Miracle-dz/UniFAR-FTUs huggingface dataset:
Counterexamples in Model Checking - A survey

Model checking is a formal method used for the verification of finite-state systems. Given a system model and such specification, which is a set of formal properties, the model checker verifies whether or not the model meets the specification. One of the major advantages of model checking over other formal methods its ability to generate a counterexample when the model falsifies the specification. Although the main purpose of the counterexample is to help the designer to find the source of the error in complex systems design, the counterexample has been also used for many other purposes, either in the context of model checking itself or in other domains in which model checking is used. In this paper, we will survey algorithms for counterexample generation, from classical algorithms in graph theory to novel algorithms for producing small and indicative counterexamples. We will also show how counterexamples are useful for debugging, and how we can benefit from delivering counterexamples for other purposes.
The Logical Execution Time Paradigm: New Perspectives for Multicore Systems (Dagstuhl Seminar 18092)

This report documents the program and the outcomes of Dagstuhl Seminar 18092 "The Logical Execution Time Paradigm: New Perspectives for Multicore Systems". The seminar brought together academic and industrial researchers working on challenges related to the Logical Execution Time Paradigm (LET). The main purpose was to promote a closer interaction between the sub-communities involved in the application of LET to multicore systems, with a particular emphasis on the automotive domain.
Poznań Contribution to TREC-PM 2020

This paper describes our contribution to TREC PM 2020. We discuss the retrieval architecture used for our contribution. We split our system into four layers - preprocessing, representation, baseline and neural layer. We go over goals and speciﬁcation of each layer. We conclude the section with description of our hardware setup. Then, we describe experiments conducted within this contribution - we discuss used data and retrieval models.The reranking gives little but noticeable improvement. Our results are signiﬁcantly better than the median. Paper is concluded with a discussion over weaknesses and strengths of our approach, we brieﬂy formulate what has to be done in the future.
Jointly Learning Network Connections and Link Weights in Spiking Neural Networks

Spiking neural networks (SNNs) are considered to be biologically plausible and power-efficient on neuromorphic hardware. However, unlike the brain mechanisms, most existing SNN algorithms have fixed network topologies and connection relationships. This paper proposes a method to jointly learn network connections and link weights simultaneously. The connection structures are optimized by the spike-timing-dependent plasticity (STDP) rule with timing information, and the link weights are optimized by a supervised algorithm. The connection structures and the weights are learned alternately until a termination condition is satisfied. Experiments are carried out using four benchmark datasets. Our approach outperforms classical learning methods such as STDP, Tempotron, SpikeProp, and a state-of-the-art supervised algorithm. In addition, the learned structures effectively reduce the number of connections by about 24%, thus facilitate the computational efficiency of the network.
MIComp: 3D on-chip magneto-inductive computing with simultaneous wireless information and power transfer

On-chip computing platforms have bottlenecks including cost and physical limits of scaling transistors, communication bottleneck, energy efficiency and speed costs for memory. Three dimensional (3D) design, carbon nanotube materials, memristor based neuromorphic computing, and optical, RF and magneto-inductive (MI) wireless communication solutions are recently proposed. MI channels are non-radiative and non-interfering by forming coupled networks. They are future promising with capabilities of THz frequency, Tbit/s data rate, hundreds of zJ/bit and 109 W/mm2 communication and power transfer (PT) efficiencies, respectively. In addition, recently introduced network topology modulation (NTM) for MI channels provides network communication with low complexity, low latency and simultaneous wireless information and power transfer (SWIPT). In this article, unique advantages of THz MI channels, NTM design, nanoscale materials including graphene and single molecular magnets (SMMs), and 3D design are combined in a novel on-chip computing architecture denoted by MIComp by introducing fully efficient SWIPT for computing purposes. The system is theoretically modeled while the state space of the system obtained with nanoscale size coils and SMMs achieves 1010 to 1016 bits in each cycle and per mm3 volume of chip compared with the current transistor counts of on the orders of 109 per mm2. Furthermore, each MIComp cycle has ability to perform for multiple purposes consisting of computing operations, memory state implementations and on-chip communications. It promises a novel solution for communication, energy and space bottlenecks for on-chip computing design.
Small Cells Placement for Crowd Networks

