Miracle-dz/UniFAR-FTUs huggingface dataset:
International Journal of Advanced Research in Computer Science and Software Engineering

— This paper present an intelligent text processing technique for identifying the emotion contains in the text data. Users share their opinions and sentiment in the form of comments in different online marketing websites for different products. Emotion detection in these customer reviews for different products in one of the challenging task. In this paper, we proposed efficient technique for detection of emotion in customer reviews for different products.
Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping (Supplementary Material)

In the main paper, we introduce the selected three representative styles from the collected data and show three examples in Figure 2: the first style is from Yann Legendre and Charles Burns where parallel lines are used to draw shadows; the second style is from Kathryn Rathke where few dark regions are used and facial features are drawn using simple flowing lines; the third style is from vectorportal.com where continuous thick lines and large dark regions are utilized. Here we provide more examples in Figure S1.
Designing single-cell experiments to harvest fluctuation information while rejecting measurement noise

Measurement error is a complicating factor that reduces or distorts the information contained in any experiment. This problem becomes even more serious in the context of experiments that are specifically designed to quantify single-cell gene expression heterogeneity, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. It is not clear how measurement noise should be managed, in addition to other experiment design variables such as sampling size and frequency, to ensure that collected data will provide useful insights on gene expression mechanisms of interest. To address these fundamental single-cell experiment design challenges, we propose a computational framework that makes explicit use of measurement error modeling and Fisher Information Matrix-based criteria to decide between experimental methods. This unified approach not only quantifies how different noise characteristics affect uncertainty in parameter estimation, but also enables a systematic approach to design hybrid experiments that combine different measurement modalities.
Anthropogenic influences on spotted hyaenas in a protected area the Kruger National Park

25 Introduction . 26 Research methods and design. 28 Study site. 28 Study animals and data collection . 29 Statistical Analysis. 32 Results . 33 Discussion. 33 Conclusion. 35 References . 36 © University of Pretoria vii Tables. 40 Figures . 42 Chapter 3: Anthropogenic influences on spotted hyaena diet in the Kruger National Park, South Africa44 Submitted to Mammal ResearchAbstract . 44 Introduction . 46 Materials and methods. 50 Study area . 50 Anthropogenic litter survey . 51 Defining anthropogenic sites . 51 Scat collection and diet estimation . 52 Data analyses . 54 Results . 55 Anthropogenic litter Density. 55 Overall diet content . 56 Anthropogenic content of scats. 56 Discussion. 57 References . 61 Figures . 65 Chapter 4: Spotted hyaena space use in relation to infrastructure in a protected area . 68 Abstract . 69 Introduction . 70 Materials and methods. 74 Study area . 74 Study animals and instrumentation. 76 © University of Pretoria viii Data collection, classification and analyses. 79 Results . 84 Discussion. 86 References . 91 Tables. 97 Figures. 100 Supplementary Information . 101 Chapter 5: Social networks of spotted hyaenas in areas of contrasting human infrastructure and activity . 102 Abstract . 103 Methods . 106 Study area . 106 Study population . 107 Evaluation of human infrastructure and activity . 108 Behavioural observations . 108 Age and sex classification. 110 Determination of dominance structures. 110 Quantification of interaction structures. 111 Statistical analyses . 113 Results . 115 Discussion. 117 References . 120 Tables. 124 Figures. 125 © University of Pretoria ix Chapter 6: Discussion. 128 Conclusions and suggestions for future research. 130 References . 131 © University of Pretoria 1 Chapter 1: Introduction
Semantics for the Internet of Things: Early Progress and Back to the Future

The Internet of Things IoT has recently received considerable interest from both academia and industry that are working on technologies to develop the future Internet. It is a joint and complex discipline that requires synergetic efforts from several communities such as telecommunication industry, device manufacturers, semantic Web, and informatics and engineering. Much of the IoT initiative is supported by the capabilities of manufacturing low-cost and energy-efficient hardware for devices with communication capacities, the maturity of wireless sensor network technologies, and the interests in integrating the physical and cyber worlds. However, the heterogeneity of the "Things" makes interoperability among them a challenging problem, which prevents generic solutions from being adopted on a global scale. Furthermore, the volume, velocity and volatility of the IoT data impose significant challenges to existing information systems. Semantic technologies based on machine-interpretable representation formalism have shown promise for describing objects, sharing and integrating information, and inferring new knowledge together with other intelligent processing techniques. However, the dynamic and resource-constrained nature of the IoT requires special design considerations to be taken into account to effectively apply the semantic technologies on the real world data. In this article the authors review some of the recent developments on applying the semantic technologies to IoT.
Coupling power and frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks

