Miracle-dz/UniFAR-FTUs huggingface dataset:
Negative Selection and Neural Network Based Algorithm for Intrusion Detection in IoT

Internet of Things expands the boundaries of the Internet to encompass many devices with constraint computational and power capabilities. This limits the implementation of security techniques such as Intrusion Detection Systems. In this paper, we propose a novel classification algorithm specifically designed for Internet of Things Intrusion Detection Systems. Our solution consists of two distinct layers. First, we employ a Negative Selection algorithm for creating a training set based only on the knowledge of the normal network behavior. Based on this data we train a simple Neural Network that is used to do the actual classification. This multilayer approach allows to distance the training complexity from the computationally and power constrained IoT devices. Furthermore, the addition of Negative Selection layer allows us to train a Neural Network only based on the self/normal behavior of the network, without the need for nonself/attack data. We call this algorithm Negative Selection Neural Network (NSNN). We test the algorithm against the KDD NSL dataset. The test results lead to the conclusion that the proposed algorithm is capable of functioning as network intrusion detection classifier.
Smart Contracts: Automated Stipulations on Blockchain

Contracts, a set of legally negotiated rules between the transacting parties are often the prime cause of legal as well as business disputes. Due to this discord, they are generally viewed with contempt. The need to revolutionize contracts has been much felt by the attorneys and the business professionals, so as to avoid these daedal contract conflicts. This resulted in the advent of smart contracts that was led by Blockchain technology. It is a blend of legalese from lawyers and computer code. A smart contract is a versatile system capable of facilitating, automating and enforcing an agreement (i.e. contract). In this paper, we preview as well as analyze smart contract based on blockchain technology for a decentralized system.
A Survey of Mobile Information-Centric Networking: Research Issues and Challenges

Recently, a series of innovative information-centric networking (ICN) architectures have been designed to better address the shift from host-centric end-to-end communication to requester-driven content retrieval. With the explosive increase of mobile data traffic, the mobility issue in ICN is a growing concern and a number of approaches have been proposed to deal with the mobility problem in ICN. Despite the potential advantages of ICN in mobile wireless environments, several significant research challenges remain to be addressed before its widespread deployment, including consistent routing, local cached content discovery, energy efficiency, privacy, security and trust, and practical deployment. In this paper, we present a brief survey on some of the works that have already been done to achieve mobile ICN, and discuss some research issues and challenges. We identify several important aspects of mobile ICN: overview, mobility enabling technologies, information-centric wireless mobile networks, and research challenges.
Using Taste Groups for Collaborative Filtering

Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and domain independent. However, there is a lack of negative examples. Existing works circumvent this problem by making various assumptions regarding the unconsumed items, which fail to hold when the user did not consume an item because she was unaware of it. In this paper, we propose as a novel method for addressing the lack of negative examples in implicit feedback. The motivation is that if there is a large group of users who share the same taste and none of them consumed an item, then it is highly likely that the item is irrelevant to this taste. We use Hierarchical Latent Tree Analysis(HLTA) to identify taste-based user groups and make recommendations for a user based on her memberships in the groups.
Amber in prehistoric Iberia: New data and a review

Provenancing exotic raw materials and reconstructing the nature and routes of exchange is a major concern of prehistoric archaeology. Amber has long been recognised as a key commodity of prehistoric exchange networks in Europe. However, most science-based studies so far have been localised and based on few samples, hence making it difficult to observe broad geographic and chronological trends. This paper concentrates on the nature, distribution and circulation of amber in prehistoric Iberia. We present new standardised FTIR analyses of 22 archaeological and geological samples from a large number of contexts across Iberia, as well as a wide scale review of all the legacy data available. On the basis of a considerable body of data, we can confirm the use of local amber resources in the Northern area of the Iberian Peninsula from the Palaeolithic to the Bronze Age; we push back the arrival of Sicilian amber to at least the 4th Millennium BC, and we trace the appearance of Baltic amber since the last quarter of the 2nd Millennium BC, progressively replacing Sicilian simetite. Integrating these data with other bodies of archaeological information, we suggest that the arrival of Baltic amber was part of broader Mediterranean exchange networks, and not necessarily the result of direct trade with the North. From a methodological perspective, thanks to the analyses carried out on both the vitreous core and the weathered surfaces of objects made of Sicilian simetite, we define the characteristic FTIR bands that allow the identification of Sicilian amber even in highly deteriorated archaeological samples.
Investigations on Statistical Classification Methods for Use in Breast Thermography

