Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional correlation between habitat use and leg morphology in birds (Aves)

Many of the morphological features of animals are considered to be adaptations to the habitat that the animals utilize. The habitats utilized by birds vary, perhaps more than for any other group of vertebrates. Here, we study possible adaptations in the morphology of the skeletal elements of the hind limbs to the habitat of birds. Measurements of the lengths of the femur, tibiotarsus and tarsometatarsus of 323 bird species from 74 families are used together with body mass data, taken from the literature. The species are separated into six habitat groups on the basis of literature data on leg use. A discriminant analysis of the groups based on leg morphology shows that swimming birds, wading birds and ground living species are more easily identified than other birds. Furthermore, functional predictions are made for each group based on ecological and mechanical considerations. The groups were tested for deviation from the norm for all birds for three indices of size- and leg-length-independent measures of the bones and for a size-independent-index of leg length. Several of the groups deviate significantly from the norm for one or more of the indices used, suggesting habitat-related adaptations in the leg morphology of birds. The results indicate that stability is an important factor affecting the leg morphology of primarily long-legged birds. The femur seems to be more important than previously thought because several of the groups have high femur indices, suggesting a positive selection pressure on this bone. On a general basis, the results suggest that the effect of leg length should be taken into consideration when discussing adaptations of mass-independent lengths of the long bones of the legs of birds (Less)
Discovering System Health Anomalies Using Data Mining Techniques

We present a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an Integrated System Health Monitoring system. We specifically treat the problem of discovering anomalous features in the time series that may be indicative of a system anomaly, or in the case of a manned system, an anomaly due to the human. Identification of these anomalies is crucial to building stable, reusable, and cost-efficient systems. The framework consists of an analysis platform and new algorithms that can scale to thousands of sensor streams to discovers temporal anomalies. We discuss the mathematical framework that underlies the system and also describe in detail how this framework is general enough to encompass both discrete and continuous sensor measurements. We also describe a new set of data mining algorithms based on kernel methods and hidden Markov models that allow for the rapid assimilation, analysis, and discovery of system anomalies. We then describe the performance of the system on a real-world problem in the aircraft domain where we analyze the cockpit data from aircraft as well as data from the aircraft propulsion, control, and guidance systems. These data are discrete and continuous sensor measurements and are dealt with seamlessly in order to discover anomalous flights. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.
Episodic memory for words enhances the language familiarity effect in talker identification

Speech perception and talker identification are intertwined. Speech from a single talker is recognized more efficiently than speech from multiple talkers; likewise, in the “language familiarity effect”, talker identification is more accurate in one's native language than a foreign one. Models of speech perception implicate episodic memory to explain effects of phonetic variability. We investigated whether these models can also account for the language familiarity effect. Listeners learned to identify voices speaking English and Mandarin in conditions differentially favoring episodic memory: (1) all talkers repeated the same sentences and (2) each talker said completely unique sentences with no repeated words. The language familiarity effect was stronger when talkers' speech had identical content, suggesting that episodic lexical access enhances talker identification in a native language. Foreign-language talker identification did not differ between the conditions, suggesting that episodic memory for voices is filtered by lexical abstraction possible only in a familiar language.
A comparative study and simulation of AODV MANET routing protocol in NS2 & NS3

Networking community is largely depending on simulation to evaluate the performance and behavior of protocols for networks. A network simulator is a program which models the behavior of a network without an actual network being present. Different network simulation tools have been developed during the past years in the field of communication. In this article, present a comparison and simulation study of Adhoc On demand Distance Vector (AODV) MANET routing protocol in two discrete event network simulators namely: NS2 and NS3. The goal is to simulate the performance and compare the two popular network simulators NS2 and NS3 with different aspects. The PING command is also considered as an application level for simulation of AODV routing protocol in MAENT environment.
A Multi-Label Classification Approach for Coding Cancer Information Service Chat Transcripts

National Cancer Institute's (NCI) Cancer Information Service (CIS) offers online instant messaging based information service called LiveHelp to patients, family members, friends, and other cancer information consumers. A cancer information specialist (IS) 'chats' with a consumer and provides information on a variety of topics including clinical trials. After a LiveHelp chat session is finished, the IS codes about 20 different elements of metadata about the session in electronic contact record forms (ECRF), which are to be later used for quality control and reporting. Besides straightforward elements like age and gender, more specific elements to be coded include the purpose of contact, the subjects of interaction, and the different responses provided to the consumer, the latter two often taking on multiple values. As such, ECRF coding is a time consuming task and automating this process could help ISs to focus more on their primary goal of helping consumers with valuable cancer related information. As a first attempt in this task, we explored multi-label and multi-class text classification approaches to code the purpose, subjects of interaction, and the responses provided based on the chat transcripts. With a sample dataset of about 673 transcripts, we achieved example-based F-scores of 0.67 (for subjects) and 0.58 (responses). We also achieved label-based micro F-scores of 0.65 (for subjects), 0.62 (for responses), and 0.61 (for purpose). To our knowledge this is the first attempt in automatic coding of Live-Help transcripts and our initial results on the smaller corpus indicate promising future directions in this task.
Hydropower Production in Future Climate Scenarios: The Case for Kwanza River, Angola

