Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender identities and farm survival: Women's activities in the agricultural sector in remote rural areas

Despite the structurally deterministic leanings of the literature about women and farms over time, new studies have attempted to identify the ways in which women’s positions in the rural household are reinforced, stressing the factor of visibility in the labor force. The entrepreneurial processes and commodification practices through which women engage in a range of different kinds of productive activity, such as agrotourism and the production of organic crops, are frequently cited as sources of empowerment for women on farms . However, since this new trend is not a unitary and homogeneous process, the present study examines the role of women in agriculture by taking into account the positionality of the specific actors, as well as their conflicts and negotiations within particular sociocultural contexts. Our analysis is based on sets of in-depth interviews with full-time farm women in Jirestan, north-eastern Iran. Findings indicate that Jirestan’s women identify themselves both as “producers” and “entrepreneurs. ” Commercial gardens, in addition to new farming styles and the reproduction of sales of traditional local produce, have created this “hybrid” identity. Although farms are being re-constructed as shared businesses with an increase in the role of women, the latter still adhere to patriarchal traditions and social norms and consider themselves helpers and housewives.
Cluster-based RF fingerprint positioning using LTE and WLAN outdoor signals

In this paper we evaluate user-equipment (UE) positioning performance of three cluster-based RF fingerprinting methods using LTE and WLAN signals. Real-life LTE and WLAN data were collected for the evaluation purpose using consumer cellular-mobile handset utilizing `Nemo Handy' drive test software tool. Test results of cluster-based methods were compared to the conventional grid-based RF fingerprinting. The cluster-based methods do not require grid-cell layout and training signature formation as compared to the grid-based method. They utilize LTE cell-ID searching technique to reduce the search space for clustering operation. Thus UE position estimation is done in short time with less computational cost. Among the cluster-based methods Agglomerative Hierarchical Cluster based RF fingerprinting provided best positioning accuracy using a single LTE and six WLAN signal strengths. This method showed an improvement of 42.3 % and 39.8 % in the 68th percentile and 95th percentile of positioning error (PE) over the grid-based RF fingerprinting.
Distributed intrusion detection scheme for wireless Ad-Hoc Networks: A review

Wireless adhoc network is a network which does not have a fixed infrastructure; it works in open medium so it is more prone to attacks. Security is main element for a network to show good performance. Intrusion detection is to protect network from an unknown or known attack. This system acts as a second line of defense for adhoc networks. In this paper we study about the adhoc network, its characteristics, about its attacks, routing protocols and show there insecurity due to attacks. Most importantly we present distributed intrusion detection system of adhoc networks and give a comparative study with the existing intrusion detection system (IDS).
An efficient approach for Color Image Retrieval using Haar wavelet

Content-Based Image Retrieval (CBIR) allows automatic extraction of target images from a database of images according to objective visual contents of the image itself. Texture analysis is a popular operation for CBIR. In this paper we propose a texture analysis based scheme for Color Image Retrieval. We use Haar wavelet to decompose color images into multilevel scale and wavelet coefficients. From the data of low frequency channel we extract features by means of F-norm theory and from data of high frequency channels we extract statistical features by computing gradient directions. Feature vectors extracted from images are then subjected to fuzzy logic based similarity comparison to obtain degree of similarity. A database of 100 color images each of size 256 × 256 pixels is used for evaluation. The results are encouraging with best retrieval accuracy of 90.33%.
Changing partners in the dark: isotopic and molecular evidence of ectomycorrhizal liaisons between forest orchids and trees

In the mycorrhizal symbiosis, plants exchange photosynthates for mineral nutrients acquired by fungi from the soil. This mutualistic arrangement has been subverted by hundreds of mycorrhizal plant species that lack the ability to photosynthesize. The most numerous examples of this behaviour are found in the largest plant family, the Orchidaceae. Although these non-photosynthetic orchid species are known to be highly specialized exploiters of the ectomycorrhizal symbiosis, photosynthetic orchids are thought to use free–living saprophytic, or pathogenic, fungal lineages. However, we present evidence that putatively photosynthetic orchids from five species which grow in the understorey of forests: (i) form mycorrhizas with ectomycorrhizal fungi of forest trees; and (ii) have stable isotope signatures indicating distinctive pathways for nitrogen and carbon acquisition approaching those of non–photosynthetic orchids that associate with ectomycorrhizal fungi of forest trees. These findings represent a major shift in our understanding of both orchid ecology and evolution because they explain how orchids can thrive in low–irradiance niches and they show that a shift to exploiting ectomycorrhizal fungi precedes viable losses of photosynthetic ability in orchid lineages.
Effective resource multiplexing for scientific workflows

