Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Goal Directed Techniques for Journey Planning with Multi-criteria Range Queries in Public Transit

One of the main problems for a realistic journey planning in public transit is the need to give the user multiple qualitative choices. Usually, public transit journeys involve 4 main criteria: the departure time, the arrival time, the number of transfers and the walking distance. The problem of computing Pareto sets with these criteria is called the Pareto range query problem. This problem is complex and difficult to solve within the constraints of the industrial world of smartphone applications, like a response time of the order of a second. In this paper, we present the Goal Directed Connection Scan Algorithm (GDCSA), an algorithm that allows, for the first time, to solve this problem with run times of less than 0.5 seconds on most European city or country-wide networks, like Berlin or Switzerland. In addition, GDCSA satisfies other industrial needs: it is conceptually simple and easy to implement. It partitions the graph in geographically small areas and precomputes some lower bounds on the duration of a trip in order to select for each itinerary a sub-set of these areas to decrease the number of scanned connections. Combining this sub-set and a journey planning using 4 criteria, the number of scanned connections is lowered by a factor of up to 17 times compared to the best algorithms (CSA and RAPTOR), the number of nodes opened during the search is lowered by a factor of up to 2.9 and the query times are lowered by a factor of up to 9 on metropolitan networks. The integration of GDCSA in a smartphone app backend server led to an improvement in results by a factor of 5.
Reinforcement Learning with Quantitative Verification for Assured Multi-Agent Policies

: In multi-agent reinforcement learning, several agents converge together towards optimal policies that solve complex decision-making problems. This convergence process is inherently stochastic, meaning that its use in safety-critical domains can be problematic. To address this issue, we introduce a new approach that combines multi-agent reinforcement learning with a formal veriﬁcation technique termed quantitative veriﬁcation . Our assured multi-agent reinforcement learning approach constrains agent behaviours in ways that ensure the satisfaction of requirements associated with the safety, reliability, and other non-functional aspects of the decision-making problem being solved. The approach comprises three stages. First, it models the problem as an abstract Markov decision process , allowing quantitative veriﬁcation to be applied. Next, this abstract model is used to synthesise a policy which satisﬁes safety, reliability, and performance constraints. Finally, the synthesised policy is used to constrain agent behaviour within the low-level problem with a greatly lowered risk of constraint violations. We demonstrate our approach using a safety-critical multi-agent patrolling problem.
Training an Agent to Find and Reach an Object in Different Environments using Visual Reinforcement Learning and Transfer Learning

This paper consists of a study on deep learning by visual reinforcement for autonomous robots through transfer learning techniques. The simulation environments tested in this study are realistic environments where the challenge of the robot was to learn and transfer knowledge in different contexts, taking advantage of the experience of previous environments in future environments. This type of approach, besides adding knowledge to autonomous robots, reduces the number of training epochs for the algorithm even in complex environments, justifying the use of transfer learning techniques.
Process Digitalization using Blockchain: EU Parliament Elections Case Study

Blockchain aims to be a disruptive technology in many aspects of our lives. It attempts to disrupt the aspect of our lives that were hard to digitize, such as democratic elections or were digitized in a centralized way, such as the financial system or social media. Despite the initial success of blockchain platforms, many hacks, design errors, and a lack of standards was encountered. This paper aims to provide a methodical approach to the design of the blockchain-based systems and help to eliminate these issues. The approach is then demonstrated in an EU parliament elections case study.
Explaining Inaccurate Predictions of Models through k-Nearest Neighbors

Deep Learning (DL) models exhibit dramatic success in a wide variety of fields such as human-machine interaction, computer vision, speech recognition, etc. Yet, the widespread deployment of these models partly depends on earning trust in them. Understanding how DL models reach a decision can help to build trust on these systems. In this study, we present a method for explaining inaccurate predictions of DL models through post-hoc analysis of k-nearest neighbours. More specifically, we extract k-nearest neighbours from training samples for a given mispredicted test instance, and then feed them into the model as input to observe the model’s response which is used for post-hoc analysis in comparison with the original mispredicted test sample. We apply our method on two different datasets, i.e. IRIS and CIFAR10, to show its feasibility on
Graph Convolution Networks for Cell Segmentation