One of the main problems affecting reliable transmission in wireless body sensor networks based on IEEE 802.15. 4 is interference caused by sharing the unlicensed 2.4 GHz ISM band with other technologies such as Wifi. One possible approach to deal with this problem is to allow a body sensor network to change its operating frequency and its transmit power in response to changes in the observed interference. In this paper we consider the effect of the variation of transmit power in the presence and absence of frequency adaptation.
AIS Electronic Library (AISeL) AIS Electronic

: Users’ satisfaction is an important indicator of App quality, and version updating is an important means for developers to respond user requirements. Incorporating user reviews into iterative delivery of new App versions would improve the quality and ratings of Apps. Most of existing research analyzed App attributes’ influences on customers’ satisfaction from the application level, but the influences on users from the perspective of the version level has more practical implications and may has the underlying relationship with customers’ satisfaction. According to the dimensions of managerial response, this paper explores the impacts of response consistency and response timeliness on users’ satisfaction. Empirical analysis was conducted based on 4797 versions and 463176 comments from 241 shopping apps in Apple App Store on “qimai” website. The results show that response consistency and response timeliness both have a significant positive impact on users’ satisfaction.
K-UNN: k-Space Interpolation With Untrained Neural Network

Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approach does not fully use the MR image physical priors, resulting in poor performance in some common scenarios (e.g., partial Fourier, regular sampling, etc.) and the lack of theoretical guarantees for reconstruction accuracy. To bridge this gap, we propose a safeguarded k-space interpolation method for MRI using a specially designed UNN with a tripled architecture driven by three physical priors of the MR images (or k-space data), including sparsity, coil sensitivity smoothness, and phase smoothness. We also prove that the proposed method guarantees tight bounds for interpolated k-space data accuracy. Finally, ablation experiments show that the proposed method can more accurately characterize the physical priors of MR images than existing traditional methods. Additionally, under a series of commonly used sampling trajectories, experiments also show that the proposed method consistently outperforms traditional parallel imaging methods and existing UNNs, and even outperforms the state-of-the-art supervised-trained k-space deep learning methods in some cases.
Training Methods for Adaptive Boosting of Neural Networks for Character Recognition

Submission to NIPS*97, Category: Algorithms & Architectures, Preferred: Oral ”Boosting” is a general method for improving the performance of any learning algorithm that consistently generates classifiers which need to perform only slightly better than random guessing. A recently proposed and very promising boosting algorithm is AdaBoost [5]. It has been applied with great success to several benchmark machine learning problems using rather simple learning algorithms [4], in particular decision trees [1, 2, 6]. In this paper we use AdaBoost to improve the performances of neural networks applied to character recognition tasks. We compare training methods based on sampling the training set and weighting the cost function. Our system achieves about 1.4% error on a data base of online handwritten digits from more than 200 writers. Adaptive boosting of a multi-layer network achieved 2% error on the UCI Letters offline characters data set.
A unified approach to architecture conformance checking

Architectural decisions can be interpreted as structural and behavioral constraints that must be enforced in order to guarantee overarching qualities in a system. Enforcing those constraints in a fully automated way is often challenging and not well supported by current tools. Current approaches for checking architecture conformance either lack in usability or offer poor options for adaptation. To overcome this problem we analyze the current state of practice and propose an approach based on an extensible, declarative and empirically-grounded specification language. This solution aims at reducing the overall cost of setting up and maintaining an architectural conformance monitoring environment by decoupling the conceptual representation of a user-defined rule from its technical specification prescribed by the underlying analysis tools. By using a declarative language, we are able to write tool-agnostic rules that are simple enough to be understood by untrained stakeholders and, at the same time, can be can be automatically processed by a conformance checking validator. Besides addressing the issue of cost, we also investigate opportunities for increasing the value of conformance checking results by assisting the user towards the full alignment of the implementation with respect to its architecture. In particular, we show the benefits of providing actionable results by introducing a technique which automatically selects the optimal repairing solutions by means of simulation and profit-based quantification. We perform various case studies to show how our approach can be successfully adopted to support truly diverse industrial projects. We also investigate the dynamics involved in choosing and adopting a new automated conformance checking solution within an industrial context. Our approach reduces the cost of conformance checking by avoiding the need for an explicit management of the involved validation tools. The user can define rules using a convenient high-level DSLwhich automatically adapts to emerging analysis requirements. Increased usability and modular customization ensure lower costs and a shorter feedback loop.
Depth guided image inpainting algorithm for free- viewpoint video

