Miracle-dz/UniFAR-FTUs huggingface dataset:
Information visualization: data infrastructure architectures

Information visualization extends the scope of scientific visualization from physical phenomena to informational entities. This broadening is a direct reflection of the needs expressed by industry, national security, health and other national agendas to include all forms of information within the information analysis processes. It includes: masses of text, forms, and documents; business, manufacturing, and scientific databases; videos; audio; imagery; and most importantly the interactions with all these media types used to enable an enhanced process of discovery understanding, and presentation of situation assessments. In order for information visualization to become a reality, a rich, flexible data infrastructure must be established. The paper defines the scope of information visualization, the emerging opportunities and uses, the related and supporting technologies, and presents a data infrastructure architecture. This architecture supports full distributed multimedia information spaces, including technologies such as information agents, metadata transformations, and local derived metadata management which turns data and information into knowledge. <<ETX>>
An empirical assessment of the business value derived from implementing mobile technology: a case study of two organisations

Mobile technologies are argued to offer unprecedented opportunities for organizations and individuals. In order for organizations to be persuaded that investment in mobile technologies is not only worthwhile, but also important to the achievement of corporate goals and objectives, then it is important to evaluate the potential of mobile technology so that the derivation of business value and the related risks involved in implementing mobile devices and services in an organization can be understood. This paper aims at understanding the organizational value that could be derived from investments in mobile technology. We present two in-depth case studies of mobile technology implementation in health care organizations. These studies show that deriving business value from the adoption and implementation of mobile devices does not seem at all certain, but is contingent upon clear business objectives and a willingness to make business changes to embrace the transformation to core business processes which are driven by the mobile technologies.
Stereoscopic Thumbnail Creation via Efficient Stereo Saliency Detection

In this paper, we propose a framework for automatically producing thumbnails from stereo image pairs. It has two components focusing respectively on stereo saliency detection and stereo thumbnail generation. The first component analyzes stereo saliency through various saliency stimuli, stereoscopic perception and the relevance between two stereo views. The second component uses stereo saliency to guide stereo thumbnail generation. We develop two types of thumbnail generation methods, both changing image size automatically. The first method is called content-persistent cropping (CPC), which aims at cropping stereo images for display devices with different aspect ratios while preserving as much content as possible. The second method is an object-aware cropping method (OAC) for generating the smallest possible thumbnail pair that retains the most important content only and facilitates quick visual exploration of a stereo image database. Quantitative and qualitative experimental evaluations demonstrate promising performance of our thumbnail generation methods in comparison to state-of-the-art algorithms.
Broadening the scope of gaussian processes for large-scale learning. (Élargir la portée des processus gaussiens pour l'apprentissage à grande échelle)

The renewed importance of decision making under uncertainty in practical applications of machine learning calls for a re-evaluation of Bayesian inference techniques targeting this goal in the big data regime. Gaussian processes (GPs) are a fundamental building block of many probabilistic kernel methods that can be applied in a large variety of modelling scenarios; however, the computational and storage complexity of GPs presents the primary barrier to their scaling to large modern datasets. The contributions presented in this thesis are two-fold. We first tackle the problem of preserving good predictive performancewhile enabling GPs to be applied to larger datasets by proposing a novel scheme for accelerating regression and classification by way of preconditioned conjugate gradient. This approach is exact in the limit of iterations, and is consequently the best alternative to exact GP inference when Cholesky decompositions are no longer feasible. The presentation of this methodology is complemented by a thorough analysis of how to design and select suitable preconditioners for this purpose. In the spirit of probabilistic numerics, we also show how the numerical uncertainty introduced by formulations of GPs relying on approximated linear algebra should be adequately evaluated and sensibly incorporated. The resilient appeal of models relying on Bayesian inference in the advent of competitive deep learning techniques can be attributed to their well-founded quantification of uncertainty. Bridging the gap between GPs and deep learning techniques remains a pertinent research goal, and the second broad contribution of this thesis is to establish and reinforce the role of GPs, and their deep counterparts (DGPs), in this setting. The AutoGP model outlined in this thesis sets a new standard for evaluating the performance of GPs in comparison to deep models, and the results obtained on several benchmark datasets are considered to be state-of-the-art among competing GP models. Meanwhile, the scarce use of DGPs within the wider machine learning body can be partly attributed to the fact that they are still widely regarded as interesting theoretical constructions with limited practical
Molecular dynamics calculations of CH 3 sticking coefficient onto diamond surfaces

