Miracle-dz/UniFAR-FTUs huggingface dataset:
The Internet : An investigation into the changing face of the TAM and it ' s application to Internet Search Engines

Widely accepted for the last 15 years as a reliable method to understand and predict user adoption of Information Systems has been the Davis' Technology Acceptance Model (TAM). With the advent and meteoric growth of the Internet, a number of researchers are now asking if TAM can be applied to Internet Technologies. The purpose of this paper is to investigate whether TAM is a model upon which a survey can be constructed in regards to users' acceptance and use of Internet Search Engines.
The clusterhead chaining scheme considering scalability of the wireless sensor networks

A wireless sensor network is the network consisting of numerous small sensor nodes with sensing, processing and wireless communication capabilities. Many routing protocols are suggested for wireless sensor networks due to the several limited resources of a sensor node such as its limited CPU, memory size, and battery. They can be divided into flat and hierarchical routing protocols. The hierarchical routing protocol uses the clustering scheme and shows better performance than flat routing protocols. However, there is an assumption that sensor nodes can communicate with the base station by a one-hop routing in the hierarchical routing protocol. However, if the network size become larger, the hierarchical routing protocol is unsuitable because a long distance between a clusterhead and the base station can cause some communication problems. In this paper, we propose the clusterhead chaining scheme to solve this problem. Our scheme is suitable for vast wireless sensor networks and it was found from the simulation result that the proposed scheme shows better performance than the general hierarchical routing protocol.
Deriving change architectures from RCS history

As software systems evolve over a series of releases, it becomes important to know which components show repeated need for maintenance. Deterioration of a single component manifests itself in repeated and increasing problems that are local to the component. A second type of deterioration is related to interactions between components, that is, components are repeatedly change-prone in their relationships with each other. The latter requires changes to code in multiple components and is a sign of problems with the software architecture of the system. Software architecture problems are by far more costly to fix and thus it is very desirable to identify potential architectural problems early and to track them across multiple releases. This paper uses Revision Control System (RCS) change history to determine which system parts are the most change-prone, both locally and in their interactions with other parts of the systems. Relationships among system components are identified based on whether they are involved in the same group of changes, and how many lines of code are changed. The resulting change architecture figures show what the system’s most change-prone components and relationships are. We illustrate our technique on a large commercial system consisting of over 800 KLOC of C, C++, and microcode.
Humane Orientation as a Moral Construct in Ethical Leadership Theories:A Comparative Analysis of Transformational, Servant, and Authentic Leadership in the United States, Mexico, and China

The present paper presents the GLOBE leadership behaviour of humane orientation as a moral construct within the ethical leadership theories of transformational leadership, servant leadership, and authentic leadership. A discussion of the country cultures of the United States, China, and Mexico and an articulation of how each of these theories is received and practiced in response to the leadership behaviour of humane orientation support the use of these theories in all three countries’ cultures. In particular, the use of these theories may be advantageous in increasing the use of humane-oriented behaviours in these country cultures, which supports an expressed humane orientation value in the United States, China, and Mexico.
Using DiAML and ANVIL for multimodal dialogue annotations

This paper shows how interoperable dialogue act annotations, using the multidimensional annotation scheme and the markup language DiAML of ISO standard 24617-2, can conveniently be obtained using the newly implemented facility in the ANVIL annotation tool to produce XML-based output directly in the DiAML format. ANVIL offers the use of multiple user-defined `tiers' for annotating various kinds of information. This is shown to be convenient not only for multimodal information but also for dialogue act annotation according to ISO standard 24617-2 because of the latter's multidimensionality: functional dialogue segments are viewed as expressing one or more dialogue acts, and every dialogue act belongs to one of a number of dimensions of communication, defined in the standard, for each of which a different ANVIL tier can conveniently be used. Annotations made in the multi-tier interface can be exported in the ISO 24617-2 format, thus supporting the creation of interoperable annotated corpora of multimodal dialogue.
Probabilistic Exploitation of the Lucas and Kanade Smoothness Constraint

