Miracle-dz/UniFAR-FTUs huggingface dataset:
The design of a live social observatory system

With the emergence of social networks and their potential impact on society, many research groups and originations are collecting huge amount of social media data from various sites to serve different applications. These systems offer insights on different facets of society at different moments of time. Collectively they are known as social observatory systems. This paper describes the architecture and implementation of a live social observatory system named 'NExT-Live'. It aims to analyze the live online social media data streams to mine social senses, phenomena, influences and geographical trends dynamically. It incorporates an efficient and robust set of crawlers to continually crawl online social interactions on various social network sites. The data crawled are stored and processed in a distributed Hadoop architecture. It then performs the analysis on these social media streams jointly to generate analytics at different levels. In particular, it generates high-level analytics about the sense of different target entitles, including People, Locations, Topics and Organizations. NExT-Live offers a live observatory platform that enables people to know the happenings of the place in order to lead better life.
PENERAPAN METODE PEMBELAJARAN MANDIRI DALAM MENINGKATKAN HASIL BELAJAR PESERTA DIDIK (Studi pada Program Pendidikan Kesetaraan Paket C di PKBM Bina Mandiri Cipageran)

Penelitian ini dilakukan dengan mengacu pada permasalahan yang dihadapi dengan latar belakang yang berbeda, sehingga berdampak pada motivasi dan pengalaman belajarnya. Metode yang diterapkan pada penelitian ini yaitu metode pembelajaran mandiri. Metode pembelajaran mandiri dilaksanakan untuk mengatasi permasalahan tersebut sehingga peserta didik memiliki motivasi yang lebih dan pengalaman belajar yang lebih banyak, pembelajaran dapat dilakukan dengan baik dan peserta didik akan lebih mandiri. PKBM Bina Mandiri Cipageran merupakan salah satu lembaga yang menyelenggarakan pendidikan kesetaraan menggunakan metode pembelajaran mandiri. Tujuan penelitian ini secara umum adalah untuk mengetahui sejauh mana penerapan metode pembelajaran mandiri dalam meningkatkan hasil belajar peserta didik. Secara khusus, tujuan penelitian ini adalah mendeskripsikan langkah-langkah penerapan metode pembelajaran mandiri yang meliputi perencanaan, pelaksanaan dan evaluasi, serta mendeskripsikan hasil penerapan metode pembelajaran mandiri. Penelitian ini dilakukan dengan menggunakan metode deskriptif dengan pendekatan kualitatif, dengan subjek penelitian seorang pengelola, dua orang selaku tutor kesetaraan paket C dan dua orang peserta didik paket C. Adapun teknik pengumpulan data yang digunakan adalah wawancara, observasi dan studi dokumentasi serta triangulasi data sebagai telaah untuk melihat keabsahan data. Hasil penelitian diperoleh dari data informasi mengenai pembelajaran mandiri yang dilakukan setelah pembelajaran tatap muka dan tutorial dan dirancang dalam bentuk pemberian tugas mandiri melalui modul atau buku. Selanjutnya untuk melihat hasil penerapan metode mandiri ini peneliti melakukan tes kognitif, afektif dan psikomotor, dan dari hasil tes tersebut dapat diketahui bahwa ada peningkatan dari aspek kognitif, afektif dan psikomotor.
A Divide and Conquer Approach for Construction of Large-Scale Signaling Networks from PPI and RNAi Data Using Linear Programming

Inference of topology of signaling networks from perturbation experiments is a challenging problem. Recently, the inference problem has been formulated as a reference network editing problem and it has been shown that finding the minimum number of edit operations on a reference network to comply with perturbation experiments is an NP-complete problem. In this paper, we propose an integer linear optimization (ILP) model for reconstruction of signaling networks from RNAi data and a reference network. The ILP model guarantees the optimal solution; however, is practical only for small signaling networks of size 10-15 genes due to computational complexity. To scale for large signaling networks, we propose a divide and conquer-based heuristic, in which a given reference network is divided into smaller subnetworks that are solved separately and the solutions are merged together to form the solution for the large network. We validate our proposed approach on real and synthetic data sets, and comparison with the state of the art shows that our proposed approach is able to scale better for large networks while attaining similar or better biological accuracy.
Table-centric interactive spaces for real-time collaboration

