Miracle-dz/UniFAR-FTUs huggingface dataset:
Inland river ship supervision system based on ZigBee and RFID technology

In view of the current conditions that the network of rivers and the inland waterway in southeast coastal area are dense and numerous, a kind of inland ship monitoring system based on ZigBee networking system and RFID recognition technology is proposed by the author. Several advantages of nRF24LE1 SCM are given full play in this system, such as good anti-interference capability, high transmission stability and bulk-storage memory. By the high speed communication between the ZigBee network system and monitoring center, and also through the advantage of infinite extension of ZigBee network, several inland river ways can be covered. Many functions of inland river ways such as ship positioning and ship control are achieved through the comprehensive utilizations of RFID and ZigBee technology. The hardware management scheme of this system and the design flow of RFID and other related programs are described in detail in this paper.
Person Identification Using Ear Biometrics

In this paper, a method to recognize human using ear biometrics has been proposed. Traditional image-based approaches to person identification almost exclusively employ front views of the individual's face. Ear on the other hand, has a more uniform distribution of color, so almost all information is conserved when converting the original image into gray scales. When employing face or lip as a biometric, changing of their appearance with the expression of the subject creates problem but in case of ear the shape and appearance is fixed. We extract the features from an ear image by measuring geometric relations between predetermined points (lengths, angles). Here, the geometrical structures observed from pixel value distances are used for the successful recognition of objects. Recognition of objects is based on these geometrical structures. Experimental result reveals that the proposed identification method can achieve almost 89% accuracy. The complexity of this process is less since the method does not require to consider the inner boundary of ear.
INCREASING PERFORMANCE AND GRANULARITY IN ROLE-BASED ACCESS CONTROL SYSTEMS A CASE STUDY IN GRSECURITY

There is a movement in progress among operating system vendors to bring greater security through preventative measures. One such measure making headway in the Linux operating system is Role-Based Access Control (RBAC). For such a measure to be widely accepted, it needs to have an insignificant performance hit and scale well in large corporate environments. Such a measure also needs to be intuitive and require as little manual configuration as possible, while at the same time providing an effective level of security. This paper provides a case study of an RBAC system and illustrates changes that were made in order to help it become more widely accepted and provide a strong level of protection while still being administrator-friendly. The changes involved altering data structures to remove algorithmically complex bottlenecks, providing dynamic unions of policy to ease configuration and reduce memory usage, allowing dynamic interpretation of wildcards in security policy, implementing an anticipatory memory allocator, and providing a robust method of defining allowed user and group transitions on a Linux system.
From Facepalm to Brain Bender: Exploring Client-Side Cross-Site Scripting

Although studies have shown that at least one in ten Web pages contains a client-side XSS vulnerability, the prevalent causes for this class of Cross-Site Scripting have not been studied in depth. Therefore, in this paper, we present a large-scale study to gain insight into these causes. To this end, we analyze a set of 1,273 real-world vulnerabilities contained on the Alexa Top 10k domains using a specifically designed architecture, consisting of an infrastructure which allows us to persist and replay vulnerabilities to ensure a sound analysis. In combination with a taint-aware browsing engine, we can therefore collect important execution trace information for all flaws. Based on the observable characteristics of the vulnerable JavaScript, we derive a set of metrics to measure the complexity of each flaw. We subsequently classify all vulnerabilities in our data set accordingly to enable a more systematic analysis. In doing so, we find that although a large portion of all vulnerabilities have a low complexity rating, several incur a significant level of complexity and are repeatedly caused by vulnerable third-party scripts. In addition, we gain insights into other factors related to the existence of client-side XSS flaws, such as missing knowledge of browser-provided APIs, and find that the root causes for Client-Side Cross-Site Scripting range from unaware developers to incompatible first- and third-party code.
ROBUST DECENTRALIZED CONTROL OF A HVDC SYSTEM USING AN LMI APPROACH

Abstract A procedure for the design and tuning of two control loops of a High Voltage DC system is presented. The controlled variables are direct current on the rectifier side and direct voltage on the inverter side. The dynamics of both control loops interact with each other and are determined by the overall properties of the combined AC/DC system. Constraints on the information available for feedback in each loop require a decentralized controller structure. The AC system impedance is determined by the transmission lines, generators and loads and can suddenly change significantly so the designed controllers must be robust against these variations. An LMI based iterative approach – referred to as PK-Iteration – is demonstrated to be an efficient tool for the design and tuning of such a decentralized control system. Moreover, even though the plant is of high dynamic order, the design procedure can be used to construct a low order controller.
COMPRESSION STRENGTH AND FRACTURE PROPERTIES OF MODEL PARTICULATE FOOD COMPOSITES IN RELATION TO THEIR MICROSTRUCTURE AND PARTICLE‐MATRIX INTERACTION

