Miracle-dz/UniFAR-FTUs huggingface dataset:
Akış Çizgesi Tabanlı Sızma Tahmin Yöntemi (Intrusion Prediction Method Based on Flow Graph)

Bilgi Teknolojilerinin gelisimi ile birlikte sistem ve sisteme dahil olan varliklarin icinde bulundugu tehditler artmaktadir. Bu sebeple bilgi guvenliginin saglanmasi daha da onem kazanmaktadir. Gunumuzde tasarlanan bilgi sistemlerinin altyapisi genellikle bilgisayar aglarina dayanmaktadir ve bu da bilgi sistemlerini saldirilara daha acik hale getirmektedir. Ag guvenliginin saglanmasinda Sizma Tespit Sistemleri (Intrusion Detection Systems, IDS) onemli bir aractir. Sizma Tespit Sisteminde uygulanan teknikler ile saldirilar, ancak kismen ya da tamamen gerceklestikten sonra saptanabilmektedir; buna bagli olarak saldirinin kontrol altinda tutulmasi ya da durdurulmasi zor olmaktadir. Bu nedenle yeni gelistirilecek olan IDS sistemlerine saldiriyi tahmin etme ozelligi dahil edilmelidir. Bu calismada Bilgisayar aglarina olan sizmalarin onceden tahmin edilmesine yonelik bir yontem onerisinde bulunulmustur. Onerilen yontem test edilerek sonuclar verilmistir. Abstract Along with the improvement of Information Technologies, the threat; that the system and the included entities are in, has been increasing. Therefore ensuring information security gains more importance. The infrastructure of the information systems designed lately, is generally built upon computer networks; which makes information systems more prone to attacks. Intrusion Detection Systems are important tools in providing network security. The attacks in Intrusion Detection Systems (IDS) can only be detected after they occur partially or fully. And because of this, taking attacks under control or ceasing them is difficult. For this reason, attack prediction feature should be included to the new IDS systems to be designed. In this research; a method to predict intrusions through computer networks is suggested. Experimental results has been presented after testing the method.
Attention-Based Convolutional and Recurrent Neural Networks for Driving Behavior Recognition Using Smartphone Sensor Data

Driving behavior recognition is a challenging task that exploits the acceleration and angular velocity information of the vehicle collected by smartphone to identify various driving events. Traditional methods usually extract hand-crafted features from raw data, leading to under-explored temporal features of driving behaviors. To address the issue of hand-designed limitation for features, this paper proposes an end-to-end deep learning framework to automatically extract the features of driving behaviors. The mechanism behind our method is to model temporal features, capture salient structure features, and explore the correlation among the high-dimensional sensor data by fusing convolutional neural network (CNN) and recurrent neural network (RNN) with an attention unit. Moreover, a novel approach is introduced to build driving behavior dataset, which considers the effect of gravity in modeling smartphone sensor data. Subsequently, sensor data with device position independence is collected, and six types of driving events (straight driving, static, left turn, right turn, breaking, and acceleration) are annotated, which provides rich sensor information compared with other methods. The experimental results indicate that the proposed model outperforms other competing methods significantly, which possesses good generalization ability in the identification of driving behaviors.
Meeting Intents Detection Based on Ontology for Automatic Email Answering

Automatic email answering is a difficult AI problem that combines classification, natural language understanding and text generation techniques. We present an original approach and a tool based on an ontology to automatically reply to meeting emails. We constructed the ontology from a French corpus of 1150 emails in which the concepts represent detailed meeting intents (proposing a meeting, cancelling a meeting, rescheduling a meeting) and different answer templates. Each intent concept is a semantic rule formalized according to the FrameNet methodology. These rules are used to detect intents in emails and also to extract relevant information (such as date, time or person) used for generating replies. The main advantage of our approach is the generation of more precise answers than those proposed by other approaches. We tested the intent detection step on a set of 297 emails and compared it with different supervised machine learning algorithms. Obtained results are encouraging, with an accuracy 20% higher than results obtained with other algorithms. keywords : Ontology engineering, knowledge acquisition from text, knowledge-based recommendation systems.
A blockchain‐based architecture for securing electronic health record systems

This paper presents a blockchain‐based architecture for our current electronic health record (EHR) systems. Being built on top of existing databases maintained by health providers, the architecture implements a blockchain solution to ensure the integrity of data records and improve interoperability of the systems through tracking all events that happen to the data in the databases. In this proposed architecture, we also introduce a new incentive mechanism for the creation of new blocks on the blockchain. The architecture is independent of any specific blockchain platforms and open to further extensions; hence, it potentially fits in with other electronic record systems that require protection against data misuse.
Smart wonder: cute, helpful, secure domestic social robots

