Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Cost IMU, GPS and Camera Integration for Handheld Indoor Positioning

Low cost inertial sensors are one potential method for positioning indoors. However, such sensors typically provide poor quality measurements which are only suitable for positioning for a few seconds. For inertial navigation to be useful, it is necessary to combine the sensors with measurements from other systems. This paper explores the integration of an Inertial Measurement Unit (IMU) with measurements from a computer vision algorithm, for indoor pedestrian navigation. The concept is to make use of sensors that are already available in modern smartphones. It is assumed that a pedestrian user is walking with the mobile device held out in front of them with the camera pointing approximately towards the ground. Therefore the camera is taking images of the ground immediately in front of the user. The computer vision algorithm matches features between pairs of successive images. Typically, many of these features will fall on the ground plane. The relative positions of features on the ground plane are related by a homography which describes the rotation and translation of the camera between images. The robust BaySAC framework is used to simultaneously identify which features lie on the ground plane, while estimating the homography relating the two views. From the homography, the camera’s orientation and 3-dimensional body frame translation relative to its previous position are computed. This information, along with measurements from other systems such as GPS when they are available, is used to aid the IMU using a Kalman filter, to reduce the position drift. This paper describes the implementation of the combined computer vision and inertial navigation approach. A microelectromechanical (MEMS) IMU is used along with a consumer grade digital camera to capture data. It is demonstrated that the drift of the inertial sensor is significantly reduced by incorporating measurements from the computer vision algorithm. The algorithm is relatively computationally expensive, therefore this paper explores the computational requirements and identifies two methods that may be used to improve efficiency. A method of reducing the sample rate of the computer vision algorithm is demonstrated to provide a significant reduction in processor requirements with only a small reduction in positioning accuracy.
Image Enhancement Technique by Mean Partitioning of Multipeaks Histogram and Contrast Control Parameters

- In agriculture, research of automatic leaf disease detection is an essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detects symptoms of disease as soon as they appear on leaves of the plant. As a first step in leaf disease detection, the image may have to undergo prepossessing. In the preprocessing step, the image is enhanced for the better machine vision or human vision and hence helps in accurate analysis. The most commonly used image enhancement techniques is the histogram based enhancement such as histogram equalization and histogram specification. In this paper, histogram based enhancement techniques proposed which is modification of the traditional histogram equalization. In this method, we consider the fact that it is not necessary to apply the same transformation function to all the pixels, unlike in the tradition histogram equalization. The pixels with lower intensity need a different transformation and the brighter pixels need a different transformation. The output images are subjected to subjective and objective quality analysis. It is observed that the proposed method results are better than the Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE).
The Development of Scoring Criteria for a New Picture Naming Task

Objective: The purpose of the study was to develop a scoring system for a novel naming task suitable for assessing naming performance in younger (18-30 years) and older (65+ years) adults in monolingual English, monolingual French, and English-French bilingual groups. This novel naming task will serve as an important health service to help diagnose and assess cognitively impaired older individuals, while also serving as an educational tool for healthcare providers.Materials and Methods: The Naming Task consists of 120 images organized in the same randomized order, and are shown on a white background displayed on a computer screen using PowerPoint. Participants are instructed to name the image displayed. Monolinguals completed the test in their native language and bilinguals completed the test in English only, French only, and a bilingual administration. Scoring criteria was established based on the responses from testing.Results: Strict and lenient scoring criteria developed for the Naming Task are presented. Eight items were removed from the original Naming Task due to quality and/or clarity, inability to name the image, or too many alternate responses. Performance in monolingual English and French was similar in younger and older adults for strict and lenient scoring. Bilinguals performed better with bilingual administration and worse with French administration, where scores were the lowest of all age and language groups.Conclusion: The Naming Task appears to be suitable for monolingual French and English individuals. Results suggest that a bilingual administration should be used when testing English-French bilinguals.
Developing collaborative learning practices in an online language course