Foods containing particles in a matrix were modelled by setting glass spheres, varying in size and surface chemistry, and oil droplets in heat-denatured whey protein gels. Composites containing particles with hydrophilic surfaces were much stronger in compression, the strength being dependent on particle surface area, than those with hydrophobia surfaces. The relationship between strength and particle size was compared with existing rheological and composite theories. SEM examination of fracture surfaces, resulting from compression, showed that particles with an hydrophilic surface were an integral part of the composite, failure occurring within the protein matrix. Gels made from particles with an hydrophobic surface fractured adjacent to the particle surface, indicating little or no interaction between particle and matrix.
Perspectives andPotential Applications of Nanomedicine inBreastandProstateCancer

Nanomedicine is a branch of nanotechnology that includes the development of nanostructures and nanoanalytical systems for various medical applications. Among these applications, utilization of nanotechnology in oncology has captivated the attention of many research endeavors in recent years. The rapid development of nano-oncology raises new possibilities in cancer diagnosis and treatment. It also holds great promise for realization of point-of-care, theranostics, and personalized medicine. In this article, we review advances in nano-oncology, with an emphasis on breast and prostate cancer because these organs are amenable to the translation of nanomedicine from small animals to humans. As new drugs are developed, the incorporation of nanotechnology approaches into medicinal research becomes critical. Diverse aspects of nano-oncology are discussed, including nanocarriers, targeting strategies, nanodevices, as well as nanomedical diagnostics, therapeutics, and safety. The review concludes by identifying some limitations and future perspectives of nano-oncology in breast and prostate cancer management. & 2010 Wiley Periodicals, Inc. Med Res Rev
A fast parallel algorithm for counting triangles in graphs using dynamic load balancing

Finding the number of triangles in a graph (network) is an important problem in graph analysis. The number of triangles also has important applications in graph mining. Big graphs emerging from numerous application areas pose a significant challenge for the analysis and mining since these graphs consist of millions, or even billions, of nodes and edges. Graphs of such scale necessitate the development of efficient parallel algorithms. Existing distributed memory parallel algorithms for counting exact triangles are either Map-Reduce or message passing interface (MPI) based. Map-Reduce based algorithms generate prohibitively large intermediate data and do not demonstrate reasonably good runtime efficiency. The MPI based algorithms offer fast computation of the number of triangles. However, the partitioning and load balancing schemes these algorithms employ are static in nature - the partitions are precomputed based on some estimations. In this paper, we present an efficient MPI-based parallel algorithm for counting triangles in large graph. We consider the case where the main memory of each compute node is large enough to contain the entire graph. We observe that for such a case, computation load can be balanced dynamically and present a dynamic load balancing scheme which improves the performance of the algorithm significantly. Our algorithm demonstrates very good speedups and scales to a large number of processors. The algorithm computes the exact number of triangles in a network with 1 billion edges in 2 minutes with only 100 processors. Our results demonstrate that the algorithm is significantly faster than the related algorithms with static partitioning. In fact, for the real-world networks we experimented on, our algorithm achieves at least 2 times runtime efficiency over the fastest algorithm with static load balancing.
Automatic speech recognition of isolated words in Hindi language

Speech recognition is a broad subject as speech is natural way of communication. The acoustic and language model for this system are available but mostly in English language [15]. In India there are so many peoples who can't understand or speak English. So the speech recognition system in English language is of no use for these people. Here we presented Isolated Hindi words recognition system which is a part of Automatic Speech Recognition (ASR) system. Automatic Speech Recognition (ASR) is also called as computer speech recognition. The main goal of ASR system understands a voice by computer or microphone and converts it into the text to perform required task. The performance accuracy of speech recognition is highly depends on feature extraction and pattern recognition technique. In this paper, we are using MFCC as feature extraction technique, K-Nearest Neighbor (KNN) with GMM (Gaussian Mixture Model) for recognition of Hindi isolated words. For practical analysis we will prepare the Hindi words speech dataset of different males and females speakers.
An Otx/Nodal Regulatory Signature for Posterior Neural Development in Ascidians