Tables have historically played a key role in many real-time collaborative environments, often referred to as "war rooms". Today, these environments have been transformed by computational technology into spaces with large vertical displays surrounded by numerous desktop computers. However, despite significant research activity in the area of tabletop computing, very little is known about how to best integrate a digital tabletop into these multi-surface environments. In this paper, we identify various design requirements for the implementation of a system intended to support such an environment. We then present a set of designs that demonstrate how an interactive tabletop can be used in a real-time operations center to facilitate collaborative situation-assessment and decision-making.
Neural network-based software for fertilizer optimization in precision farming

A novel technique for providing fertilizer recommendation in precision agriculture is proposed. The method is based on the maximization of the profit function approximated using a decision support system based on artificial neural networks. The software implementation of the proposed approach is described and its use is illustrated on simulated realistic data. Experimental results suggest that the proposed technique is applicable for site-specific crop management.
Fast single frame super-resolution using scale-invariant self-similarity

Example-based super-resolution (SR) attracts great interest due to its wide range of applications. However, these algorithms usually involve patch search in a large database or the input image, which is computationally intensive. In this paper, we propose a scale-invariant self-similarity (SiSS) based super-resolution method. Instead of searching patches, we select the patch according to the SiSS measurement, so that the computational complexity is significantly reduced. Multi-shaped and multi-sized patches are used to collect sufficient patches for high-resolution (HR) image reconstruction and a hybrid weighting method is used to suppress the artifacts. Experimental results show that the proposed algorithm is 20~1,800 times faster than several state-of-the-art approaches and can achieve comparable quality.
Comparing OpenFlow Controller Paradigms Scalability: Reactive and Proactive

The OpenFlow architecture is a proposal from the Clean Slate initiative to define a new Internet architecture where the network devices are simple, and the control and management plane is performed by a centralized controller. The simplicity and centralization architecture makes it reliable and inexpensive, but the centralization causes problems concerning controller scalability. An OpenFlow controller has two operations paradigm: reactive and proactive. This paper proposes a methodology to evaluate OpenFlow controller performance. The performances of both paradigms were analyzed in different known controllers. The performance evaluation was done in a real environment and emulation. It was tested distinct OpenFlow controller and using a different amount of OpenFlow devices. Finally, we will present some conclusions about the controller scalability.
Feedforwarding Channel State Information in Relay Networks

Channel estimation via training is a common method for provi ding imperfect channel state information (CSI) in a network. In a relay network, imperfect CSI c an be efficiently provided at the relays by sending training signals from the source and the destinat io nodes to the relays. Providing imperfect CSI at the destination is more challenging since each relay i s required to send training signals to the destination. We introduce the feedforwarding method, wher e each relay simply forwards the imperfect CSI to the destination in such a way that we benefit from the relay n etwork structure to carry the information for us. This method significantly lowers the network resourc es spent on providing the imperfect CSI at the destination. In addition, we prove that the feedforward ing method provides better average effective signal to noise ratio (eSNR) compared to the training method s currently known in the literature. The eSNR is an important parameter in the channel capacity expre ssions. Therefore, the parameters of a network with training could be optimized in order to maximiz e the eSNR. In our setup, source, relay and destination nodes have M , N andM antennas, respectively. We use matched filtering at the rela ys and single antenna decoding at the destination, where each s ource antenna communicates to a separate destination antenna via K relays. We achieve our results at large K. Index Terms Relay networks, imperfect channel state information (CSI) , training. C. M. Yetis is with Informatics Institute, Satellite Remote S nsing and Communication, Istanbul Technical University , Maslak, Istanbul, 34469, TURKEY. Email: cenkmyetis@yahoo.com. Th e author is supported in part by The Scientific and Technologi cal Research Council of Turkey (TUBITAK). The author is on leave at University of California, Irvine. A. H. Kayran is with Department of Electronics and Communica tions Engineering, Istanbul Technical University, Maslak , Istanbul, 34469, TURKEY. Email: kayran@itu.edu. tr. November 8, 2008 DRAFT
Cancerous stem cells can arise from pediatric brain tumors