The basic idea of Lucas and Kanade is to constrain the local motion measurement by assuming a constant velocity within a spatial neighborhood. We reformulate this spatial constraint in a probabilistic way assuming Gaussian distributed uncertainty in spatial identification of velocity measurements and extend this idea to scale and time dimensions. Thus, we are able to combine uncertain velocity measurements observed at different image scales and positions over time. We arrive at a new recurrent optical flow filter formulated in a dynamic Bayesian network applying suitable factorisation assumptions and approximate inference techniques. The introduction of spatial uncertainty allows for a dynamic and spatially adaptive tuning of the constraining neighborhood. Here, we realize this tuning dependent on the local structure tensor of the intensity patterns of the image sequence. We demonstrate that a probabilistic combination of spatiotemporal integration and modulation of a purely local integration area improves the Lucas and Kanade estimation.
Robust Image Analysis With Sparse Representation on Quantized Visual Features

Recent techniques based on sparse representation (SR) have demonstrated promising performance in high-level visual recognition, exemplified by the highly accurate face recognition under occlusion and other sparse corruptions. Most research in this area has focused on classification algorithms using raw image pixels, and very few have been proposed to utilize the quantized visual features, such as the popular bag-of-words feature abstraction. In such cases, besides the inherent quantization errors, ambiguity associated with visual word assignment and misdetection of feature points, due to factors such as visual occlusions and noises, constitutes the major cause of dense corruptions of the quantized representation. The dense corruptions can jeopardize the decision process by distorting the patterns of the sparse reconstruction coefficients. In this paper, we aim to eliminate the corruptions and achieve robust image analysis with SR. Toward this goal, we introduce two transfer processes (ambiguity transfer and mis-detection transfer) to account for the two major sources of corruption as discussed. By reasonably assuming the rarity of the two kinds of distortion processes, we augment the original SR-based reconstruction objective with \mmbl0-norm regularization on the transfer terms to encourage sparsity and, hence, discourage dense distortion/transfer. Computationally, we relax the nonconvex \mmbl0-norm optimization into a convex \mmbl1-norm optimization problem, and employ the accelerated proximal gradient method to optimize the convergence provable updating procedure. Extensive experiments on four benchmark datasets, Caltech-101, Caltech-256, Corel-5k, and CMU pose, illumination, and expression, manifest the necessity of removing the quantization corruptions and the various advantages of the proposed framework.
REST Web Services in Collaborative Work Environments

Integration of heterogeneous applications offers many benefits; as a consequence, the goal is not to try and eliminate the heterogeneity, but to help developers to master it and manage it. There have been many approaches to integrate heterogeneous technologies in the context of the Web. Most of them cannot provide a common solution because they try to solve the problem using an incomplete set of proprietary technologies. To establish a holistic, commonly accepted and standardised approach would be preferable. This paper presents a case study for the development of an integration framework in the area of collaborative work environments. This framework is based on REST Web services, and has been designed and implemented as part of the Laboranova European project.
Improving the efficiency of a hardware transactional memory on an NoC-based MPSoC

Transactional Memories (TM) have attracted much interest as an alternative to lock-based synchronization in shared-memory multiprocessors. Considering the use of TM on an embedded, NoC-based MPSoC, this work evaluates a LogTM implementation. It is shown that the time an aborted transaction waits before restarting its execution (the backoff delay) can seriously affect the overall performance and energy consumption of the system. This work also shows the difficulty to find a general and optimal solution to set this time and analyzes three backoff policies to handle it. A new solution to this issue is presented based on a handshake between transactions. Results suggest up to 20% in performance gains and up to 53% in energy savings when comparing our new solution to the best backoff delay alternative found in our experiments.
GDCSim: A tool for analyzing Green Data Center design and resource management techniques

Energy consumption in data centers can be reduced by efficient design of the data centers and efficient management of computing resources and cooling units. A major obstacle in the analysis of data centers is the lack of a holistic simulator, where the impact of new computing resource (or cooling) management techniques can be tested with diffierent designs (i.e., layouts and configurations) of data centers. To fill this gap, this paper proposes Green Data Center Simulator (GDCSim) for studying the energy efficiency of data centers under various data center geometries, workload characteristics, platform power management schemes, and scheduling algorithms. GDCSim is used to iteratively design green data centers. Further, it is validated against established CFD simulators. GDCSim is developed as a part of the BlueTool infrastructure project at Impact Lab.
Targeting Technology to Reduce Poverty and Conserve Resources: Experimental Delivery of Laser Land Leveling to Farmers in Uttar Pradesh, India