Scientific workflows feature complex precedence constraints that are mostly dictated by data dependencies between tasks. The inter-task communication (data staging) in these complex workflow applications incurs significant overheads resulting in a major hindering factor of high performance and effective resource utilization. As the scale of these applications becomes increasingly large due primarily to the recent explosive growth of data, addressing this hindrance is of great practical importance. In this paper, we present a resource multiplexing (RM) technique, which leverages data staging aiming to minimize idle times between execution of tasks due to inter-task communication overheads. In particular, we incorporate RM into our DEWE framework 1 with making a set of extensions to the framework. The rationale behind RM is each slot or core pairs up the actual workflow task and the RM-enabled file loading DEWE extension (File Client) in the way that their resource usage is complementary. We demonstrate the efficacy of our multiplexing technique in a data-intensive computing environment using an astronomy application. Our results from experiments conducted in Amazon EC2 demonstrate that our multiplexing technique is effective with the reduction in resource idle time between jobs by 57% on average and up to 91%.
MRT/MRC for cognitive AF relay networks under feedback delay and channel estimation error

In this paper, we examine the performance of multiple-input multiple-output (MIMO) cognitive amplify-and-forward (AF) relay systems with maximum ratio transmission (MRT). In particular, closed-form expressions in terms of a tight upper bound for outage probability (OP) and symbol error rate (SER) of the system are derived when considering channel estimation error (CEE) and feedback delay (FD) in our analysis. Through our works, one can see the impact of FD and CEE on the system as well as the benefits of deploying multiple antennas at the transceivers utilizing the spatial diversity of an MRT system. Finally, we also provide a comparison between analytical results and Monte Carlo simulations for some examples to verify our work.
Phytophthora ramorum and Sudden Oak Death in California: II. Transmission and Survival 1

The newly discovered Phytophthora ramorum canker disease of oak (Sudden Oak Death Syndrome) threatens millions of acres of California woodlands where coast live oak (Quercus agrifolia), tanoak (Lithocarpus densiflorus), or black oak (Quercus kelloggii) are dominant species. An important step in controlling this disease involves understanding how it is spread. The presence of diseased oaks at all elevations on hillsides and the above-ground nature of the disease suggest wind-blown rain or rain splash as a common mechanism for movement of spores. Although viable spores have yet to be found on infected oak tissue, other hosts may serve as sources of rain-dispersed inoculum. In the laboratory, abundant sporangia form on moistened leaves of infected bay (Umbellularia californica) and Rhododendron spp. within 72 hours. These sporangia break off and easily disperse in water. Chlamydospores were also observed on the surface of moistened bay leaves. Consistent with these results, P. ramorum has been recovered from rain, soil, litter, and stream water from woodlands with infected oak and bay trees. Spores of P. ramorum do not survive drying, but in moist conditions can survive for at least one month.
2015 Ieee International Conference on Big Data (big Data) Recommending Forum Posts to Designated Experts

—There are users who generate significant amounts of domain knowledge in online forums or community question and answer (CQA) websites. Existing literature defines them as 'experts. ' These users attain such statuses by providing multiple relevant answers to the question askers. Past works have focused on recommending relevant posts to these users. With the rise of web forums where certified experts answer questions, strategies that are tailored towards addressing the new type of experts will be beneficial. In this paper, we identify a new type of user called 'designated experts' (i.e., users designated as domain experts by the web administrators). These are the experts who are guaranteed by web administrators to be an expert in a given domain. Our focus is on how we can capture the unique behavior of designated experts in an online domain. We have noticed designated experts have different behaviors compared to CQA experts. In particular, unlike existing CQAs, only one designated expert responds to any given thread. To capture this intuition, we introduce a matrix factorization algorithm with regularization to capture the behavior. Our results show that the regularization method improves the performance significantly compared to the baseline approach.
Ordered Classes and Incomplete Examples in Classification

The classes in classification tasks often have a natural ordering, and the training and testing examples are often incomplete. We propose a nonlinear ordinal model for classification into ordered classes. Predictive, simulation-based approaches are used to learn from past and classify future incomplete examples. These techniques are illustrated by making prognoses for patients who have suffered severe head injuries.
Is learning to rank effective for Web search ?

