Miracle-dz/UniFAR-FTUs huggingface dataset:
EEGRA: Energy Efficient Geographic Routing Algorithms for Wireless Sensor Network

Energy efficiency is critical in wireless sensor networks (WSN) for system reliability and deployment cost. The power consumption of the communication in multi-hop WSN is primarily decided by three factors: routing distance, signal interference, and computation cost of routing. Several routing algorithms designed for energy efficiency or interference avoidance had been proposed. However, they are either too complex to be useful in practices or specialized for certain WSN architectures. In this paper, we propose two energy efficient geographic routing algorithms (EEGRA) for wireless sensor networks, which are based on existing geographic routing algorithms and take all three factors into account. The first algorithm combines the interference into the routing cost function, and uses it in the routing decision. The second algorithm transforms the problem into a constrained optimization problem, and solves it by searching the optimal discretized interference level. We integrate four geographic routing algorithms: GOAFR+, Face Routing, GPSR, and RandHT, to both EEGRA algorithms and compare them with three other routing methods in terms of power consumption and computation cost for the grid and irregular sensor topologies. The results of our experiments show both algorithms conserve sensor's routing energy 30% ~ 50% comparing to general geographic routing algorithms. In addition, the time complexity of EEGRA algorithms is similar to the geographic greedy routing methods, which is much faster than the optimal SINR-based algorithm.
Coordinated Locomotion and Monitoring Using Autonomous Mobile Sensor Nodes

Stationary wireless sensor networks (WSNs) fail to scale when the area to be monitored is unbounded and the physical phenomenon to be monitored may migrate through a large region. Deploying mobile sensor networks (MSNs) alleviates this problem, as the self-configuring MSN can relocate to follow the phenomenon of interest. However, a major challenge here is to maximize the sensing coverage in an unknown, noisy, and dynamically changing environment with nodes having limited sensing range and energy, and moving under distributed control. To address these challenges, we propose a new distributed algorithm, Causataxis, which enables the MSN to relocate toward the interesting regions and adjust its shape and position as the sensing environment changes. (In Latin, causa means motive/interest. A taxis (plural taxes) is an innate behavioral response by an organism to a directional stimulus. We use Causataxis to refer to an interest driven relocation behavior. ) Unlike conventional cluster-based systems with backbone networks, a unique feature of our proposed approach is its biosystem inspired growing and rotting behaviors with coordinated locomotion. We compare Causataxis with a swarm-based algorithm, which uses the concept of virtual spring forces to relocate mobile nodes based on local neighborhood information. Our simulation results show that Causataxis outperforms the swarm-based algorithm in terms of the sensing coverage, the energy consumption, and the noise tolerance with a slightly high communication overhead.
Study of active force control in biodynamic hand model

This simulation work provides the investigation of the performance active vibration method in suppressing human hand tremor. The Active Force Control (AFC) and classic Proportional-Derivative (PD) controller is proposed to control linear electromagnet actuator and applied at a 4-DOFs biodynamic model of human hand to investigate the performance of the controller. From the findings, the combination of AFC and classic PD controller shows excellent performance in reducing tremor error. The two degree of freedoms controller gives a significant improvement in tremor cancellation compared to merely using PD controller. The simulation work could be used as initial stage to study and develop anti tremor device.
Queries over Large-Scale Log Data of Hybrid Granularities

Log data is of great value for operation and maintenance of systems and networks. With log data, system behaviors can be monitored, traced, analyzed, to detect unusual circumstances and identify warnings, for the purpose of taking timely measure. However, nowadays many general systems making queries and retrieval over log data is traditionally based on the full amount of raw data. When the amount of log data increase substantially, it's difficult to execute queries based on the raw data of extremely large scales to meet response time requirements. As log data will not be updated after generated, this paper proposes that data and queries can be preprocessed to form data preprocessing results of different granularities. Queries submitted by users can take advantage of corresponding preprocessing results, to improve the query response time. This paper proposes a model for queries over log data of various granularities. The main work includes the follows (1) develop a query model based on hybrid granularity, which enables a query to execute on various granularities of data sets after preprocessing, (2) analyze and prove the completeness and correctness of the proposed query model based on hybrid granularity, (3) describe the query action based on hybrid granularity model formally and present the algorithm framework of the query transformation, (4) analyze and demonstrate the advantages of efficiency of the proposed query model compared to the original data query model. The proposed solution is used in some practical systems. The results show that this solution can guarantee the correctness of the query results while it is able to improve the responsive efficiency of the query significantly. Preprocessing, (2) analyze and prove the completeness and correctness of the proposed query model based on hybrid granularity, (3) describe the query action based on hybrid-granularity model formally and present the algorithm framework of the query transformation, (4) analyze and demonstrate the advantages in efficiency of the proposed query model compared to the original data query model. The proposed solution is used in some practical systems. The results show that this solution can guarantee the correctness of query results while it is able to improve the responsive efficiency of the query significantly.
A New Method for Obtaining Binary Pulsar Distances and its Implications for Tests of General Relativity

