Miracle-dz/UniFAR-FTUs huggingface dataset:
Muscle fatigue tracking based on stimulus evoked EMG and adaptive torque prediction

Functional electrical stimulation (FES) is effective to restore movement in spinal cord injured (SCI) subjects. Unfortunately, muscle fatigue constrains the application of FES so that output torque feedback is interesting for fatigue compensation. Whereas, inadequacy of torque sensors is another challenge for FES control. Torque estimation is thereby essential in fatigue tracking task for practical FES employment. In this work, the Hammstein cascade with electromyography (EMG) as input is applied to model the myoelectrical mechanical behavior of the stimulated muscle. Kalman filter with forgetting factor is presented to estimate the muscle model and track fatigue. Fatigue inducing protocol was conducted on three SCI subjects through surface electrical stimulation. Assessment in simulation and with experimental data reveals that the muscle model properly fits the muscle behavior well. Moreover, the time-varying parameters tracking performance in simulation is efficient such that real time tracking is feasible with Kalman filter. The fatigue tracking with experimental data further demonstrates that the proposed method is suitable for fatigue tracking as well as adaptive torque prediction at different prediction horizons.
The Light Field Camera: Extended Depth of Field, Aliasing, and Superresolution

Portable light field (LF) cameras have demonstrated capabilities beyond conventional cameras. In a single snapshot, they enable digital image refocusing and 3D reconstruction. We show that they obtain a larger depth of field but maintain the ability to reconstruct detail at high resolution. In fact, all depths are approximately focused, except for a thin slab where blur size is bounded, i.e., their depth of field is essentially inverted compared to regular cameras. Crucial to their success is the way they sample the LF, trading off spatial versus angular resolution, and how aliasing affects the LF. We show that applying traditional multiview stereo methods to the extracted low-resolution views can result in reconstruction errors due to aliasing. We address these challenges using an explicit image formation model, and incorporate Lambertian and texture preserving priors to reconstruct both scene depth and its superresolved texture in a variational Bayesian framework, eliminating aliasing by fusing multiview information. We demonstrate the method on synthetic and real images captured with our LF camera, and show that it can outperform other computational camera systems.
Architectural-enhanced intrusion detection and memory authentication schemes in embedded systems

A number of shocking cyber-attacks have happened in recent years, and the damage they have caused has led to the emergence of cyber-security as a consideration when designing embedded systems. Software vulnerability and physical attack are the most severe threats the system face. This paper provides information about hardware designed to monitor potential intrusions and incidences of unauthorized access. Crucially, it can also trace execution patterns and cryptographic schemes in relation to memory authentication. The automated compiler extracts the intrusion detection model and covers the important instructions with cipher text at the compile time. At runtime, the proposed hardware monitors the instructions that change program trace and access memory data, which ensure the process and data follow the permissible behavior and resist the potential attacks. The security analysis shows that the proposed techniques can recognize and eliminate a wide range of common software and physical threats with low performance penalties and minimal overhead.
A framework for interference control in Software-Defined mobile radio networks

To cope up with the booming of data traffic and to accommodate new and emerging technologies such as machine-type communications, the 5th Generation (5G) of mobile networks must be empowered with efficient resource allocation schemes that benefit from the adoption of the Software-Defined networking (SDN) paradigm. In radio communications, allocation of resources is tightly connected with interference. In this paper, we revisit the way wireless interference is managed and avoided relying on the SDN paradigm for controlling the network. The SDN approach is exploited to expose the lower layers of the stack (e.g., Physical and Medium Access Control) to the controller and its applications by making system parameters available, such that it is possible to dynamically configure the network in a logically centralized fashion, by means of specifically designed algorithms. The contribution of this work is threefold. First, we show how to adapt the SDN paradigm to mobile networks. Second, we propose the interference graph as an abstraction that can be used to control interference. Last, we formulate a throughput optimization tool that uses the proposed interference graph as an input.
5 Nonintentional Similarity Processing

