Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantics-based software watermarking by abstract interpretation

Software watermarking is a software protection technique used to defend the intellectual property of proprietary code. In particular, software watermarking aims at preventing software piracy by embedding a signature, i.e. an identifier reliably representing the owner, in the code. When an illegal copy is made, the owner can claim his/her identity by extracting the signature. It is important to hide the signature in the program in order to make it difficult for the attacker to detect, tamper or remove it. In this work, we present a formal framework for software watermarking, based on program semantics and abstract interpretation, where attackers are modelled as abstract interpreters. In this setting, we can prove that the ability to identify signatures can be modelled as a completeness property of the attackers in the abstract interpretation framework. Indeed, hiding a signature in the code corresponds to embed it as a semantic property that can be retrieved only by attackers that are complete for it. Any abstract interpreter that is not complete for the property specifying the signature cannot detect, tamper or remove it. We formalize in the proposed framework the major quality features of a software watermarking technique: secrecy, resilience, transparence and accuracy. This provides a unifying framework for interpreting both watermarking schemes and attacks, and it allows us to formally compare the quality of different watermarking techniques. Indeed, a large number of watermarking techniques exist in the literature and they are typically evaluated with respect to their secrecy, resilience, transparence and accuracy to attacks. Formally identifying the attacks for which a watermarking scheme is secret, resilient, transparent or accurate can be a complex and error-prone task, since attacks and watermarking schemes are typically defined in different settings and using different languages (e.g. program transformation vs. program analysis), complicating the task of comparing one against the others.
Trajectory Network Assessment Based on Analysis of Stay Points Cluster

The popularization of GPS has generated a massive amount of geographic data organized in trajectories. Trajectories are ordered sequences of geographic points that represent a path of any moving object, which provides information on the mobility behavior of this moving objects. To improve the understanding of trajectories, places of greater importance are referred to as stay points and indicate that a user has remained in this correspondent place for a significant time. In the literature, stay points are commonly represented through networks to facilitate trajectory mining. Nonetheless, to the best of our knowledge, there is a lack of studies addressing the quality of these networks. This article addresses this gap and presents a network construction analysis through stay points by using external validity criteria.
Particle-swarm Krill Herd Algorithm

To improve krill herd’s (KH) ability for solving global optimization, by combining the advantage of KH and accelerated particle swarm optimization (APSO) method, an enhanced version of KH technique based on KH and accelerated APSO method, called particle-swarm krill herd (PKH) approach is designed. In this way, the advantage of the APSO and KH approaches are fully exploited, and the proposed PKH algorithm has the useful features originated from APSO and KH optimization techniques. The improvement includes the addition of APSO method serving as mutation operator when the krill updating for solving optimization problems. In PKH method, we used a novel type of elitism to store some best krill during the evolving stage. Eighteen standard testing problems are applied to evaluate these improvements and the results indicate that this metaheuristic method has much better performance than the original KH approach and six state-of-the-art metaheuristic algorithms. Our proposed PKH method significantly overtakes the other seven approaches. Moreover, KH is only inferior to PKH among eight methods. APSO and DE perform only worse than PKH and KH on eighteen functions. In addition, the parametric studies are also implemented to further show the robustness and generality of the proposed PKH approach in the present work.
Operational Aircraft Routing Problem: Some Insights in the Capacitated Maintenance Resources

In aviation industry, airlines solve operational aircraft routing problems (OARP) to assign maintenance-feasible routes to a fleet of aircraft. Note that the scheduled aircraft must be provided with sufficient maintenance resources to prevent costly recovery. Most existing OARP models assume that airlines are given fixed amount of maintenance resources. However, in the case of a capacitated maintenance stations serving more than one airline, resources available to an airline may be highly susceptible to the resource allocation scheme of maintenance providers (MPs). This paper aims to capture the uncertain supply of maintenance resources by modelling the market-based resource allocation schemes of MPs. In particular, two possible scenarios in maintenance stations are explored: (1) each airline has no information about other airlines that shares the same maintenance station; and (2) each airline has complete information about other airlines. This paper discusses game-theoretic models that can be used in these scenarios. It is shown that the equilibrium strategies of airlines in such games contribute to make proper OARP decisions by reducing maintenance misalignments.
An Agent-Based Fog Computing Architecture for Resilience on Amazon EC2 Spot Instances

Cloud computing providers have started offering their idle resources as transient servers. Spot instances are transient servers offered by Amazon, whose prices dynamically change over time based on supply and demand. By using appropriate strategies and fault-tolerant mechanisms, users can effectively use spot instances to run applications at a lower price. This paper presents a resilient agent-based fog computing architecture that combines machine learning and a statistical model to predict time to instance revocation and helps to refine fault tolerance parameters and reduce total execution time. The experiments demonstrate that our model predicts with high levels of accuracy reaching 94% success rate what indicates the model is effective under realistic working conditions.
Modelling and Control of a Dielectric Elastomer Actuator

