Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Efficient and Congestion Control Multipath Routing in Clustered Wireless Sensor Network

In this paper we propose redundancy management of heterogeneous wireless sensor networks (HWSNs) where we are utilizing multipath routing to answer user queries in the presence of unreliable and malicious nodes which cause packet loss in the network. The research problem we are addressing in this paper is, effective Redundancy Management of Multipath routing in a clustered Heterogeneous Wireless Sensor Network to tolerate intrusions which are responsible for packet loss and jamming attack. The main goal and objective of the paper is to use redundancy management on multipath routing to tolerate and detect intrusions. The key concept of our redundancy management is to exploit the tradeoff between energy consumption vs. the gain in reliability, timeliness, and security to maximize the system useful lifetime. In our work, we consider redundancy management of multipath routes, based on trust and energy values, for intrusion detection, and to maximize the system lifetime of a HWSN in the presence of unreliable and malicious nodes. Finally to protect confidentiality we are using symmetric encryption technique in this paper. To increase security we discovered more extensive attacks by the malicious nodes such as packet dropping attack and jamming attacks, each attack is having different energy requirement as well as security and reliability.
Digital Commons @ Michigan Tech Digital Commons @ Michigan Tech Environmental Impacts of Distributed Manufacturing from 3-D Environmental Impacts of Distributed Manufacturing from 3-D Printing of Polymer Components and Products Printing of Polymer Components and Products

Although additive layer manufacturing is well established for rapid prototyping the low throughput and historic costs have prevented mass-scale adoption. The recent development of the RepRap, an open source self-replicating rapid prototyper, has made low-cost 3-D printers readily available to the public at reasonable prices (<$1,000). The RepRap (Prusa Mendell variant) currently prints 3-D objects in a 200x200x140 square millimeters build envelope from acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA). ABS and PLA are both thermoplastics that can be injection-molded, each with their own benefits, as ABS is rigid and durable, while PLA is plant-based and can be recycled and composted. The melting temperature of ABS and PLA enable use in low-cost 3-D printers, as these temperature are low enough to use in melt extrusion in the home, while high enough for prints to retain their shape at average use temperatures. Using 3-D printers to manufacture provides the ability to both change the fill composition by printing voids and fabricate shapes that are impossible to make using tradition methods like injection molding. This allows more complicated shapes to be created while using less material, which could reduce environmental impact. As the open source 3-D printers continue to evolve and improve in both cost and performance, the potential for economically-viable distributed manufacturing of products increases. Thus, products and components could be customized and printed on-site by individual consumers as needed, reversing the historical trend towards centrally mass-manufactured and shipped products. Distributed manufacturing reduces embodied transportation energy from the distribution of conventional centralized manufacturing, questions remain concerning the potential for increases in the overall embodied energy of the manufacturing due to reduction in scale. In order to quantify the environmental impact of distributed manufacturing using 3-D printers, a life cycle analysis was performed on a plastic juicer. The energy consumed and emissions produced from conventional large-scale production overseas are compared to experimental measurements on a RepRap producing identical products with ABS and PLA. The results of this LCA are discussed in relation to the environmental impact of distributed manufacturing with 3-D printers and polymer selection for 3-D printing to reduce this impact. The results of this study show that distributed manufacturing uses less energy than conventional manufacturing due to the RepRap's unique ability to reduce fill composition. Distributed manufacturing also has less emissions than conventional manufacturing when using PLA and when using ABS with solar photovoltaic power. The results of this study indicate that open-source additive layer distributed manufacturing is both technically viable and beneficial from an ecological perspective.
The challenge of information visualization evaluation

As the field of information visualization matures, the tools and ideas described in our research publications are reaching users. The reports of usability studies and controlled experiments are helpful to understand the potential and limitations of our tools, but we need to consider other evaluation approaches that take into account the long exploratory nature of users tasks, the value of potential discoveries or the benefits of overall awareness. We need better metrics and benchmark repositories to compare tools, and we should also seek reports of successful adoption and demonstrated utility.
Agent-based Similarity-aware Web Document Pre-fetching

This paper presents an agent-based similarity-aware Web document pre-fetching scheme that is built on the similarity-aware Web caching architecture. A set of agents are employed to carry out certain duties such as document similarity detection, identification of relevant access patterns, document prediction and network traffic monitoring for document pre-fetching. Preliminary simulations have been conducted to evaluate the proposed scheme, and the results have shown that the new pre-fetching scheme outperforms existing Web-document pre-fetching algorithms
Vector quantization using survival Cauchy-Schwartz divergence