Climate change is altering hydrological processes with varying degrees in various regions of the world and remains a threat to water resources projects in southern Africa. The likely negative impacts of changes in Africa may be worse than in most other regions of the world. This study is an evaluation of the possible impacts of climate change on water resources and hydropower production potential in Kwanza River Basin, Angola. The regional climate data, the basis for future climate scenarios, is used in the hydrological model HBV to assess climate change impacts on water resources in the Kwanza River Basin. Evaluation of changes in hydropower production potential is carried out using an energy model. The simulations show that annual rainfall in 2080 would increase by approximately 16% with increasing inter-annual variability of rainfall and dry season river flow and later onset of the rainy season. The simulation results show that for the Kwanza River Basin the effects as a result of changes in the future climate, in general, will be positive. Consequently, the increase in water resources will lead to increased hydropower production potential in the basin by up to 10%.
Gesture based painting system

Many painting software is available in the software industry. Each of them has its own pros and cons. One of the main disadvantages of these painting software is the need of accurate pointing devices. Here we introduce the Smart Paint, which is a gesture based painting software. It uses the hand movements for drawing. User doesn't need an external hardware pointing device to draw. Instead the different gestures of the fingertips are used for drawing purposes. We designed this system as a primary level software product which aims to provide entertainment to the user. The software can be further upgraded by improving the current features. The disadvantage of the traditional drawing software is that they use traditional drawing devices like mouse light pens etc. Now it overcomes by touch screen technology. The next evolutionary technology that will take over the world will be the gesture technology. By using gestures for communicating with system, we have unlimited ways to pass different messages to the system.
Zonal liver cell heterogeneity : effects of oxygen on metabolic functions of hepatocytes

The morphology and function of hepatocytes are known to vary with position along the liver sinusoids from the portal triad to the central vein. Functions such as urea synthesis and the activity of the cytochrome P-450 system are different, depending on whether the cell is located in a peri-portal zone or in a peri-venous zone. The factors determining this heterogeneous function are not fully understood. Possible explanations of this phenomenon include gradients in hormones, substrates, oxygen, pH, innervation and extracellular matrix composition. Past studies are reviewed, and some recent efforts are described that examine the potential role of oxygen in directly or indirectly inducing differences in hepatocyte function. Partial pressure of oxygen appears to have a role in urea synthesis, lipid metabolism, cytochrome P-450 activity and gluco-neogenesis, which corresponds to in vivo zonal localisation of these functions. Thus, as part of the overall design criteria for a bio-artificial liver device, oxygen distribution at the tissue surface should be included with the more standard criteria, such as optimisation of flow rate, cell dispersal and cell density within these systems. Keywords—Bio-artificial liver, Hepatocyte, Heterogeneity, Oxygen Cel l . Eng., 1996,1 , 125-135
Teknologi Partisipatif Dalam Rangka Penguatan Kelembagaan Pertanian Organik Yang Berkelanjutan