Pediatric brain tumors are significant causes of morbidity and mortality. It has been hypothesized that they derive from self-renewing multipotent neural stem cells. Here, we tested whether different pediatric brain tumors, including medulloblastomas and gliomas, contain cells with properties similar to neural stem cells. We find that tumor-derived progenitors form neurospheres that can be passaged at clonal density and are able to self-renew. Under conditions promoting differentiation, individual cells are multipotent, giving rise to both neurons and glia, in proportions that reflect the tumor of origin. Unlike normal neural stem cells, however, tumor-derived progenitors have an unusual capacity to proliferate and sometimes differentiate into abnormal cells with multiple differentiation markers. Gene expression analysis reveals that both whole tumors and tumor-derived neurospheres express many genes characteristic of neural and other stem cells, including CD133, Sox2, musashi-1, bmi-1, maternal embryonic leucine zipper kinase, and phosphoserine phosphatase, with variation from tumor to tumor. After grafting to neonatal rat brains, tumor-derived neurosphere cells migrate, produce neurons and glia, and continue to proliferate for more than 4 weeks. The results show that pediatric brain tumors contain neural stem-like cells with altered characteristics that may contribute to tumorigenesis. This finding may have important implications for treatment by means of specific targeting of stem-like cells within brain tumors.
SEMI-INFINITE MULTIOBJECTIVE PROGRAMMING WITH GENERALIZED INVEXITY

Motivated by important applications, the theory of mathematical programming has been extended to the case of infinitely many restrictions. At the same time, this theory knew remarcable developments since invexity and its further generalizations have been introduced as substitute of convexity. Here, we consider the multiobjective programming with a set of restrictions indexed in a compact. We obtain optimality criteria of Kuhn-Tucker type under new weaker invexity conditions. Also some dual probles are introduced and it is proved that the weak and strong duality properties hold within the same environement. AMS 2000 Subject Classification: 90C34, 90C29.
Ferroportin/IREG-1/MTP-1/SLC40A1 modulates the uptake of iron at the apical membrane of enterocytes

Background: Absorption of non-haeme iron occurs mainly in the duodenum. It involves the divalent metal transporter 1 (DMT1) in the uptake of ferrous Fe(II) iron and the basolateral transporter ferroportin/IREG-1/MTP-1/SLC40A1 in its release. Whether ferroportin functions in this process at other sites in the enterocyte is unknown. In this study the effect of a blocking antibody to ferroportin on the uptake and release of iron was evaluated in enterocyte-like cells (IEC-6 and Caco-2) and in freshly isolated duodenal enterocytes from rats. Methods: Uptake of 1 μM Fe(II) and its release by cells was studied in the presence of the antibody. Ferroportin expression was determined by western blot analysis of duodenal mucosa enriched microvillus membranes, Caco-2 cells, IEC-6 cells, and freshly isolated enterocytes. Immunofluorescent detection of ferroporitn was performed on frozen sections of duodenum from rats with variations in body iron stores. Results: Ferroportin was expressed in all cell types. In these cells, the antibody significantly reduced (p<0.05) uptake of Fe(II) by 40–50% but had no effect on the release of iron. In Caco-2 cells, Fe(II) uptake was reduced only when the antibody was in contact with the apical membrane. Ferroportin protein was enriched in microvillus membrane preparations. In enterocytes from iron deficient rats, ferroportin was expressed along the brush border where it colocalised with lactase. Ferroportin was seen in the basal cytoplasm and along the basolateral membranes. Iron loading markedly reduced intracellular expression of ferroportin. In Caco-2 cells, ferroportin also localised to the microvillus and lateral and basal membranes. Conclusions: In addition to release, ferroportin functions in the uptake of iron at the apical membrane, possibly by modulating the activity of DMT1.
Cox's Proportional Hazards Model with Lp Penalty for Biomarker Identification and Survival Prediction