We demonstrate how measuring orbital period derivatives can lead to more accurate distance estimates and transverse velocities for some nearby binary pulsars. In many cases this method will estimate distances more accurately than is possible by annual parallax, as the relative error decreases as t-5/2. Unfortunately, distance uncertainties limit the degree to which nearby relativistic binary pulsars can be used for testing the general relativistic prediction of orbital period decay to a few percent. Nevertheless, the measured orbital period derivative of PSR B1534+12 agrees within the observational uncertainties with that predicted by general relativity if the proper-motion contribution is accounted for.
In vitro Fermentation, Digestion Kinetics and Methane Production of Oilseed Press Cakes from Biodiesel Production

Following the extraction of oil for biodiesel production, oilseed press cakes are high in fat. As the dietary supplementation of fat is currently considered the most promising strategy of consistently depressing methanogenesis, it follows that oilseed press cakes may have a similar potential for CH4 abatement. As such, this study aimed to characterise the nutritive value of several oilseed press cakes, glycerine and soybean meal (SBM) and to examine their effects on in vitro ruminal fermentation, digestion kinetics and CH4 production. Moringa press oil seeds exhibited the greatest in sacco effective degradability (ED) of DM and CP (p<0.05). In vitro gas production (ml/g digested DM) was not affected (p = 0.70) by supplement at 48 h of incubation. In vitro DMD was increased with the supplementation of glycerine and SBM at all levels of inclusion. Moringa oilseed press cakes produced the lowest CH4 (mg/g digested DM) at 6 and 12 h of incubation (p<0.05). The findings suggest that moringa oilseed press cake at 400 g/kg DM has the greatest potential of the oilseed press cakes examined in this study, to reduce CH4 production, without adversely affecting nutrient degradability.
Metadata and the world wide web

Metadata is of paramount importance for persons, organizations, and endeavors of every dimension that are increasingly turning to the World Wide Web (hereafter referred to as the Web) as a chief conduit for accessing and disseminating information. This is evidenced by the development and implementation of metadata schemas supporting projects ranging from restricted corporate Intranets, data warehouses, and consumer-oriented electronic commerce enterprises—to freely accessible digital libraries, educational initiatives, virtual museums, and other public websites. Today’s metadata activities are unprecedented because they extend beyond the traditional library environment in an effort to deal with the Web’s exponential growth. This article considers metadata in today’s Web environment. The article defines metadata, examines the relationship between metadata and cataloging, provides definitions for key metadata vocabulary terms, and explores the topic of metadata generation. Metadata is an extensive and expanding subject that is prevalent in many environments. For practical reasons, this article has elected to concentrate on the information resource domain, which is defined by electronic textual documents, graphical images, archival materials, museum artifacts, and other objects found in both digital and physical information centers (e.g., libraries, museums, record centers, archives, etc.). To show the extent and larger application of metadata, examples are also drawn from the news channel, data warehouse, electronic commerce, open source, and medical communities.
Association rule mining algorithms and Genetic Algorithm: A comparative study

Generally frequent itemsets are extracted from large databases by applying association rule mining (ARM) algorithms like Apriori, Partition, Pincer-Search, Incremental, and Border algorithm etc. Genetic Algorithm (GA) can also be applied to discover the frequent patterns from databases. The main advantage of using GA in the discovery of frequent patterns or itemsets is that they can perform global search and its time complexity is lesser compared to other Apriori-based algorithms as because it is based on the greedy approach. But the FP-tree algorithm is considered to be the best among the ARM algorithms, because its candidate sets generation procedure is completely different from Apriori-based algorithms. The major aim of this paper is to present a comparative study among ARM-based and GA-based approaches to data mining.
Reinforcement Learning of Dimensional Attention for Categorization

The ability to selectively focus attention on stimulus dimensions appears to play an important role in human category learning. This insight is embodied by learned dimensional attention weights in the ALCOVE model (Kr-uschke, 1992). The success of this psychological model suggests its use as a foundation for efforts to understand the neural basis of category learning. One obstacle to such an effort is ALCOVE’s use of the biologically implausible backpropagation of error algorithm to adapt dimensional attention weights. This obstacle may be overcome by replacing this attention mechanism with one grounded in the reinforcement learning processes of the brain’s dopamine system. In this paper, such a biologically-based mechanism for dimensional attention is proposed, and the ﬁt of this mechanism to human performance is shown to be comparable to that of ALCOVE.
How the media coverage affects the stock returns of listed real estate companies in China

