Miracle-dz/UniFAR-FTUs huggingface dataset:
Athletic Training Analysis System Research based on Physiological Calculation

The physiological state data is the more important on the athletes' aided training and competition. Physiological Plan system is designed and implemented in this study, the system is divided into hardware layer, data processing layer, algorithm layer and interface layer. The hardware of Physiological Plan is based on the Berkeley Tricorder platform, data processing level is aiming to transform kinds of sensors to a uniform format, algorithm level includes algorithms of filter, peak finder, outlier detection, etc, coaches interact with the system through the interface level. Physiological Plan can collect ECG, EMG, bioimpedance, 3-axis accelerations of the player, real-time feedback of the data is provided to coaches, and the physiological states of the player are monitored and analyzed to help them. The experiment shows that Physiological Plan is valuable for coaches to monitor and it analyze the states of players.
KAPEAN: A supportive tool for observing performance and concentration of children with learning difficulties

In emergent economies such as Mexican, most schools lack resources to cover or develop programs when teachers assess children with special needs. Teaching practice for children with learning difficulties require, for instance, well-trained therapists, special learning materials, adequate classroom settings, and supportive technology equipment. KAPEAN is a software tool that offers a set of educational activities that can help therapists organize interventions with children where attention, memory, visouspatial, and reasoning skills are reinforced. Data collected from the participant's experiences is processed in order to get insights of the performance and concentration exposed by children. Results inform that there are activities with a higher level of difficulty than others regarding cognition and interactional experience. The activity demanding memory skills was especially difficult for all of the participants. It was also found that the level of concentration children have when developing a task might be influencing their performance.
Noise Makers Need to Know Where to be Silent – Producing Schedules That Find Bugs

A noise maker is a tool that seeds a concurrent program with conditional synchronization primitives, such as yield(), for the purpose of increasing the likelihood that a bug manifest itself. We introduce a novel fault model that classifies locations as "good", "neutral", or "bad," based on the effect of a thread switch at the location. Using the model, we explore the terms under which an efficient search for real-life concurrent bugs can be conducted. We accordingly justify the use of probabilistic algorithms for this search and gain a deeper insight of the work done so far on noise- making. We validate our approach by experimenting with a set of programs taken from publicly available multi-threaded benchmarks. Our empirical evidence demonstrates that real-life behavior is similar to one derived from the model.
Coupling Aware Explicit Delay Metric for On- Chip RLC Interconnect for Ramp input

Recent years have seen significant research in finding closed form expressions for the delay of the RLC interconnect which improves upon the Elmore delay model. However, several of these formulae assume a step excitation. But in practice, the input waveform does have a non zero time of flight. There are few works reported so far which do consider the ramp inputs but lacks in the explicit nature which could work for a wide range of possible input slews. Elmore delay has been widely used as an analytical estimate of interconnect delays in the performance-driven synthesis and layout of VLSI routing topologies. However, for typical RLC interconnections with ramp input, Elmore delay can deviate by up to 100% or more than SPICE computed delay since it is independent of rise time of the input ramp signal. We develop a novel analytical delay model based on the first and second moments of the interconnect transfer function when the input is a ramp signal with finite rise/fall time. Delay estimate using our first moment based analytical model is within 4% of SPICE-computed delay, and model based on first two moments is within 2.3% of SPICE, across a wide range of interconnects parameter values. Evaluation of our analytical model is several orders of magnitude faster than simulation using SPICE. We also discuss the possible extensions of our approach for estimation of source-sink delays for an arbitrary interconnects trees.
Statistical-Computational Tradeoffs in Planted Models : The High-Dimensional Setting

The planted models assume that a graph is generated from a set of clusters by randomly placing edges between nodes according to their cluster memberships; the task is to recover the clusters given the graph. Special cases include planted clique, planted partition and planted coloring. This paper studies the statisticalcomputational tradeoffs of these models. Our focus is the high-dimensional setting, where the number of clusters is allowed to grow with the number of nodes. We show that the complexities of cluster recovery exhibit phase transitions. In particular, the space of model parameters can be partitioned into four regions with decreasing statistical and computational complexities: (1) the impossible regime, where all algorithms fail; (2) the hard regime, where the exponential-time Maximum Likelihood Estimator (MLE) succeeds; (3) the easy regime, where a polynomial-time convexified MLE succeeds; (4) the simple regime, where a simple algorithm based on counting degrees and common neighbors succeeds. Moreover, each of these algorithms is likely to fail in the harder regime.
Architecture support for intrusion detection systems