Advances in high throughput technology provide massive high dimensional data. It is very important and challenging to study the association of genes with various clinical outcomes. Due to large variability in time to certain clinical event among patients, studying possibly censored survival data can be more informative than classification. We proposed the Cox's proportional hazards model with Lp penalty method for simultaneous feature (gene) selection and survival prediction. Lp penalty shrinks coefficients and produces some coefficients that are exactly zero. It has been shown that Lp (p < 1) regularization performs better than L1 in the regression and classification framework (Knight & Fu 2000, Liu et al. 2007). Experimental results with different data demonstrate that the proposed procedures can be used for identifying important genes (features) that are related to time to death due to cancer and for building parsimonious model for predicting the survival of future patients.
Temperature reduction analysis in Sentry Tag cache systems

Power and temperature management continue to impose challenging issues in high-performance processor design. Processor power density is growing and has made building efficient cooling systems expensive. While Dynamic Thermal Management (DTM) techniques aim at reducing cooling systems cost, previously suggested low-temperature design could potentially provide further temperature reductions or help DTM operate more effectively. Different processor components have different levels of impact on the overall temperature. Meantime, reducing the temperature of different processor components could result in both transient and steady temperature changes. In this paper, we study the temperature impact of exploiting Sentry Tag caches on cache temperature. We show that by using the Sentry Tag it is possible to reduce cache temperature effectively. In addition, we show that using Sentry Tag can impact transient temperature behavior which in turn enhances DTM efficiency.
Baobab ( Adansonia digitata L.) seed protein utilization in young albino rats I: Biochemical ingredients and performance characteristics

Raw, cooked and HCl-extracted baobab, Adansonia digitata seed meals were used for biological and nutritional evaluation studies. The seed is low in protein (16.60g/100g DM) but could be a good source of oil (17.50g/100g) and minerals, particularly sodium, potassium and phosphorus, which contained 228.0, 1429.0 and 924.5 mg/100gDM respectively. Low levels of antinutritional factors such as tannin, phytate, cyanide, oxalate, nitrate/nitrite and absence of trypsin inhibitors were observed. Seed protein is high in sulfur-amino acid, with a chemical score (CS) of 126.80, but marginally limiting in lysine and threonine, with CS of 64.31 and 85.59 respectively based on the preschool age (2-5yrs) reference protein requirement. The seed oil contain appreciable level of unsaturated fatty acids with oleic and linoleic acids making up 66.32% of total fatty acids. The raw diet was similar to the casein diet in weight gain, feed intake, net protein retention (NPR) and true digestibility (TD) but significantly inferior in protein efficiency ratio (PER). Cooking did not have any significant effect on feed intake but significantly lowered the weight gain relative to the raw and casein diets. HCl-extracted meal exerted significantly lower weight gain compared to the raw, cooked and casein diets. It is concluded that the raw seed showed promise as a source of food supplement and is likely to be satisfactory in supporting growth and maintenance in livestock feeding.
Feature-Based Volumetric Terrain Generation and Decoration