ABSTRACT There is much evidence to support the occurrence of learning in connection with collaboration, both in face-to-face and online teaching situations, but much less research on how such collaboration develops over the duration of a course. Using Dillenbourg's concepts of situation, interactions, mechanisms, and effects as a framework, this study aims to fill that gap by following a group of 47 university students participating in a 15-week online English course for low- to mid-intermediate learners. Data gathered included participants’ reflective journals, questionnaires, written assignments, comments posted on Discussion Forums in a Learning Management System, and e-mail exchanges between individual students and the instructor. These were analysed using open, axial, and selective coding. Three different forms of collaboration were identified – reading each other's work, providing feedback, and working in groups. The main points to emerge from the study were the importance of the social aspect of collaboration, of allowing time for collaboration to develop, and the effect of the gradual development of confidence among the students. Patterns of collaboration changed as the group developed a sense of community and mutual trust, with students who initially held back from offering their work for comment gradually posting their work early to encourage feedback from others.
Parameter Optimization and Application of SVM Based on Chaos Gravitational Search Algorithm

Chaotic series and the crossover of genetic algorithm were introduced into gravitational search algorithm to overcome its poor local optimization problem.The improved algorithm was used for the SVM parameters optimization.And the simulated experiments show that the SVM model has a higher accuracy.At last the model was applied to the condition monitoring of primary air fan in power plant.And the experimental results indicate that the model is valid.
NetWatch: End-to-End Network Performance Measurement as a Service for Cloud

Accurate and comprehensive end-to-end network performance measurement is critical for the automatic troubleshooting and optimized provision of various services in Cloud. However, cloud providers and tenants still rely on rudimentary and separate tools for end-to-end performance measurement, which are inflexible, tedious, and error-prone. In this paper, we present NetWatch, a system that provides measurement as a service through open APIs for both cloud providers and tenants to measure end-to-end performance on-demand. In this system, measurement requests are first delivered to NetWatch Controller by open APIs, which transforms the request to configure specific Probes to fulfill the requests by active measurement. We make delicate design choices and address several challenges to enable NetWatch offering accurate and low-overhead measurement service for multiple users simultaneously and efficiently. A prototype implementation and experiments with diverse network settings link and traffic demonstrate that NetWatch can support flexible and accurate measurement of end-to-end network performance with small overhead.
The Institutionalization of Inter-Municipal Arrangements in Europe: Findings from the Unusual Suspects

Research on Inter-Municipal Cooperation (IMC) in Europe suffers from a deficit of international comparative studies. This article presents an in-depth analysis of the level of institutionalization of IMC arrangements, claiming its relevance for future research and suggesting a set of post-typological dimensions needed for its characterization. It ends with a recommendation of new research agendas. In addition to bringing up-to date information, it aims at contributing with new knowledge by including countries not involved in previous comparative research and new ‘after crisis’ data. The research methodology also allowed the analysis of individual institutions.
Efficacy of Social Accounting Practices on the Financial Performance: Evidence from Ethiopia

This aims of this paper is to examine the effects of social accounting practices on the financial performance of business organizations in the city of Dessie, South Wollo, Ethiopia. To fulfill this objective, a stratified-random sampling design was used, followed by proportional sampling techniques. The research data were obtained from primary and secondary sources and analyzed using multiple linear regression to understand the effect of corporate social responsibility accounting practices on financial performance. The results of the study indicated that report on social accounting was indeed necessary, however, there was no standard guideline followed when reporting except the Ethiopian commercial code and statement of socio-economic operations. Likewise, the observation during this research showed that an increase in corporate social responsibility scores would lead to an increase in the company's financial performance.
Research on Path-finding Algorithm of Player in 3D Scene

In the process of player auto find path in large 3D scenes,an efficient path-finding method based on A* algorithm is proposed.The model of regular-square-grid(RSG) was selected to organize the large scale scene to realize higher efficient path-finding in such a common larger-scale game scene.Also,the navigation mesh grid data was generated based on the RSG in a more accurate and editable way.The global path-finding was an emphasisly interesting based on the RSG,this method improves the heuristic searching ability of the A* algorithm.This algorithm was used to auto path-finding by player in 3D games,and it has efficient practical performance.
Super-Resolution through Dictionary Learning and Sparse Representation

The overcomplete dictionary extracted from large scale dataset and sparse representation have been widely applied in image denoise,deblocking and inpainting in recent years.However,this technique can not be directly employed to deal with heterogeneous low resolution and high resolution image patches and relevant image reconstruction with super-resolution as well.The method to yield the sparse representation meeting two overcomplete dictionaries of different scales at the same time is proposed in this paper and the super-resolution reconstruction of image sparse representation is implemented by it.To further improve the super-resolution effect of color images,the UV chroma super-resolution reconstruction based on super-resolution luminance information is put forward as well.The experimental results show the method in this paper obtain better outcome no matter in visual effects or in root mean squared(RMS)error.
Qualitative Assessment of Key Beliefs in Regards to Consumers’ Food Truck Visits