The deployment of small cells is one of the technical solutions to meet the challenge of data traffic rise. It reduces the cost of radio access networks. The efficiency of this solution depends on the success of cell planning to mitigate the interference. This work aims to optimize the incremental cell planning scheme that considers a preliminary macro-cell network infrastructure and expands it with new small cells to enhance the coverage and the capacity. In order to reduce the cost of installing new small cells, we consider a crowd networking business model where the Mobile Network Operator retrieves sites to deploy small cells above. We formulate the small cells placement problem as an Integer Linear Programming. It aims to maximize the coverage, ensure the required capacity and mitigate the cross-tier interference. Since the problem is NP-hard with large number of sites, we propose an heuristic to select the optimal sites locations around each macro cell. We tested the proposed heuristic with different simulation scenarios. The proposed Sequential Deployment scenario is better in coverage uniformity among dense and non dense zones whilst the Dense Zones First ensures more optimized selected sites number with better capacity in the dense zones in terms of users. The last scenario proves that higher candidate sites optimize the number of small cells in the non dense areas without compromising the coverage.
Critical Failure: Computer-Aided Instruction and the Fantasy of Information

Abstract:The history of the use of various kinds of computers in education involves frequent triumphalist claims about the inevitable automation of instruction and equally frequent declarations of the failure of this project. This article situates both types of claims within a broader cultural understanding, one that holds that the human world is fundamentally informational and therefore amenable to improvement by computers.
Pictographs in Disaster Communication for Linguistically Challenged and Illiterate Populations: A Survey on Background and Existing Practices

This﻿article﻿describes﻿how﻿pictographs﻿(or﻿pictograms)﻿can﻿be﻿an﻿important﻿means﻿to﻿communicate﻿ information﻿about﻿natural﻿disasters﻿to﻿people﻿that﻿are﻿lacking﻿the﻿capability﻿to﻿understand﻿written﻿text. ﻿ This﻿does﻿not﻿only﻿include﻿illiterates,﻿but﻿also﻿foreigners﻿not﻿speaking﻿the﻿local﻿language. ﻿While﻿it﻿is﻿ widely﻿accepted﻿that﻿pictograph-based﻿communication﻿can﻿play﻿a﻿major﻿role,﻿there﻿is﻿no﻿established﻿ workflow﻿to﻿include﻿this﻿kind﻿of﻿symbols﻿in﻿early﻿warning﻿or﻿disaster﻿reporting﻿practice. ﻿This﻿article﻿ investigates﻿the﻿topic﻿of﻿pictograph-based﻿communication﻿systems,﻿especially﻿in﻿the﻿field﻿of﻿natural﻿ disasters,﻿with﻿a﻿focus﻿on﻿the﻿use﻿of﻿linguistically﻿challenged﻿populations. ﻿The﻿authors﻿analyze﻿existing﻿ literature,﻿take﻿a﻿look﻿at﻿existing﻿pictograph﻿resources,﻿analyze﻿a﻿first﻿field﻿study﻿with﻿marginalized﻿ populations﻿and﻿derive﻿conclusions﻿for﻿a﻿design﻿process﻿for﻿pictograph-based﻿disaster﻿communication﻿ systems. KEywoRDS Disaster Communication, Early Warning, Cognitive Aspects, Cognitive Systems, Illiteracy, Icons, User Experience, User Interfaces, Design, Participatory Design, User-Centered Design
Young People's Purchase Intentions in Online Flea Market