Graph signal processing is an emerging field in deep learning, aiming to solve various non-Euclidean domain problems. Pathologist have difficulty detecting diseases at an early stage due to the limitations of clinical methods and image analysis. For more accurate diagnosis of disease and early detection, automated segmentation can play a vital role. However, efficiency and accuracy of the system depends on how the model learned. We have found that traditional machine-learning methods, such as clustering and thresholding, are unsuited for precise cell segmentation. Furthermore, the recent development of deep-learning techniques has demonstrated promising results, especially for medical images. In this paper, we proposed two graph-based convolution methods for cell segmentation to improve analysis of immunostained slides. Our proposed methods use advanced deep-learning, spectral-, and spatial-based graph signal processing approaches to learn features. We have compared our results with state-of-the-art fully convolutional networks(FCN) method and found a significant of improvement of 2.2% in the spectral-based approach and 3.94% in the spatial-based approach in pixel based accuracy.
Sensor Fusion Neural Networks for Gesture Recognition on Low-power Edge Devices

: The goal of hand gesture recognition based on time-of-ﬂight and radar sensors is to enhance the human-machine interface, while taking care of privacy issues of camera sensors. Additionally, the system needs to be deployable on low-power edge devices for applicability in serial-produced vehicles. Recent advances show the capabilities of deep neural networks for gesture classiﬁcation but they are still limited to high performance hardware. Embedded neural network accelerators are constrained in memory and supported operations. These limitations form an architectural design problem that is addressed in this work. Novel gesture classiﬁcation networks are optimized for embedded deployment. The new approaches perform equally compared to high-performance neural networks with 3D convolutions, but need only 8.9% of the memory. These lightweight network architectures allow deployment on constrained embedded accelerator devices, thus enhancing human-machine interfaces.
ArabiaNer: A System to Extract Named Entities from Arabic Content

The extraction of named entities from unstructured text is a crucial component in numerous Natural Language Processing (NLP) applications such as information retrieval, question answering, machine translation, to name but a few. Named-entity Recognition (NER) aims at locating proper nouns from unstructured text and classifying them into a predefined set of types, such as persons, locations, and organizations. There has been extensive research on improving the accuracy of NER in English text. For other languages such as Arabic, extracting Named-entities is quite challenging due to its morphological structure. In this paper, we introduce ArabiaNer, a system employing Conditional Random Field (CRF) learning algorithm with extensive feature engineering steps to effectively extract Arabic named Entities. ArabiaNer produced state-of-the-art results with f1-score of 91.31% when applied on the ANERcrop dataset.
Development of a Purchasing Portfolio Model for the Health Sector: A Case Study of a Central Hospital

Over the years, the purchasing area has taken on an essential role in the management of companies in all activity sectors. In the health sector, purchasing medicines is highly important considering the amounts involved, the impact on the service quality, and the wide variety of purchased products. This research work combined action research with a case study and aims to apply to a Central Hospital a Purchasing Portfolio Model based on the Kraljic Purchasing Matrix (KPM). The KPM allows for the classification of different classes of medicines in accordance with their impact on profits and supply risk dimensions, making it possible to define differentiated purchasing strategies. This application used the Analytical Hierarchical Process (AHP) tool for criteria prioritization and used direct measurement for criterion rating. By applying the model to a Central Hospital, this study seeks to increase the areas of applicability of purchasing portfolio models. Moreover, the results confirmed the model's value in defining medicine purchasing strategy at the Central Hospital and also gave rise to guidelines for applying the model.
Object Tracking using Correction Filter Method with Adaptive Feature Selection

: Correlation filter based tracking algorithms have shown favourable performance in recent years. Nonetheless, the fixed feature selection and potential model drift limit their effectiveness. In this paper, we propose a novel adaptive feature selection based tracking method which keeps the strong discriminating ability of the correlation filter. The proposed method can automatically select either the HOG feature or color feature for tracking based on the confidence scores of the features in each frame. Firstly, the response map of the color features and the HOG features are extracted respectively using correlation filter. The Lab color space is used to extract the color features which separate the luminance from the color. Secondly, the confidence region and the possible location of the target are estimated using the average peak-to-correlation energy. Thirdly, three criteria are used to select the proper feature for the current frame to perform tracking adaptively. The experimental results demonstrate that the proposed tracker performs superiorly comparing with several state-of-the-art algorithms on the OTB benchmark datasets.
Short-reach 200 Gb/s SDM Network Employing Direct-detection and Optical SSBI Mitigation

We propose a new transmission scheme for direct-detection (DD) short-reach networks based on transmitting the carriers and the data signals in separated cores of a multicore-fibre (MCF). With this scheme, a low complexity signal-signal beat interference (SSBI) mitigation approach is proposed at the receiver side, which may be required to compensate electronically the chromatic dispersion of the singlemode fibre. The performance of a 200 Gb/s binary NRZ signal in a MCF short-reach network employing the proposed transmission scheme is assessed by numerical simulation. The combined effect of the skew and the laser phase noise on the system performance is evaluated. It is shown that the SSBI mitigation technique enables distances up to 180 m when dispersion is not compensated, showing the potential to be implemented in intra data-centre (DC) networks, when the signal mean optical power is much higher than the carrier mean optical power, and when the SSBI estimation is not corrupted by electrical noise. The results also show that in systems with full dispersion compensation, a significant performance improvement is achieved by the proposed SSBI mitigation approach, enabling higher connection lengths.
An Effective Driver Intention and Trajectory Prediction for Autonomous Vehicle based on LSTM