Vector quantization (VQ) is a data compression method in machine learning and data mining field by representing a larger data set with a smaller number of vectors in a possible way. Several vector quantization algorithms have been proposed in recent years. Different from the classic vector quantization algorithms such as LBG and K-means, the algorithms based on information theoretic learning try to make the code book distribution similar to the original data distribution. The computational complexity of such algorithms is however very high. In this paper, a novel vector quantization algorithm is proposed, which is based on a parameter-free information theoretic quantity, namely the survival Cauchy-Schwartz divergence (SCSD). Minimizing the SCSD between code book and the original data set yields a code book that has similar distribution with the data set. The computational cost of the new algorithm is relatively small due to the computational simplicity of the survival information potential (SIP). Simulation results show that the proposed algorithm works well.
Effects of exercise training on heart rate and QT interval in healthy young individuals: are there gender differences?

AIMS The aim of the present study was to assess the effects of exercise training on heart rate, QT interval, and on the relation between ventricular repolarization and heart rate in men and women. METHODS AND RESULTS A 24 h Holter recording was obtained in 80 healthy subjects (40 males) who differed for the degree of physical activity. Trained individuals showed a lower heart rate and a higher heart rate variability than sedentary subjects, independent of the gender difference in basal heart rate. Mean 24 h QTc was similar in trained and non-trained men, while a significant difference was observed between trained and non-trained women. Exercise training reduced the QT/RR slope in both genders. This effect on the QT/RR relation was more marked in women; in fact, the gender difference in the ventricular repolarization duration at low heart rate observed in sedentary subjects was no longer present among trained individuals. CONCLUSION The results of this study suggest that the cardiovascular response to exercise training may be different in men and women. Women may benefit more from interventions aimed to increase physical activity as a tool for prevention of cardiovascular morbidity and mortality.
Theta-RBM: Unfactored Gated Restricted Boltzmann Machine for Rotation-Invariant Representations

Learning invariant representations is a critical task in computer vision. In this paper, we propose the Theta-Restricted Boltzmann Machine ({\theta}-RBM in short), which builds upon the original RBM formulation and injects the notion of rotation-invariance during the learning procedure. In contrast to previous approaches, we do not transform the training set with all possible rotations. Instead, we rotate the gradient filters when they are computed during the Contrastive Divergence algorithm. We formulate our model as an unfactored gated Boltzmann machine, where another input layer is used to modulate the input visible layer to drive the optimisation procedure. Among our contributions is a mathematical proof that demonstrates that {\theta}-RBM is able to learn rotation-invariant features according to a recently proposed invariance measure. Our method reaches an invariance score of ~90% on mnist-rot dataset, which is the highest result compared with the baseline methods and the current state of the art in transformation-invariant feature learning in RBM. Using an SVM classifier, we also showed that our network learns discriminative features as well, obtaining ~10% of testing error.
Stunting, adiposity, and the individual-level “dual burden” among urban lowland and rural highland peruvian children

The causes of the “dual burden” of stunting and obesity remain unclear, and its existence at the individual level varies between populations. We investigate whether the individual dual burden differentially affects low socioeconomic status Peruvian children from contrasting environments (urban lowlands and rural highlands), and whether tibia length can discount the possible autocorrelation between adiposity proxies and height due to height measurement error.
Howard Gardner And Multiple Intelligences

I want my children to understand the world, but not just because the world is fascinating and the human mind is curious. I want them to understand it so that they will be positioned to make it a better place. Knowledge is not the same as morality, but we need to understand if we are to avoid past mistakes and move in productive directions. An important part of that understanding is knowing who we are and what we can do. Ultimately, we must synthesize our understandings for ourselves. The performance of understanding that try matters are the ones we carry out as human beings in an imperfect world which we can affect for good or for ill. (Howard Gardner 1999: 180-181)
Nationwide Surveillance for Pathogenic Microorganisms in Groundwater near Carcass Burials Constructed in South Korea in 2010