System security is a prerequisite for efficient day-to-day transactions. As a consequence, Intrusion Detection Systems (IDS) are commonly used to provide an effective security ring to systems in a network. An IDS operates by inspecting packets flowing in the network for malicious content. To do so, an IDS like Snort[49] compares bytes in a packet with a database of prior reported attacks. This functionality can also be viewed as string matching of the packet bytes with the attack string database. Snort commonly uses the Aho-Corasick algorithm[2] to detect attacks in a packet. The Aho-Corasick algorithm works by first constructing a Finite State Machine (FSM) using the attack string database. Later the FSM is traversed with the packet bytes. The main advantage of this algorithm is that it provides a linear time search irrespective of the number of strings in the database. The issue however lies in devising a practical implementation. The FSM thus constructed gets very bloated in terms of the storage size, and so is area inefficient. This also affects its performance efficiency as the memory footprint also grows. Another issue is the limited scope for exploiting any parallelism due to the inherent sequential nature in a FSM traversal. This thesis explores hardware and software techniques to accelerate attack detection using the Aho-Corasick algorithm. In the first part of this thesis, we investigate techniques to improve the area and performance efficiency of an IDS. Notable among our contributions, includes a pipelined architecture that accelerates accesses to the most frequently accessed node in the FSM. The second part of this thesis studies the resilience of an IDS to evasion attempts. In an evasion attempt an adversary saturates the performance of an IDS to disable it, and thereby gain access to the network. We explore an evasion attempt that significantly degrades the performance of the Aho-Corasick al- gorithm used in an IDS. As a counter measure, we propose a parallel architecture that improves the resilience of an IDS to an evasion attempt. The final part of this thesis explores techniques to exploit the network traffic characteristic. In our study, we observe significant redundancy in the payload bytes. So we propose a mechanism to leverage this redundancy in the FSM traversal of the Aho-Corasick algorithm. We have also implemented our proposed redundancy-aware FSM traversal in Snort.
A Semantic Search Algorithm for Peer-to-Peer Open Hypermedia Systems

We consider a collaborative application scenario in Open Hypermedia Systems. We describe a semantic search algorithm to discover semantically equivalent or related resources across distributed link databases, otherwise known as linkbases. Our approach differs from traditional crawler based search mechanisms because it relies on clustering of semantically related entities. It creates clusters of related semantic entities to expedite the search for resources in a random network. It uses a distance-vector based heuristic to guide the search. Our results confirm that the algorithm yields high search efficiency in collaborative environments where the change in content published by each participant is rapid and random.
Adult learning for social change in museums: An exploration of sociocultural learning approaches to community engagement

This integrative literature review critically examined how scholars were articulating the work of museums to make a space for ‘adult learning for social change through community engagement’. We applied sociocultural adult learning theories (situated learning and cultural-historical activity theory), to 25 theoretical and empirical studies in predominantly museum and culture-based journals but also, specific adult education and lifelong learning journals. We found the studies shared in the works underscored the importance of social, collective interactions (adult learning for social change through community engagement) in the informal settings of museums and the work of various learning mediators of learning such as educators, tour guides and even, curators. In particular, we found a belief in the value of participatory collaborations based on community needs. However, when it came to practice, most studies illuminated passive, individualised processes of learning confined to pre-organised education activities. We argue therefore, that although museums have great potential to become meaningful spaces for adult learning for social change through community engagement, there is a disconnection between a belief/application of more critical, sociocultural learning perspectives and the actual practices under study. We argue that an application of what these theories suggest is needed to strengthen adult learning for social change through community engagement in museums.
Transformation of Survey Coordinates—Another Look at an Old Problem

A procedure is discussed that combines the best aspects of the usual “rigid body” (three parameter affine) and the similarity (Helmert) least-squares procedures found in most civil engineering enhancements to popular computer-aided design programs. The procedure will work to transform North American Datum of 1927 coordinates into the North American Datum of 1983 (NAD83) system, and will work in transforming arbitrary coordinates (coordinates derived in an arbitrary system) to NAD83. In all of these transformations, coordinates of a number of points are required in both systems, and provide the basis for the transformation.
HvHMA2, a P1B-ATPase from Barley, Is Highly Conserved among Cereals and Functions in Zn and Cd Transport

Manipulation of crops to improve their nutritional value (biofortification) and optimisation of plants for removal of toxic metals from contaminated soils (phytoremediation) are major goals. Identification of membrane transporters with roles in zinc and cadmium transport would be useful for both aspects. The P1B-ATPases play important roles in heavy metal allocation and detoxification in Arabidopsis and it is now important to elucidate their roles in monocots. We identified nine P1B-ATPases in barley and this study focuses on the functional characterization of HvHMA2, providing evidence for its role in heavy metal transport. HvHMA2 was cloned using information from EST analysis and 5′ RACE. It possesses the conserved aspartate that is phosphorylated during the reaction cycle of P-type pumps and has motifs and key residues characteristic of P1B-ATPases, falling into the P1B-2 subclass. Homologous sequences occur in three major sub-families of the Poaceae (Gramineae). Heterologous expression in Saccharomyces cerevisiae demonstrates that HvHMA2 functions as a Zn and Cd pump. Mutagenesis studies show that proposed cation coordination sites of the P1B-2 pumps are crucial for the metal responses conferred by HvHMA2 in yeast. HvHMA2 expression suppresses the Zn-deficient phenotype of the Arabidopsis hma2hma4 mutant indicating that HvHMA2 functions as a Zn pump in planta and could play a role in root to shoot Zn transport. When expressed in Arabidopsis, HvHMA2 localises predominantly to the plasma membrane.
Building and navigating a network of local minima