A molecular dynamics model is implemented in a way dedicated to simulate initial conditions close to microwave plasma deposition of thick single crystal diamond films. The sticking coefficient of CH 3 radical is obtained for (111) and (100) surface at three gas and substrate temperatures: 1120, 1200 and 1500 K. A low value of 5 · 10 −4 is obtained, which is consistent with experimental findings claiming values less than 0.01 or equating to 8 · 10 −3 at 1120 K.
Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo

Enhancers constitute one of the major components of regulatory machinery of metazoans. Although several genome-wide studies have focused on finding and locating enhancers in the genomes, the fundamental principles governing their internal architecture and cis-regulatory grammar remain elusive. Here, we describe an extensive, quantitative perturbation analysis targeting the dorsal-ventral patterning gene regulatory network (GRN) controlled by Drosophila NF-κB homolog Dorsal. To understand transcription factor interactions on enhancers, we employed an ensemble of mathematical models, testing effects of cooperativity, repression, and factor potency. Models trained on the dataset correctly predict activity of evolutionarily divergent regulatory regions, providing insights into spatial relationships between repressor and activator binding sites. Importantly, the collective predictions of sets of models were effective at novel enhancer identification and characterization. Our study demonstrates how experimental dataset and modeling can be effectively combined to provide quantitative insights into cis-regulatory information on a genome-wide scale. DOI: http://dx.doi. org/10.7554/eLife.08445. 001
Formation Control of Multiple Mobile Robots Incorporating an Extended State Observer and Distributed Model Predictive Approach

This paper studies the extended state observer (ESO)-based distributed model predictive control (DMPC) approach to deal with multiple mobile robot formation with unknown disturbances. The distributed control problem with path parameters synchronization and disturbance rejection is formulated for formation system according to the tracking error dynamic model, where the reference paths are parameterized. A local distributed controller is designed by using DMPC strategy for each mobile robot in the absence of disturbance by including parameter synchronization constraints in the quadratic performance index as coupling terms. The DMPC optimization problem is solved by using Nash-optimization iteration strategy with the maximum number of iteration constraint. To improve the ability of anti-jamming, a feedforward compensation controller is designed by using ESO method, where the ESO is designed by pole assignment. The convergence of the proposed iterative algorithm is given. Furthermore, the input-to-state stability property of the proposed composite controller, combining a feedforward compensation controller and local distributed controller, is analyzed for the closed-loop system. Finally, the validity of the proposed algorithm is verified by two simulation examples.
Augmenting Environmental Interaction in Audio Feedback Systems

Audio feedback is defined as a positive feedback of acoustic signals where an audio input and output form a loop, and may be utilized artistically. This article presents new context-based controls over audio feedback, leading to the generation of desired sonic behaviors by enriching the influence of existing acoustic information such as room response and ambient noise. This ecological approach to audio feedback emphasizes mutual sonic interaction between signal processing and the acoustic environment. Mappings from analyses of the received signal to signal-processing parameters are designed to emphasize this specificity as an aesthetic goal. Our feedback system presents four types of mappings: approximate analyses of room reverberation to tempo-scale characteristics, ambient noise to amplitude and two different approximations of resonances to timbre. These mappings are validated computationally and evaluated experimentally in different acoustic conditions.
Improved personalized recommendation system with better user experience

With the development of Internet e-commerce, shopping online has become very popular. Online stores have the advantage of having more items in their catalogue without worrying about shop constraints. In this scenario, recommendation systems are useful as they can help users in identifying items that they might be interested in. After shopping users usually provide feedback in the form review and ratings. This feedback is very useful for recommendation systems. Most of the current implementations of recommendation engines makes use of ratings only. The review text contain important aspect on why a particular rating was given. If the review text can be analyzed and used for recommendation, the user experience can be improved. In this paper we propose an approach to identify ‘aspects’ in the review and build user and item profiles to reflect ‘aspects’. The users' preference to ‘aspects’ are considered during recommendation. The proposed approach was tested with data from Amazon dataset and our proposed recommendation system showed RMSE MAE value below one. The salient feature of this approach is that it combines review text and rating to remove sparsity and cold start problem in a limited sense.
Rinnegan: Efficient resource use in heterogeneous architectures