Perkembangan sistem pertanian organik tidak hanya menunjukkan adanya perubahan dalam teknik budidaya tanaman dari sistem pertanian konvensional menjadi organik, namun juga meliputi perubahan-perubahan dalam interaksi dan relasi aktor, tata aturan, nilai, dan norma yang dibangun, dan pengelolaan sumber daya yang dimanfaatkan. Perubahan-perubahan tersebut mendorong terbangunnya kelembagaan yang berperan untuk memperkuat dan meningkatkan manfaat dari sistem pertanian organik dari aspek sosial, ekonomi, maupun ekologi. Kelembagaan pertanian organik yang berkelanjutan diharapkan dapat terbangun untuk menjamin pemanfaatan yang terus-menerus dari penerapan sistem pertanian tersebut. Adapun teknologi partisipatif menjadi upaya untuk mewujudkan dan memperkuat kelembagaan tersebut. Tujuan dari penulisan makalah ini adalah untuk menganalisis penerapan teknologi partisipatif dalam mendukung penguatan kelembagaan pertanian organik yang berkelanjutan. Tulisan ini merupakan hasil dari studi pendahuluan yang dilakukan pada bulan Desember 2014 sampai Mei 2015 di Gabungan Kelompok Tani Simpatik Kabupaten Tasikmalaya. Penelitian dilakukan dengan pendekatan kualitatif dengan metode studi kasus. Pengambilan data dilakukan dengan teknik wawancara mendalam, pengamatan berperan serta dan didukung oleh data sekunder yang relevan. Hasil analisis menunjukkan bahwa proses diskusi melalui metode ORID, upaya membangun konsensus melalui kegiatan lokakarya, dan proses perencanaan aksi melalui workshop merupakan rangkaian teknologi partisipatif dalam pengorganisasian komunitas dan penerapan teknik sosial yang menjadi salah satu alternatif dalam rangka penguatan kelembagaan pertanian organik yang berkelanjutan
The inefficiency of batch training for large training sets

Multilayer perceptrons are often trained using error backpropagation (BP). BP training can be done in either a batch or continuous manner. Claims have frequently been made that batch training is faster and/or more "correct" than continuous training because it uses a better approximation of the true gradient for its weight updates. These claims are often supported by empirical evidence on very small data sets. These claims are untrue, however, for large training sets. This paper explains why batch training is much slower than continuous training for large training sets. Various levels of semi-batch training used on a 20,000-instance speech recognition task show a roughly linear increase in training time required with an increase in batch size.
Effect of Slow Power Control Error on the Reverse Link of OSTBC DS-CDMA in a Cellular System With Nakagami Frequency-Selective MIMO Fading

In this paper, the performance of a multicell orthogonal space-time block-coding direct-sequence code-division-multiple-access system with base station diversity is studied for the reverse link in terms of bit error rate, taking into account the effects of frequency-selective Nakagami-m fading with arbitrary parameters, correlated lognormal shadowing, power control imperfections, selection-based macroscopic diversity, and space-time rake receiver diversity. How the transmitter and receiver antenna configuration setups, the number of rake fingers, and the number of resolvable paths affect the reverse-link capacity of the system is discussed in detail. Analytical results are also given for systems with different processing gains and for propagation environments with different multipath intensity profile distributions
A language identifier for Indonesian and Malay text document

There is huge growth of online text documents in the Internet today. We can easily find documents written in languages from all over part of the just from a single click. Increasing number of online text document in Internet makes the increased availability of information on the Internet. In fact that none in the world can understand all languages of the digital documents. Hence, there is a significant need to have a language identifier to assist user to understand the information. Up to now, the language identification is more focused in European languages and still limited for Asian languages. Whilst the research of language identification for similar languages from popular languages has attracted the attention of many researchers. In this research, a new language identification for language with similar topology, Malay and Indonesian language, is proposed. The algorithm is experimented on a set of Indonesian and Malay text documents to support the limited research of language identification for Asian language. An experiment done on 100 Indonesian and Malay text documents has produced a number of satisfactorily accurate results.
Concept Name Similarity Calculation Based on WordNet and Ontology

Concept name similarity calculation between ontologies is the basis of ontology mapping. Many concept name words are polysemous, but in traditional word similarity algorithms, the similarity between the most similar senses of two words was regarded as the word similarity. It were ignored that the true meanings of concept names in the context, which resulted in falling of the precision of similarity calculation and ontology mapping. A new algorithm was presented in this paper: The true meaning of hypernym concept of an ontology concept and the comment of this concept were seen as two conceptual features. The conceptual WordNet sense the most similar to the features in all senses was determined as the true sense of this concept in ontology context. Each of the two features can generate a corresponding concept sense determination method and a kind of concept name similarity. At last, the weighted sum of the two kinds of similarities was seen as the final concept name similarity. The experimental result showed that our algorithm outperforms other existing algorithms. The ambiguity of the concept name words can be eliminated, and the precision of both of concept name similarity calculation and ontology mapping can be increased dramatically.
A visualization interface for interactive search refinement

It is common practice nowadays to find, assess and explore the Web by groping scattered information presented through many search results. Browsing interfaces and query suggestion techniques attempt to guide the user by providing term recommendations and query phrases. In this paper, we introduce the browsing interface of Kolline, a community search engine under development. Two case studies are described and two distinct web browsing interfaces are analyzed. Based on this analysis, we present a new browsing interface, describing our design decisions and providing directions for future work.
A low complexity hybrid precoding scheme for massive MIMO system