We present a novel method that constructs and navigates a network of local minima of potential fields defined over multidimensional spaces. Though motivated by problems of motion planning for robotic manipulators, similar techniques have been proposed for use in other domains such as molecular chemistry and drug design. The method is based on building a roadmap of paths connecting local minima of a potential function. The novel approach consists of an up-hill search strategy used to climb out of local minima and find new nearby local minima, without doubling back on previous local minima. With this up-hill search strategy, one can find local minima otherwise difficult to encounter, and one can focus the search to specific local minima and specific directions from those local minima. The construction of the roadmap can be done in parallel with very little communication. We present extensive simulation results. © 2001 John Wiley & Sons, Inc.
Detecting indexical signs in film audio for scene interpretation

In this paper, we study the sound tracks in films and their indexical semiotic usage by developing a classification system that detects complex sound scenes and their constituent sound events in cinema. We investigate two main issues in this paper: Determination of what constitutes the presence of a high level sound scene and inferences about the thematic content of the scene that can be drawn from this presence, and classification of environmental sounds in the audio track of the scene, to assist in the automatic detection of the high level scene. Experiments with our classification system on pure sounds resulted in a correct event classification rate of 88.9%. When the audio content of a number of film scenes was examined, though a lower accuracy resulted with sound event detection due to the presence of mixed sounds, the film audio samples were generally classified with the correct high-level sound scene label, enabling correct inferences about the story content of the scenes.
Sparse coding and its application to modeling the zebra finch's auditory system

In the first part of this thesis I discuss characterizations of neuronal functionalities especially in response to sensory stimuli, followed by an overview over the knowledge about the zebra finch’s auditory system as far as it is known today. The main part of the thesis is based on and partly reproduced1 from our publication (Blättler and Hahnloser, 2011). I present a new nonsymmetric sparse coding algorithm and its application to modeling the zebra finch’s neuronal activity in response to auditory stimuli. In contrast to other, symmetric sparse coding algorithms it is adapted to neuronal modeling as biological neurons themselves are only able to relay unsigned messages (action potential). However, models based on sparse coding schemes have successfully been applied in the past to model low-level sensory systems, mainly the primary visual cortex. But whether such models will be successful in explaining the more complex behavior of neurons in higher sensory brain areas is unknown. I show that applying our nonsymmetric sparse coding algorithm on zebra finch vocalizations we are able not only to model neuronal behavior in low-level brain areas such as Field L, but also in high-level areas such as HVC (used as a proper name). In our model one single parameter controls the transition between these behaviors: the firing threshold of the neurons. In the last part of the thesis four possible applications in machine learning based on our nonsymmetric sparse coding algorithm and inspired by the zebra finch’s auditory system will be presented: The first application is a simple method to automatically identify sound files containing subsong. As a second application a direct way to track development of song learning 1 sections 4.3, 5.2, 5.3, 6.3, and chapter 7
Twisting graphene nanoribbons into carbon nanotubes

Although carbon nanotubes consist of honeycomb carbon, they have never been fabricated from graphene directly. Here, it is shown by quantum molecular-dynamics simulations and classical continuum-elasticity modeling, that graphene nanoribbons can, indeed, be transformed into carbon nanotubes by means of twisting. The chiralities of the tubes thus fabricated can be not only predicted but also externally controlled. This twisting route is an opportunity for nanofabrication, and is easily generalizable to ribbons made of other planar nanomaterials.
Evaluating social and cognitive effects of video games using electroencephalography.