In order to make the navigation system of autonomous vehicle more robust and safe in urban environment we propose in this paper a model for driver intention prediction and trajectory prediction. The proposed model is based on LSTM (long short term memory). The model was trained on database of features collected from the driving simulator CARLA. This paper treats four type of intentions, turn left, turn right, go straight and stopping intention. Two cases were treated, the first case is to predict intention before it occurs, the second case corresponds to intention recognition, where the driver already starts maneuvering the intention. Both cases are treated by the same model. The model shows better performances for the second case than the first case with small differences. The main strength of our model is that it gives good performances with a small set of features. The accuracy of the model is 96% for intention prediction and 97% for the intention recognition. The proposed method for trajectory prediction reach an accuracy of 99.9%. Those accuracies are higher than what we found in state of art.
Word Reordering and Comma Insertion Integrated with Shift-Reduce Dependency Parsing

Japanese has widely recognized as relatively free word orde r language. However, since Japanese word order is not completely arbitrary and has some sort of preference, even native Japanese writers often write Japanese sentences that are grammatically well-formed but not easy t o read. Furthermore, in Japanese sentences, a comma plays an important role in explicitly separating the c onstituents such as words and phrases. This paper proposes a method of word reordering and comma inserti on for hard-to-read Japanese sentences so that they become easier to read, as basic technique. Our contribu tion is to show the feasibility of concurrently performing word reordering, comma insertion, and dependen cy parsing.
Synthesis of polypropylene graft copolymers by the combination of a polypropylene copolymer containing pendant vinylbenzene groups and atom transfer radical polymerization

This article discusses a facile and inexpensive reaction process for preparing polypropylene-based graft copolymers containing an isotactic polypropylene (i-PP) main chain and several functional polymer side chains. The chemistry involves an i-PP polymer precursor containing several pendant vinylbenzene groups, which is prepared through the Ziegler-Natta copolymerization of propylene and 1,4-divinylbenzene mediated by an isospecific MgCl 2 -supported TiCl 4 catalyst. The selective monoenchainment of 1,4-divinylbenzene comonomers results in pendant vinylbenzene groups quantitatively transformed into benzyl halides by hydrochlorination. In the presence of CuCl/pentamethyldiethylenetriamine, the in situ formed, multifunctional, polymeric atom transfer radical polymerization initiators carry out graft-from polymerization through controlled radical polymerization. Some i-PP-based graft copolymers, including poly(propylene-g-methyl methacrylate) and poly(propylene-g-styrene), have been prepared with controlled compositions.
Stopping DNS Rebinding Attacks in the Browser

: DNS rebinding attacks circumvent the same-origin policy of browsers and severely jeopardize user privacy. Although recent studies have shown that DNS rebinding attacks pose severe security threats to users, up to now little effort has been spent to assess the effectiveness of known solutions to prevent such attacks. We have carried out such a study to assess the protective measures proposed in prior studies. We found that none of the recommended techniques can entirely halt this attack due to various factors, e.g., network layer encryption renders packet inspection infeasible. Examining the previous problematic factors, we realize that a protective measure must be implemented at the browser-level. Therefore, we propose a defensive measure, a browser plug-in called Fail-rebind, that can detect, inform, and protect users in the event of an attack. Afterwards, we discuss the merits and limitations of our method compared to prior methods. Our ﬁndings suggest that Fail-rebind does not necessitate expert knowledge, works on different OSes and smart devices, and is independent of networks and location.
Remote WebAuthn: FIDO2 Authentication for Less Accessible Devices

Nowadays, passwords are the prevalent authentication mechanism, even though it is proven to offer insufficient protection against cyber crime. Thus, FIDO2 was released with a more secure authentication mechanism. FIDO2 enables authentification with cryptographic hardware, such as USB sticks, NFC cards or in the smartphone integrated chips. A device with FIDO2 support is required to implement the whole FIDO2 stack and offer the required interfaces for the security hardware. However, many systems like for example Smart TVs can not make use of FIDO2 due to the lack of HW interfaces or the usage of outdated software. To overcome this, we present Remote WebAuthn, which enables secure authentification on such restricted devices through a remote authentication from a secondary, FIDO2 compatible device, such as a smartphone. We evaluate our approach to have better usability compared to FIDO2 while maintaining most security advantages.
An Algorithmic Approach to Online Multi-Facility Location Problems

