Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPLEMENTATION AND EVALUATION OF AN INTRUSION-RESILIENT SYSTEM FOR A DNS SERVICE

The Internet has increased its inclusion of highly available servers. These servers require not only fault tolerance but intrusion resilience. DNS servers are particularly critical components on the Internet, since a DNS service is an infrastructure for the Internet. This paper proposes an implementation of an intrusion-resilient system for the DNS service using an active redundancy approach with diversiﬁcation. This system is composed of heterogeneous virtual machines running diﬀerent operating systems and diﬀerent server implementations of the same server protocol speciﬁcation. The approach is founded on the observation that not all implementations will be aﬀected by the same vulnerability, apart from vulnerabilities in the speciﬁcations and shared libraries. The intrusion-resilient system needs to run only two application servers in parallel whereas others, such as intrusion-tolerant systems, require many physical resources to diversify an application server and operating systems. Tests of the intrusion-resilient system demonstrated that stab resolvers did not fail name resolution even during downtime due to cyberattacks. In addition, the overhead of the intrusion-resilient system had negligible eﬀect on DNS service performance.
A Music System based on Paintings

Olivier Messiaen once mentioned: “I am affected by a kind of synopsia which allows me, when I hear music, and equally when I read it, to see inwardly, in the mind’s eye, colors which move with the music”. It is now known that a number of artists have a condition called synaesthesia. Synaesthesia is neurological phenomenon where the stimulus of a sense induces the stimulus of another sense. This means there are people who see colours when looking at some specific numbers, or when hearing some specific sound. Since it enables the association of typically non-associated concepts, it is believed that this phenomenon is the key to creativity [1]. It is easily perceivable that some rhythms convey a more relaxed experience than others. This also applies to shapes. We can call this the instability/stability of a rhythm or shape. Using a mathematical model created by us, we quantify the instability of a shape present in a painting and then, using the theory of Pulse Salience [2], generate a rhythm with a similar instability value. We present a computational system which is able to play the music/rhythms of paintings. The system is organized in three layers, and there are two key modules: an image analyzer tool and a rhythm generator tool. The former extracts the image characteristics and assigns an instability/stability value to its shapes, while the latter generates and plays rhythms according to the instability/stability values. The aim of the system is to provide a multi-sensorial experience similar to synaesthesia, using the cognitive and perceptual influence of rhythm in humans. We believe that if people can be exposed to a similar experience, this will improve their overall perception of a work of art as well as enhance their own creativity.
Malware Detection via Machine Learning

— The use of supervised learning techniques for malware detection has been used increasingly to aid classical classiﬁcation methods. In this work we aim at developing a malware detection model, analyzing the impact of the reliability of the training dataset on the ﬁnal result of the classiﬁer, and metrics to deﬁne the ground-truth . For this, we propose three datasets’ scenarios whose content range from unambiguous malware and goodware samples to more ambiguous and real ones. We analyze each scenario in laboratory conditions, where standard cross-validation methodologies are applied, discarding the importance of time in malware detection, and also in real-world conditions, where temporal-based dependencies are proposed and applied. Furthermore, we modify our original model to both enrich the extractable information, by implementing a multi layer model, and to improve the ﬁnal results, by using dynamic information about the samples. We then use our temporal-based methodologies to reduce the size of the training dataset without compromising optimal results, concluding that there exists an ideal number of necessary training folds, temporally consistent with the validation fold, that maximizes the overall score.
SEIMI: Efficient and Secure SMAP-Enabled Intra-process Memory Isolation

Memory-corruption attacks such as code-reuse attacks and data-only attacks have been a key threat to systems security. To counter these threats, researchers have proposed a variety of defenses, including control-flow integrity (CFI), code-pointer integrity (CPI), and code (re-)randomization. All of them, to be effective, require a security primitive—intra-process protection of confidentiality and/or integrity for sensitive data (such as CFI’s shadow stack and CPI’s safe region). In this paper, we propose SEIMI, a highly efficient intra-process memory isolation technique for memory-corruption defenses to protect their sensitive data. The core of SEIMI is to use the efficient Supervisor-mode Access Prevention (SMAP), a hardware feature that is originally used for preventing the kernel from accessing the user space, to achieve intra-process memory isolation. To leverage SMAP, SEIMI creatively executes the user code in the privileged mode. In addition to enabling the new design of the SMAP-based memory isolation, we further develop multiple new techniques to ensure secure escalation of user code, e.g., using the descriptor caches to capture the potential segment operations and configuring the Virtual Machine Control Structure (VMCS) to invalidate the execution result of the control registers related operations. Extensive experimental results show that SEIMI outperforms existing isolation mechanisms, including both the Memory Protection Keys (MPK) based scheme and the Memory Protection Extensions (MPX) based scheme, while providing secure memory isolation.
Computer-Assisted Rehabilitation

