Biblio

Heart rate monitoring has become increasingly popular in the industry through mobile phones and wearable devices. However, current determination of heart rate through mobile applications suffers from high corruption of signals during intensive physical exercise. In this paper, we present a novel technique for accurately determining heart rate during intensive motion by classifying PPG signals obtained from smartphones or wearable devices combined with motion data obtained from accelerometer sensors. Our approach utilizes the Internet of Things (IoT) cloud connectivity of smartphones for selection of PPG signals using deep learning. The technique is validated using the TROIKA dataset and is accurately able to predict heart rate with a 10-fold cross validation error margin of 4.88%.

This paper proposes a method for segmentation of nuclei of single/isolated and overlapping/touching immature white blood cells from microscopic images of B-Lineage acute lymphoblastic leukemia (ALL) prepared from peripheral blood and bone marrow aspirate. We propose deep belief network approach for the segmentation of these nuclei. Simulation results and comparison with some of the existing methods demonstrate the efficacy of the proposed method.

Abnormality detection is useful in reducing the amount of data to be processed manually by directing attention to the specific portion of data. However, selections of suitable features are important for the success of an abnormality detection system. Designing and selecting appropriate features are time-consuming, requires expensive domain knowledge and human labor. Further, it is very challenging to represent high-level concepts of abnormality in terms of raw input. Most of the existing abnormality detection system use handcrafted feature detector and are based on shallow architecture. In this work, we explore Deep Belief Network for abnormality detection and simultaneously, compared the performance of classic neural network in terms of features learned and accuracy of detecting the abnormality. Further, we explore the set of features learn by each layer of the deep architecture. We also provide a simple and fast mechanism to visualize the feature at the higher layer. Further, the effect of different activation function on abnormality detection is also compared. We observed that deep learning based approach can be used for detecting an abnormality. It has better performance compare to classical neural network in separating distinct as well as almost similar data.

Augmented reality (AR) technologies, such as those in head-mounted displays like Microsoft HoloLens or in automotive windshields, are poised to change how people interact with their devices and the physical world. Though researchers have begun considering the security, privacy, and safety issues raised by these technologies, to date such efforts have focused on input, i.e., how to limit the amount of private information to which AR applications receive access. In this work, we focus on the challenge of output management: how can an AR operating system allow multiple concurrently running applications to safely augment the user's view of the world? That is, how can the OS prevent apps from (for example) interfering with content displayed by other apps or the user's perception of critical real-world context, while still allowing them sufficient flexibility to implement rich, immersive AR scenarios? We explore the design space for the management of visual AR output, propose a design that balances OS control with application flexibility, and lay out the research directions raised and enabled by this proposal.

In this demo, we present an immersive virtual reality (VR) system which integrates multimodal interaction sensors (i.e., smartphone, Kinect v2, and Myo armband) and streaming technology to improve the VR experience. The integrated system solves the common problems in most VR systems: (1) the very limited playing area due to transmission cable between computer and display/interaction devices, and (2) non-intuitive way of controlling virtual objects. We use Unreal Engine 4 to develop an immersive VR game with 6 interactive levels to demonstrate the feasibility of our system. In the game, the user not only can freely walk within a large playing area surrounded by multiple Kinect sensors but also select the virtual objects to grab and throw with the Myo armband. The experiment shows that our idea is workable for VR experience.

As the oil and gas industry's ultimate goal is to uncover efficient and economic ways to produce oil and gas, well optimization studies are crucially important for reservoir engineers. Although this task has a major impact on reservoir productivity, it has been challenging for reservoir engineers to perform since it involves time-consuming flow simulations to search a large solution space for an optimal well plan. Our work aims to provide engineers a) an analytical method to perform static connectivity analysis as a proxy for flow simulation, b) an application to support well optimization using our method and c) an immersive experience that benefits engineers and supports their needs and preferences when performing the design and assessment of well trajectories. For the latter purpose, we explore our tool with three immersive environments: a CAVE with a tracked gamepad; a HMD with a tracked gamepad; and a HMD with a Leap Motion controller. This paper describes our application and its techniques in each of the different immersive environments. This paper also describes our findings from an exploratory evaluation conducted with six reservoir engineers, which provided insight into our application, and allowed us to discuss the potential benefits of immersion for the oil and gas domain.