Widespread outbreaks of foot-and-mouth disease and avian influenza occurred in South Korea during 2010. In response to the culling of many animals to attenuate the spread of disease, South Korea used mass burial sites to dispose of the large number of carcasses; consequently, concerns about groundwater contamination by leachate from these burial sites are increasing. Groundwater is one of the main sources of drinking water, and its cleanliness is directly related to public health. Thus, this study aimed to evaluate the safety of groundwater around the burial sites (total of 600 sites). A total of 1,200 groundwater samples were collected though the country, and microbial analysis was conducted during two time periods: during the spring (n = 600; April to June 2012) and after rainfall (n = 600; August to October, 2012; fall). Fecal coliform and Escherichia coli were detected in 173 (14.4%) and 85 (7.1%) of the 1,200 samples, respectively. Salmonella spp. and Shigella spp. each were detected only once (0.083%). Clostridium perfringens was detected from 7 groundwater samples (0.583%), and E. coli O157:H7 was not detected. With respect to norovirus, only the GII type was detected from six groundwater samples (0.5%), and enterovirus was detected in 15 groundwater samples (1.25%). The frequency of E. coli that we detected was lower than that found in previous studies conducted in South Korea, but we detected higher frequency of fecal coliform than that observed in a previous report. The contamination frequencies of Salmonella spp. and Shigella spp. were very low, but C. perfringens, which could be an indicator of fecal pollution, was detected in seven regions. Overall, the results of the present study indicate a low possibility of contamination from burial sites. However, consistent monitoring is required to prevent microbial contamination of groundwater near the burial sites.
Definitions and Properties

Constrained orthogonal polynomials have been recently introduced in the study of the Hohenberg-Kohn functional to provide basis functions satisfying particle number conservation for an expansion of the particle density. More generally, we define block orthogonal (BO) polynomials which are orthogonal, with respect to a first Euclidean scalar product, to a given i-dimensional subspace E i of polynomials associated with the constraints. In addition, they are mutually orthogonal with respect to a second Euclidean scalar product. We recast the determination of these polynomials into a general problem of finding particular orthogonal bases in an Euclidean vector space endowed with distinct scalar products. An explicit two step Gram-Schmidt orthogonalization (G-SO) procedure to determine these bases is given. By definition, the standard block orthogonal (SBO) polynomials are associated with a choice of E i equal to the subspace of polynomials of degree less than i. We investigate their properties, emphasizing similarities to and differences from the standard orthogonal polynomials. Applications to classical orthogonal polynomials will be given in forthcoming papers.
A secant method for nonsmooth optimization

The notion of a secant for locally Lipschitz continuous functions is introduced and a new algorithm to locally minimize nonsmooth, nonconvex functions based on secants is developed. We demonstrate that the secants can be used to design an algorithm to find descent directions of locally Lipschitz continuous functions. This algorithm is applied to design a minimization method, called a secant method. It is proved that the secant method generates a sequence converging to Clarke stationary points. Numerical results are presented demonstrating the applicability of the secant method in wide variety of nonsmooth, nonconvex optimization problems. We also compare the proposed algorithm with the bundle method using numerical results.
The distribution of polynomials over finite fields, with applications to the Gowers norms

In this paper we investigate the uniform distribution properties of polynomials in many variables and bounded degree over a fixed finite field F of prime order. Our main result is that a polynomial P : F^n -> F is poorly-distributed only if P is determined by the values of a few polynomials of lower degree, in which case we say that P has small rank. We give several applications of this result, paying particular attention to consequences for the theory of the so-called Gowers norms. We establish an inverse result for the Gowers U^{d+1}-norm of functions of the form f(x)= e_F(P(x)), where P : F^n -> F is a polynomial of degree less than F, showing that this norm can only be large if f correlates with e_F(Q(x)) for some polynomial Q : F^n -> F of degree at most d. The requirement deg(P) < |F| cannot be dropped entirely. Indeed, we show the above claim fails in characteristic 2 when d = 3 and deg(P)=4, showing that the quartic symmetric polynomial S_4 in F_2^n has large Gowers U^4-norm but does not correlate strongly with any cubic polynomial. This shows that the theory of Gowers norms in low characteristic is not as simple as previously supposed. This counterexample has also been discovered independently by Lovett, Meshulam, and Samorodnitsky. We conclude with sundry other applications of our main result, including a recurrence result and a certain type of nullstellensatz.
Fast and space-efficient virtual machine checkpointing

Checkpointing, i.e., recording the volatile state of a virtual machine (VM) running as a guest in a virtual machine monitor (VMM) for later restoration, includes storing the memory available to the VM. Typically, a full image of the VM's memory along with processor and device states are recorded. With guest memory sizes of up to several gigabytes, the size of the checkpoint images becomes more and more of a concern. In this work we present a technique for fast and space-efficient checkpointing of virtual machines. In contrast to existing methods, our technique eliminates redundant data and stores only a subset of the VM's memory pages. Our technique transparently tracks I/O operations of the guest to external storage and maintains a list of memory pages whose contents are duplicated on non-volatile storage. At a checkpoint, these pages are excluded from the checkpoint image. We have implemented the proposed technique for paravirtualized as well as fully-virtualized guests in the Xen VMM. Our experiments with a paravirtualized guest (Linux) and two fullyvirtualized guests (Linux, Windows) show a significant reduction in the size of the checkpoint image as well as the time required to complete the checkpoint. Compared to the current Xen implementation, we achieve, on average, an 81% reduction in the stored data and a 74% reduction in the time required to take a checkpoint for the paravirtualized Linux guest. In a fully-virtualized environment runningWindows and Linux guests, we achieve a 64% reduction of the image size along with a 62% reduction in checkpointing time.
A comprehensive study on the power-sensitivity trade-off in TRF receivers

