Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Action Description

This master thesis describes a supervised approach to recognize human actions in video sequences. With human action we can understand human action classification and human action localization. In human action classification, the goal is to issue an action class label to sequences with pre-defined temporal extent. In human action localization, the objective is to localize human actions in space (the 2D image region) and in time (temporal extent). In this thesis, we are interested in action classification and localization tasks. The type of video data used are controlled and uncontrolled (realistic) video sequences. In realistic type of video data, human action recognition is especially difficult due to variations in motion, view-point, illumination, camera ego-motion, and partial occlusion of humans. In addition to that, action classes are subject to large intra-class variabilities due to the anthropometric differences across humans. In the case of action localization, the search is computationally expensive due to the large volume of video data. Recently, global representations have shown impressive results for action localization in realistic videos. Nevertheless, these representations have not been applied to repetitive actions, such as running, walking etc. In fact, they even seem not appropriate for this type of actions. Therefore, we propose and evaluate in this work a novel approach that addresses this problem by representing actions as loose collection of movement patterns. To recognize actions in video, we first detect and track human positions throughout time. We then represent human tracks by a series of overlapping chunks segments. These track chunks are processed independently. For our action representation, we extract appearance and motion information with histograms of spatio-temporal gradient orientations. Since chunks with a high affinity to an action of interest will be classified with a larger score, the beginning and end of an action (i.e., action localization) can be determined with clustering approaches. For our method, we employ a variable band-with meanshift. Our results on publicly available datasets demonstrate and advantage of our method, especially for repetitive type of actions. On realistic data and for non-repetitive actions, we are able to compete with the state-of-the-art results for some action classes and loose for other classes.
The Paderkicker Team RoboCup 2006

The Paderkickers are a heterogenuous robot soccer team that makes heavy use of automotive technology like C167 microcontrollers or communication over CAN bus. All sensor data is processed on these decentralized embedded nodes for better reliability and hardware layer abstraction. In this paper we present how we integrate and process the extracted real-time data in our behavior-based system.
“I've Not Got a Job Sir; I'm Only Teaching" : Dynamics of Teacher Identity in an Era of Globalisation

When compared with most other professions, teaching appears to have a low status even in some of the developed countries where it possesses most, if not all the qualities of a profession. Hence, as rightly observed by Yoloye (1992), The Webster New Collegiate Dictionary defi nes a “doctor” as one duly licensed to practise “medicine” but quite interestingly, the same dictionary defines a “teacher” as one who teaches or instructs, especially one whose occupation is to instruct. This concept or perceived identity of the teacher raises three inter-related questions: • Is teaching a process of instructing or a process of cultivating, facilitating and sustaining desirable behavioural changes in learners? • Is teaching supposed to be a profession or an occupation? • How much education does a person need to become a teacher?
An Automated Method to Segment and Classify Masses in Mammograms

Mammography is the most effective procedure for an early diagnosis of the breast cancer. Nowadays, peo ple are trying to find a way or method to support as much as possible to the radiologists in diagnosis process. The most popular way is now being developed is using Computer-Aided Detection (CAD) s ystem to process the digital mammograms and prompt the suspi cious region to radiologist. In this paper, an automated CAD system for detection and classification of massive lesions in mammograph ic images is presented. The system consists of three processing teps: Regions-OfInterest detection, feature extraction and classifi cation. Our CAD system was evaluated on Mini-MIAS database consisti g 322 digitalized mammograms. The CAD system’s performanc e is evaluated using Receiver Operating Characteristics (ROC) and Freeresponse ROC (FROC) curves. The archived results ar e 3.47 false positives per image (FPpI) and sensitivity of 85%. Keywords—classification, computer-aided detection, feature extraction, mass detection.
Speech and Gesture in Spatial Language and Cognition Among the Yucatec Mayas

In previous analyses of the influence of language on cognition, speech has been the main channel examined. In studies conducted among Yucatec Mayas, efforts to determine the preferred frame of reference in use in this community have failed to reach an agreement (Bohnemeyer & Stolz, 2006; Levinson, 2003 vs. Le Guen, 2006, 2009). This paper argues for a multimodal analysis of language that encompasses gesture as well as speech, and shows that the preferred frame of reference in Yucatec Maya is only detectable through the analysis of co-speech gesture and not through speech alone. A series of experiments compares knowledge of the semantics of spatial terms, performance on nonlinguistic tasks and gestures produced by men and women. The results show a striking gender difference in the knowledge of the semantics of spatial terms, but an equal preference for a geocentric frame of reference in nonverbal tasks. In a localization task, participants used a variety of strategies in their speech, but they all exhibited a systematic preference for a geocentric frame of reference in their gestures.
Mining Frequent Subgraph by Incidence Matrix Normalization