Similarity is a compelling part of everyday experience . In the visual world, objects that are similar in shape or color may seem to leap to our attention . In conceptual processing, we have an immediate sense of whether a pair of concepts is similar . The prominence of similarity in conscious experience has made it an important explanatory construct in psychological theories . New problems are assumed to be solved on the basis of their similarity to known problems (e .g ., Reed, Ernst, & Banerji, 1974; Ross, 1987) . Objects are assumed to be classified on the basis of their similarity to some stored category representation (e .g ., Medin & Schaffer, 1978 ; Reed, 1972). Predictions of new features of an item may be based on what other similar items have those features (e .g ., Blok & Gentner, 2000 ; Heit & Rubinstein, 1994 ; Osherson, Smith. Wilkie, Lopez, & Shafir, 1990 ; Sloman, 1993) . Yet despite extensive work on mechanisms of similarity, there has been very little discussion of why and how similarity is important in cognitive processing beyond the general recognition that similarity often provides a good basis for generalization (Shepard, 1987) . In this chapter, we consider the role of similarity in the cognitive architecture and the relationship of similarity to automatic processing . We suggest that some types of similarity are determined automatically . When the cognitive system recognizes similarities . they influence cognitive processing, even when the person does not intend processing to be affected by similarities . To support this claim, we first outline three approaches to similarity . Then, we examine how similarity can influence both low-level processes like attention and memory retrieval and higher cognitive processes like analogical reasoning and decision making . Next, we explore a number of examples in which cognitive processing is in-
Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning

In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel algorithm is proposed by adapting the simultaneous codeword optimization (SimCO) algorithm, based on the sparse synthesis model, to the sparse analysis model. This algorithm assumes that the analysis dictionary contains unit l2-norm atoms and learns the dictionary by optimization on manifolds. This framework allows multiple dictionary atoms to be updated simultaneously in each iteration. However, similar to several existing analysis dictionary learning algorithms, dictionaries learned by the proposed algorithm may contain similar atoms, leading to a degenerate (coherent) dictionary. To address this problem, we also consider restricting the coherence of the learned dictionary and propose Incoherent Analysis SimCO by introducing an atom decorrelation step following the update of the dictionary. We demonstrate the competitive performance of the proposed algorithms using experiments with synthetic data and image denoising as compared with existing algorithms.
Mediterranean Conference on Information Systems ( MCIS ) Summer 9-4-2014 CULTURAL CONFLICTS AND KNOWLEDGE SHARING : AN EXPLORATORY ANALYSIS OF SINO-AUSTRIAN PROJECTS

This paper provides an exploratory analysis of cultural conflicts that potentially hinder knowledge sharing in the context of cross-national and knowledge-intensive projects including Austrian and Chinese project members. We firstly give an overview of central cultural concepts from a national viewpoint, highlight typical conflicts in projects, and discuss key factors that stimulate knowledge sharing. Next, we provide results gained from a series of interviews with practitioners and academics, and an exploratory workshop on global knowledge sharing held in Hong Kong. From the qualitative analysis, we posit six central cultural conflicts that emerged over task responsibilities, attitudes, work execution, power, communication, and time-orientation. We consequently classify these conflicts, discuss their causal placements within Hofstede’s cultural dimensions and suggest implications for knowledge sharing. Our research findings provide more groundwork for better understanding cultural conflicts and implications for knowledge sharing in the context of Sino-Austrian projects.
A Systematic Enhancer Screen Using Lentivector Transgenesis Identifies Conserved and Non-Conserved Functional Elements at the Olig1 and Olig2 Locus

Finding sequences that control expression of genes is central to understanding genome function. Previous studies have used evolutionary conservation as an indicator of regulatory potential. Here, we present a method for the unbiased in vivo screen of putative enhancers in large DNA regions, using the mouse as a model. We cloned a library of 142 overlapping fragments from a 200 kb-long murine BAC in a lentiviral vector expressing LacZ from a minimal promoter, and used the resulting vectors to infect fertilized murine oocytes. LacZ staining of E11 embryos obtained by first using the vectors in pools and then testing individual candidates led to the identification of 3 enhancers, only one of which shows significant evolutionary conservation. In situ hybridization and 3C/4C experiments suggest that this enhancer, which is active in the neural tube and posterior diencephalon, influences the expression of the Olig1 and/or Olig2 genes. This work provides a new approach for the large-scale in vivo screening of transcriptional regulatory sequences, and further demonstrates that evolutionary conservation alone seems too limiting a criterion for the identification of enhancers.
Choosing Whom to Target: Horizontal Inequality and the Risk of Civil and Communal Violence

Do different types of inequality spur violence with different targets? This article explores whom violent movements choose to target when they take to arms, by comparing civil conflicts, which target the state, to communal conflicts, which target other ethnic groups. Different types of ethnic group disadvantage relate to conflict through different mechanisms. Political exclusion is expected to promote the choice to target the central government rather than other ethnic groups, while economic disadvantages should increase the risk of both civil and communal conflicts. The different expectations stem from two important differences between political and economic horizontal inequalities: only the government has the authority to change the political distribution, while there can be many avenues to economic redistribution; and blame is more straightforwardly assigned to the government for political than for economic disadvantages. Statistical analysis of 155 politically relevant ethnic groups in Africa (1991–2009) provides support for both propositions.
Knowledge-based Segmentation and Feature Analysis of Hand-wrist Radiographs 1