With increasing penetration of smartphone usage, many people enjoy online purchasing with their smartphones. Lately, many young people have been purchasing goods in the online flea market through smartphones and the market has been growing. Despite the online flea market developing rapidly, the critical factors that influence consumers' purchase intention (PI) in the flea market are not clear. Only a few studies have attempted to explain the factors influencing the adoption of the online flea market. This study examines young people's acceptance of the online flea market on smartphones. It identifies the factors of PI, the relative importance of the factors, and relationships among the variables in the online flea market. This study indicates that the online flea market has become a channel for online purchasing among young people. This study also reveals that the PI with flea market applications is directly influenced by both perceived usefulness (PU) and trust (TR) of the online flea market, and indirectly influenced by credibility of personal information protection (PIP). In addition, credibility of PIP strongly influences TR and the system quality (SQ) of the online flea market strongly influences the credibility of PIP.
PANDORA: A Scalable and Efficient Scheme to Extract Version of Binaries in IoT Firmwares

Open source components are widely used by IoT vendors to develop firmwares in devices. The exposure of vulnerabilities existing in some specific versions of the core components may cause severe security incidents such as the Heartbleed event in 2014 and the Sambacry event in 2016. Extracting the version information from various firmware binaries is significant for evaluating the influence of such incidents and providing emergency response services. To the best of our knowledge, there are still no scalable and efficient extraction methods for binary version information in IoT firmwares. The commonly used method for traditional softwares requires the running up of the firmwares and interaction such as '-version' to obtain the version information. This method is not applicable for IoT devices, as they are built from various platforms which makes it impossible to simulate all of the interested firmwares at large scale. In this paper, we design, implement and evaluate a scalable and efficient binary version extraction framework (termed as PANDORA) for IoT firmwares, which does not rely on the real runtime environment. The main idea of our methodology is to leverage version strings in binaries to get version information. We design a string recover engine (SRE) to recover the missing pieces of those incomplete version strings. We test PANDORA in a dataset containing 2683 IoT binary files. Surprisingly 2267 of them are version- extractable and the recognition rate can reach 84.5%
An implicit frictional contact solver for adaptive cloth simulation

Cloth dynamics plays an important role in the visual appearance of moving characters. Properly accounting for contact and friction is of utmost importance to avoid cloth-body and cloth-cloth penetration and to capture typical folding and stick-slip behavior due to dry friction. We present here the first method able to account for cloth contact with exact Coulomb friction, treating both cloth self-contacts and contacts occurring between the cloth and an underlying character. Our key contribution is to observe that for a nodal system like cloth, the frictional contact problem may be formulated based on velocities as primary variables, without having to compute the costly Delassus operator. Then, by reversing the roles classically played by the velocities and the contact impulses, conical complementarity solvers of the literature can be adapted to solve for compatible velocities at nodes. To handle the full complexity of cloth dynamics scenarios, we have extended this base algorithm in two ways: first, towards the accurate treatment of frictional contact at any location of the cloth, through an adaptive node refinement strategy; second, towards the handling of multiple constraints at each node, through the duplication of constrained nodes and the adding of pin constraints between duplicata. Our method allows us to handle the complex cloth-cloth and cloth-body interactions in full-size garments with an unprecedented level of realism compared to former methods, while maintaining reasonable computational timings.
Mining Software Repositories to Support OSS Developers: A Recommender Systems Approach

To facilitate the development activities, software developers frequently look up external sources for related information. Consulting data available at open source software (OSS) repositories can be considered as their daily routine. Nonetheless, the heterogeneity of resources and their corresponding dependencies are the main obstacles to the effective mining and exploitation of the data. Given the context, the manual search for every single resource to find the most suitable ones is a daunting and inefficient task. Thus, equipping developers with techniques and tools to accelerate the search process as well as to improve the search results will help them enhance the work efficiency. Within the scope of the EU funded CROSSMINER project, advanced techniques and tools are being conceived for providing open software developers with innovative features aiming at obtaining improvements in terms of development effort, cost savings, developer productivity, etc. To this end, cutting-edge technologies are applied, such as information retrieval and recommender systems to solve the problem of mining the rich metadata available at OSS repositories to support software developers. In this paper, we present the main research problems as well the proposed approach together with some preliminary results.
WIP: Daily Life Oriented Indoor Localization by Fusion of Smartphone Sensors and Wi-Fi