ABSTRACT Despite the recent booming of the food truck industry, no research has identified consumers’ underlying beliefs in regards to their food truck visits. To discover underlying cognitive structure about consumers’ food truck visits, this study utilized the theory of planned behavior (TPB) and examined salient behavioral beliefs, normative beliefs, and control beliefs with an elicitation study. A content analysis was performed with a total of 200 responses using Nvivo 10. Consequently, this study identified a range of advantages (e.g., unique and/or different food), disadvantages (e.g., sanitation), referent groups (e.g., family), and barriers and facilitators (e.g., proximity). Based on the findings, practical and theoretical implications were discussed.
Job enrichment, organizational commitment, and intention to quit: the mediating role of employee engagement

In human resource accounting, personnel recruitment cost is an important issue. High turnover of employees triggers this charge. This research focused on the non-financial issue in employee turnovers for various professions. This study examines the effect of job enrichment, a self-development process on the working environment, to the employee’s engagement and organizational commitment, as well as the intention to quit in a different profession. The respondents of this research are 154 workers who have worked at their institution for at least two years. Using moderate regression analysis and testing two models, the first with linear regression, and the second with moderate regression analysis, this research shows that in the first model, job enrichment and employee engagement affect organizational commitment directly. However, in the second model, this study shows that employee engagement cannot moderate the effect of job enrichment on organizational commitment or the intention to quit. This research also finds that employee engagement affects negatively the intention to quit at work. The research findings strengthen the theory that a self-development process capable of generating employee engagement can assist management in controlling employee turnover rates.
First person HCI research:Tapping into designers’ tacit experiences

The emphasis on user experience, and bodily-driven approaches in HCI has paved the way towards a richer understanding of felt-life experience. Unlike users however, designers need specific methods to access such experiences and to work with them in their design. This paper presents three cases studies where we employed first person research method to design or evaluate interactive systems, or where we directly explored professional designers’ first person accounts of felt-life experiences in their practice. We conclude with a reflection on opportunities and challenges of this methodological approach in HCI, and three suggestions for first person HCI research.
Evaluating 3D registration of CT-scan images using crest lines

We consider the issue of matching 3D objects extracted from medical images. We show that crest lines computed on the object surfaces correspond to meaningful anatomical features, and that they are stable with respect to rigid transformations. We present the current chain of algorithmic modules which automatically extract the major crest lines in 3D CT-Scan images, and then use differential invariants on these lines to register together the 3D images with a high precision. The extraction of the crest lines is done by computing up to third order derivatives of the image intensity function with appropriate 3D filtering of the volumetric images, and by the 'marching lines' algorithm. The recovered lines are then approximated by splines curves, to compute at each point a number of differential invariants. Matching is finally performed by a new geometric hashing method. The whole chain is now completely automatic, and provides extremely robust and accurate results, even in the presence of severe occlusions. In this paper, we briefly describe the whole chain of processes, already presented to evaluate the accuracy of the approach on a couple of CT-scan images of a skull containing external markers.
Inspiration from Neurosciences to emulate Cognitive Tasks at different Levels of Time

Our team has been working for more than ten years on the modelling of biologically inspired artificial neural networks. Today, our models are used to different cognitive tasks like autonomous behavior and exploration for a robot, planning, reasoning, and other tasks linked to memory and internal representation building. We present the framework that underlies these models through the time delays related to several fundamental properties like information coding, learning, planning, motivation.
Calibration Method for 3-D Vision System Using Phase Mapping

A calibration method for three-dimensional sensing scheme based on phase mapping is described.The calibration procedure is based on the definition of a unique coordinate system in the structure system,and thus,a constraint is introduced between two devices.Fringe projection and phase mapping are used to convert the phase map to world coordinates of the object surface by a mathematical model.Using the model,the 3-D coordinates of range image can be obtained with moderate accuracy,which will be compared with the prescribed benchmark in order to build up a square-error based objective function,leading to a non-linear equation.A two-step procedure is developed to solve the equation,and all structural parameters involved in 3-D sensing system can be determined with appropriate accuracies.Experimental results show that these proposed approaches are accurate and robust.
Slow Feature Analysis as a Potential Preprocessing Tool in BCI