The latest advances in head-mounted displays (HMDs) for augmented reality (AR) and mixed reality (MR) have produced commercialized devices that are gradually accepted by the public. These HMDs are generally equipped with head tracking, which provides an excellent input to explore immersive visualization and interaction techniques for various AR/MR applications. This paper explores the head tracking function on the latest Microsoft HoloLens – where gaze is defined as the ray starting at the head location and points forward. We present a gaze-directed visualization approach to study ensembles of 2D oil spill simulations in mixed reality. Our approach allows users to place an ensemble as an image stack in a real environment and explore the ensemble with gaze tracking. The prototype system demonstrates the challenges and promising effects of gaze-based interaction in the state-of-the-art mixed reality.

Generic multi-button controllers are the most common input devices used for video games. In contrast, dedicated game controllers and gestural interactions increase immersion and playability. Room-sized gaming has opened up possibilities to further enhance the immersive experience, and provides players with opportunities to use full-body movements as input. We present a purpose-centric approach to appropriating everyday objects as physical game controllers, for immersive room-sized gaming. Virtual manipulations supported by such physical controllers mimic real-world function and usage. Doing so opens up new possibilities for interactions that flow seamlessly from the physical into the virtual world. As a proof-of-concept, we present a 'Tower Defence' styled game, that uses four everyday household objects as game controllers, each of which serves as a weapon to defend the base of the players from enemy bots. Players can use 1) a mop (or a broom) to sweep away enemy bots directionally; 2) a fan to scatter them away; 3) a vacuum cleaner to suck them; 4) a mouse trap to destroy them. Each controller is tracked using a motion capture system. A physics engine is integrated in the game, and ensures virtual objects act as though they are manipulated by the actual physical controller, thus providing players with a highly-immersive gaming experience.

Virtual Reality and immersive experiences, which allow players to share the same virtual environment as the characters of a virtual world, have gained more and more interest recently. In order to conceive these immersive virtual worlds, one of the challenges is to give to the characters that populate them the ability to express behaviors that can support the immersion. In this work, we propose a model capable of controlling and simulating a conversational group of social agents in an immersive environment. We describe this model which has been previously validated using a regular screen setting and we present a study for measuring whether users recognized the attitudes expressed by virtual agents through the realtime generated animations of nonverbal behavior in an immersive setting. Results mirrored those of the regular screen setting thus providing further insights for improving players experiences by integrating them into immersive simulated group conversations with characters that express different interpersonal attitudes.

This project shows a procedure-training simulator targeted at the operation and maintenance of overland distribution power lines. This simulator is focused on workplace safety and risk assessment of common daily operations such as fuse replacement and power cut activities. The training system is implemented using VR goggles (Oculus Rift) and a mixture of a real scenario matched perfectly with its Virtual Reality counterpart. The real scenario is composed of a real "basket" and a stick - both of the equipment is the actual ones used in daily training. Both, equipment are tracked by high precision infrared cameras system (OptiTrack) providing a high degree of immersion and realism. In addition to tracking the scenario, the user is completely tracked: heads, shoulders, arms and hands are tracked. This tracking allows a perfect simulation of the participant's movements in the Virtual World. This allows precise evaluation of movements as well as ergonomics. The virtual scenario was carefully designed to accurately reproduce in a coherent way all relevant spatial, architectonic and natural features typical of the urban environment, reflecting the variety of challenges that real cities might impose on the activity. The system consists of two modules: the first module being Instructor Interface, which will help create and control different challenging scenarios and follow the student's reactions and behavior; and the second module is the simulator itself, which will be presented to the student through VR goggles. The training session can also be viewed on a projected screen by other students, enabling learning through observation of mistakes and successes of their peers, such as a martial arts dojo. The simulator features various risk scenarios such as: different climates - sun, rain and wind; different lighting conditions - day, night and artificial; different types of electrical structures; transformer fire and explosion; short-circuit and electric arc; defective equipment; many obstacles - trees, cars, windows, swarm of bees, etc.