Dielectric elastomer actuators (DEAs) stick out among explored soft actuators due to their advantages of large deformation, high energy density, fast response and low cost. However, DEAs still are far away from robotic practical applications because of the difficulty of the actuation control. This paper presents a novel control approach for control a 3D DEA control. The fabrication of the proposed soft actuator is introduced and the dynamics of the DEA is analyzed via several experiments. Furthermore, a bio-inspired cerebellum nonlinear controller for achieving accurate movements of the DEAs are designed and analyzed. Finally, the simulation and experiments have been carried out for the developed system validation.
Correctness of Tendermint-Core Blockchains

Tendermint-core blockchains (e.g. Cosmos) are considered today one of the most viable alternatives for the highly energy consuming proof-of-work blockchains such as Bitcoin and Ethereum. Their particularity is that they aim at offering strong consistency (no forks) in an open system combining two ingredients (i) a set of validators that generate blocks via a variant of Practical Byzantine Fault Tolerant (PBFT) consensus protocol and (ii) a selection strategy that dynamically selects nodes to be validators for the next block via a proof-of-stake mechanism. The exact assumptions on the system model under which Tendermint underlying algorithms are correct and the exact properties Tendermint verifies, however, have never been formally analyzed. The contribution of this paper is as follows. First, while formalizing Tendermint algorithms we precisely characterize the system model and the exact problem solved by Tendermint, then, we prove that in eventual synchronous systems a modified version of Tendermint solves (i) under additional assumptions, a variant of one-shot consensus for the validation of one single block and (ii) a variant of the repeated consensus problem for multiple blocks. These results hold even if the set of validators is hit by Byzantine failures, provided that for each one-shot consensus instance less than one third of the validators is Byzantine.
On a Discrete-time Epidemic Model based on a Continuous-time SEIR Model Under Feedback Vaccination Controls

This paper discusses a discrete-time epidemic model which is based on a continuous-time SEIR (susceptible-exposed-infections-recovered) one. The equilibrium points are obtained and their local stability properties are characterized in both the vaccination-free case and the vaccination control one. The vaccination control is of an additive full linear feedback-type one involving constant gains for all the subpopulations.
Hotel Cancellation Strategies Under Online Advanced Booking

We segment heterogeneous customers that are different with each other in arriving probability into two types and study possible pricing strategies together with two types of booking cancellation policies, deadline cancellation and refund cancellation. We find that if hotel is risk neutral, the two types of cancellation contracts are the same as each other. However, for a risk-averse hotel, booking contract with refund cancellation policy is better than that with deadline cancellation policy whereas for a risk-seeking hotel, booking contract with deadline cancellation policy is better.
Deep Learning for People Detection on Beach Images

Convolutional architectures have in recent years become state-of-the-art for several object detection tasks. However, these detectors have not yet been evaluated for detection and monitoring of beach areas. As some of these areas need to be continually monitored for dangerous situations, such as shark attacks, an automated system would be an effective risk control measure. The most significant and specific challenges for this problem are variable scene illumination, partial occlusion and distant camera position. In this work we present a study on three recent convolutional architectures for the task of people detection in beach scenarios. Our dataset is composed of images taken in the Boa Viagem beach, in Brazil, and is used to evaluate Faster R-CNN, R-FCN and SSD in terms of quality and speed of detection. The detectors are pretrained on a dataset containing 91 classes of objects, including people with different levels of scale and occlusion. The results suggest that the Faster R-CNN meta-architecture with the Resnet 101 feature extractor generates significantly better detections in terms of F-measure, while performing at 5.6 fps on a GTX 1080 Ti GPU.
Involving the Manufacturing System within its Planning Phase

Nowadays, manufacturing systems are equipped with a huge number of digital components. Along with the increasing computing power and connectivity, they are becoming more intelligent and can make decisions on their own. Amongst others, these tasks include the self-optimization and self-configuration of the production system. In addition, such cyber-physical production systems can contribute to the planning phase of a manufacturing system, e.g. when the production process must be adapted to a new product. However, neither appropriate means nor a practical mode of operation are available yet. Hence, it is unclear how manufacturing systems can work concurrently with humans in this phase of planning. Within this paper, we present a method of concurrent engineering that enables manufacturing systems to participate in the planning of production systems.
Frame based representation and compression of still images