LETOR, the benchmark collection for learning to rank, helps make comparative study on different approaches in experimental research. Since the collection is constructed mainly based on TREC datasets, queries and documents in LETOR differ from true Web search scenario on some aspects, such as its incomplete link information, limited documents’ domain, and lack of user click information. Hence the observations derived by the collection could be different from that in real Web environment. This paper empirically studies the effectiveness of the state-of-art learning to rank algorithms, especially in Web search scenario. Besides LETOR, a Web search collection is constructed based on the search log of a commercial search engine. Five approaches have been studied, including linear regression, RankBoost, ListNet, top k optimization of ListMLE, and SVM-MAP. Comparative study has been made among algorithms and across different datasets. Furthermore, the effects of learning to rank algorithms are compared with that of content-based and link-based ranking features. Essential differences have been observed and analyzed in the paper in terms of the effectiveness and stability of the algorithms and the feature selection. We believe this study will help the Web search community for better knowledge about whether and to what extent learning to rank algorithms are effective in real applications.
Point Based Rendering of Implicit 4-Dimensional Surfaces

We present a point based rendering algorithm that uses hyper-cubes to perform spatial subdivision in a 4D volume. A 4D function interval exclusion test is used to speed up the rendering of 4D Implicit surfaces in this hyper-volume. A 4D orthonormal basis function is used to define a 4D camera, which projects isometric or perspective views onto a plane in 4D for viewing. The technique requires evaluations of 4D implicit surface functions and gradients to render shaded images. The technique also allows for hidden surface elimination using a z-buffer modified for use in a 4D space. We give examples of its use in rendering some 4D surfaces and discuss problems with the technique. The algorithm can be generalised to higher dimensions.
UNSTRUCTURED MESHING FOR TWO ASSET BARRIER

Discretely observed barriers introduce discontinuities in the solution of two asset option pricing partial diierential equations (PDE's) at barrier observation dates. Consequently, accurate solution of the pricing PDE requires a ne mesh spacing near the barriers. Non-rectangular barriers pose diiculties for nite diierence methods using structured meshes. We show that the nite element method (FEM) with standard unstructured meshing techniques can lead to signiicant eeciency gains over structured meshes with a comparable number of vertices. The greater accuracy achieved with unstructured meshes is shown to more than compensate for a greater solve time due to an increase in sparse matrix condition number. Results are presented for a variety of barrier shapes, including rectangles, ellipses, and rotations of these shapes.
Supervisor ostracism and defensive silence: a differential needs approach

ABSTRACT This study investigates the sequential mediating effects of threats to efficacy needs and defensive silence between supervisor ostracism and emotional exhaustion, explained through need-threat/need-fortification framework. We collect time-lagged data at two measurement points from 300 employees working in service sector organizations in Pakistan. We find that supervisor ostracism threatens employees’ efficacy needs which results in reduced evaluation of resources. Consequently, employees seek to fortify these endangered needs through defensive silence, a proactive and self-protective behaviour. However, defensive silence affects employees’ trust, morale, motivation and eventually elicits emotional exhaustion. Our results show that supervisor ostracism, threat to efficacy needs and defensive silence contribute towards emotional exhaustion, and we offer several corrective options. We believe that one direct path involves actions that discourage supervisor ostracism through training and role plays. Another indirect step highlights competence of firm to create a perception of high status and influence. It may even involve managers to improve employees’ perception of work control through job redesign. Finally, we propose that supervisors may anticipate motives for defensive silence and plan targeted strategies to facilitate employees’ psychological safety.
Software partitioning for effective automated unit testing

A key problem for effective unit testing is the dificulty of partitioning large software systems into appropriate units that can be tested in isolation. We present an approach that identifies control and data inter-dependencies between software components using static program analysis, and divides the source code into units where highly-intertwined components are grouped together. Those units can then be tested in isolation using automated test generation techniques and tools, such as dynamic software model checkers. We discuss preliminary experimental results showing that automatic software partitioning can significantly increase test coverage without generating too many false alarms caused by unrealistic inputs being injected at interfaces between units.
Decentralized cooperative compressed spectrum sensing for block sparse signals

In cognitive radio (CR), compressed sensing (CS) and cooperative sensing are two emerging techniques enabling sub-Nyquist sampling for wide-band sensing and improving the detection performance under fading channels. In this paper, a decentralized cooperative compressed spectrum sensing scheme for block sparse signals is proposed. The traditional block sparse assumption of the multi-band signal is extended to a generalized case viewing the fact that each occupied block may both have zero and nonzero elements. Thus, a Binary Tree-based Block Adaptive Matching Pursuit (BT-BAMP) algorithm is presented at local CR to achieve more accurate detection results. BT-BAMP estimates the signal supports by separating each candidate block into two half smaller sub-blocks iteratively until the final block size is one. At each block size level, adaptive block updating and backtracking based on greedy pursuit are employed to estimate the block sparsity of the multi-band signal, where the block detection results are exchanged among the cognitive radio network (CRN) and merged as a priori information for local iteration. The efficiency and superiority of the proposed approach are validated via simulations.
Design of high gain multiple U-slot microstrip patch antenna for wireless system