Current processors provide a variety of different processing units to improve performance and power efficiency. For example, ARM's big.LITTLE, AMD's APUs, and Oracle's M7 provide heterogeneous processors, on-die GPUs, and on-die accelerators. However, the performance experienced by programs using these processing units can vary widely due to contention from multiprogramming, thermal constraints and other issues. In these systems, the decision of where to execute a task must consider not only execution time of the task, but also current system conditions. We built Rinnegan, a Linux kernel extension and runtime library, to perform scheduling and handle task placement in heterogeneous systems. The Rinnegan kernel extension monitors and reports the utilization of all processing units to applications, which then makes placement decisions at user level. The Rinnegan runtime provides a performance model to predict the speedup and overhead of offloading a task. With this model and the current utilization of processing units, the runtime can select the task placement that best achieves an application's performance goals, such as low latency, high throughput, or real-time deadlines. When integrated with StarPU, a runtime system for heterogeneous architectures, Rinnegan improves StarPU by performing 1.5- 2× better than its native scheduling policies in a shared heterogeneous environment.
Magnetic Resonance Imaging of the Elbow

Despite being one of the more commonly injured joints in recreational and elite athletes, diagnosis of soft-tissue injuries of the elbow remains problematic. Although plain radiographs remain the initial mainstay for imaging of the elbow, the superior soft-tissue contrast and direct multiplanar capabilities of magnetic resonance (MR) imaging has afforded the ability to more accurately diagnose the site and extent of soft-tissue pathology, including tendinous injuries and trauma to the static stabilizers. In addition, the ability of MR imaging to noninvasively assess the integrity of the articular cartilage provides for more comprehensive information that was previously unavailable and has prognostic significance in the athlete.
Comparing Interaction Techniques to Help Blind People Explore Maps on Small Tactile Devices

Exploring geographic maps on touchscreens is a difficult task in the absence of vision as those devices miss tactile cues. Prior research has therefore introduced non-visual interaction techniques designed to allow visually impaired people to explore spatial configurations on tactile devices. In this paper, we present a study in which six blind and six blindfolded sighted participants evaluated three of those interaction techniques compared to a screen reader condition. We observed that techniques providing guidance result in a higher user satisfaction and more efficient exploration. Adding a grid-like structure improved the estimation of distances. None of the interaction techniques improved the reconstruction of the spatial configurations. The results of this study allow improving the design of non-visual interaction techniques that support a better exploration and memorization of maps in the absence of vision.
An Agent-Based Solution for Dynamic Supply Chain Management

Supply chain management (SCM) deals with planning and coordinating activities such as material procurement, product assembly, and the distribution of manufactured products. This paper offers an agent-based solution as a potentially adequate approach for the automation of supply chain management. The greatest obstacle in SCM research is obtaining benchmark designed solutions since it is difficult to simulate real business environments, while live testing in real-world systems is not an option. The Trading Agent Competition Supply Chain Management (TAC SCM) scenario provides a unique testbed for studying and prototyping SCM agents by providing a challenging game environment where competing agents engage in complex decision-making activities with the purpose of maximizing their profit. In this paper, we describe the TAC SCM environment and present the main features of the CrocodileAgent, our TAC SCM 2007 entry. Additionally, the CrocodileAgent’ s performance in the competition, as well as in a series of controlled experiments, is discussed.
Immediate Rewards Predict Adherence to Long-Term Goals

People primarily pursue long-term goals, such as exercising, to receive delayed rewards (e.g., improved health). However, we find that the presence of immediate rewards is a stronger predictor of persistence in goal-related activities than the presence of delayed rewards. Specifically, immediate rewards (e.g., enjoyment) predicted current persistence at New Year’s resolutions whereas delayed rewards did not (Study 1). Furthermore, immediate rewards predicted persistence in a single session of studying and exercising whereas delayed rewards did not, even though people report primarily pursuing these activities for delayed rewards (Studies 2 and 3). This is true for both short (1 week) and long (3 month) time frames (Study 4), and regardless of whether anticipated or materialized rewards are assessed (Study 5). Overall, whereas delayed rewards may motivate goal setting and the intentions to pursue long-term goals, a meta-analysis of our studies finds that immediate rewards are more strongly associated with actual persistence in a long-term goal.
A simple and efficient method for accurate collision detection among deformable polyhedral objects in arbitrary motion