We find that real estate companies are usually the focus of media, and the real estate industry is always hot for most inventors. The article discusses the relation between the media coverage and the stock returns of listed real estate companies in China in univariate and multivariate analysis. We find that stocks not covered by the media earn significant higher future returns than stocks that are heavily covered. We also compare the different effects of positive news and negative news, and the results support the result. Our findings suggest that the media can alleviate information frictions of inventors to affect stock returns by disseminating information broadly. However, the return premium has nothing to do with the content of the news, which shows that we should take account of the complexity of the media coverage to invest in real estate companies.
User Managed Virtual Clusters in Comet

Hardware virtualization has been gaining a significant share of computing time in the last years. Using virtual machines (VMs) for parallel computing is an attractive option for many users. A VM gives users a freedom of choosing an operating system, software stack and security policies, leaving the physical hardware, OS management, and billing to physical cluster administrators. The well-known solutions for cloud computing, both commercial (Amazon Cloud, Google Cloud, Yahoo Cloud, etc.) and open-source (OpenStack, Eucalyptus) provide platforms for running a single VM or a group of VMs. With all the benefits, there are also some drawbacks, which include reduced performance when running code inside of a VM, increased complexity of cluster management, as well as the need to learn new tools and protocols to manage the clusters. At SDSC, we have created a novel framework and infrastructure by providing virtual HPC clusters to projects using the NSF sponsored Comet supercomputer. Managing virtual clusters on Comet is similar to managing a bare-metal cluster in terms of processes and tools that are employed. This is beneficial because such processes and tools are familiar to cluster administrators. Unlike platforms like AWS, Comet's virtualization capability supports installing VMs from ISOs (i.e., a CD-ROM or DVD image) or via an isolated management VLAN (PXE). At the same time, we're helping projects take advantage of VMs by providing an enhanced client tool for interaction with our management system called Cloudmesh client. Cloudmesh client can also be used to manage virtual machines on OpenStack, AWS, and Azure. The article describes our design and approach to running virtual clusters, the tools we developed, and initial user experience.
Integrated Approach for the Detection of Learning Styles & Affective States

Detecting the needs of the learner is a challenging task for systems addressing issues related to either adaptivity or personalization. In this paper we present a student-driven learning and assessment tool along with a tool for detecting/calculating learning styles and affective states. The learning and assessment tool enables learning, while the detection/calculation tool detects learning styles and affective states from the behavior of learners. The resulting behavior is then reflected in the database. This method does not require learners to fill out a lengthy questionnaire. Also, there is no need to monitor observational cues, such as gesture, posture, conversation etc. of the learner; the detection/calculation tool automatically identifies learning styles and affective states.
The feature selection for classification by applying the Significant Matrix with SPEA2

This paper presents a novel application of Genetic Algorithm for the feature selection. The main purpose is to provide proper subset features for decision tree construction in the classification task. New method with the use of “Significant Matrix” on genetic algorithm is presented. The main function is to calculate the relationship between the feature and class label assigned to a fitness value for the population. The algorithm presented important features selected by considering the class of the data and number of features for the least amount in the Significant Matrix. The next step will then update the feature number and the record number to repeat the process until a stop condition is met. Classification by decision tree is used to verify the importance of the features selected by the proposed method. The model tested with 11 different datasets. The results show that the method yields high accuracy of the classification and higher satisfaction compared to classification using artificial neural network. Experimental results show that the proposed method not only provides a higher accuracy, but also reduce the complexity by using less features of the dataset.
A Bayesian non-parametric method for clustering high-dimensional binary data

In many real life problems, objects are described by large number of binary features. For instance, documents are characterized by presence or absence of certain keywords; cancer patients are characterized by presence or absence of certain mutations etc. In such cases, grouping together similar objects/profiles based on such high dimensional binary features is desirable, but challenging. Here, I present a Bayesian non parametric algorithm for clustering high dimensional binary data. It uses a Dirichlet Process (DP) mixture model and simulated annealing to not only cluster binary data, but also find optimal number of clusters in the data. The performance of the algorithm was evaluated and compared with other algorithms using simulated datasets. It outperformed all other clustering methods that were tested in the simulation studies. It was also used to cluster real datasets arising from document analysis, handwritten image analysis and cancer research. It successfully divided a set of documents based on their topics, hand written images based on different styles of writing digits and identified tissue and mutation specificity of chemotherapy treatments.
A low-cost portable 3D laser scanning system with aptness from acquisition to visualization