In this paper, we propose a hybrid precoding scheme utilizing traditional linear precoding methods to achieve approximate optimal performance with low complexity. The proposed scheme implements phase control using linear precoding methods at the analog precoding domain and employs the same linear precoding method in the digital domain to maintain the antenna gain. We simulate our proposed schemes in both Rayleigh fading channels and a geometric channel based on the extended Saleh-Valenzuela model. The simulation results prove that the proposed scheme can achieve desirable performance in both channel models and has flexibility for various network conditions by combining with different linear precoding methods.
Learning about belief about inflation target and Stabilisation Policy

We analyse the interaction between the lack of credibility (or uncertainty) regarding central bank’s inflation target and central bank’s stabilisation policy. We consider a model in which private sector is uncertain about the bank’s inflation target and update its beliefs on it. Furthermore, we assume that the bank cannot observe the private-sector belief about the inflation target as well as fundamental economic shocks, and has to estimate them. We consider the implications of this ‘double-guessing’ about inflation target. We show that this uncertainty generates a negative feedback on stabilisation policy by making the bank’s estimation of economic shocks more dicult. Also, it generates inflation persistence even when shocks are purely transitory. We also draw some implications of movements in yield curves in response to monetary policy changes. Very preliminary. Please do not circulate. Appendices missing.
Semantic subspace learning for text classification using hybrid intelligent techniques

A vast data repository such as the web contains many broad domains of data which are quite distinct from each other e.g. medicine, education, sports and politics. Each of these domains constitutes a subspace of the data within which the documents are similar to each other but quite distinct from the documents in another subspace. The data within these domains is frequently further divided into many subcategories. In this paper we present a novel hybrid parallel architecture using different types of classifiers trained on different subspaces to improve text classification within these subspaces. The classifier to be used on a particular input and the relevant feature subset to be extracted is determined dynamically by using maximum significance values. We use the conditional significance vector representation which enhances the distinction between classes within the subspace. We further compare the performance of our hybrid architecture with that of a single classifier - full data space learning system and show that it outperforms the single classifier system by a large margin when tested with a variety of hybrid combinations on two different corpora. Our results show that subspace classification accuracy is boosted and learning time reduced significantly with this new hybrid architecture.
Decentralized Control of Large Scale Systems Modeled with Continuous Marked Graphs

Abstract This paper addresses the decentralized control of large scale systems modeled with timed continuous Marked Graphs ( ContMG ). Decentralized structures are first obtained by decomposing the system into subnets by cutting the original net through sets of places, and adding marking structurally implicit places. Then, first local control laws are computed separately. Later, algorithms are proposed to make the locally computed laws to be compatible and fireable when the global state is considered. It is proved that using the control laws computed with the proposed algorithms, the final state of the overall system can be reached in minimum time. A manufacturing system is taken as case study to illustrate the control method.
Towards a Typology of Green IS Strategies: Insights from Case Study Research

In this paper we develop a typology of Green IS strategies and address two research questions: 1) What different types of Green IS strategies can be identified in a real-life context? 2) How do firms with distinct Green IS strategies conceive the role of Green IS within their organization? We begin with the a priori definition of "Green IS strategy" and conduct an exploratory case study that examines multinational companies from different industries. Across these cases, four distinct Green IS strategies are identified as a result of a within-case analysis and the emerging patterns are more closely defined using a cross-case analysis. Insights are compared with extant literature and findings are consolidated in five propositions that outline characteristics of Green IS strategies. These propositions guide the conceptualization of a typology of Green IS strategies which illustrates four generic strategies: Green IS for Efficiency, for Innovation, for Transformation, and for Credibility.
The mediating effect of parental involvement between family socioeconomic status and academic performance: Meta-analysis structural equation modeling

: A meta-analysis Structural equation modelling was conducted to investigate the relationship between family socioeconomic status, parental involvement and academic performance of students on the basic of family stress model and family investment model. Analysis of 25 studies ( N = 79665) met the criteria for inclusion in the meta-analysis. The result of meta-analysis showed: family socioeconomic status had medium positive correlation to academic performance of students. Moderator analyses revealed that measurement method had significant moderating effects on the relationship between family socioeconomic status and academic performance, and the moderating effects of grade was not significant. Mediation analysis revealed that parental involvement plays partial mediation effect between family socioeconomic status and academic performance. Moreover, partial mediation effect of parental involvement was significant when students were from kindergarten or elementary school. The results suggest that family socioeconomic status is an important influencing factor to academic performance from kindergarten to secondary school. And before secondary school, family socioeconomic status partially affects academic performance by parental involvement.