: Recent decades brought technological advances able to improve the life quality of people with disabilities. However, practical developments are still scarce to what tetraplegics and their Rehabilitation is concerned. Therapeutic processes are lengthy and require great dedication from both the therapist and the patient. They consist in carrying out repetitive movement patterns across sessions, giving relevance to a suitable observation and accompaniment process. Our goal is to provide a system with accurate monitoring and evolution analysis assisting therapists in current rehabilitation procedures. We analyzed therapy sessions with tetraplegic patients to better understand the rehabilitation process and highlight the major requirements for a technology-enhanced tool. We then developed a prototype able to automate and improve the current monitoring and follow-up processes. Results indicate that computational movement analysis and comparison can improve the quality of a rehabilitation session and overall patient evolution analysis. The capture and virtual playback of motion in physical therapy sessions increases therapist awareness of patient condition and evolution.
Decentralized and Emergent NPC Coordination using Behavior Trees

- Artificial Intelligence in videogames is being more used nowadays than it was before and an area that needs to have better AI is definitely coordination between individual characters. The aim of this project is to use Behavior trees, a recent structure introduced in the game industry, to enable coordination in a decentralized way, giving more focus on the characters behaviors. The StarCraft commercial game was used to show and evaluate our implementation.
Optimal Object Retrieval Paths in Indoor Spaces

We present and describe an implementation of a system that utilizes indoor positioning and an optimal path algorithm to help users find their products while grocery shopping. Our objective was to create a system that could, based on a personalized shopping list created by the user, navigate the user through the supermarket. With the objective of helping the user to get his products in a much more efficient manner. This paper describes in some detail the implementation process and discusses the results that were obtained.
Coordination of tilt and touch in one- and two-handed use

Our goal is to enhance navigation in mobile interfaces with quick command gestures that do not make use of explicit mode-switching actions. TilTouch gestures extend the vocabulary of navigation interfaces by combining motion tilt with directional touch. We consider sixteen directional TilTouch gestures that rely on tilt and touch movements along the four main compass directions. An experiment explores their effectiveness for both one-handed and two-handed use. Results identify the best combinations of TilTouch gestures in terms of performance, motor coordination, and user preferences.
University of Birmingham Fine-grained Concurrency with Separation Logic

Reasoning about concurrent programs involves representing the information that concurrent processes manipulate disjoint portions of memory. In sophisticated applications, the division of memory between processes is not static. Through operations, processes can exchange the implied ownership of memory cells. In addition, processes can also share ownership of cells in a controlled fashion as long as they perform operations that do not interfere, e.g., they can concurrently read shared cells. Thus the traditional paradigm of distributed computing based on locations is replaced by a paradigm of concurrent computing which is more tightly based on program structure. Concurrent Separation Logic with Permissions, developed by O’Hearn, Bornat et al., is able to represent sophisticated transfer of ownership and permissions between processes. We demonstrate how these ideas can be used to reason about finegrained concurrent programs which do not employ explicit synchronization operations to control interference but cooperatively manipulate memory cells K. Kapoor Department of Mathematics, Indian Institute of Technology Guwahati, Guwahati 781 039, India e-mail: kalpesh@iitg.ernet. in K. Lodaya (B) The Institute of Mathematical Sciences, C.I. T. Campus, Chennai 600 113, India e-mail: kamal@imsc.res.in U. S. Reddy School of Computer Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom e-mail: u.s.reddy@cs.bham. ac.uk 584 K. Kapoor et al. so that interference is avoided. Reasoning about such programs is challenging and appropriate logical tools are necessary to carry out the reasoning in a reliable fashion. We argue that Concurrent Separation Logic with Permissions provides such tools. We illustrate the logical techniques by presenting the proof of a concurrent garbage collector originally studied by Dijkstra et al., and extended by Lamport to handle multiple user processes.
Combining Relations and Text in Scientific Network Clustering

In this paper, we present different combined clustering methods and we evaluate their performances and their results on a dataset with ground truth. This dataset, built from several sources, contains a scientific social network in which textual data is associated to each vertex and the classes are known. Indeed, while the clustering task is widely studied both in graph clustering and in non supervised learning, combined clustering which exploits simultaneously the relationships between the vertices and attributes describing them, is quite new. We argue that, depending on the kind of data we have and the type of results we want, the choice of the clustering method is important and we present some concrete examples for underlining this.
REHABILITATION IN A WARM CLIMATE IS EFFECTIVE FOR YOUNG ADULTS WITH INFLAMMATORY ARTHRITIS: A 12 MONTHS RANDOMIZED CONTROLLED TRIAL