Demand heterogeneity often makes it profitable for firms to price and promote goods and services differently in different market segments. When private consumption brings public benefits, this same heterogeneity can be used to target public subsidies. We explore the design of publicâ€“private targeting and segmentation strategies in the case of a resource-conserving agricultural technology in India. To understand farmersâ€™ heterogeneous demand for laser land leveling (LLL), we conducted an experimental auction for LLL services with an integrated randomized controlled trial to estimate the private benefits of the technology. We use graphical and econometric approaches to characterize farmer demand for LLL. We then add detailed cost data from LLL providers to simulate and evaluate several potential targeted delivery strategies based on measures of (1) the cost-effectiveness of expanding LLL dissemination, (2) water savings, and (3) market surplus in a welfare framework. These simulations demonstrate inherent tradeoffs between increasing the amount of land that is leveled and expanding the number of farmers who adopt the technology, and between adoption and water savings. While segmenting and targeting are popular elements of many publicâ€“private partnerships to develop and disseminate agricultural technologies, formulating and implementing effective delivery strategies requires a rich understanding of costs, benefits, and demand. Our experimental approach generates such an understanding and may be relevant in other contexts.
Content Caching Problem Formulation

Introduction Content networks are usually virtual networks based over the IP infrastructure of Internet or of a corporative network, which use mechanisms to allow accessing a content when there is no fixed, single, link between the content and the host or the hosts where this content is located. Even more, the content is usually subject to reallocations , replications, and even deletions from the different nodes of the network [6][7][9]. In the last years many different kinds of content networks have been developed and deployed in widely varying contexts: they include peer-to-peer networks, collaborative networks, cooperative Web caching, content distribution networks, subscribe-publish networks, content-based sensor networks, backup networks, distributed computing, instant messaging, and multiplayer games. As a general rule, every content network is actually a knowledge network, where the knowledge is the information about the location of the nodes where each specific content is to be found: this is "meta-information", in the sense of being the information about the information contents themselves. The objective of the network is to be able to answer each content query with the most complete possible set of nodes where this content is to be found; this corresponds to discover the content location in the most effective and efficient possible way. As both nodes and contents are continuously going in and out of the network, the task of maintaining updated the network meta-information is very difficult and represents an important communication cost. In this context, cache nodes are used to hold the available meta-information; as this information is continuously getting outdated, the cache nodes must decide when to discard it, which means increasing communication overhead for the sake of improving the quality of the answers. The policy employed for determining these cache expiration dates have a large impact in the performance of a network; this problem is related but at the same time different to other problems involving caches, as in this case the cached information does not take too much place, but gets outdated very quickly (usually in caching problems the cached information uses up significant space, and is valid for more extended time periods). In this work, we model different policies for fixing the cache expiration dates for the information about the contents' location; in particular, we give mathematical programming formulations which represent the case where the cache expiration dates are equal for all contents, the case where the cache expiration dates are …
Pre-processing aspects for complexity reduction of the QSAR problem

Predictive Toxicology (PT) is one of the newest targets of the Knowledge Discovery in Databases (KDD) domain. Its goal is to describe the relationships between the chemical structure of chemical compounds and biological and toxicological processes. In real PT problems there is a very important topic to be considered: the huge number of the chemical descriptors. Irrelevant, redundant, noisy and unreliable data have a negative impact, therefore one of the main goals in KDD is to detect these undesirable proprieties and to eliminate or correct them. This assumes data cleaning, noise reduction and feature selection because the performance of the applied Machine Learning algorithms is strongly related with the quality of the data used. In this paper, we present some of the issues that can be taken into account for preparing data before the actual knowledge discovery is performed.
Is It the Typeset or the Type of Statistics? Disfluent Font and Self-Disclosure