Sci-fi authors and start-ups alike claim that socially enabled technologies like companion robots will become widespread. However, current attempts to push companion robots to the market often end in failure, with consumers finding little value in the products offered. Technology acceptance frameworks describe factors that influence robot acceptance. It is unclear how to design a companion robot based on them, however, as they were derived from much more primitive, asocial technology. Based on two frameworks of robot acceptance as a starting point, this thesis highlights the value socially enabled technologies could bring as conveyed by the views and experiences of three user groups: the potential users of companion robots being exposed to adverts; the people who lived with smart speakers – a successful socially enabled technology with a dedicated embodiment; and the people who lived with companion robots long-term. By discussing both the frameworks of acceptance, and how real people used and anticipated real socially enabled technologies, this thesis draws broad considerations for companion robot designers concerning form factor, (non-)acceptance over time, robot’s personality, trust, and human-robot relationships. The implication is for the valuable traits to be replicated in the future iterations of companion robots. Findings include the tension between familiarity and strangeness of robotic form factors and faces; the specifics of how socially enabled technologies fit and do not fit within existing frameworks of acceptance; the need for both authenticity and pro-activity in companion robot’s personality; the differences between views and actions on security and trust towards autonomous devices in the domestic environment; and the construction of human-machine relationships between people and socially enabled technologies. These findings highlight the need to extend existing frameworks of robot acceptance to include unique factors pertaining to socially enabled technologies. They also highlight the need to highlight the value of companion robots, dismissing the assumption of automatic robot acceptance by people.
Platelet concentrates for transfusion: Preparation, principle and safety criteria for optimal tolerance and reduction of hazards

Les concentres plaquettaires (CP) – comme les autres produits sanguins labiles (PSL) – sont issus de dons de sang ou de ses composants. La reglementation impose des criteres stricts pour le don, la preparation des produits individuels ou de melanges, la qualification et la conservation, mais aussi pour les specifications qui definissent le produit, en particulier pour ce qui concerne son contenu en principe actif therapeutique, ainsi – le cas echeant – que les etapes et produits intermediaires, qui – tous – doivent figurer sur l’etiquette du PSL. Malgre les efforts considerables d’homogeneisation qui ont ete entrepris, il demeure des facteurs de variabilite dans les produits, dont tous ceux qui sont lies aux caracteristiques genetiques et, le cas echeant, metaboliques des donneurs. A cela s’ajoutent des facteurs de variabilite dans les processus de preparation, qui utilisent des dispositifs differents, ainsi que la variabilite de l’âge du PSL a delivrance, puisqu’il s’agit d’un produit vivant, ce qui est particulierement sensible avec les produits cellulaires comme les CP. Enfin, les receveurs (patients transfuses), tres divers de par leurs caracteristiques genetiques, d’une part, et pathologiques (cause de la transfusion) d’autre part, sont une troisieme composante de la variabilite de la triade transfusionnelle : donneur/don-produit-patient. Un certain nombre d’effets indesirables des receveurs (EIR) peuvent ainsi apparaitre au decours des episodes transfusionnels, et particulierement apres les transfusions de CP. Cette revue a pour principaux objectifs de presenter les differents modes de preparation des CP et de souligner les etapes qui peuvent donner lieu a des alterations des cellules (plaquettes) ainsi que les pistes d’amelioration qui peuvent etre retenues. Les modes de preparation et de conservation des CP s’averent en effet etre des facteurs tres sensibles, rendant compte de la survenue possible d’EIR en ce sens qu’en sus du produit actif, il existe des derives biologiquement actifs, soit declenches par le processus, soit amplifies pendant la conservation des produits. Un focus particulier sera fait sur les EIR revetant des aspects inflammatoires, qui pour une large part deroulent de la physiologie des plaquettes elles-memes ; cette revue permettra de rappeler les mecanismes d’activation des plaquettes, dont certains – steriles – sont ou peuvent etre mis en jeu pendant la preparation et la conservation des produits.
Ranking Absorption Practices of Knowledge for Collaborative Innovation: Which is the Ideal Multi Criteria Decision Method