Smartphones are the best personal assistants in our lives on several counts. However, their services can still be improved for a better quality of life. In this paper, we aim to determine the exact location of a smartphone in a room, i.e, on a study desk, a television table, etc. By this way, our daily settings may be automatically activated from the smartphone itself. For example, if a user puts his/her phone on the bed commode, then the phone would be able to switch itself to the night mode on its own. A successful localization in a room should be able to distinguish different corners from each other so that it can be used in various applications as a supported technology. Hence, in this work, we are proposing an indoor localization system that can distinguish different indoor places by using the smartphones' sensors and Wi-Fi services. Unlike the common location-based services, our solution is not a server-client based system. In order to enhance feasibility and availability, we only use the mobile device but no additional infrastructure. We developed two applications on Android platform. The first one allows the user to easily collect sensor data from his/her living places, such as home and office settings. The second one is a data mining application sourced by Weka. The tests were performed in different rooms of a house and office environment. We achieved 86% accuracy for room level localization.
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams

Many tasks in machine learning and data mining, such as data diversification, non-parametric learning, kernel machines, clustering etc., require extracting a small but representative summary from a massive dataset. Often, such problems can be posed as maximizing a submodular set function subject to a cardinality constraint. We consider this question in the streaming setting, where elements arrive over time at a fast pace and thus we need to design an efficient, low-memory algorithm. One such method, proposed by Badanidiyuru et al. (2014), always finds a $0.5$-approximate solution. Can this approximation factor be improved? We answer this question affirmatively by designing a new algorithm SALSA for streaming submodular maximization. It is the first low-memory, single-pass algorithm that improves the factor $0.5$, under the natural assumption that elements arrive in a random order. We also show that this assumption is necessary, i.e., that there is no such algorithm with better than $0.5$-approximation when elements arrive in arbitrary order. Our experiments demonstrate that SALSA significantly outperforms the state of the art in applications related to exemplar-based clustering, social graph analysis, and recommender systems.
Global implementation survey of Integrated Management of Childhood Illness (IMCI): 20 years on

Objective To assess the extent to which Integrated Management of Childhood Illness (IMCI) has been adopted and scaled up in countries. Setting The 95 countries that participated in the survey are home to 82% of the global under-five population and account for 95% of the 5.9 million deaths that occurred among children less than 5 years of age in 2015; 93 of them are low-income and middle-income countries (LMICs). Methods We conducted a cross-sectional self-administered survey. Questionnaires and data analysis focused on (1) giving a general overview of current organisation and financing of IMCI at country level, (2) describing implementation of IMCI’s three original components and (3) reporting on innovations, barriers and opportunities for expanding access to care for children. A single data file was created using all information collected. Analysis was performed using STATA V.11. Participants In-country teams consisting of representatives of the ministry of health and country offices of WHO and Unicef. Results Eighty-one per cent of countries reported that IMCI implementation encompassed all three components. Almost half (46%; 44 countries) reported implementation in 90% or more districts as well as all three components in place (full implementation). These full-implementer countries were 3.6 (95% CI 1.5 to 8.9) times more likely to achieve Millennium Development Goal 4 than other (not full implementer) countries. Despite these high reported implementation rates, the strategy is not reaching the children who need it most, as implementation is lowest in high mortality countries (39%; 7/18). Conclusion This survey provides a unique opportunity to better understand how implementation of IMCI has evolved in the 20 years since its inception. Results can be used to assist in formulating strategies, policies and activities to support improvements in the health and survival of children and to help achieve the health-related, post-2015 Sustainable Development Goals.
Re-ADP: Real-Time Data Aggregation with Adaptive ω-Event Differential Privacy for Fog Computing