We propose an accurate collision detection algorithm for use in virtual reality applications. The algorithm works for three-dimensional graphical environments where multiple objects, represented as polyhedra (boundary representation), are undergoing arbitrary motion (translation and rotation). The algorithm can be used directly for both convex and concave objects and objects can be deformed (non-rigid) during motion. The algorithm works efficiently by first reducing the number of face pairs that need to be checked accurately for interference by first localizing possible collision regions using bounding box and spatial subdivision techniques; face pairs that remain after this pruning stage are then accurately checked for interference. The algorithm is efficient, simple to implement, and does not require any memory intensive auxiliary data structures to be precomputed and updated. Since polyhedral shape representation is one of the most common shape representation schemes, this algorithm should be useful to a wide audience. Performance results are given to show the efficiency of the proposed method.
Test Selection Based on Finite State Models

A method for the selection of appropriate test case, an important issue for conformance testing of protocol implementations as well as software engineering, is presented. Called the partial W-method, it is shown to have general applicability, full fault-detection power, and yields shorter test suites than the W-method. Various other issues that have an impact on the selection of a suitable test suite including the consideration of interaction parameters, various test architectures for protocol testing and the fact that many specifications do not satisfy the assumptions made by most test selection methods (such as complete definition, a correctly implemented reset function, a limited number of states in the implementation, and determinism), are discussed. >
Active Trace Clustering for Improved Process Discovery

Process discovery is the learning task that entails the construction of process models from event logs of information systems. Typically, these event logs are large data sets that contain the process executions by registering what activity has taken place at a certain moment in time. By far the most arduous challenge for process discovery algorithms consists of tackling the problem of accurate and comprehensible knowledge discovery from highly flexible environments. Event logs from such flexible systems often contain a large variety of process executions which makes the application of process mining most interesting. However, simply applying existing process discovery techniques will often yield highly incomprehensible process models because of their inaccuracy and complexity. With respect to resolving this problem, trace clustering is one very interesting approach since it allows to split up an existing event log so as to facilitate the knowledge discovery process. In this paper, we propose a novel trace clustering technique that significantly differs from previous approaches. Above all, it starts from the observation that currently available techniques suffer from a large divergence between the clustering bias and the evaluation bias. By employing an active learning inspired approach, this bias divergence is solved. In an assessment using four complex, real-life event logs, it is shown that our technique significantly outperforms currently available trace clustering techniques.
Bulwark AODV against Black hole and Gray hole attacks in MANET

As the technology gets advanced so as the security issues come into the picture. Here we will discuss about one such advancement in the modern world for communication purpose which is ruled by wireless networking. Precisely MANET (Mobile Adhoc Network) plays a huge role in transmission of data without any wired structure. Its distinct nature of maintaining the nodes and links to transmit the data at faster rate has proven its importance in communication media. The major issue with mobile adhoc network is security, there are two major such type of security issues which affects the MANET while transmitting data are Black hole and Gray hole attacks. This paper proposes Bulwark-AODV which prevents single or cooperative Black hole attacks by identifying malicious route replies at source and intermediate nodes and finds a shortest valid path. It also works in case of single adjacent node to the source and when there are multiple Black holes in the network. It detects Gray holes by snooping the ACKs of data packets. Forgery of ACK if any is identified by the source through checking the return time of ACK. Simulation results indicates better performance even though special cases are considered.
SenSkin: adapting skin as a soft interface

We present a sensing technology and input method that uses skin deformation estimated through a thin band-type device attached to the human body, the appearance of which seems socially acceptable in daily life. An input interface usually requires feedback. SenSkin provides tactile feedback that enables users to know which part of the skin they are touching in order to issue commands. The user, having found an acceptable area before beginning the input operation, can continue to input commands without receiving explicit feedback. We developed an experimental device with two armbands to sense three-dimensional pressure applied to the skin. Sensing tangential force on uncovered skin without haptic obstacles has not previously been achieved. SenSkin is also novel in that quantitative tangential force applied to the skin, such as that of the forearm or fingers, is measured. An infrared (IR) reflective sensor is used since its durability and inexpensiveness make it suitable for everyday human sensing purposes. The multiple sensors located on the two armbands allow the tangential and normal force applied to the skin dimension to be sensed. The input command is learned and recognized using a Support Vector Machine (SVM). Finally, we show an application in which this input method is implemented.