Existing frequent subgraph mining algorithms can operate efficiently on graphs that are sparse, have vertices with low and bounded degrees, and contain welllabeled vertices and edges. However, there are a number of applications that lead to graphs that do not share these characteristics, for which these algorithms highly become inefficient. In this paper we propose a fast algorithm for mining frequent subgraphs in large database of labeled graphs. The algorithm uses incidence matrix to represent the labeled graphs and to detect their isomorphism. Starting from the frequent edges from the graph database, the algorithm searches the frequent subgraphs by adding frequent edges progressively. By normalizing the incidence matrix of the graph, the algorithm can effectively reduce the computational cost on verifying the isomorphism of the subgraphs. Experimental results show that the algorithm has higher speed and efficiency than that of other similar ones.
Networking in curriculum planning : the development and application of a model

The purpose of this study was to create an analytic and programmatic model of networking for application to an existing educational setting. The model created was designed to bring social studies teachers together along with community people for the purpose of informal curriculum planning. A model was created which used the settings models of Sarason and Brubaker as guides for the basic structure of the final product. Theory and practice were joined together in the model in terms of using settings theory within the context of traditional teacher attitudes toward involvement in curriculum planning. In creating the model, the aspects of the before-the-beginning history of the setting, emergence of leadership, formation of a core group, goal setting and articulation of network concerns, and resource exchange were utilized.
On the noise impact in the L1 based reconstruction of the sparse time-frequency distributions

Time-frequency distributions (TFDs) are powerful tools for analysis of nonstationary signals, which allow us to observe signal energy distribution as a function of both time and frequency simultaneously. However, TFDs are often corrupted by artifacts, a mathematical byproduct of TFD quadratic nature. A method for artefact suppression is compressive sensing (CS) of signal ambiguity function (AF) in such a way to discard corrupted samples, and leaving only a small subset of artifact-free signal samples. However, in order to obtain a high-resolution TFD, one needs to solve a linear unconstrained optimization problem. In this paper, we analyse noise effect on TFD reconstruction based on various ℓ1 norm algorithms for different sizes of CSAF regions of interest.
Relationship Between Jump Performance and Sport Ages in U16 Basketball Players

This study was conducted to evaluate the jump performance of youth basketball players according to their sport ages. 26 male basketball players (14.1±1.6 year) who participated in the study were divided into two groups of sport ages of 4 and below (≤4) and 6 and above (≥6). The group with sports ages ≤4 consisted of 12 male basketball players with a height of 162±2.56 cm, a body weight of 51.4±3.04 kg, a body mass index of 19.4±0.74 kg/m². The other group with sports ages ≥6 consisted of 14 male basketball players with a height of 155.9±1.98 cm, a body weight of 45.7±1.85 kg, a body mass index of 18.8±0.69 kg/m. All basketball players’ squat jump (SJ) and countermovement jump (CMJ) were measured (Optojump Microgate Bolzano, Italy). The Mann Whitney U test was used to determine whether there were differences between groups in terms of T flighttimes and jump heights. Statistically significant level of p<0.05 was accepted. As a result of the study, no statistically significant difference was observed between the sport ages and SJ and CMJ splashes. In this respect, it can be considered that the Jump performance does not develop in parallel with the training age, and that the jump ability of this cause may be more related to motor skill and ability than the training age.
Experimental Characterization and Modeling of High-Frequency Transformers for SMPS

A complete characterization and modeling procedure for high-frequency transformers is proposed. The procedure is based on one-port measurements and on the approximation of the entries of the admittance matrix of the transformer by real rational functions. Parasitics effects between the windings and between the windings and the reference plane are included by means of a 4-port framework. The use of a transformer model for the simulation of a dc-dc converter is demonstrated.
Approximate diagonalization approach to blind source separation with a subset of matrices

In blind source separation problems it is assumed that the approximate diagonalization of a matrix set achieves more robust solutions than the simultaneous diagonalization of a matrix pencil. In this work we will analyse approximate diagonalization methods using a generalized eigendecomposition (GED) of any pair of a given matrix set. The constraints of GHD solutions provide a criterion to choose a matrix subset even when none of the matrices follows an ideal model. We also present some numerical simulations comparing the performance of the solutions achieved by the referred approaches.
Iterative temporal learning and prediction with the sparse online echo state gaussian process

