adaptable to practical environment. Future application of the improved protocol is also envisioned.
Wide matching — An approach to improving noise robustness for speech enhancement

It is shown that under certain conditions it is possible to obtain a good speech estimate from noise without requiring noise estimation. We study an implementation of the theory, namely wide matching, for speech enhancement. The new approach performs sentence-wide joint speech segment estimation subject to maximum recognizability to gain noise robustness. Experiments have been conducted to evaluate the new approach with variable noises and SNRs from -5 dB to noise free. It is shown that the new approach, without any estimation of the noise, significantly outperformed conventional methods in the low SNR conditions while retaining comparable performance in the high SNR conditions. It is further suggested that the wide matching and deep learning approaches can be combined towards a highly robust and accurate speech estimator.
A SIP-Based Mobility Management Architecture Supporting TCP with Handoff Optimization

Keeping a session connected as a mobile host traverses across networks is the responsibility of a handoff management protocol. An effective protocol should support various transport protocols, require minimum changes, and manage handoff with acceptable delay and signaling cost. In this paper, a handoff management architecture that utilizes application layer gateway (ALG) and network address translation (NAT) mechanisms based on the session initiation protocol (SIP) is proposed. In addition to supporting mobility for the TCP session, the proposed architecture also optimizes real-time handoff delay by reducing delays caused by layer independency and duplication address detection (DAD) procedure and head of train delay caused by sequential execution of handoff procedures at different layers with few changes to the existing protocols and infrastructure.
A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

This paper presents a novel brain-computer interface (BCI)-based healthcare control system, which is based on steady-state visually evoked potential (SSVEP) and P300 of electroencephalography (EEG) signals. The proposed system is composed of two modes, a brain switching mode and a healthcare function selection mode. The switching mode can detect whether a user has the intent to activate the function selection mode by detecting SSVEP in an ongoing EEG. During the function selection mode, the user is able to select any functions that he/she wants to activate through a healthcare control panel, and the function selection is done by detecting P300 in the user’s EEG signals. The panel provides 25 functions representing 25 frequently performed activities of daily life. Therefore, users with severe motor disabilities can activate the system and any functions in a self-paced manner, achieving the goal of autonomous healthcare. To achieve high P300 detection accuracy, a novel P300 detector based on kernel Fisher’s discriminant analysis (kernel FDA) and support vector machine (SVM) is also proposed. Experimental results, carried out on five subjects, show that the proposed BCI system achieves high SSVEP detection (93%) and high P300 detection (95.5%) accuracies, meaning that the switching mode has a high sensitivity, and the function selection mode has the ability to accurately detect the functions that the users want to trigger. More important, only three electrodes (Oz, Cz, and Pz) are required to measure EEG signals, enabling the system to have good usability in practical use.
Video reconstruction using inductive three dimensional sparsity measure

Compressive sensing (CS) aims at acquiring and reconstructing sparse signals at a low sampling rate, and thus has wide applications in video processing. In this paper, an inductive three-dimensional sparsity measure (I_3DSM) is proposed for real-time video reconstruction. In the proposed sparsity measure, we utilize an online trained projection matrix to exploit the low-rank property of video sequence in the sparse transform domain. A large number of experiments are conducted to illustrate the superior performance of I_3DSM as compared with some known sparsity measures.
Unsupervised Bayesian Parameter Estimation for Dependency Parsing

We explore a new Bayesian model for probabilistic grammars, a family of distributions over discrete structures that includes hidden Markov models and probabilitsic context-free grammars. Our model extends the correlated topic model framework to probabilistic grammars, exploiting the logistic normal prior as a prior over the grammar parameters. We derive a variational EM algorithm for that model, and then experiment with the task of unsupervised grammar induction for natural language dependency pasring, and show that our model achieves superior results over previous models that differ mostly in the choice of a prior different than the logistic-normal prior. For a full version of this paper, see [5].
Design and Implementation of Optimal Winner Determination Algorithm in Combinatorial e- Auctions

— The one of best robust search technique on large scale search area is heuristic and meta heuristic approaches. Especially in issue that the exploitation of combinatorial status in the large scale search area prevents the solution of the problem via classical calculating methods, so such problems is NP-complete. in this research, the problem of winner determination in combinatorial auctions have been formulated and by assessing older heuristic functions, we solve the problem by using of genetic algorithm and would show that this new method would result in better performance in comparison to other heuristic function such as simulated annealing greedy approach.
Bakery Production Scheduling Optimization ?a PSO Approach

Production of bakery goods is strictly time sensitive due to the yeast proofing of doughs. That causes special requirements for production planning and scheduling, which is in bakeries often completely based on the practical experience of the responsible employee instead of mathematical methods. This often leads to suboptimal performance of companies due to the sometimes “chaotic” scheduling approach. This work presents an approach to use PSO as a highly efficient numerical method to solve complex scheduling problems. In all probability bakeries will be able to increase efficiency by optimizing their production planning with numerical methods like the one presented here. After modelling the production line with a limited range of input data and a pre-processing of the necessary data to match the real process induced requirements, a PSO algorithm handles the optimization task. Results show the high potential of this method to solve the scheduling problem in good computational time.
Antifungal Attributes of Lactobacillus plantarum MYS6 against Fumonisin Producing Fusarium proliferatum Associated with Poultry Feeds

Fumonisins, being common in occurrence in maize-based feeds, pose a great threat to animal and human health. The present study is aimed at determining the antifungal activity of Lactobacillus plantarum MYS6 against a fumonisin producing fungus, Fusarium proliferatum MYS9. The isolate was subjected to standard tests for determining its probiotic attributes and antifungal properties. L. plantarum MYS6 thrived well at pH 3.0 and 6.0, and exhibited strong resistance up to 3% bile. The isolate showed a high degree of cell surface hydrophobicity corresponding to its strong adhesion to chicken crop epithelial cells. Co-inoculation with the fungus on modified de Man Rogosa Sharpe medium revealed the inhibitory effect of L. plantarum MYS6 on fungal growth and biomass. Observation using scanning electron microscopy showed distortion of hyphal structures, swollen tips and disrupted conidia. Conidia germination inhibition assay restrained germination and showed deformed hyphae. The bioprotective feature of the isolate was evident by the inhibition of fungal development in maize-kernel treated with the cell free supernatant of L. plantarum MYS6. Both the isolate and its extracellular metabolites lowered fumonisin content in feed model up to 0.505 mg/Kg of feed and 0.3125 mg/Kg of feed respectively when compared to the level of 0.870 mg/Kg of feed in control. The major antifungal compounds produced by the isolate were 10-Octadecenoic acid, methyl ester; palmitic acid, methyl ester; heptadecanoic acid, 16-methyl ester; stearic acid and lauric acid. L. plantarum MYS6 reduced 61.7% of fumonisin possibly by a binding mechanism. These findings suggest the application of L. plantarum MYS6 as an efficient probiotic additive and biocontrol agent in feed used in poultry industry. Additionally, the antifungal metabolites pose a conspicuous inhibition of Fusarium growth and fumonisin production.
ERP global template and organizational informal structures (a practice-based study)

Based on an interpretive case study and Activity Theory as a theoretical framework, this contribution shows how local users of an enterprise resource planning system in a Chinese joint-venture of a French multinational corporation have developed an informal organizational structure which eventually led to workaround work practices. The development and quest for alternative practices was justified as indispensable and appropriate to respond to local needs. In that perspective, the formal structure was not sufficient to cover the problems met at the Chinese subsidiary. Our research tackles an original aspect of post ERP implementation since it suggests that informal work practices might eventually lead to the adoption of global IS.