Free-Viewpoint Video (FVV) is a novel technique which creates virtual images of multiple direction by view synthesis. In this paper, an exemplar-based depth-guided inpainting algorithm is proposed to fill disocclusions due to uncovered areas after projection. We develop an improved priority function which uses the depth information to impose a desirable inpainting order. We also propose an efficient background-foreground separation technique to enhance the accuracy of hole filling. Furthermore, a gradient-based searching approach is developed to reduce the computational cost and the location distance is incorporated into patch matching criteria to improve the accuracy. The experimental results have shown that the gradientbased search in our algorithm requires a much lower computational cost (factor of 6 compared to global search), while producing significantly improved visual results.
Decomposition of timed automata for solving scheduling problems

A decomposition algorithm for scheduling problems based on timed automata (TA) model is proposed. The problem is represented as an optimal state transition problem for TA. The model comprises of the parallel composition of submodels such as jobs and resources. The procedure of the proposed methodology can be divided into two steps. The first step is to decompose the TA model into several submodels by using decomposable condition. The second step is to combine individual solution of subproblems for the decomposed submodels by the penalty function method. A feasible solution for the entire model is derived through the iterated computation of solving the subproblem for each submodel. The proposed methodology is applied to solve flowshop and jobshop scheduling problems. Computational experiments demonstrate the effectiveness of the proposed algorithm compared with a conventional TA scheduling algorithm without decomposition.
The role of academic motivation in Computer-Supported Collaborative Learning

Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. 47 courses at various faculties of the Open University in Israel and found that the majority (80%) of students contributed only a small amount (20%) of messages. But not only differences in the amount of contributions by students have been found. For example, De Laat and Lally (2003) showed that students in an online E-learning Master's programme also differed with respect to the type (cognitive, affective, metacognitive) of contributions. In a Bachelor's freshman course of educational science, Schellens and Valcke (2005) found significant differences with respect to both amount and type (social, cognitive) of discourse. Although recent research findings indicate that learners differ with respect to the amount and type of discourse contributed, little is known about what the causes of these differences are. Within the field of Computer-Supported Collaborative Learning, researchers try to understand what the underlying reasons for a lack of contributions to cognitive discourse are and how to solve them. For example, Lowry and colleagues (2006) found that informatics learners who in an experimental design collaborated in class and were complemented with ICT established higher levels of communication quality than learners who collaborated only virtually. Hurme and colleagues (2007) analysed the interaction patterns among secondary school children who worked online in pairs on mathematical problems. Metacognitive activity varied among participants, which subsequently influenced the interaction among pairs of learners. Furthermore, by using Social Network Analysis 2 some pairs became central contributors to discourse, while others were less active and were positioned on the outer fringe of the social network (Hurme, Palonen, & Järvelä, 2007). Recently several researchers have investigated the role of motivation in CSCL. For example, by measuring goal-oriented motivation (Pintrich & De Groot, 1990), Yang and colleagues (2006) found evidence that motivation is positively related with how learners perceive each other's presence in online courses. Järvelä and colleagues (2008) found that students who were working together in groups of 3-5 students reported more (favourable) learning goals and fewer performance goals in the face-to-face setting than students in virtual groups. Besides goal-oriented motivation, several other factors might influence motivation like the degree of self-determination of learners (Ryan & Deci, 2000a, 2000b). For example, in an online setting learners …
A Large-Scale Model of the Functioning Brain

Modeling the Brain Neurons are pretty complicated cells. They display an endless variety of shapes that sprout highly variable numbers of axons and dendrites; they sport time- and voltage-dependent ion channels along with an impressive array of neurotransmitter receptors; and they connect intimately with near neighbors as well as former neighbors who have since moved away. Simulating a sizeable chunk of brain tissue has recently become achievable, thanks to advances in computer hardware and software. Eliasmith et al. (p. 1202; see the Perspective by Machens) present their million-neuron model of the brain and show that it can recognize numerals, remember lists of digits, and write down those lists—tasks that seem effortless for a human but that encompass the triad of perception, cognition, and behavior. Two-and-a-half million model neurons recognize images, learn via reinforcement, and display fluid intelligence. A central challenge for cognitive and systems neuroscience is to relate the incredibly complex behavior of animals to the equally complex activity of their brains. Recently described, large-scale neural models have not bridged this gap between neural activity and biological function. In this work, we present a 2.5-million-neuron model of the brain (called “Spaun”) that bridges this gap by exhibiting many different behaviors. The model is presented only with visual image sequences, and it draws all of its responses with a physically modeled arm. Although simplified, the model captures many aspects of neuroanatomy, neurophysiology, and psychological behavior, which we demonstrate via eight diverse tasks.
Programmable VLIW and SIMD Architectures for DSP and Multimedia Applications