This paper focuses on evolving an absorptive capacity (ACAP) assessment tool designed to help firms understand their ACAP maturity in processing external knowledge. ACAP maturity is evaluated based on a firm's capacity and willingness to do relevant ACAP practices. Although an earlier version of the ACAP tool was able to evaluate maturity and highlight immature practice, it could not determine how critical these practices were for improvement action. Thus, a means of eliciting the importance of practices and aggregating it with their ACAP maturity evaluations is needed. This paper provides summaries of the subjective weight elicitation methods and aggregation techniques which were identified from the domain of multi-criteria decision making. Criteria for comparing these methods are defined and analyzed to determine the most appropriate methods for the current application. The SRF method for subjective weight elicitation, aggregated with the maturity evaluations through weight sum models, is deemed the most appropriate for the current application. During testing with users, the SRF procedure was found to suffer from various usability concerns which will be investigated in future work.
Alternating optimization to solve penalized regression‐based clustering model

Two previously proposed heuristic algorithms for solving penalized regression‐based clustering model (PRClust) are (a) an algorithm that combines the difference‐of‐convex programming with a coordinate‐wise descent (DC‐CD) algorithm and (b) an algorithm that combines DC with the alternating direction method of multipliers (DC‐ADMM). In this paper, a faster method is proposed for solving PRClust. DC‐CD uses p × n × (n − 1)/2 slack variables to solve PRClust, where n is the number of data and p is the number of their features. In each iteration of DC‐CD, these slack variable and cluster centres are updated using a second‐order cone programming (SOCP). DC‐ADMM uses p × n × (n − 1) slack variables. In each iteration of DC‐ADMM, these slack variables and cluster centres are updated using ADMM. In this paper, PRClust is reformulated into an equivalent model to be solved using alternating optimization. Our proposed algorithm needs only n × (n − 1)/2 slack variables, which is much less than that of DC‐CD and DC‐ADMM and updates them analytically using a simple equation in each iteration of the algorithm. Our proposed algorithm updates only cluster centres using an SOCP. Therefore, our proposed SOCP is much smaller than that of DC‐CD, which is used to update both cluster centres and slack variables. Experimental results on real datasets confirm that our proposed method is faster and much faster than DC‐ADMM and DC‐CD, respectively.
Stealthy Attack Against Redundant Controller Architecture of Industrial Cyber-Physical System

In an industrial cyber-physical system (iCPS), the controller plays a critical role in guaranteeing reliability and stability. Therefore, redundant controller architecture is a well-adopted approach by distributed control systems (DCS), supervisory control and data acquisition (SCADA), and other typical iCPSs. They monitor and control the critical industrial process, such as power generation, chemical industry, water treatment plant, etc. Redundant controller architecture has been designed and largely implemented in response to unpredictable mechanical failures. However, this structure initially proposed for guaranteeing reliability and safety may expand the cyber-attack surface, posing the risk that an attacker may take advantage of this architecture for stealthy attacks. In this article, we analyze the vulnerability arising from the redundant controller architecture and propose a combined attack methodology against these redundant controller architecture systems in a stealthy manner. We find several 0-day vulnerabilities of the real-world devices from three manufacturers and further implement the combined attack over these devices. Our experimental results over various types of real-world devices show that the redundant controller architecture can be exploited to compromise all tested systems stealthily. We also present guidelines for mitigating this risk.
Paralympic VR Game Immersive Game using Virtual Reality Technology

Throughout the years virtual reality has been used for a wide range of applications, and several types of research have been made in order to improve its techniques and technology. In the last few years, the interest in virtual reality has been increasing partially due to the emergence of cheaper and more accessible hardware, and the increase in content available. One of the possible applications for virtual reality is to lead people into seeing situations from a different perspective, which can help change opinions. This thesis uses virtual reality to help people better understand paralympic sports by allowing them to experience the sports’ world from the athletes’ perspective. For the creation of the virtual environment, both computer-generated elements and 360 video are used. The integration of these two components presented a challenge to explore. This thesis focused on wheelchair basketball, and a simulator of this sport was created resorting to the use of a game engine (Unity 3D). For the development of this simulator, computer-generated elements were built, and the interaction with them implemented. Besides allowing the users to play the sport as if they are in the athlete’s shoes, users can also watch 360 videos in which explanations of the modality (rules and classification) are presented. They are also capable of interacting with some of these videos through virtual elements that are placed over the videos. User studies were conducted to evaluate the sense of presence, motion sickness and usability of the system developed. The results were positive although there are still some aspects that should be improved.
SVM approach for non-parametric method in classification and regression learning process on feature selection with $epsilon$-insensitive region