There has always been a trade-off between the sensitivity of an RF receiver and its total power consumption meaning that in order to design a receiver with a high sensitivity, more power must be dissipated. However, this inevitable trade-off can be tolerated in applications where only one of these two margins is of interest. For example, ultra-low power receivers in general and wake-up receivers in particular may still be applicable with a sensitivity of better than -70 dBm if the power consumption requirement has been satisfied. We present in this paper a through analytical/graphical approach in order to derive the power-sensitivity characteristics for a general Tuned Radio Frequency receiver based on practical assumptions.
GMDH-type neural networks with a feedback loop and their application to the identification of large-spatial air pollution patterns

The GMDH (group method of data handling)-type neural networks with a feedback loop have been proposed in our early work. The architecture of these networks is generated by using the heuristic self-organization method that is the basic theory of the GMDH method. The number of hidden layers and the number of neurons in the hidden layers are determined so as to minimize the error criterion defined by Akaike's information criterion (AIC). Furthermore, the optimum neurons that can handle the complexity of the nonlinear system are selected from a variety of prototype functions, such as the sigmoid function, the radial basis function, the high order polynomial and the linear function. In this study, the GMDH-type neural networks with a feedback loop is applied to the identification of large-spatial air pollution patterns. The source-receptor matrix that represents a relationship between the multiple air pollution sources and the air pollution concentration at the multiple monitoring stations is accurately identified by using the GMDH-type neural networks with a feedback loop. The identification results of the GMDH-type neural networks are compared with those identified by other identification methods.
Neural Programmer: Inducing Latent Programs with Gradient Descent

Deep neural networks have achieved impressive supervised classification performance in many tasks including image recognition, speech recognition, and sequence to sequence learning. However, this success has not been translated to applications like question answering that may involve complex arithmetic and logic reasoning. A major limitation of these models is in their inability to learn even simple arithmetic and logic operations. For example, it has been shown that neural networks fail to learn to add two binary numbers reliably. In this work, we propose Neural Programmer, an end-to-end differentiable neural network augmented with a small set of basic arithmetic and logic operations. Neural Programmer can call these augmented operations over several steps, thereby inducing compositional programs that are more complex than the built-in operations. The model learns from a weak supervision signal which is the result of execution of the correct program, hence it does not require expensive annotation of the correct program itself. The decisions of what operations to call, and what data segments to apply to are inferred by Neural Programmer. Such decisions, during training, are done in a differentiable fashion so that the entire network can be trained jointly by gradient descent. We find that training the model is difficult, but it can be greatly improved by adding random noise to the gradient. On a fairly complex synthetic table-comprehension dataset, traditional recurrent networks and attentional models perform poorly while Neural Programmer typically obtains nearly perfect accuracy.
Fault-tolerant continuous flow systems modelling

ABSTRACT This paper presents a structural modelling of faults with hybrid Petri nets (HPNs) for the analysis of a particular class of hybrid dynamic systems, continuous flow systems. HPNs are first used for the behavioural description of continuous flow systems without faults. Then, faults’ modelling is considered using a structural method without having to rebuild the model to new. A translation method is given in hierarchical way, it gives a hybrid automata (HA) from an elementary HPN. This translation preserves the behavioural semantics (timed bisimilarity), and reflects the temporal behaviour by giving semantics for each model in terms of timed transition systems. Thus, advantages of the power modelling of HPNs and the analysis ability of HA are taken. A simple example is used to illustrate the ideas.
Tracking and Predicting Moving Targets in Hierarchical Sensor Networks

Target tracking is an important application of newly developed wireless sensor networks (WSN). Much work has been done on this topic using a plane network architecture. We propose a scheme, namely hierarchical prediction strategy (HPS), for target prediction in hierarchical sensor networks. The network is divided into clusters, which are composed of one cluster-head and many normal nodes, by Voronoi division. For an existing target, cluster-heads only selectively activate nearby sensor nodes to perform tracking. Moreover, Recursive Least Square technique is used to predict the target trajectory and help activate next-round sensor nodes. Extended simulations show the properties of the proposed network architecture and the efficiency of the prediction scheme.