In chordates, neural induction is the first step of a complex developmental process through which ectodermal cells acquire a neural identity. In ascidians, FGF-mediated neural induction occurs at the 32-cell stage in two blastomere pairs, precursors respectively of anterior and posterior neural tissue. We combined molecular embryology and cis-regulatory analysis to unveil in the ascidian Ciona intestinalis the remarkably simple proximal genetic network that controls posterior neural fate acquisition downstream of FGF. We report that the combined action of two direct FGF targets, the TGFβ factor Nodal, acting via Smad- and Fox-binding sites, and the transcription factor Otx suffices to trigger ascidian posterior neural tissue formation. Moreover, we found that this strategy is conserved in the distantly related ascidian Phallusia mammillata, in spite of extreme sequence divergence in the cis-regulatory sequences involved. Our results thus highlight that the modes of gene regulatory network evolution differ with the evolutionary scale considered. Within ascidians, developmental regulatory networks are remarkably robust to genome sequence divergence. Between ascidians and vertebrates, major fate determinants, such as Otx and Nodal, can be co-opted into different networks. Comparative developmental studies in ascidians with divergent genomes will thus uncover shared ascidian strategies, and contribute to a better understanding of the diversity of developmental strategies within chordates.
Multi-Valued Decision Diagram-Based Reliability Analysis of $k$ -out-of-$n$  Cold Standby Systems Subject to Scheduled Backups

To improve the system reliability while conserving the limited system resources, cold standby sparing is often used. In computing tasks, because active components fail randomly, and the standby component has to pick up the mission task whenever required, scheduled backups are often implemented to save the completed portions of the task. The backups can facilitate an effective system recovery where the standby component can take over the mission task from the last backup point instead of resuming the mission task from the very beginning. This paper considers a k-out-of- n cold standby system subject to scheduled backups, where k components are online and operating, with the remaining components waiting in the unpowered, cold standby mode. Whenever an online component fails, a cold standby component is activated to take over the mission task from the last backup point. The backup intervals are deterministic, but can be even or uneven. As the component may fail due to an imperfect switching from the standby state to the fully powered up state, the switching failure is also considered in the system model. A multi-valued decision diagram (MDD)-based analytical approach is proposed to evaluate the reliability of the considered system, and its complexity is analyzed. The proposed method is applicable to systems with non-identical components following arbitrary lifetime distributions. Examples are given to illustrate the MDD-based method. The correctness and efficiency of the proposed method are verified using Monte Carlo simulations.
Spatial Learning Using Locomotion Interface to Virtual Environment

The inability to navigate independently and interact with the wider world is one of the most significant handicaps that can be caused by blindness, second only to the inability to communicate through reading and writing. Many difficulties are encountered when visually impaired people (VIP) need to visit new and unknown places. Current speech or haptics technology does not provide a good solution. Our approach is to use treadmill-style locomotion interface, unconstrained walking plane (UWP), to allow a richer and more immersive form of virtual environment (VE) exploration to enable VIP to create cognitive maps efficiently and thereby to enhance their mobility. An experimental study is reported that tests design of UWP for both straight walking and turning motions. Two groups of participants, blind-folded-sighted and blind, learned spatial layout in VE using two exploration modes: guided (training phase) and unguided (testing phase). Spatial layout knowledge was assessed by asking participants to perform object-localization task and target-object task. Our results showed a significant decrease in time and helps taken to complete tasks, subjective workload, and errors in a post-training trial as compared to a partial-training trial. UWP has been found to significantly improve interaction with VE with visualizations such as spatial information.
Altered resting state attentional networks in diabetic neuropathic pain

Background Chronic pain can be considered as a highly salient stimulus that continuously taxes the attentional and salience processing networks, thus interfering with cognitive abilities and, more specifically, consuming attentional resources. The aim of the paper was to explore whether and how diabetic neuropathic pain (NP) affects attentional networks. Methods The authors sought to achieve this by investigating resting state functional connectivity (rsFC) in diabetic NP patients and comparing it with that of matched healthy controls. Results NP patients showed a widespread reduction in connectivity in both the dorsal and ventral attentional networks, as well as in the dorsal anterior cingulated cortex (ACC), typically implicated in salience processing. The authors also found a generalised reduction in the length of functional connections in the NP group: in all the examined networks, the Euclidean distance between connected voxels was significantly shorter in patients than in controls. Conclusion In diabetic NP, a parieto-fronto-cingulate network controlling attention to external stimuli is impaired. In line with previous studies, chronic pain can disrupt the synchrony of a common pool of brain areas, involved in self-monitoring, pain processing and salience detection.
Sieve: A Tool for Automatically Detecting Variations Across Program Versions