We present a study examining the impact of physical and cognitive challenge on reported immersion for a mixed reality game called Beach Pong. Contrary to prior findings for desktop games, we find significantly higher reported immersion among players who engage physically, regardless of their actual game performance. Building a mental map of the real, virtual, and sensed world is a cognitive challenge for novices, and this appears to influence immersion: in our study, participants who actively attended to both physical and virtual game elements reported higher immersion levels than those who attended mainly or exclusively to virtual elements. Without an integrated mental map, in-game cognitive challenges were ignored or offloaded to motor response when possible in order to achieve the minimum required goals of the game. From our results we propose a model of immersion in mixed reality gaming that is useful for designers and researchers in this space.

In this paper we present work-in-progress toward a vision of personalized views of visual analytics interfaces in the context of collaborative analytics in immersive spaces. In particular, we are interested in the sense of immersion, responsiveness, and personalization afforded by gaze-based input. Through combining large screen visual analytics tools with eye-tracking, a collaborative visual analytics system can become egocentric while not disrupting the collaborative nature of the experience. We present a prototype system and several ideas for real-time personalization of views in visual analytics.

Beyond other domains, the field of immersive analytics makes use of Augmented Reality techniques to successfully support users in analyzing data. When displaying ubiquitous data integrated into the everyday life, spatial immersion issues like depth perception, data localization and object relations become relevant. Although there is a variety of techniques to deal with those, they are difficult to apply if the examined data or the reference space are large and abstract. In this work, we discuss observed problems in such immersive analytics systems and the applicability of current countermeasures to identify needs for action.

In multi-server environments, remote user authentication is an extremely important issue because it provides authorization while users access their data and services. Moreover, the remote user authentication scheme for multi-server environment has resolved the problem of users needing to manage their different identities and passwords. For this reason, many user authentication schemes for multi-server environments have been proposed in recent years. In 2015, Lu et al. improved Mishra et al.'s scheme, and claimed that their scheme is a more secure and practical remote user authentication for multi-server environments. However, we found that Lu et al.'s scheme is actually insecure and incorrect. In this paper, we demonstrate that their scheme is vulnerable to outsider attack, user forgery attack. We then propose a new biometrics and smart card-based authentication scheme. Finally, we show that our proposed scheme is more secure and supports security properties.

The average computer user is no longer restricted to one device. They may have several devices and expect their applications to work on all of them. A challenge arises when these applications need the cryptographic private key of the devices' owner. Here the device owner typically has to manage keys manually with a "keychain" app, which leads to private keys being transferred insecurely between devices – or even to other people. Even with intuitive synchronization mechanisms, theft and malware still pose a major risk to keys. Phones and watches are frequently removed or set down, and a single compromised device leads to the loss of the owner's private key, a catastrophic failure that can be quite difficult to recover from. We introduce Shatter, an open-source framework that runs on desktops, Android, and Android Wear, and performs key distribution on a user's behalf. Shatter uses threshold cryptography to turn the security weakness of having multiple devices into a strength. Apps that delegate cryptographic operations to Shatter have their keys compromised only when a threshold number of devices are compromised by the same attacker. We demonstrate how our framework operates with two popular Android apps (protecting identity keys for a messaging app, and encryption keys for a note-taking app) in a backwards-compatible manner: only Shatter users need to move to a Shatter-aware version of the app. Shatter has minimal impact on app performance, with signatures and decryption being calculated in 0.5s and security proofs in 14s.