In the Internet of Things (IoT), aggregation and release of real-time data can often be used for mining more useful information so as to make humans lives more convenient and efficient. However, privacy disclosure is one of the most concerning issues because sensitive information usually comes with users in aggregated data. Thus, various data encryption technologies have emerged to achieve privacy preserving. These technologies may not only introduce complicated computing and high communication overhead but also do not work on the protection of endless data streams. Considering these challenges, we propose a real-time stream data aggregation framework with adaptive ω-event differential privacy (Re-ADP). Based on adaptive ω-event differential privacy, the framework can protect any data collected by sensors over any dynamic ω time stamp successively over infinite stream. It is designed for the fog computing architecture that dramatically extends the cloud computing to the edge of networks. In our proposed framework, fog servers will only send aggregated secure data to cloud servers, which can relieve the computing overhead of cloud servers, improve communication efficiency, and protect data privacy. Finally, experimental results demonstrate that our framework outperforms the existing methods and improves data availability with stronger privacy preserving.
Peerus Review: Un outil de recherche d'experts scientifiques

Nous proposons un outil de recherche d’experts applique au monde academique sur les donnees generees par l’entreprise DSRT dans le cadre de son application Peerus 1 . Un utilisateur soumet le titre, le resume et optionnellement les auteurs et le journal de publication d’un article scientifique et se voit proposer une liste d’experts, potentiels reviewers de l’article soumis. L’algorithme de recherche est un systeme de votes reposant sur un modele du langage entraine a partir d’un ensemble de plusieurs millions d’articles scientifiques. L’outil est accessible a chacun sous la forme d’une application web intitulee Peerus Review
Graphe de connaissances et folksonomie : leur performance comparative dans le calcul de l'afinité

Résumé : L’affinité est un élément essentiel dans bien des systèmes d’information centrés sur l’utilisateur comme les systèmes de recommandation. Le graphe de connaissances et la folksonomie sont respectivement des jalons importants pour le Web Sémantique et le Web Social. Nonobstant leur trait collaboratif partagé (du moins quelques grands graphes de connaissances le sont), les données codées diffèrent tant par la nature (fait versus expérience) que par la structure (formelle versus lâche). Dans ce papier, nous tentons d’éclaircir leur performance comparative dans la tâche du calcul de l’affinité à travers deux expériences dans le domaine du etourisme. Nos résultats montrent que le graphe de connaissances permet de calculer l’affinité avec plus de précision alors que la folksonomie augmente la diversité et la nouveauté. Ces constatations nous ont motivés à développer le Framework d’Affinité Sémantique pour bénéficier de leurs avantages respectifs. L’original de ce papier est publié par ESWC 2017.
Prototyping for Investigating Affective Objects with and for Children

If designers give users the means to create or experience interactive prototypes with them, users can contribute and form realistic expectations and experiences. That is especially relevant in case of novel interactive solutions and children as users. In 2017, two prototyping workshops concerning affective objects were organised with children and designers across Europe: one in Germany in a summer camp, the other in Italy during a science festival. The former workshop helped designers inspect children’s expectations for affective objects. The latter workshop helped designers assess other children’s experience with the prototypes created by the former children. The paper presents the prototyping workshops with in total 81 children, aged 11 to 16, and reflects on what designers learnt from them.
Five Challenges for Intelligent Cinematography and Editing

In this position paper, we propose five challenges for advancing the state of the art in intelligent cinematography and editing by taking advantage of the huge quantity of cinematographic data (movies) and metadata (movie scripts) available in digital formats. This suggests a data-driven approach to intelligent cinematography and editing, with at least five scientific bottlenecks that need to be carefully analyzed and resolved.we briefly describe them and suggest some possible avenues for future research in each of those new directions.