This paper presents the design perspectives of a low-cost portable 3D laser scanning mechanism. The hardware is simple in design, easy to fabricate, and costs much less compared to the cheapest ones available in the market. Moreover, it is noninvasive and able to generate high-quality data sets. We present the complete design pipeline and discuss how to implement and put together the most recent algorithms. We propose this design for its wide applications to virtual reality simulation, 3D modeling, digitization of archaeological artifacts, automated defect inspection, object recognition etc.
Vitamin D as a protective factor in multiple sclerosis

ABSTRACT Objective: To examine the association between 25-hydroxyvitamin D (25[OH]D) levels and the risk of multiple sclerosis (MS) in blood samples collected prospectively and during gestation. Methods: In this nested case-control study, 2 population-based biobanks with 291,500 samples from 164,000 persons collected since 1975 in the northern half of Sweden were used. We identified prospectively collected blood samples from MS cases (n = 192, controls matched 2:1) and gestational samples from pregnant mothers where the offspring had later developed MS (n = 37, control mothers matched 5:1). 25(OH)D levels were measured using an ELISA, and the risk of MS was analyzed using matched logistic regression. Results: Levels of 25(OH)D ≥75 (vs <75) nmol/L in prospectively collected blood samples were associated with a decreased risk of MS (odds ratio [OR] 0.39, 95% confidence interval [CI] 0.16–0.98). No decrease in MS risk was found in the offspring exposed to gestational 25(OH)D levels ≥75 (vs <75) nmol/L (OR 1.8, 95% CI 0.53–5.8). The prevalence of 25(OH)D levels ≥75 nmol/L in female controls decreased gradually during 1976–2005 (p trend = 0.005). Conclusion: This study supports the presence of an association between high 25(OH)D levels during the years preceding disease onset and a decreased risk of MS. In the very limited material with samples drawn in early pregnancy, where month-of-birth effects were controlled for, we found no association between gestational 25(OH)D levels and MS risk in the offspring. Decreasing 25(OH)D levels in the population may contribute to explain the increasing MS incidence that is suggested from epidemiologic studies.
Boolean Algebras with an Automorphism Group: a Framework for Lukasiewicz Logic

We introduce a framework within which reasoning according to ˆ logic can be represented. We consider a separable Boolean algebra B endowed with a (certain type of) group G of automorphisms; the pair (B,G) will be called a Boolean ambiguity algebra. B is meant to model a system of crisp properties; G is meant to express uncertainty about these properties. We define fuzzy propositions as subsets of B which are, most importantly, closed under the action of G. By defining a conjunction and implication for pairs of fuzzy propositions in an appropriate manner, we are led to the algebraic structure characteristic for ˆ Lukasiewicz logic.
Terrestrial Species in Protected Areas and Community-Managed Lands in Arunachal Pradesh, Northeast India

Protected areas (including areas that are nominally fully protected and those managed for multiple uses) encompass about a quarter of the total tropical forest estate. Despite growing interest in the relative value of community-managed lands and protected areas, knowledge about the biodiversity value that each sustains remains scarce in the biodiversity-rich tropics. We investigated the species occurrence of a suite of mammal and pheasant species across four protected areas and nearby community-managed lands in a biodiversity hotspot in northeast India. Over 2.5 years we walked 98 transects (half of which were resampled on a second occasion) across the four paired sites. In addition, we interviewed 84 key informants to understand their perceptions of species trends in these two management regimes. We found that protected areas had higher overall species richness and were important for species that were apparently declining in occurrence. On a site-specific basis, community-managed lands had species richness and occurrences comparable to those of a protected area, and in one case their relative abundances of mammals were higher. Interviewees indicated declines in the abundances of larger-bodied species in community-managed lands. Their observations agreed with our field surveys for certain key, large-bodied species, such as gaur and sambar, which generally occurred less in community-managed lands. Hence, the degree to which protected areas and community-managed lands protect wildlife species depends upon the species in question, with larger-bodied species usually faring better within protected areas.
Absorptive Capacity and Enterprise Performance: An Empirical Study

This is the time of knowledge economy and knowledge becomes an important factor for enterprises. Enterprises external network as the main source of enterprise knowledge, can affect enterprise performance by way of absorptive capacity. Thus, enterprise’s knowledge absorption capacity becomes a hot issue. While, however, most of related researches lack deeply analyses on absorptive capacity, and can’t clearly explain the impact of absorptive capacity on profit performance and innovation performance. Based on 205 valid questionnaires, the paper conducts a further study on enterprise absorptive capacity, profit performance and innovation performance, sets up and verifies the mechanism that absorptive capacity affecting its profit and innovation performance.