Federated identity providers, e.g., Facebook and PayPal, offer a convenient means for authenticating users to third-party applications. Unfortunately such cross-site authentications carry privacy and tracking risks. For example, federated identity providers can learn what applications users are accessing; meanwhile, the applications can know the users' identities in reality. This paper presents Crypto-Book, an anonymizing layer enabling federated identity authentications while preventing these risks. Crypto-Book uses a set of independently managed servers that employ a (t,n)-threshold cryptosystem to collectively assign credentials to each federated identity (in the form of either a public/private keypair or blinded signed messages). With the credentials in hand, clients can then leverage anonymous authentication techniques such as linkable ring signatures or partially blind signatures to log into third-party applications in an anonymous yet accountable way. We have implemented a prototype of Crypto-Book and demonstrated its use with three applications: a Wiki system, an anonymous group communication system, and a whistleblower submission system. Crypto-Book is practical and has low overhead: in a deployment within our research group, Crypto-Book group authentication took 1.607s end-to-end, an overhead of 1.2s compared to traditional non-privacy-preserving federated authentication.

In this paper, we propose the first identity-based broadcast encryption scheme, which can simultaneously achieves confidentiality and full anonymity against adaptive chosen-ciphertext attacks under a standard assumption. In addition, two further desirable features are also provided: one is fully-collusion resistant which means that even if all users outside of receivers S collude they cannot obtain any information about the plaintext. The other one is stateless which means that the users in the system do not need to update their private keys when the other users join or leave our system. In particular, our scheme is highly efficient, where the public parameters size, the private key size and the decryption cost are all constant and independent to the number of receivers.

This work is motivated by the rapid increase of the number of attacks in computer networks and software engineering. In this paper we study identity snowball attacks and formally prove the correctness of suggested solutions to this type of attack (solutions that are based on the graph reachability reduction) using a proof assistant. We propose a model of an attack graph that captures technical informations about the calculation of reachability of the graph. The model has been implemented with the proof assistant PVS 6.0 (Prototype Verification System). It makes it possible to prove algorithms of reachability reduction such as Sparsest\_cut.

User identity linkage across social platforms is an important problem of great research challenge and practical value. In real applications, the task often assumes an extra degree of difficulty by requiring linkage across multiple platforms. While pair-wise user linkage between two platforms, which has been the focus of most existing solutions, provides reasonably convincing linkage, the result depends by nature on the order of platform pairs in execution with no theoretical guarantee on its stability. In this paper, we explore a new concept of ``Latent User Space'' to more naturally model the relationship between the underlying real users and their observed projections onto the varied social platforms, such that the more similar the real users, the closer their profiles in the latent user space. We propose two effective algorithms, a batch model(ULink) and an online model(ULink-On), based on latent user space modelling. Two simple yet effective optimization methods are used for optimizing objective function: the first one based on the constrained concave-convex procedure(CCCP) and the second on accelerated proximal gradient. To our best knowledge, this is the first work to propose a unified framework to address the following two important aspects of the multi-platform user identity linkage problem –- (I) the platform multiplicity and (II) online data generation. We present experimental evaluations on real-world data sets for not only traditional pairwise-platform linkage but also multi-platform linkage. The results demonstrate the superiority of our proposed method over the state-of-the-art ones.

The purpose of this research is to propose architecture-driven, penetration testing equipped with a software reverse and forward engineering process. Although the importance of architectural risk analysis has been emphasized in software security, no methodology is shown to answer how to discover the architecture and abuse cases of a given insecure legacy system and how to modernize it to a secure target system. For this purpose, we propose an architecture-driven penetration testing methodology: 4+1 architectural views of the given insecure legacy system, documented to discover program paths for vulnerabilities through a reverse engineering process. Then, vulnerabilities are identified by using the discovered architecture abuse cases and countermeasures are proposed on identified vulnerabilities. As a case study, a telecommunication company's Identity Access Management (IAM) system is used for discovering its software architecture, identifying the vulnerabilities of its architecture, and providing possible countermeasures. Our empirical results show that functional suggestions would be relatively easier to follow up and less time-consuming work to fix; however, architectural suggestions would be more complicated to follow up, even though it would guarantee better security and take full advantage of OAuth 2.0 supporting communities.