Important procedures exist in paediatric radiology for the assessment of skeletal maturity from radiographs of a child's hand. Typically, these procedures involve comparison of a radiograph with textual and pictorial information from an atlas. This is a tedious and time-consuming process, and there has been widespread interest in the possibility of automation using computers. We have devised a segmentation algorithm which makes it possible to automate the TW2 method for skeletal maturity assessment. Our algorithm exploits knowledge of the anatomical structure of the hand and capitalises on the fact that large structures (the radius, the ulna, the hand itself) are easier to detect than smaller, fainter features (e.g., the carpals). We begin by separating the hand from the background in a radiograph and verifying, in as eecient a manner as possible, that it is normal and suitable for further analysis. At this stage, we can also isolate those regions of the image corresponding to the ngers, thereby tightly constraining attempts to segment the associated phalanges. Observables such as nger orientation can be propagated inward to constrain the position and orientation of other bones. Other techniques applied at this early stage include analysis of one-dimensional projections of grey level data within the hand. This yields further information on the location of features such as the metacarpals and the capitate. Detection of bone boundaries is achieved using deformable models. We have developed an approach which uses template matching techniques to initialise a snake, the latter then being deformed subject to externally-imposed constraints. A parametrically deformable model has also been investigated; here, the primary constraints on model deformation are internal, being derived through a process of training. We have shown that many of the written criteria used in the TW2 method can be readily implemented in software, e.g., as rules in an expert system. Ultimately, it may be possible to augment or even replace certain of these criteria with more quantitative tests based on shape analysis of the detailed bone boundary data obtained from segmentation. The measurement of boundary curvature could be particularly useful in this regard.
Workplace bullying among nurses in the Jordanian private hospitals

Workplace bullying is a wide spread phenomenon that constitutes negative impact to individuals and organizations. Prior studies have proven the negative implications of bullying behaviors in workplaces on the health of the victims and the performance of organizations. This study aims to examine the relationship between the perception of injustice, job insecurity, trait anger, and negative affectivity with workplace bullying among nurses in private hospitals in Jordan. The role of self-control as the moderating factor in this relationship is also examined. The study used a survey method and questionnaires were distributed to 500 nurses at five private hospitals in Amman, Jordan. The data then were analysed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) and it is found that there is a positive and significant relationship between the perception of distributive injustice, perception of interactional injustice, trait anger, negative affectivity and workplace bullying. On the contrary, it is discovered that there is no positive and significant relationship between perception of procedural injustice and workplace bullying; and perception of job insecurity and workplace bullying. However, it is discovered that self-control serves as moderator in the relationship between the perception of distributive injustice and workplace bullying; and between trait anger and workplace bullying. In general, these findings support the view that self-control can override predispositions of individuals to engage in workplace bullying. Theoretical and practical implications of this study are also discussed
Particle methods enable fast and simple approximation of Sobolev gradients in image segmentation

Bio-image analysis is challenging due to inhomogeneous intensity distributions and high levels of noise in the images. Bayesian inference provides a principled way for regularizing the problem using prior knowledge. A fundamental choice is how one measures "distances" between shapes in an image. It has been shown that the straightforward geometric L2 distance is degenerate and leads to pathological situations. This is avoided when using Sobolev gradients, rendering the segmentation problem less ill-posed. The high computational cost and implementation overhead of Sobolev gradients, however, have hampered practical applications. We show how particle methods as applied to image segmentation allow for a simple and computationally efficient implementation of Sobolev gradients. We show that the evaluation of Sobolev gradients amounts to particle-particle interactions along the contour in an image. We extend an existing particle-based segmentation algorithm to using Sobolev gradients. Using synthetic and real-world images, we benchmark the results for both 2D and 3D images using piecewise smooth and piecewise constant region models. The present particle approximation of Sobolev gradients is 2.8 to 10 times faster than the previous reference implementation, but retains the known favorable properties of Sobolev gradients. This speedup is achieved by using local particle-particle interactions instead of solving a global Poisson equation at each iteration. The computational time per iteration is higher for Sobolev gradients than for L2 gradients. Since Sobolev gradients precondition the optimization problem, however, a smaller number of overall iterations may be necessary for the algorithm to converge, which can in some cases amortize the higher per-iteration cost.
Fast Automatic Optic Disc Localization in Retinal Images