Ingrid REKAA NILSSEN, MSc1, Hege Svean KOKSVIK, MSc1, Kjersti GRØNNING, PhD2,3 and Aslak STEINSBEKK, PhD2 From the 1Norwegian National Advisory Unit on Pregnancy and Rheumatic Diseases, St Olavs Hospital, Trondheim University Hospital, 2Department of Public Health and Nursing, Norwegian University of Science and Technology and 3Department of Rheumatology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
The Impact of Ethereum Throughput and Fees on Transaction Latency During ICOs

The Ethereum blockchain has gained popularity for its ability to implement Initial Coin Offerings (ICOs), whereby a buyer enters a market order agreement with a seller in order to purchase cryptographic tokens at an agreed price. The popularity of ICOs in 2017 has created an increasingly adversarial environment among potential buyers, who compete for what is often a fixed supply of tokens offered for a limited period of time. We study the impact of a series of ICOs in order to understand the relationship between transaction fees, throughput and latency in Ethereum. Our analysis considers the effects on both Ethereum’s service providers, known as miners, and users who issue transactions in the network. Our results show that while buyers incentivise miners generously to include their transactions during ICOs, the latency of these transactions is predominantly determined by the levels of supply and demand in the network.
Prediction of Speech Intelligibility in Fluctuating Noise for Listeners with Normal and Impaired Hearing

Speech intelligibility prediction based exclusively on the pure-tone audiogram (e.g. Speech Intelligibility Index (SII)-based models) shows some limitations. For listeners with similar pure-tone audiograms the same speech reception thresholds (SRT) are predicted even though the observed SRT values may differ. For SRT values measured in fluctuating noises, these differences are even larger than in stationary noises. One hypothesis is that cognitive parameters significantly influence the observed SRT in fluctuating noise.
Creating Recipes using Machine Learning and Computational Creativity

—The area of computational creativity focuses on automating tasks linked to creative thinking. Our work focuses on this area, more precisely in the area of culinary. There are some works done in the ﬁeld of computational creativity applied to culinary, but we believe that there are many improvements to be made in these systems and new ways to be explored. In this paper we present a system that is capable of creating creative recipes for cakes. Our system begins by creating a combination of ingredients from a previously categorized list of ingredients. This creation is done through a genetic algorithm that uses a metric that tries to balance the ﬂavor and the novelty of each combination. Both the ﬂavor assessment and the eternal structure used to generate the combinations are based on culinary knowledge drawn from various experts. After obtaining the ﬁnal combination of ingredients, a preparation is calculated. This preparation is a ﬁxed preparation, chosen from a list of three possible preparations, the ﬁnal preparation is only adjusted for the ingredients which are in the combination. Finally, a quantity is generated for each of the ingredients using learning based on a data set of recipes. We have done three different types of evaluation, in all of them we evaluate the creativity of the various recipes and compare them with real world recipes and randomly generated recipes. All of them have shown that our recipes have achieved excellent results in terms of originality and satisfactory results in terms of ﬂavor.
SRoCE: Software RDMA over Commodity Ethernet

RDMA networks are used in datacenters and high performance computing clusters to support high-throughput, low-latency networking by allowing specialized hardware to directly copy to and from the application memory and network. We propose a software-based flexible RDMA verbs implementation that uses TAS - high-performance user-space TCP stack - as the underlying transport layer to allow similar semantics without hardware and network requirements. We discuss the design space that we explore and evaluate our prototype. The single-connection throughput and latency achieved by our implementation indicate not only that providing RDMA interface over TCP using commodity NICs is feasible but also comparable to the hardware implementation.
On asymptotically efficient simulation of large deviation probabilities

Let {νε, ε>0} be a family of probabilities for which the decay is governed by a large deviation principle, and consider the simulation of νε0 (A) for some fixed measurable set A and some ε0>0. We investigate the circumstances under which an exponentially twisted importance sampling distribution yields an asymptotically efficient estimator. Varadhan's lemma yields necessary and sufficient conditions, and these are shown to improve on certain conditions of Sadowsky. This is illustrated by an example to which Sadowsky's conditions do not apply, yet for which an efficient twist exists.
Activity Sculptures: Exploring the Impact of Physical Visualizations on Running Activity

Data sculptures are a promising type of visualizations in which data is given a physical form. In the past, they have mostly been used for artistic, communicative or educational purposes, and designers of data sculptures argue that in such situations, physical visualizations can be more enriching than pixel-based visualizations. We present the design of Activity Sculptures: data sculptures of running activity. In a three-week field study we investigated the impact of the sculptures on 14 participants' running activity, the personal and social behaviors generated by the sculptures, as well as participants' experiences when receiving these individual physical tokens generated from the specific data of their runs. The physical rewards generated curiosity and personal experimentation but also social dynamics such as discussion on runs or envy/competition. We argue that such passive (or calm) visualizations can complement nudging and other mechanisms of persuasion with a more playful and reflective look at ones' activity.
The Initial Allocation of CO2 Emission Allowances: A Theoretical and Experimental Study