In this work, we contribute the online echo state gaussian process (OESGP), a novel Bayesian-based online method that is capable of iteratively learning complex temporal dynamics and producing predictive distributions (instead of point predictions). Our method can be seen as a combination of the echo state network with a sparse approximation of Gaussian processes (GPs). Extensive experiments on the one-step prediction task on well-known benchmark problems show that OESGP produced statistically superior results to current online ESNs and state-of-the-art regression methods. In addition, we characterise the benefits (and drawbacks) associated with the considered online methods, specifically with regards to the trade-off between computational cost and accuracy. For a high-dimensional action recognition task, we demonstrate that OESGP produces high accuracies comparable to a recently published graphical model, while being fast enough for real-time interactive scenarios.
Navigation systems for increasing the autonomy and security of mobile bases for disabled people

A navigation module for technological aids for the disabled user is presented. Usability and acceptability criteria are considered in the design of this module. Different levels of autonomy for the navigation module are considered for allowing an active interaction of the user with the technological aids. A standardized protocol for the integration of input-output devices for robotic assisted systems is also used. The navigation module is tested on an powered wheelchair and an autonomous mobile base. The sonar sensors are used for online detection of possible obstacle collisions. The reliability of sonar readings is increased by the use of a probabilistic map of the environment to support the decisions on obstacle detection.
Towards The Deployment Of Fastflow On Distributed Virtual Architectures

In this paper we investigate the deployment of FastFlow applications on multi-core virtual platforms. The overhead introduced by the virtual environment has been measured using a well-known application benchmark both in the sequential and in the FastFlow parallel setting. The overhead introduced for both the sequential and the parallel executions of CPU and memory-intensive applications is in the range of 2-30%, while the execution speedup is almost preserved. Additionally, we have ported the FastFlow benchmark to a cloud-based distributed environment in which a task-intensive application has been tested and the performance compared with the corresponding run on a smaller cluster of multi-core machines without virtualisation. From a parallel programming perspective, we have demonstrated how a unique programming framework based on the structured parallel programming paradigm can cope with very different kind of target architectures without any (or minimal) code intervention.
Hepatic Cannabinoid Receptor Type 1 Mediates Alcohol-Induced Regulation of Bile Acid Enzyme Genes Expression Via CREBH

Bile acids concentration in liver is tightly regulated to prevent cell damage. Previous studies have demonstrated that deregulation of bile acid homeostasis can lead to cholestatic liver disease. Recently, we have shown that ER-bound transcription factor Crebh is a downstream effector of hepatic Cb1r signaling pathway. In this study, we have investigated the effect of alcohol exposure on hepatic bile acid homeostasis and elucidated the mediatory roles of Cb1r and Crebh in this process. We found that alcohol exposure or Cb1r-agonist 2-AG treatment increases hepatic bile acid synthesis and serum ALT, AST levels in vivo alongwith significant increase in Crebh gene expression and activation. Alcohol exposure activated Cb1r, Crebh, and perturbed bile acid homeostasis. Overexpression of Crebh increased the expression of key bile acid synthesis enzyme genes via direct binding of Crebh to their promoters, whereas Cb1r knockout and Crebh-knockdown mice were protected against alcohol-induced perturbation of bile acid homeostasis. Interestingly, insulin treatment protected against Cb1r-mediated Crebh-induced disruption of bile acid homeostasis. Furthermore, Crebh expression and activation was found to be markedly increased in insulin resistance conditions and Crebh knockdown in diabetic mice model (db/db) significantly reversed alcohol-induced disruption of bile acid homeostasis. Overall, our study demonstrates a novel regulatory mechanism of hepatic bile acid metabolism by alcohol via Cb1r-mediated activation of Crebh, and suggests that targeting Crebh can be of therapeutic potential in ameliorating alcohol-induced perturbation of bile acid homeostasis.
Agent Based Models A New Tool for Economic and Policy Analysis

Are current economic models well equipped to provide useful policy prescriptions? Many economists would have certainly answered, "yes" before the recent Global Recession. This economic crisis has not only demonstrated the importance of banking and financial markets for the dynamics of real economies. It has also revealed the inadequacy of the dominant theoretical framework. Standard models have indeed failed to forecast the advent of the crisis. In addition, they have been unable to indicate a therapy able to restore economic growth (.).
Hosting an object heap on manycore hardware: an exploration

In order to construct a test-bed for investigating new programming paradigms for future "manycore" systems (i.e. those with at least a thousand cores), we are building a Smalltalk virtual machine that attempts to efficiently use a collection of 56-on-chip caches of 64KB each to host a multi-megabyte object heap. In addition to the cost of inter-core communication, two hardware characteristics influenced our design: the absence of hardware-provided cache-coherence, and the inability to move a single object from one core's cache to another's without changing its address. Our design relies on an object table, and the exploitation of a user-managed caching regime for read-mostly objects. At almost every stage of our process, we obtained measurements in order to guide the evolution of our system. The architecture and performance characteristics of a manycore platform confound old intuitions by deviating from both traditional multicore systems and from distributed systems. The implementor confronts a wide variety of design choices, such as when to share address space, when to share memory as opposed to sending a message, and how to eke out the most performance from a memory system that is far more tightly integrated than a distributed system yet far less centralized than in a several-core system. Our system is far from complete, let alone optimal, but our experiences have helped us develop new intuitions needed to rise to the manycore software challenge.
Can Camera Traps Monitor Komodo Dragons a Large Ectothermic Predator?