Here we present initial results of the unsupervised preprocessing method Slow Feature Analysis (SFA) for a BCI data set. It is the first time SFA is applied to EEG. SFA optimizes the signal representation with respect to temporal slowness. Its objective as well as its computational properties render it a possibly useful candidate for the preprocessing of BCI EEG data in order to detect task relevant components as well as components that represent artifacts or non-stationarities of the background brain activity or external sources.
Effective and Secure Access Control for Multi-Authority Cloud Storage Systems

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a promising cryptographic tool to implement access control for secure cloud storage systems. However, most existing access control schemes based on CP-ABE for the multi-authority cloud storage systems rely on a fully trusted global certificate authority. It is just an ideal assumption while there never exists a fully trusted global certificate authority in reality. In this paper, we construct a system with multiple certificate authorities (CA). The parameters of those CAs could be verified when an authority suspects the messages received from the correlative certificate authority. Besides, we construct a verifiable secret sharing (VSS) scheme to realize the decentralization of the certificate authority in our scheme. The scheme adopts the Pedersen commitment in combination with the properties of bilinear-pairs on elliptic curve and bilinear DiffieHellman problem. The analysis shows that our scheme is highly efficient, authentic and provably secure under the security model. Our scheme simultaneously supports efficient attribute revocation.
Context-Based Spelling Correction for the Dutch Language: Applied on spelling errors extracted from the Dutch Wikipedia revision history

In this thesis we did research on context-based spellchecking approaches for the Dutch language. Context-based approaches enable the detection of real-word spelling errors by using the context in which the errors occur. We also assessed if we could improve the ranking of replacement candidates by using the context. To be able to measure the performance of the different techniques used, a dataset containing erroneous-corrected sentence pairs was obtained from the Dutch Wikipedia revision history. This dataset contains a wide variety of human generated spelling errors, and consists of over 1.4 million instances. It can serve as a basis for further research. The obtained dataset showed to be a valuable source for the creation of an error model, with which we could improve the ranking of candidate replacement words. This model takes the character context in which erroneous edit operations occur into account, and therefore reflects what kind of edit operations are more likely to occur. The spellchecking results using our dataset show that the context-based approach used, works for both the detection of errors and the ranking of candidate replacements. A comparison with literature was made to assess if the technique used performs as good for Dutch as for English and we conclude that the performance is comparable. The error model trained on our dataset was shown to work better than the context-based approach for the task of candidate ranking.
Proactive learning: towards learning with multiple imperfect predictors

Label scarcity is a serious problem in many machine learning applications. In many domains such as classifying texts, images, etc., unlabeled data is readily available whereas labels are fairly expensive to obtain due to labeler availability, cost, and difficulty. Active learning is a paradigm that addresses this challenge by carefully selecting instances to he labeled. The goal is to improve the generalization performance of the learner with fewer labeling requests. Although active learning is well studied in the literature, it makes unrealistic assumptions. For instance, active learning assumes there is a unique omniscient oracle that works for free or charges uniform fee. In many real-world applications, it is quite possible there are multiple imperfect predictors with differing but unknown qualities. These qualities may vary from providing incorrect labels, failing to provide a label at all or charging non-uniform fees. The proactive learning paradigm addresses these problems to bridge the gap between active learning and more practical real-life scenarios. In this thesis, we first propose novel active learning methods for classification and rank learning problems that are shown to be quite effective in various real-world domains. We then describe a decision-theoretic framework that addresses learning with multiple predictors having non-ideal characteristics mentioned above with no apriori information, introducing proactive learning and how it can address various scenarios. Later, we focus on more specific aspects of proactive learning, especially coping with multiple fallible (noisy) predictors. We are interested in estimating the labeling accuracy of the predictors to select the most reliable ones in the absence of ground truth. We propose two novel approaches that achieve this goal when first the labeling accuracies are stationary and second when they vary with time. Our empirical evaluation demonstrates the ability to infer the predictor accuracy without any prior information in both synthetic and real-world datasets. Finally, we frame the problem as unsupervised risk estimation of multiple predictors as an alternative to the above approaches. The benefit is that it allows risk to he defined as a parametric function where the parameter governs the generation of the noisy predictor output. We propose maximum likelihood estimation framework as a solution. One of the most crucial consequences of this framework is to train classifiers that will minimize margin-based risk without using a single labeled example. The likelihood maximization framework yields statistically consistent estimators and hence effective estimation and training capabilities as supported by the thorough empirical evaluation.