Optic disc (OD) localization is an essential step of developing computer-assisted diagnostics. In this paper, we present a fast and robust method to locate the OD center in digital retinal images. The horizontal and vertical location of the OD are be detected respectively, using two main features of the OD, vascular direction and intensity variance. Four public datasets, including 89 normal images and 251 diseased images, were used to evaluate the performance of the proposed method. The success rate is 96.6% with an average computing time of 1.3 s for each image.
Learning Classifier Systems Approach for Automated Discovery of Crisp and Fuzzy Hierarchical Production Rules

— This research presents a system for post processing of data that takes mined flat rules as input and discovers crisp as well as fuzzy hierarchical structures using Learning Classifier System approach. Learning Classifier System (LCS) is basically a machine learning technique that combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. Crisp description for a concept usually cannot represent human knowledge completely and practically. In the proposed Learning Classifier System initial population is constructed as a random collection of HPR–trees (related production rules) and crisp / fuzzy hierarchies are evolved. A fuzzy subsumption relation is suggested for the proposed system and based on Subsumption Matrix (SM), a suitable fitness function is proposed. Suitable genetic operators are proposed for the chosen chromosome representation method. For implementing reinforcement a suitable reward and punishment scheme is also proposed. Experimental results are presented to demonstrate the performance of the proposed system.
Cognitive and Non-Cognitive Dysfunction in a Mouse Model of Alzheimer's Disease

s and Presentations: June 2017. Poster Presentation Robarts Research Retreat (London, CA) Cognitive and Non-Cognitive Phenotypes in the 5xFAD mouse model of Alzheimer's Disease June 2017. Poster Presentation Canadian Association for Neuroscience Annual Meeting (Montreal, CA) Cognitive and Non-Cognitive Phenotypes in the 5xFAD mouse model of Alzheimer's Disease May 2017. Poster Presentation Southern Ontario Neuroscience Association Annual Meeting (St.Catherines, CA) Cognitive and Non-Cognitive Phenotypes in the 5xFAD mouse model of Alzheimer's Disease
Error detection by refactoring reconstruction

In many cases it is not sufficient to perform a refactoring only at one location of a software project. For example, refactorings may have to be performed consistently to several classes in the inheritance hierarchy, e.g. subclasses or implementing classes, to preserve equal behavior.In this paper we show how to detect incomplete refactorings - which can cause long standing bugs because some of them do not cause compiler errors - by analyzing software archives. To this end we reconstruct the class inheritance hierarchies, as well as refactorings on the level of methods. Then, we relate these refactorings to the corresponding hierarchy in order to find missing refactorings and thus, errors and inconsistencies that have been introduced in a software project at some point of the history.Finally. we demonstrate our approach by case studies on two open source projects.
The Swift Discovery of X-Ray Afterglows Accompanying Short Bursts from SGR 1900+14

The discovery of X-ray afterglows accompanying two short bursts from SGR 1900+14 is presented. The afterglow luminosities at the end of each observation are lower by 30%-50% than their initial luminosities, and decay with power-law indices p ∼ 0.2–0.4. Their initial bolometric luminosities are L ∼ 1034-1035 erg s−1. We discuss analogies and differences between the X-ray afterglows of SGR short bursts and short gamma-ray bursts.
STATISTICAL ANALYSIS ON SOFTWARE METRICS AFFECTING MODULARITY IN OPEN SOURCE SOFTWARE

Modularity has been identified by many researchers as one of the success factors of Open Source Software (OSS) Projects. This modularity trait are influenced by some aspects of software metrics such as size, complexity, cohesion, and coupling. In this research, we analyze the software metrics such as Size Metrics (NCLOC, Lines, and Statements), Complexity Metrics (McCabe's Cyclomatic Complexity), Cohesion Metrics (LCOM4), and Coupling Metrics (RFC, Afferent coupling and Efferent coupling) of 59 Java-based OSS Projects from Sourceforge.net. By assuming that the number of downloads can be used as the indication of success of these projects, the OSS Projects being selected are the projects which have been downloaded more than 100,000 times. The software metrics reflecting the modularity of these projects are collected using SONAR tool and then statistically analyzed using scatter graph, Pearson r product-moment correlation, and least-square-fit linear approximation. It can be shown that there are only three independent metrics reflecting modularity which are NCLOC, LCOM4, and Afferent Coupling, whereas there is also one inconclusive result regarding Efferent Coupling