Games User Research (GUR) is an area of evaluative player research and human-computer interaction (HCI) that aims to improve games, focused on a player’s understanding and on their experience when playing games. In this field, techniques are available to measure and understand user experience. These techniques each have their own strengths and weaknesses. To improve and extend GUR methodology, this thesis explores ways that electroencephalography (EEG) can be used as an evaluative measure as part of a mixed methodology. The thesis aims to improve the accuracy and richness of GUR results obtained using EEG. Hemispheric Frontal Alpha Asymmetry (HFAA) is reviewed in depth as a useful EEG technique to measure arousal in real time. HFAA, the EEG methodology proposed in this thesis is used in several experimental studies reported here to show new insights into the social and cognitive factors of gaming. The research presented in this thesis shows that player experience related to the social environment of a game does not necessarily arise from gameplay, but instead relies more on the expectations of a player than the current literature suggests. Additionally, the thesis introduces a new way to investigate player understanding and learning in games, using real-time data about the player’s brain state. This is particularly useful for game designers creating introductory tutorial mechanisms for their games. The result of this research is useful for both researchers investigating the human brain immersed in the virtual world of a video game and game designers wanting to use real-time user feedback to build their games.
Biglycan Induces the Expression of Osteogenic Factors in Human Aortic Valve Interstitial Cells via Toll-Like Receptor-2

Objective—Although biglycan (BGN) and oxidized low-density lipoprotein (oxLDL) accumulation has been observed in calcific, stenotic aortic valves, their role in the pathogenesis of calcific aortic valve disease is poorly understood. We hypothesized that soluble BGN induces the osteogenic response in human aortic valve interstitial cells via Toll-like receptor (TLR) 2 and TLR4 and mediates the proosteogenic effect of oxLDL. Methods and Results—Aortic valve interstitial cells of stenotic valves express higher levels of BGN. Stimulation of cells from normal valves with BGN increased the expression of bone morphogenetic protein-2 (BMP-2) and alkaline phosphatase (ALP) among the chondrogenic/osteogenic markers examined and caused accumulation of calcium deposits. TLR2 silencing, but not TLR4 silencing, reduced BMP-2 and ALP levels after BGN stimulation although coimmunoprecipitation revealed that BGN interacts with both TLR2 and TLR4. BGN induced the phosphorylation of extracellular signal-regulated protein kinase-1/2, p38 mitogen-activated protein kinase and nuclear factor-&kgr;B. Inhibition of extracellular-regulated kinase-1/2 markedly reduced the upregulation of BMP-2 and ALP expression by BGN whereas inhibition of p38 mitogen-activated protein kinase or nuclear factor-&kgr;B had a moderate effect. Stimulation of aortic valve interstitial cells with oxLDL upregulated BGN expression and release. Knockdown and neutralization of BGN reduced the effect of oxLDL on BMP-2 and ALP expression. Conclusion—Extracellular soluble BGN induces the expression of BMP-2 and ALP in human aortic valve interstitial cells primarily via TLR2 and contributes to the proosteogenic effect of oxLDL. These findings highlight the potential role of soluble BGN and oxLDL in the development of calcific aortic valve disease.
Angular momentum conservative algorithm of collisional process in DSMC method

The traditional algorithm of collisional process in DSMC method, used for calculation of velocities of molecules after collision, is based on the conservation laws for linear momentum and total energy. This algorithm does not provide conservation of angular momentum with respect to some axis, but for non-rotating flow this circumstance is insignificant, since the mean value of angular momentum of such kind of flow is equal to zero. The situation changes for axisymmetric flow with rotation. In this case the traditional procedure leads to some internal source or sink of angular momentum that may distort the flowfield. In the paper the discussed effect is recognized and investigated. The angular momentum conservative (AMC) algorithm of collisional process is proposed.
An Agent-Based Approach to Reconciling Data Heterogeneity in Cyber-Physical Systems

Computing and communication devices in any cyber-physical system (CPS) of non-trivial scale exhibit significant heterogeneity. Critical infrastructure systems, which are prime examples of CPSs, are no exception. The extent of networking capability, decentralized control, and more generally, integration between the cyber and physical infrastructures can vary greatly within a large-scale CPS. Other manifestations of heterogeneity in CPSs are in the resolution, syntax, and semantics of data collected by sensors from the physical infrastructure. Similar challenges complicate the use of databases that maintain past sensor data, device settings, or information about the physical infrastructure. The work presented in this paper aims to address these challenges by using the summary schemas model (SSM), which enables heterogeneous data sources to be queried with an unrestricted view and/or terminology. This support for imprecise queries significantly broadens the scope of data that can be used for intelligent decision support and carries the promise of increased reliability and performance for the CPS. We seek to ensure that ambiguity and imprecision do not accompany this expanded scope. %a.r. (note imprecise query also may bring ambiguity and not exact answer into the picture) The ultimate goal of a CPS is to fortify and streamline the operation of its physical infrastructure. The success of this task is contingent upon correct and efficient interpretation of data describing the state of the physical components, and the constraints to which it is subject. To this end, we propose agent-based semantic interpretation services that extract meaningful and useful information from raw data from heterogeneous sources, aided by the SSM. The proposed approach is described in the context of intelligent water distribution networks, which are cyber-physical critical infrastructure systems responsible for reliable delivery of potable water. The methodology is general, and can be extended to a broad range of CPSs, including smart power grids and intelligent transportation systems.