Recent studies have stated that thermography has shown to be very promising as an auxiliary tool in the task of early detection of breast cancer, which is a fundamental factor to increase the chances of cure of the patient. The present work aims to analyze methods of images classification of breast thermographic and to evaluate the results obtained with the purpose of investigating the feasibility of the use of infrared thermography for the detection of breast cancer. Initially, the thermographic image is obtained and processed. Then, the feature extraction occurs that is based on the temperature ranges obtained from the thermogram, determining the input data for the classification process. Eight statistical classifiers were evaluated. Finally, 93.42% of accuracy, 94.73% of sensitivity and 92.10% of specificity were obtained for the Cancer class in a binary analysis (Cancer versus Non-cancer) and in a multiclass analysis (Malignant, Benign, Cyst and Normal), 63.46% of accuracy, 80.77% of sensitivity and 86.54% of specificity were obtained for the Malignant class.
Towards security defect prediction with AI

In this study, we investigate the limits of the current state of the art AI system for detecting buffer overflows and compare it with current static analysis tools. To do so, we developed a code generator, s-bAbI, capable of producing an arbitrarily large number of code samples of controlled complexity. We found that the static analysis engines we examined have good precision, but poor recall on this dataset, except for a sound static analyzer that has good precision and recall. We found that the state of the art AI system, a memory network modeled after Choi et al. [1], can achieve similar performance to the static analysis engines, but requires an exhaustive amount of training data in order to do so. Our work points towards future approaches that may solve these problems; namely, using representations of code that can capture appropriate scope information and using deep learning methods that are able to perform arithmetic operations.
Traffic-Aware Resource Allocation with Aggregation in Heterogeneous Networks with WLANs

We consider resource allocation with aggregation for different types of traffic in heterogeneous networks, including WLANs. While mobile data traffic is expected to increase, efficient management of multiple bands including unlicensed band becomes increasingly important. In this context, we formulate a resource allocation problem using utility functions for heterogeneous traffic and propose a novel algorithm that considers the estimated UE speed, traffic types and channel quality. Simulation results illustrate performance of the proposed algorithm in terms of higher utility value and fairness, even at high traffic loads. Additional improvements in resource utilization through estimating UE speed and allocating low-mobility UEs to Wi-Fi are shown.
No Target Function Classifier - Fast Unsupervised Text Categorization using Semantic Spaces

We describe a Text Categorization (TC) classifier that does not require a target function. When performing TC, there is a set of predefined, labeled categories that the documents need to be assigned to. Automated TC can be done by either describing fixed classification rules or by applying machine learning. Machine learning based TC usually occurs in a supervised learning fashion. The learner generally uses example document-to-category assignments (the target function) for training. When TC is introduced for any application or when new topics emerge, such examples are not easy to obtain because they are timeintensive to create and can require domain experts. Unsupervised document classification eliminates the need for such training examples. We describe a method capable of performing unsupervised machine learning-based TC. Our method provides quick, tangible classification results that allow for interactive user feedback and result validation. After uploading a document, the user can agree or correct the category assignment. This allows our system to incrementally create a target function that a regular supervised learning classifier can use to produce better results than the initial unsupervised system. To do so, the classifications need to be performed in a time acceptable for the user uploading documents. We based our method on word embedding semantics with three different implementation approaches; each evaluated using the reuters21578 benchmark (Lewis, 2004), the MAUI citeulike180 benchmark (Medelyan et al., 2009), and a self-compiled corpus of 925 scientific documents taken from the Cornell University Library arXiv.org digital library (Cornell University Library, 2016). Our method has the following advantages: Compared to key word extraction techniques, our system can assign documents to categories that are labeled with words that do not literally occur in the document. Compared to usual supervised learning classifiers, no target function is required. Without the requirement of a target function the system cannot overfit. Compared to document clustering algorithms, our method assigns documents to predefined categories and does not create unlabeled groupings of documents. In our experiments, the system achieves up to 66.73 % precision, 41.8 % recall and 41.09% F1 (all reuters21578) using macroaveraging. Using microaveraging, similar effectiveness is obtained. Even though these results are below those of contemporary supervised classifiers, the system can be adopted in situations where no training data is available or where text needs to be assigned to new categories capturing freshly emerging knowledge. It requires no manually collected resources and works fast enough to gather feedback interactively thereby creating a target function for a regular classifier.
PD1-CD28 Fusion Protein Enables CD4+ T Cell Help for Adoptive T Cell Therapy in Models of Pancreatic Cancer and Non-hodgkin Lymphoma