Camera trapping has greatly enhanced population monitoring of often cryptic and low abundance apex carnivores. Effectiveness of passive infrared camera trapping, and ultimately population monitoring, relies on temperature mediated differences between the animal and its ambient environment to ensure good camera detection. In ectothermic predators such as large varanid lizards, this criterion is presumed less certain. Here we evaluated the effectiveness of camera trapping to potentially monitor the population status of the Komodo dragon (Varanus komodoensis), an apex predator, using site occupancy approaches. We compared site-specific estimates of site occupancy and detection derived using camera traps and cage traps at 181 trapping locations established across six sites on four islands within Komodo National Park, Eastern Indonesia. Detection and site occupancy at each site were estimated using eight competing models that considered site-specific variation in occupancy (ψ)and varied detection probabilities (p) according to detection method, site and survey number using a single season site occupancy modelling approach. The most parsimonious model [ψ (site), p (site*survey); ω = 0.74] suggested that site occupancy estimates differed among sites. Detection probability varied as an interaction between site and survey number. Our results indicate that overall camera traps produced similar estimates of detection and site occupancy to cage traps, irrespective of being paired, or unpaired, with cage traps. Whilst one site showed some evidence detection was affected by trapping method detection was too low to produce an accurate occupancy estimate. Overall, as camera trapping is logistically more feasible it may provide, with further validation, an alternative method for evaluating long-term site occupancy patterns in Komodo dragons, and potentially other large reptiles, aiding conservation of this species.
CCN1 Induces β-Catenin Translocation in Esophageal Squamous Cell Carcinoma through Integrin α11

Aims. Nuclear translocation of β-catenin is common in many cancers including esophageal squamous cell carcinoma (ESCC). As a mediator of Wnt signaling pathway, nuclear β-catenin can activate many growth-related genes including CCN1, which in turn can induce β-catenin translocation. CCN1, a matricellular protein, signals through various integrin receptors in a cell-dependent manner to regulate cell adhesion, proliferation, and survival. Its elevation has been reported in ESCC as well as other esophageal abnormalities such as Barrett's esophagus. The aim of this study is to examine the relationship between CCN1 and β-catenin in ESCC. Methods and Results. The expression and correlation between CCN1 and β-catenin in ESCC tissue were examined through immunohistochemistry and further analyzed in both normal esophageal epithelial cells and ESCC cells through microarray, functional blocking and in situ protein ligation. We found that nuclear translocation of β-catenin in ESCC cells required high level of CCN1 as knockdown of CCN1 in ESCC cells reduced β-catenin expression and translocation. Furthermore, we found that integrin α 11 was highly expressed in ESCC tumor tissue and functional blocking integrin α 11 diminished CCN1-induced β-catenin elevation and translocation. Conclusions. Integrin α 11 mediated the effect of CCN1 on β-catenin in esophageal epithelial cells.
Professionalisation and decision making in higher education management: new collegiality and academic change

This study discusses the professionalisation of higher education management and emerging patterns of decision making within a context of academic and organisational change. A total of thirty interviews were conducted across six Universities, with five similar roles interviewed in each institution. Respondents were drawn from both centralised and decentralised parts of the organisation, and represented both academic and professional services perspectives. Three ideas are proposed. The first is an emerging New Collegiality in which decision making behaviour is developing that reflects traditional collegial debate, but within new peer groups of academic and professional services managers. Academic managers are also using New Collegiality to share good management practice, with new organisational combinations offering new forms of collaborative working within and across subject disciplines. A second theme proposes that a Higher Education Professional Services Framework exists, which has situationally contingent characteristics that are unique to the professional services in higher education. These features combine decision making and management behaviours to operate as a singular body positioned throughout the organisation in context specific ways. Finally, a third concept identifies three linked levers of management used by the centre to address tensions of internal demands for decentralisation against the external pressures to centralise. These linked levers consist of the creation of an intermediate tier such as a faculty or college, a proactive use of management information as an evidential tool for decision making, and a particular use of the Higher Education Professional Services Framework. The findings suggest that Universities are ostensibly decentralising their organisational structures while simultaneously centralising decision making authority through changes in accountability. These trends raise a number of relevant issues to the professionalisation of higher education.