Software systems often undergo many revisions during their lifetime as new features are added, bugs repaired, abstractions simplified and refactored, and performance improved. When a revision, even a minor one, does occur, the changes it induces must be tested to ensure that invariants assumed in the original version are not violated unintentionally. In order to avoid testing components that are unchanged across revisions, impact analysis is often used to identify code blocks or functions that are affected by a change. In this paper, we present a novel solution to this general problem that uses dynamic programming on instrumented traces of different program binaries to identify longest common subsequences in strings generated by these traces. Our formulation allows us to perform impact analysis and also to detect the smallest set of locations within the functions where the effect of the changes actually manifests itself. Sieve is a tool that incorporates these ideas. Sieve is unobtrusive, requiring no programmer or compiler intervention to guide its behavior. Our experiments on multiple versions of op ensource C programs shows that Sieve is an effective and scalable tool to identify impact sets and can locate regions in the affected functions where the changes manifest. These results lead us to conclude that Sieve can play a beneficial role in program testing and software maintenance
Distributed transmit beamforming: challenges and recent progress

Distributed transmit beamforming is a form of cooperative communication in which two or more information sources simultaneously transmit a common message and control the phase of their transmissions so that the signals constructively combine at an intended destination. Depending on the design objectives and constraints, the power gains of distributed beamforming can be translated into dramatic increases in range, rate, or energy efficiency. Distributed beamforming may also provide benefits in terms of security and interference reduction since less transmit power is scattered in unintended directions. Key challenges in realizing these benefits, however, include coordinating the sources for information sharing and timing synchronization and, most crucially, distributed carrier synchronization so that the transmissions combine constructively at the destination. This article reviews promising recent results in architectures, algorithms, and working prototypes which indicate that these challenges can be surmounted. Directions for future research needed to translate the potential of distributed beamforming into practice are also discussed.
Robust Clothing-Invariant Gait Recognition

Robust gait recognition is a challenging problem, due to the large intra-subject variations and small inter-subject variations. Out of the covariate factors like shoe type, carrying condition, elapsed time, it has been demonstrated that clothing is the most challenging covariate factor for appearance-based gait recognition. For example, long coat may cover a significant amount of gait features and make it difficult for individual recognition. In this paper, we proposed a random subspace method (RSM) framework for clothing-invariant gait recognition by combining multiple inductive biases for classification. Even for small size training set, this method can achieve promising performance. Experiments are conducted on the OU-ISIR Treadmill dataset B which includes 32 combinations of clothing types, and the average recognition accuracy is more than 80%, which indicates the effectiveness of our proposed method.
Outcomes after two years of providing antiretroviral treatment in Khayelitsha, South Africa

Background: A community-based antiretroviral therapy (ART) programme was established in 2001 in a South African township to explore the operational issues involved in providing ART in the public sector in resource-limited settings and demonstrate the feasibility of such a service. Methods: Data was analysed on a cohort of patients with symptomatic HIV disease and a CD4 lymphocyte count < 200 × 106 cells/l. The programme used standardized protocols (using generic medicines whenever possible), a team-approach to clinical care and a patient-centred approach to promote adherence. Results: Two-hundred and eighty-seven adults naive to prior ART were followed for a median duration of 13.9 months. The median CD4 lymphocyte count was 43 × 106 cells/l at initiation of treatment, and the mean log10 HIV RNA was 5.18 copies/ml. The HIV RNA level was undetectable (< 400 copies/ml) in 88.1, 89.2, 84.2, 75.0 and 69.7% of patients at 3, 6, 12, 18 and 24 months respectively. The cumulative probability of remaining alive was 86.3% at 24 months on treatment for all patients, 91.4% for those with a baseline CD4 lymphocyte count ⩾ 50 × 106 cells/l, and 81.8% for those with a baseline CD4 lymphocyte count < 50 × 106 cells/l. The cumulative probability of changing a single antiretroviral drug by 24 months was 15.1% due to adverse events or contraindications, and 8.4% due to adverse events alone. Conclusions: ART can be provided in resource-limited settings with good patient retention and clinical outcomes. With responsible implementation, ART is a key component of a comprehensive response to the epidemic in those communities most affected by HIV.