Digital Signal Processing (DSP) and multimedia workloads are expected to be the dominant workloads on future computer systems. This is true in both low cost embedded applications that use specialized microprocessors like DSPs and in the generalpurpose processor market. Very Long Instruction Word (VLIW) architectures have multiple functional units to take advantage of vastly available Instruction Level Parallelism (ILP) in such applications. Single Instruction Multiple Data (SIMD) techniques operate on multiple data in a single instruction (exploiting data parallelism). This paper proposes to evaluate the benefits of using the above two techniques for DSP and multimedia applications. Using a modern commodity processor from each category – Texas Instruments Inc.’s TMS320C6x (VLIW) and Intel’s Pentium II with MMX (SIMD), several DSP and multimedia benchmarks will be evaluated.
S CALE - INVARIANT PROCESS REGRESSION

Gaussian processes are the leading method for non-parametric regression on small to medium datasets. One main challenge is the choice of kernel and optimization of hyperparameters. We propose a novel regression method that does not require speciﬁcation of a kernel, length scale, variance, nor prior mean. Its only hyperparameter is the assumed regularity (degree of differentiability) of the true function. We achieve this with a novel non-Gaussian stochastic process that we construct from minimal assumptions of translation and scale invariance. The process can be thought of as a hierarchical Gaussian process model, where the hyperparameters have been incorporated into the process itself. To perform inference with this process we develop the required mathematical tools. It turns out that for interpolation, the posterior is a t -process with a polyharmonic spline as mean. For regression, we state the exact posterior and ﬁnd its mean (again a polyharmonic spline) and approximate variance with a sampling method. Experiments show a performance equal to that of Gaussian processes with optimized hyperparameters. The most important insight is that it is possible to derive a working machine learning method by assuming nothing but regularity and scale- and translation invariance, without any other model assumptions.
The LAIR: Lightweight Affordable Immersion Room

This paper proposes a virtual environment system called a lightweight affordable immersion room (LAIR) that we propose for use in space and financially constrained situations. The LAIR consists of four walls arranged in a square providing a 360° degree view. By using front projection and two projectors per wall, the LAIR provides a compact environment which gives users some freedom of movement. The LAIR may be considered in terms of performance and cost as somewhere between a power wall and a CAVE—more immersive than a power wall, but less than a CAVE—and similarly moderate in terms of use of space and cost.
Learning Salient Features for Real-Time Face Veri(cid:12)cation (cid:3)

We propose a novel person veri(cid:12)cation system for real-time face identi(cid:12)cation. The main features of the system include accurate registration of face images us-ing a robust form of correlation, a framework for global registration of a face database using a minimum spanning tree algorithm and a method for selecting a subset of features optimal for discrimination between clients and impostors. We present results obtained through experiments on a large database with 295 subjects and show that the method is performing well in comparison with two standard methods based on elastic graph matching.
Near-infrared neuroimaging with NinPy

There has been substantial recent growth in the use of non-invasive optical brain imaging in studies of human brain function in health and disease. Near-infrared neuroimaging (NIN) is one of the most promising of these techniques and, although NIN hardware continues to evolve at a rapid pace, software tools supporting optical data acquisition, image processing, statistical modeling, and visualization remain less refi ned. Python, a modular and computationally effi cient development language, can support functional neuroimaging studies of diverse design and implementation. In particular, Python’s easily readable syntax and modular architecture allow swift prototyping followed by effi cient transition to stable production systems. As an introduction to our ongoing efforts to develop Python software tools for structural and functional neuroimaging, we discuss: (i) the role of non-invasive diffuse optical imaging in measuring brain function, (ii) the key computational requirements to support NIN experiments, (iii) our collection of software tools to support NIN, called NinPy, and (iv) future extensions of these tools that will allow integration of optical with other structural and functional neuroimaging data sources. Source code for the software discussed here will be made available at www.nmr. mgh.harvard. edu/Neural_SystemsGroup/software.html.