We use a frame-based representation approach for compression of still images. The frames are optimized for use on images by using a training set of six natural images in the frame design algorithm we have called method of optimal directions (MOD). The optimized frames are investigated, and a compression experiment using a frame is done and compared to JPEG. A set of frames optimized using MOD are used in a multi frame compression (MFC) scheme. A test image is compressed using the MFC scheme with the optimized frames. For low bit rates the results show improved performance compared to JPEG.
A High Throughput Device-to-Device Wireless Communication System

SUMMARY Device-to-device (D2D) communication in cellular networks is deﬁned as direct communication between two mobile users without traversing the base station (BS) or core network. D2D communication can occur on the cellular frequencies (i.e., inband) or unlicensed spectrum (i.e., outband). A high capacity IEEE 802.11-based outband device-to-device communication system for cellular networks is introduced in this paper. Transmissions in device-to-device connections are managed using our proposed medium access control (MAC) protocol. In the proposed MAC protocol, backo ﬀ window size is adjusted dynamically considering the current network status and utilizing an appropriate transmission attempt rate. We have considered both cases that the request to send / clear to send (RTS / CTS) mechanism is and is not used in our protocol design. Describing mechanisms for guaranteeing quality of service (QoS) and enhancing reliability of the system is another part of our work. Moreover, performance of the system in the presence of channel impairments is investigated analytically and through simulations. Analytical and simulation results demonstrate that our proposed system has high throughput, and it can provide di ﬀ erent levels of QoS for its users.
Persistence and Procyclicality in Margin Requirements

Margin requirements for derivative contracts serve as a buffer against the transmission of losses through the financial system by protecting one party to a contract against default by the other party. However, if margin levels are proportional to volatility, then a spike in volatility leads to potentially destabilizing margin calls in times of market stress. Risk-sensitive margin requirements are thus procyclical in the sense that they amplify shocks. We use a GARCH model of volatility and a combination of theoretical and empirical results to analyze how much higher margin levels need to be to avoid procyclicality while reducing counterparty credit risk. Our analysis compares the tail decay of conditional and unconditional loss distributions with comparable stable and risk-sensitive margin requirements. Greater persistence and burstiness in volatility leads to a slower decay in the tail of the unconditional distribution and a higher buffer needed to avoid procyclicality. The tail decay drives other measur.
Categorization of Mergers and Acquisitions in Japan Using Corporate Databases: A Fundamental Research for Prediction

Mergers and Acquisitions (M&A) are recognized important strategy for corporate growth. In practice, M&A business consumes much energy and M&A success rate is not high. Hence, scientific M&A recommendation research is needed under such condition. This paper, focusing on M&A categorization, is a fundamental research for M&A prediction and recommendation. In this paper, we used M&A data, financial data and corporate data for M&A analysis. Based on them, we designed 13 features and used K-means clustering to separate M&A cases. The 13 features are of acquirer features, target features and their relationship features. We grouped M&A cases into 5 clusters and found different characteristics in these 5 clusters. Results in this paper show that these features will be effective for future M&A prediction and recommendation.
Similarity-Based Matrix Factorization for Item Cold-Start in Recommender Systems

In recommender systems (RS) one of the most used approaches is collaborative filtering (CF), which recommends items according to the behavior of similar users. Among CF approaches, those based on matrix factorization are generally more effective because they allow the system to discover the underlying characteristics of interactions between users and items. However, this approach presents the cold-start problem, which occurs because of the system's inability to recommend new items and/or accurately predict new users' preferences. This paper proposes a novel matrix factorization approach, which incorporates similarity of items using their metadata, in order to improve the rating prediction task in an item cold-start scenario. For this purpose, we explore semantic descriptions of items which are gathered from knowledge bases available online. Our approach is evaluated in two different and publicly available datasets and compared against content-based and collaborative algorithms. The experiments show the effectiveness of our approach in the item cold-start scenario.
Analysis of Graph Construction Methods in Supervised Data Classification

Graph-based methods have attracted a lot of attention in recent years, especially due to its inherent ability to capture properties of the networked data (e.g., structural and dynamical). Clustering, semi-supervised label propagation and, more recently, data classification are examples of tasks in which graph-based learning methods have obtained relevant results. In any of these tasks, the common approach is (i) to transform the feature vector data in a graph and then (ii) exploit some property uncovered by the network structure. However, most works have focused on the development of models to exploit the graph, while the graph construction step has been little explored. In this article, we conduct a preliminary study to evaluate supervised graph construction methods based on k-nearest neighbors (kNN) and ϵ-radius neighborhood (ϵN) criteria by employing a recently proposed classification technique based on the importance concept of complex networks. Experiments were conducted on artificial and real-world data sets, including the problem of invariant pattern recognition in images. The results show that the graph construction methods under study are able to deal with different configuration of problems (e.g., domain, features, etc). They also suggest that the combination between selective kNN and ϵN is more suitable in data sets with low level of mixture among the classes, while kNN seems slightly better in problems with higher noise levels.
Modeling of CMS‐based nonuniform interconnects using FDTD technique