Background: Interaction of the programmed death receptor 1 (PD-1) and its ligand, PD-L1, suppresses T cell activity and permits tumors to evade T cell-mediated immune surveillance. We have recently demonstrated that antigen-specific CD8+ T cells transduced with a PD1-CD28 fusion protein are protected from PD-1-mediated inhibition. We have now investigated the potential of PD1-CD28 fusion protein-transduced CD4+ T cells alone or in combination with CD8+ T cells for immunotherapy of pancreatic cancer and non-Hodgkin lymphoma. Methods: OVA-specific CD4+ and CD8+ were retrovirally transduced with the PD1-CD28 fusion protein. Cytokine release, proliferation, cytotoxic activity, and phenotype of transduced T cells were assessed in the context of Panc02-OVA (murine pancreatic cancer model) and E.G7-PD-L1 (murine T cell lymphoma model) cells. Results: Stimulation of PD1-CD28 fusion protein-transduced CD4+ T cells with anti-CD3 and recombinant PD-L1 induced specific T cell activation, as measured by IFN-y release and T cell proliferation. Coculture with Panc02-OVA or E.G7-PD-L1 tumor cells also led to specific activation of CD4+ T cells. Cytokine release and T cell proliferation was most effective when tumor cells simultaneously encountered genetically engineered CD4+ and CD8+ T cells. Synergy between both cell populations was also observed for specific tumor cell lysis. T cell cytotoxicity was mediated via granzyme B release and mediated enhanced tumor control in vivo. Transduced CD4+ and CD8+ T cells in co-culture with tumor cells developed a predominant central memory phenotype over time. Different ratios of CD4+ and CD8+ transduced T cells led to a significant increase of IFN-y and IL-2 secretion positively correlating with CD4+ T cell numbers used. Mechanistically, IL-2 and MHC-I were central to the synergistic activity of CD4+ and CD8+ T cells, since neutralization of IL-2 prevented the crosstalk between these cell populations. Conclusion: PD1-CD28 fusion protein-transduced CD4+ T cells significantly improved anti-tumoral effect of fusion protein-transduced CD8+ T cells. Thus, our results indicate that PD1-CD28 fusion protein-transduced CD4+ T cells have the potential to overcome the PD-1-PD-L1 immunosuppressive axis in pancreatic cancer and non-Hodgkin lymphoma.
Sensor, Signal, and Imaging Informatics in 2017

Summary Objective: To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017. Methods: PubMed® and Web of Science® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook. Results: The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information. Conclusion: The growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics.
Transformerless Inverter using Charge Pump Circuit For Photovoltaic Application

The leakage current is reduced by implementing a new transformerless inverter along with charge pump circuit concept. This topology consists of MOSFET switches (S1-S4), two diodes (D1-D2), two capacitors (C1-C2) and output filter (Lf, Cf). The leakage current is minimized and common mode voltage is created. Negative voltage is created by the charge pump circuit. The ripple present in output side is reduced by using the technique unipolar sinusoidal pulse width modulation (SPWM). The filter requirements are also reduced in the proposed inverter. The inverter has the following advantages 1) compact size 2) high efficiency 3) leakage current is minimized. Simulation results are presented using MATLAB.
Analyzing the Deployment Challenges of Beacon Stuffing as a Discovery Enabler in Fog-to-Cloud Systems

In order to meet the needs of emerging IoT applications having tight QoS constraints, new computing paradigms have been proposed, bringing computation resources closer to the edge of the network, where IoT resides. One of such paradigms is Fog-to-Cloud (F2C), defined as a framework where the combined use of fog and cloud resources is coordinated and managed in an optimized manner to achieve the desired service requirements. Unlike cloud computing, the fog provides a heterogeneous set of resources, possibly within fixed deployments provided by city managers, or that could even be contributed by end users. This brings in many challenges yet to be addressed such as resource discovery, which is the focus of this paper. This paper digs into the utilization of 802.11 beacon stuffing as a possible solution allowing the discovery of nearby devices in an F2C system, particularly dealing with specific design and implementation details of the proposed solution and more importantly its real applicability to an F2C system through the analysis of several experiments carried out on a real world testbed.
Minimizing Indoor Localization Errors for Non-Line-of-Sight Propagation

Indoor Localization becomes more important, as it provides additional context for many applications for example in the Internet of Things (IoT), Time-of-flight measurements, as a basis for distance estimation, are susceptible for non-line-of-sight (NLOS) propagation, resulting in large distance errors. Standard least squares solutions to estimate the targets location do not account for NLOS propagation which results in large scale errors. We investigate the difference between L1- and L2-minimization and present a new framework based on a modified RANSAC approach. Additionally, we investigate a Support Vector Machine (SVM) to detect NLOS measurements. We present simulation and measurement results and evaluate our approach. We show that our framework delivers better performance in presence of NLOS propagation compared to plain Ll-or L2-minimization.
Policy Framework for the Next Generation Platform as a Service