Two-dimensional height fields are the most common data structure used for storing and rendering of terrain in offline rendering and especially real-time computer graphics. By its very nature, a height field cannot store terrain structures with multiple vertical layers such as overhanging cliffs, caves, or arches. This restriction does not apply to volumetric data structures. However, the workflow of manual modelling and editing of volumetric terrain usually is tedious and very time-consuming. Therefore, we propose to use three-dimensional curve-based primitives to efficiently model prominent, large-scale terrain features. We present a technique for volumetric generation of a complete terrain surface from the sparse input data by means of diffusion-based algorithms. By combining an efficient, feature-based toolset with a volumetric terrain representation, the modelling workflow is accelerated and simplified while retaining the full artistic freedom of volumetric terrains. Feature Curves also contain material information that can be complemented with local details by using per-face texture mapping. All stages of our method are GPU-accelerated using compute shaders to ensure interactive editing of terrain. Please note that this paper is an extended version of our previously published work [1] .
The Role of Normalization in the Belief Propagation Algorithm

An important part of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov Random Field. The belief propagation algorithm, which is an exact procedure to compute these marginals when the underlying graph is a tree, has gained its popularity as an efficient way to approximate them in the more general case. In this paper, we focus on an aspect of the algorithm that did not get that much attention in the literature, which is the effect of the normalization of the messages. We show in particular that, for a large class of normalization strategies, it is possible to focus only on belief convergence. Following this, we express the necessary and sufficient conditions for local stability of a fixed point in terms of the graph structure and the beliefs values at the fixed point. We also explicit some connexion between the normalization constants and the underlying Bethe Free Energy.
A unified approach for multi-point Cartesian compliance control of serial manipulators

This paper proposes a unified approach for controlling the Cartesian compliance of multiple points assigned along the linkage chains of the fully actuated serial manipulator. With this method, a number of points (as much as the total number of moving linkages) and their desired compliances may be specified. Then, their motion to perform a particular operation are designated, of which the compatibility is not required. Rather, it will be accounted for automatically by the controller. The controller attempts to accomplish higher-priority tasks with the available control space while the lower-priority tasks might not be completely achieved due to task confliction or inadequate dimension of the control vector space. Additionally, with the use of generalized inverse, the method is capable of controlling the manipulator at the singularities seamlessly.
Understanding domain expert's perspectives and expectations in assistive technology

Designing computer applications for blind users is a challenging task. The design requirements should encompass those feedback obtained from the target users, the domain experts as well as the designers' intuition of the system. Current practice has placed little emphasis on the involvement of domain experts when capturing the design requirements. This paper addresses this problem by highlighting the importance of such involvement. An interview was conducted to elicit a domain expert's view on how blind users used computer, the technology they used and their general requirements on an ideal system. The findings include blind users require an intelligent system that could read “the right thing” at “the right time”, provide a description of images on a document, and have shortcut keys for the system.
A k-NN Approach for Scalable Image Annotation Using General Web Data

This paper presents a simple k-NN based image annotation method that relies only on automatically gathered Web data. It can easily change or scale the list of concepts for annotation, without requiring labeled training samples for the new concepts. In terms of MAP the performance is better than the results from the ImageCLEF 2012 Scalable Web Image Annotation Task on the same dataset. Although, in terms of F-measure they are equivalent, suggesting that a better method for choosing how many concepts to select per image is required. Large-scale issues are considered by means of linear hashing techniques. The use of dictionary definitions has been observed to be a useful resource for image annotation without manually labeled training data.
Score normalization in playback attack detection

The task of a playback attack detector (PAD) is to decide whether an incoming recording shares the same originating utterance as any of N stored recordings. All recordings are noisy channel-distorted versions of the same phrase uttered by the same person; the originating utterances of the N stored recordings are assumed to be distinct. The proposed approach makes a decision based on a set of N similarity scores which quantify the similarity between the incoming recording and each of the N stored recordings. Although satisfactory results are obtained by thresholding the maximum of the N scores using speaker and phrase (SaP)-dependent thresholds, it is shown that the use of a relative similarity score (a normalized version of the maximum similarity score) results in significant performance improvements especially in the case when the incoming recording is a severely distorted version of a stored recording utterance, as well as for the case when SaP-independent thresholds are used.