This paper presents the finite‐difference time‐domain model of nonuniform interconnects including skin effect losses based on the current mode signaling (CMS). For accurate analysis, the nonlinear CMOS inverter is used as a driver for coupled nonuniform interconnects. These effects are incorporated in the proposed model using the modified alpha power law model. Additionally, high‐frequency losses are incorporated in the proposed model that further improves the accuracy. Using the proposed model, the performance of nonuniform interconnects is investigated using the CMS scheme. Time‐domain analysis model is derived from CMS nonuniform interconnects using finite‐difference time‐domain technique. Both inductive and capacitive couplings have been considered to incorporate coupling effects in interconnects. The efficiency of CMS interconnects is evaluated by comparing with conventional voltage mode signaling interconnects. The propagation delays and dynamic and functional cross talk effects at the far end of the coupled nonuniform interconnect are analyzed at the 32‐nm technology node. The proposed model results are validated using the standard HSPICE simulations.
Computation of Caging Grasps of Objects using Multi-Task Learning Method

Caging grasps would handle objects without full immobilization and enable dealing with the object‘s position and orientation uncertainties. Most previous works have constructed caging sets by using the geometric models of the object. This work aims to present a learning-based method for caging objects only with its image. A multi-task learning method is developed for caging grasps, where the caging region is directly learned from the image of the object. Furthermore, several different caging tasks are trained with a joint model using the sample data of the tasks. Therefore, we could avoid the collection of plenty of training samples. Simulations show the validity of the method.
A Novel Evolutionary Algorithm for Automated Machine Learning Focusing on Classifier Ensembles

Automated Machine Learning (Auto-ML) is an emerging area of ML which consists of automatically selecting the best ML algorithm and its best hyper-parameter settings for a given input dataset, by doing a search in a large space of candidate algorithms and settings. In this work we propose a new Evolutionary Algorithm (EA) for the Auto-ML task of automatically selecting the best ensemble of classifiers and their hyper-parameter settings for an input dataset. The proposed EA was compared against a version of the well-known Auto-WEKA method adapted to search in the same space of algorithms and hyper-parameter settings as the EA. In general, the EA obtained significantly smaller classification error rates than that Auto-WEKA version in experiments with 15 classification datasets.
Detection and variability analyses of CRISPR-like loci in the H. pylori genome

Helicobacter pylori is a human pathogenic bacterium with a high genomic plasticity. Although the functional CRISPR-Cas system has not been found in its genome, CRISPR-like loci have been recently identified. In this work, 53 genomes from different geographical areas are analyzed for the search and analysis of variability of this type of structure. We confirm the presence of a locus that was previously described in the VlpC gene in al lgenomes, and we characterize new CRISPR-like loci in other genomic locations. By studying the variability and gene location of these loci, the evolution and the possible roles of these sequences are discussed. Additionally, the usefulness of this type of sequences as a phylogenetic marker has been demonstrated, associating the different strains by geographical area.
Wetting mechanism of a starch-stabilized alkylketene dimer emulsion: A study by atomic force microscopy

Le comportement au mouillage du verre par une emulsion commerciale de dimere cetene alkyle (DCA) stabilisee par un amidon a ete analyse a l'aide de la microscopie a forces atomiques. Le but premier etait de determiner si l'amidon et le surfactif utilises pour emulsionner le DCA peuvent influencer le mouillage par DCA sur une surface hydrophile jusqu'a en permettre l'etalement complet (θ E = 0). Le DCA n'est pas reconnu pour s'etaler sur le verre ou la cellulose. Le but secondaire etait d'estimer la fraction de la composition de fabrication mouillee par les gouttelettes de DCA. La temperature est la variable qui a le plus d'influence sur le mouillage par DCA. L'etendue du mouillage par DCA augmente avec la temperature jusqu'a 70°C, apres quoi l'angle de contact du liquide atteint une valeur d'equilibre qui ne peut se differencier par la microscopie a forces atomiques. Le DCA mouille le verre a un angle de contact fini (θ E > 0). L'etalement (θ E = 0) n'a jamais ete constate avec la suspension commerciale de DCA. La methode de sechage influence aussi l'etendue du mouillage par DCA sur le verre. Cela s'explique en termes d'hydrolyse DCA, de desorption de l'amidon de la DCA et d'adsorption de l'amidon sur le verre, qui modifie les energies interfaciales et, de ce fait, la valeur d'equilibre de l'angle de contact. La fraction de la composition de fabrication mouillee par les gouttelettes (x) et la distance moyenne entre deux gouttelettes de DCA (L) ont ete calculees pour trois exemples.