The Platform-as-a-Service (PaaS) model, allows service providers to build and deploy their services following streamlined work-flows. However, platforms deployed through the PaaS model can be very diverse in terms of technologies and involved subsystems (e.g. infrastructure, orchestration). Thus, the means for deploying and managing a service can significantly vary depending on the deployed platform. To address this issue, this paper proposes a policy-based framework designed for the Next Generation Platform-as-a-Service (NGPaaS). This framework allows service providers to define platform-wide and technology-agnostic policies to NGPaaS, by means of abstraction of the underlying platforms and the use of generic interfaces. The paper also presents a specific use case for the proposed framework, which targets network-oriented policies.
Dynamic Stability of an Autonomous Microgrid Considering Active Load Impact With a New Dedicated Synchronization Scheme

Similar to distributed generation inverters, active load has to use a dedicated synchronization unit such as a phase-locked loop (PLL) to get the reference voltage of the microgrid. To avoid PLL drawbacks, a new method is proposed to track the microgrid reference voltage for active load synchronization. An autonomous microgrid is modeled and designed. Additionally, the impact of active loads on the dynamic stability of the autonomous microgrid is investigated. An optimal design is performed to obtain the optimal controller parameters. Based on the time-domain simulation, the control design problem is optimally solved by minimizing a weighted objective function to limit the error in the dc voltage and the measured active power. Different step change disturbances are applied to examine and verify the effectiveness of the new synchronization method and the proposed controllers. Finally, a comparison between the proposed synchronization scheme and the traditional PLL is provided to prove the superiority of the proposed technique. It is concluded that the proposed synchronization method has a potential to enhance the microgrid stability efficiently. With the proposed method, PLL disadvantages such as design complexity, bad accuracy, nonlinearity, tuning difficulties, slow response, and negative impact on the control performance can be avoided.
Le caractère dynamique et évolutif de la notion d’innovation : Quelles conséquences pour l’évaluation de la capacité à innover des entreprises ?

L'innovation possede intrinsequement un caractere dynamique qui fait evoluer la facon de la gerer au sein des entreprises. Tous ces changements ont des impacts sur les pratiques et les processus d'innovation rendant difficile la tâche de definir un indicateur de mesure de la capacite a innover pertinent dans le temps. Ainsi les indicateurs d'evaluation de la capacite a innover doivent aussi evoluer constamment. Dans cet article une methodologie de mise a jour iterative des indicateurs de mesure de la capacite a innover est proposee. Basee sur le cycle de Deming d'amelioration continue la methodologie propose quatre etapes : suivre, choisir, integrer et tester. Ces etapes sont configurees comme une boucle afin de repeter cette sequence selon la vitesse de changements dans l'environnement de l'innovation. La methodologie sera testee sur l'indicateur de la capacite a innover propose par l'equipe de recherche ERPI de l'Universite de lorraine, appele Indice d'Innovation Potentielle (IIP). Cette etude de cas met en evidence l'evolution de l'IIP depuis sa derniere version de 2016 vers sa version actuelle.
A Reduced Power MUX in 16Nm CMOS Technology

A new 2:1 mux in 16nm cmos design, nmux, is presented in this paper. The proposed design uses dual assignment of threshold voltage and oxide width with a specific pattern. Static power, delay and PDP of nmux is compared with high performance application and low power application multiplexer. It is found that there is an improvement of 65.5% in static power and 63.4% in delay as compared to the multiplexer designed for low power applications. Static power in case nmux is reduced to 99.9% as compared to mux designed for high performance applications. The performance of nmux here is reduced to 5.3%. There is no loss in area in proposed mux.
Bringing personalized learning into computer-aided question generation

This paper proposes a novel and statistical method of ability estimation based on acquisition distribution for a personalized computer aided question generation. This method captures the learning outcomes over time and provides a flexible measurement based on the acquisition distributions instead of precalibration. Compared to the previous studies, the proposed method is robust, especially when an ability of a student is unknown. The results from the empirical data show that the estimated abilities match the actual abilities of learners, and the pretest and post-test of the experimental group show significant improvement. These results suggest that this method can serves as the ability estimation for a personalized computer-aided testing environment.
The Raspberry Pi in an Education Process

The aim of this work is to create a working GPS tracker for cars with a web administration interface. The web application design was based on the similar solutions that already exist and needs of potential users. The GPS tracker is based on Raspberry Pi 3 board, which is widely available and is a perfect learning tool for students wanting to learn basics about software-hardware interaction. Whole work was divided into simpler tasks, which could be solved on a regular week basis. Students were divided into two groups based on their preferences. Main task for the first group was to assemble and program the GPS tracker based on the Raspberry Pi Zero board using GPS and GSM modules. The second group needed to develop a web application which will store and manage data from the tracker. The complete solution was then tested with real data and evaluated by real users. Students were then given a questionnaire to provide feedback on this type of group project and the results were mainly positive.