In this paper we describe a privacy-preserving method for commissioning an IoT device into a cloud ecosystem. The commissioning consists of the device proving its manufacturing provenance in an anonymous fashion without reliance on a trusted third party, and for the device to be anonymously registered through the use of a blockchain system. We introduce the ChainAnchor architecture that provides device commissioning in a privacy-preserving fashion. The goal of ChainAnchor is (i) to support anonymous device commissioning, (ii) to support device-owners being remunerated for selling their device sensor-data to service providers, and (iii) to incentivize device-owners and service providers to share sensor-data in a privacy-preserving manner.

Digital artifacts on social media can challenge individuals during identity transitions, particularly those who prefer to delete, separate from, or hide data that are representative of a past identity. This work investigates concerns and practices reported by transgender people who transitioned while active on Facebook. We analyze open-ended survey responses from 283 participants, highlighting types of data considered problematic when separating oneself from a past identity, and challenges and strategies people engage in when managing personal data in a networked environment. We find that people shape their digital footprints in two ways: by editing the self-presentational data that is representative of a prior identity, and by managing the configuration of people who have access to that self-presentation. We outline the challenging interplay between shifting identities, social networks, and the data that suture them together. We apply these results to a discussion of the complexities of managing and forgetting the digital past.

UnlimitID is a method for enhancing the privacy of commodity OAuth and applications such as OpenID Connect, using anonymous attribute-based credentials based on algebraic Message Authentication Codes (aMACs). OAuth is one of the most widely used protocols on the Web, but it exposes each of the requests of a user for data by each relying party (RP) to the identity provider (IdP). Our approach allows for the creation of multiple persistent and unlinkable pseudo-identities and requires no change in the deployed code of relying parties, only in identity providers and the client.

Many of the game-changing innovations the Internet brought and continues to bring to all of our daily professional and private lifes come with privacy-related costs. The more day-to-day activities are based on the Internet, the more personal data are generated, collected, stored and used. Big Data, Internet of Things, cyber-physical-systems and similar trends will be based on even more personal information all of us use and produce constantly. Three major points are to be noted here: First, there is no common European or even worldwide agreement whether and in how far these collections need to be limited. There is, though, no common privacy law âĂŞ neither in Europe nore worldwide. Second, laws that do exist constantly fail in steering the developments. Technology innovations come so fast, are so disruptive and so market-demand driven, that an ex-post control by law and courts constantly comes late and/or is circumvented and/or ignored. Third, lack of consensus and lack of steering lead to huge data accumulations and market monopolies built up very quickly and held by very few companies working on a global level with data driven business models. These early movers are in many cases in very dominant market positions making it not only more difficult to regulate their behavior but also to keep the markets open for future competitors. This workshop will evaluate current European and international attempts to deal with this situation. Although all four panelists have a legal background, the meeting will be less interested in an in-depth review of existing laws and their impact, but more in the underlying technological and ethical principles (and their inconsistencies) leading to the sitation described. Specific attention will be attributed to technology driven attempts to deal with the situation, such as privacy by design, privacy by default, usable privacy etc.

In recent years, prodigious explosion of social network services may trigger new business models. However, it has negative aspects such as personal information spill or spamming, as well. Amongst conventional spam detection approaches, the studies which are based on vertex degrees or Local Clustering Coefficient have been caused false positive results so that normal vertices can be specified as spammers. In this paper, we propose a novel approach by employing the circuit structure in the social networks, which demonstrates the advantages of our work through the experiment.