Machine Learning is considered as a subfield of Artificial Intelligence. Machine Learning is concerned with the development of techniques and design the methods which enable the computer to learn. The field of machine learning is concerned with constructing computer program that automatically improve its performance with experience. In today’s machine learning applications, support vector machines are considered (SVM) one of the most robust and accurate methods among all well-known algorithms and also being developed at a fast space. The aim of SVM is to find the best classification function, in a two-class learning task, and to distinguish between members of the two classes in the training data. Hence, the goal of machine learning is to find the output hypothesis that performed the correct classification of the training data, but the other earlier algorithms to find the hypothesis that accurate fit to the data. SVM requires that each data instance is represented as a vector of real numbers. Hence, if there are categorical attributes, convert them into numerical data, then we using m numbers to represent an m-category attribute. Only one of the m numbers is one, and others are zero. Machine learning has been applied in various field such as medical diagnosis, bioinformatics, detecting credit card fraud, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, and robot locomotion. The objective is algorithmic approach for non parametric methods to tractable for high dimensional massive datasets.
A novel deep capsule neural network for remaining useful life estimation

With the availability of cheaper multi-sensor systems, one has access to massive and multi-dimensional sensor data for fault diagnostics and prognostics. However, from a time, engineering and computational perspective, it is often cost prohibitive to manually extract useful features and to label all the data. To address these challenges, deep learning techniques have been used in the recent years. Within these, convolutional neural networks have shown remarkable performance in fault diagnostics and prognostics. However, this model present limitations from a prognostics and health management perspective: to improve its feature extraction generalization capabilities and reduce computation time, ill-based pooling operations are employed, which require sub-sampling of the data, thus loosing potentially valuable information regarding an asset’s degradation process. Capsule neural networks have been recently proposed to address these problems with strong results in computer vision–related classification tasks. This has motivated us to extend capsule neural networks for fault prognostics and, in particular, remaining useful life estimation. The proposed model, architecture and algorithm are tested and compared to other state-of-the art deep learning models on the benchmark Commercial Modular Aero Propulsion System Simulation turbofans data set. The results indicate that the proposed capsule neural networks are a promising approach for remaining useful life prognostics from multi-dimensional sensor data.
Automatic Understanding and Formalization of Plane Geometry Proving Problems in Natural Language: A Supervised Approach

Automatically understanding natural language problems is a long-standing challenging research problem in automatic solving. This paper models the understanding of geometry problems as a problem of relation extraction, instead of as the problem of semantic understanding of natural language. Then it further proposes a supervised machine learning method to extract geometric relations, targeting to produce a group of relations to represent the given geometry problem. This method identifies the actual geometric relations from the relation candidates using a classifier trained from the labelled examples. The formalized geometric relations can then be transformed into the target system-native representations for manipulation in various tasks. Experiments conducted on the test problem dataset show that the proposed method can extract geometric relations at high F1 scores. The comparisons also demonstrate that the proposed method can achieve good performance against the baseline methods. Integrating the automatic understanding method with different geometry systems will greatly enhance the efficiency and intelligence in geometry tutoring.
Usability Evaluation Metrics of Tourism Mobile Applications

The use of mobile phone applications for our touristic activity is very common nowadays with the simplification of smartphones. The tourism mobile applications currently can be argued to be one of the most useful applications that can facilitate the movement of travelers. However, existing usability evaluation metrics are too general to be applied to a more specific application, such as mobile tourism application. Thus, the objective of the study is to propose usability evaluation metrics for tourism mobile applications. The study employs four phases: identifying problem and the objective, encompassing the techniques of developing usability measurement of metrics, selecting the usability metrics of tourism mobile application and conducting expert review and verification. The verification phase was conducted using expert review approach to measure the proposed metrics in terms of its consistency ease of use, understandable, verifiable and overall impression. The finding revealed that the proposed metrics have been well received by the experts in terms of consistency, ease to use, understandable, verifiability and overall impression. Finally, this study presented usability metrics for the tourism mobile applications that can be used by designers or usability practitioners in creating a useable mobile application for the tourists.
Adaptive Lossy Image Compression Based on Singular Value Decomposition

Image compression techniques aim to reduce redundant information in order to allow data storage and transmission in an efficient way. In this work, we propose and analyze a lossy image compression method based on the singular value decomposition using an optimal choice of eigenvalues and an adaptive mechanism for block partitioning. Experiments are conducted on several images to demonstrate the effectiveness of the proposed compression method in comparison with the direct application of the singular value decomposition.