: Facility Location problems ask to optimally place facilities with respect to some objective so that all clients requesting a facility service are served. These are one of the most well-studied optimization problems spanning many research areas, such as operations research, computer science, and management science. Classical algorithmic study of Facility Location problems is based on the assumption that clients need to be served with one facility each. Nevertheless, in many real-world applications, facilities experience disruptions and to overcome their failures, a robust service is desired. To obtain this, clients are served with more than one facility, and this is commonly represented by an additional input parameter. The aim of the algorithm is then to provide a robust service to all clients while minimizing costs. This is known as the Multi-Facility Location problem (MFL), a well-known optimization problem, studied in the ofﬂine setting in which the entire input sequence is known to the algorithm in advance. In this paper, we address MFL in the online setting, in which client requests are not known in advance but revealed over time. We refer to it as the Online Multi-Facility Location problem (OMFL) and study its metric and non-metric variants. We propose the ﬁrst online algorithms for these variants and measure their performance using the standard notion of competitive analysis. The latter is a worst-case analysis that compares the cost of the online algorithm to that of the optimal ofﬂine algorithm that is assumed to know all demands in advance.
Enhanced Information Management in Inter-organisational Planning for Critical Infrastructure Protection: Case and Framework

: This paper develops an analytical framework to assess information in planning for critical infrastructure protection (CIP). Critical infrastructure concerns various societal functions that ensure the daily life, endurance and progress of societies. Thus, CIP involves a considerable number of actors in a multi-level planning that relies on inter-organisational information sharing. Based on a Swedish case of CIP, this study aims to foster information assessment and management that bridge the inherent conflicts between information sharing and information security in CIP. Analyses of the information alongside the Swedish S TYREL process first exemplify crucial deficiencies in the inter-organisational, national emergency response planning and then specify a set of dimensions and attributes as baseline for assessing information and information processing in CIP. Four stages in the Swedish approach cause a filtering and altering of information that affect the quality of decisions alongside the process and the emergency response plan that relies on them. By assessing the information basis in this large-scale approach, the paper contributes evidence-based foundations for information management in inter-organisational settings, such as the multi-level planning for CIP.
An ALPR System-based Deep Networks for the Detection and Recognition

Automatic license plates reading (ALPR), from images or videos, is a research topic that is still relevant in the field of computer vision. In this article, we propose a new dataset and a robust ALPR system based on the YOLO object detector of literature. The trained Convolutional Neural Networks (CNN) allow us to extract features from license plates and label them through Recurrent Neural Networks (RNN) specialized character recognition. RNN are supported by GRU units instead of LSTM units that are generally used in the literature. The experiments results were conclusive reaching a recognition rate of 92%.
A Crowdsourcing Methodology for Improved Geographic Focus Identification of News-stories

Past work on the task of identifying the geographic focus of news-stories has established that state-of-the-art performance can be achieved by using existing crowdsourced knowledge-bases. In this work we demonstrate that a further refinement of those knowledge-bases through an additional round of crowdsourcing can lead to improved performance on the aforementioned task. Our proposed methodology views existing knowledgebases as collections of arguments in support of particular inferences in terms of the geographic focus of a given news-story. The refinement that we propose is to associate these arguments with weights — computed through crowdsourcing — in terms of how strongly they support their inference. The empirical results that we present establish the superior performance of this approach compared to the one using the original knowledge-base.
A Probabilistic Theory of Abductive Reasoning

We present an abductive search strategy that integrates creative abduction and probabilistic reasoning to produce plausible explanations for unexplained observations. Using a graphical model representation of abductive search, we introduce a heuristic approach to hypothesis generation, comparison, and selection. To identify creative and plausible explanations, we propose 1) applying novel structural similarity metrics to a search for simple explanations, and 2) optimizing for the probability of a hypothesis’ occurrence given known observa-
Planning with Hierarchical Temporal Memory for Deterministic Markov Decision Problem

Sequential decision making is among the key problems in Artificial Intelligence. It can be formalized as Markov Decision Process (MDP). One approach to solve it, called model-based Reinforcement Learning (RL), combines learning the model of the environment and the global policy. Having a good model of the environment opens up such properties as data efficiency and targeted exploration. While most of the memorybased approaches are based on using Artificial Neural Networks (ANNs), in our work we instead draw the ideas from Hierarchical Temporal Memory (HTM) framework, which is based on human-like memory model. We utilize it to build an agent’s memory that learns the environment dynamics. We also accompany it with an example of planning algorithm, that enables the agent to solve RL tasks.