The Kyoto Protocol sets national quotas on the global pollutant CO2 and allows for international emissions trading as a way to obtain air quality standards at least costs. The economic efficiency of the system depends on firms being able to buy and sell permits, with incidental transactions costs and at competitive prices. We study the reliability of prices generated by different policy-relevant allocation rules for CO2 allowances under the EU emissions trading system (EU-ETS). We consider gratis allocation (grandfathering), auctions, and their combination in so-called hybrid systems. Regarding the auctions, we also inquire for the ”appropriate design” for carbon auctions. In a theoretical approach, where agents bid according to their marginal abatement costs, we show that the hybrid system of grandfathering and a one-sided auction (which is taken into account in the EU-ETS) does not generate reliable price signals, which should reflect the actual market scarcity of the allowances. This requirement, however, is met, if in the hybrid system the one-sided auction is replaced by a double auction or if a one-sided auction is used exclusively. The results of a laboratory experiment persuasively support our theoretical findings with respect to correct price signals. JEL-Classifications: C9, L51, Q54.
HAEMATOCHEMICAL PROFILE OF COMMERCIAL LAYERS INFLUENCED BY HEAT COMBATING SYSTEMS DURING HIGH AMBIENT TEMPERATURE

Total leukocytes appeared to be highest at peak stress as compared to pre-stress and post-stress stages reflecting its maximum level during stress (Maxwell et al., 1992). Decline in hematocrit level was observed in layers (Doerr et al., 1975; Zimmerman et al., 1975), turkey (Parker et al., 1982) and in broilers (Yahav et al., 1997) when compared at low and high temperature. Decline in haemoglobin concentration at higher environmental temperature was also presented in chicken (Yahav et al., 1997) and layer (Sahota et al., 1993; Sahota and Gilani, 1995). Higher environmental temperature has been reported to raise the level of glucose in the blood (Yang et al., 1992). Thus, adverse effects of heat make poultry production a difficult and uneconomical pursuit. It is essential to look for some methods that could prove successful in reducing environmental stress and hence improve production in birds.
Implementation of an Enterprise Architecture in SPMS and the eSIS Ecosystem using Enterprise Architecture Management Tools and Cartography Methods

—Enterprise Architecture is a discipline that focuses on the structure and organisation of information and the layers, such as strategy, business and IT infrastructure, within an enterprise, helping it maintain a coherent vision throughout all its levels. However, many organisations still struggle with succeeding with the implementation process of this discipline. In this project, we will focus on the speciﬁc case of the implementation of an EA project at a national healthcare IT system and management provider and the organisations that use its systems, all with no previous experience on the ﬁeld of EA. Following an Enterprise Cartography approach, supported by an enterprise architecture management system (Atlas), this project includes an analysis to pinpoint the main struggles of the subject organisation, such as the non-existence of an architectural meta-model, difﬁculty in managing different information sources and entities and the effort of gathering said information, the short-lived value of views produced at a certain point in time, the negative perception regarding the use of Enterprise Architecture and political pressures regarding change projects. The later part of this project focused on designing solutions that would tackle these problems with a focus on the breadth of the solutions. Using an EC project framework and the help of an application development team, a set of solutions, such as easier and more agile ways to register information, batches for mass centralisation of different sources of information and a feedback based Meta-model, allowed for the creation of a solid baseline for existing and future EA efforts.
Tenant-Aware BigData Scheduling with Software-Deﬁned Networking

The increase of data in the Internet world has created a need to process and acquire information from them using Big Data Analytics which in turn use data center for computing and storing purposes. The Big data analytics in data center needs a good network conﬁguration to avoid delay or error in the network. but the traditional network could not avoid the error or dynamically create a network architecture. This gave rise to Software Deﬁned Networking which conﬁgures, deploys and manages the network infrastructures and is most widely used in the data center network. Both these technology SDN and Big Data has beneﬁted the data center network tremendously. SDN follows logically centralized approach, with which network allocation and scheduling can be performed with increased eﬃciency and reliability. Due to the emergence of cloud services it has becoming easy to access the computational resource. The client can rent large size of the computational resources in a very reasonable price, but unfortunately there is performance degradation in the network level as many clients would be using the computational instance through same communication channel and SLA (Service-level agreement) could not guarantee the performance in the network layer. This bring about the problem in the multi-tenant data center environment.Having these two technologies in mind we propose MR-FLOOD which is a conjugation of the Hadoop MapReduce framework and Floodlight controller. We brought these technologies together to give the tenants a fair bandwidth share and less latency using bandwidth or latency based job allocation strategy.Our assessments show that the above mentioned properties are achieved, being carried out in two common data center network topologies: Tree and Fat-tree.