In this paper, a novel multi-slotted microstrip patch antenna with high gain is presented and discussed. The design adopts contemporary techniques such as probe feeding and multi-slotted patch. These techniques can contribute to the enhanced performance of the antenna. The design also employs a novel shape patch. By integrating these techniques the proposed design offers low profile, high gain and compact antenna element. The maximum gain at the resonant frequency of 2.45GHz is 11.35dBi. The lowest return loss can be −34.49 dB at 2.45GHz. The proposed design has a simple structure and a compact dimension of 87mm*51mm. The proposed design is suitable for particular wireless communication application such as WiFi and WLAN.
Reliable and Efficient Inference of Bayesian Networks from Sparse Data by Statistical Learning Theory

To learn (statistical) dependencies among random variables requires exponentially large sample size in the number of observed random variables if any arbitrary joint probability distribution can occur. We consider the case that sparse data strongly suggest that the probabilities can be described by a simple Bayesian network, i.e., by a graph with small in-degree \Delta. Then this simple law will also explain further data with high confidence. This is shown by calculating bounds on the VC dimension of the set of those probability measures that correspond to simple graphs. This allows to select networks by structural risk minimization and gives reliability bounds on the error of the estimated joint measure without (in contrast to a previous paper) any prior assumptions on the set of possible joint measures. The complexity for searching the optimal Bayesian networks of in-degree \Delta increases only polynomially in the number of random varibales for constant \Delta and the optimal joint measure associated with a given graph can be found by convex optimization.
Multiplierless FIR Filter Implementation on FPGA

Area complexity in the algorithm of finite impulse response (FIR) filter is mainly caused by multipliers. Among the multiplierless techniques of FIR filter, Distributed Arithmetic is most preferred area efficient technique. In this technique, precomputed values of inner product are stored in LUT, which are further added and shifted with number of iterations equal to the precision of input samples. But the exponential growth of LUT with the order of FIR filter, in its basic structure, makes it prohibitive for many applications. An improvement over the basic DA structure is presented in this paper, by the use of slicing of LUT to the desired length. An architecture of 16 tap FIR filter is presented, with different length of slice of LUT. Design implementation and synthesis result shown the improvement in speed of operation as well as saving in area, with more number of slices. Found drastic improvement in speed, when compared with earlier result.
Using aspect-oriented state machines for detecting and resolving feature interactions

Composing different features in a software system may lead to conflicting situations. The presence of one feature may interfere with the correct functionality of another feature, resulting in an incorrect behavior of the system. In this work we present an approach to manage feature interactions. A formal model, using Finite State Machines (FSM) and Aspect-Oriented (AO) technology, is used to specify, detect and resolve features interactions. In fact aspects can resolve interactions by intercepting the events which causes troubleshoot. Also a Domain-Specific Language (DSL) was developed to handle Finite State Machines using a pattern matching technique.
Enhanced Reputation Scoring for Online Auctions

To handle the uncertainty inherent in eCommerce transactions, reputation systems have emerged as a way to represent reliability and develop trust between transaction participants. Despite the value added by reputation systems, limitations of existent systems remain (Malaga 2001). We empirically test Porter et al.’s (2004) reputation scoring procedure, which was designed to address the shortcomings of current systems. This study uses computer simulation to replicate the auction process between buyers and sellers with reputation scores calculated using both the Porter et al. model and the eBay auction model. Results from the two models are then analyzed to show that the Porter et al. model more accurately estimates reputation scores.
Effective nearest neighbor search for aligning and merging range images

We describe a novel method, which extends the search algorithm of a k-d tree for aligning and merging range images. If the nearest neighbor point is far from a query, many of the leaf nodes must be examined during the search, which actually will not finish in logarithmic time. However, such a distant point is not as important as the nearest neighbor in many applications, such as aligning and merging range images; the reason for this is either because it is not consequently used or because its weight becomes very small. Thus, we propose a new algorithm that does not search strictly by pruning branches if the nearest neighbor point lies beyond a certain threshold. We call the technique the bounds-overlap-threshold (BOT) test. The BOT test can be applied without recreating the k-d tree if the threshold value changes. Then, we describe how we applied our new method to three applications in order to analyze its performance. Finally, we discuss the method's effectiveness.
Toward a Silent Speech Interface based on Unspoken Speech

This work aims to interpret the EEG signals associated with actions to imagine the pronunciation of words that belong to a reduced vocabulary without moving the articulatory muscles and without uttering any audible sound (unspoken speech). Specifically, the vocabulary reflects movements to control the cursor on the computer. We have recorded EEG signals from 21 subjects using a markers based basic protocol. The discrete wavelet transform (DWT) is used to extract features from the delimited windows, and a subset of them with frequency ranges below 32 Hz is further selected. These subsets are used to train four classifiers: Naive Bayes (NB), Random Forests (RF), support vector machine (SVM), and Bagging-RF. The results are still preliminary but encouraging because the accuracy rates are above 20%, i.e. up to chance for five classes. The implementation process as well as some experiments with their corresponding results are shown.