• Background. The security and privacy communities have become increasingly interested in results from behavioral economics and psychology to help frame decisions so that users can make better privacy and security choices. One such result in the literature suggests that cognitive disfluency (presenting questions in a hard-to-read font) reduces selfdisclosure. • Aim. To examine the replicability and reliability of the effect of disfluency on self-disclosure, in order to test whether such approaches might be used to promote safer security and privacy behaviors. • Method. We conducted a series of survey studies on human subjects with two conditions disfluent and fluent font. The surveys were completed online (390 participants throughout the United States), on tablets (93 students) and with pen and paper (three studies with 89, 61, and 59 students). The pen and paper studies replicated the original study exactly. We ran an independent samples t-test to check for significant differences between the averages of desirable responses across the two conditions. • Results. In all but one case, participants did not show lower self-disclosure rates under disfluent conditions using an independent samples t-test. We re-analyzed the original data and our data using the same statistical test (paired t-test) as used in the original paper, and only the data from the original published studies supported the hypothesis. • Conclusions.We argue that the effect of disfluency on disclosure originally reported in the literature might result from the choice of statistical analysis, and that disfluency does not reliably or consistently affect self-disclosure. Thus, disfluency may not be relied on for interface designers trying to improve security or privacy decision making.
Triaxial accelerometer-based real time fall event detection

Falls in older people and the injuries are a major problem for their welfare, confidence and happiness and represent a public health burden on health care cost. In this study, an automatic fall detection system consisting of a triaxial accelerometer and a smartphone is evaluated. The system classifies raw sensor data by using an online algorithm. Based on physical characteristics of activity, four time-domain features are abstracted, which are all independent of the sensor orientation with respect to the body. A decision tree is used as a classifier running on smartphone. Meanwhile, permitting control is adopted to save power by reducing data traffic. The accelerometer and Bluetooth unit are bounded as a wearable unit and placed on the subject's waist/chest. A laboratory-based trial involving ten subjects during different time was undertaken; results indicate an overall accuracy of 92% and response time of less than 6 seconds, which demonstrates excellent effectiveness of this system.
Quantifying the impact of funded research works

A number of scientific publications discuss works that have been financially supported by agencies that invest in research. We refer to those publications as funded research works. In this paper, we try to capture the impact of funded research works, in order to assist funding agencies evaluate their efficacy in identifying the best research efforts. To accomplish our goal, we firstly need a method that automatically identifies publications that correspond to funded research. Our proposed method leverages NLP techniques in order to identify acknowledgments of financial support in the publications' content. Publications containing acknowledgments of financial support are deemed as funded works. Following the identification of funded research articles, we quantify their impact by considering the number of their citations, their freshness as well as the impact of their publication venue. The application of our method to a number of publications reveals that although funded research articles account to nearly 23% of the scientific publications, their average impact is increased compared to the average impact of non-funded publications. Our findings suggest that investments made on research deliver significant results and that funding agencies are effective in judging the potential impact of research efforts.
A Unified Framework for Multioriented Text Detection and Recognition

High level semantics embodied in scene texts are both rich and clear and thus can serve as important cues for a wide range of vision applications, for instance, image understanding, image indexing, video search, geolocation, and automatic navigation. In this paper, we present a unified framework for text detection and recognition in natural images. The contributions of this paper are threefold: 1) text detection and recognition are accomplished concurrently using exactly the same features and classification scheme; 2) in contrast to methods in the literature, which mainly focus on horizontal or near-horizontal texts, the proposed system is capable of localizing and reading texts of varying orientations; and 3) a new dictionary search method is proposed, to correct the recognition errors usually caused by confusions among similar yet different characters. As an additional contribution, a novel image database with texts of different scales, colors, fonts, and orientations in diverse real-world scenarios, is generated and released. Extensive experiments on standard benchmarks as well as the proposed database demonstrate that the proposed system achieves highly competitive performance, especially on multioriented texts.
Development of Hyperspectral Imaging Technique for the Detection of Chilling Injury in Cucumbers; Spectral and Image Analysis

Hyperspectral images of cucumbers were acquired before and during cold storage treatment as well as during subsequent room temperature (RT) storage to explore the potential for the detection of chilling induced damage in whole cucumbers. Region of interest (ROI) spectral features of chilling injured areas, resulting from cold storage treatments at 0C or 5C, showed a reduction in reflectance intensity during multi-day post-chilling periods of RT storage. Large spectral differences between good-smooth skins and chilling injured skins occurred in the 700- to 850-nm visible/NIR region. A number of data processing methods, including simple spectral band algorithms and principal component analysis (PCA), were attempted to discriminate the ROI spectra of good cucumber skins from those of chilling injured skins. Results revealed that using either a dual-band ratio algorithm (Q811/756) or a PCA model from a narrow spectral region of 733- to 848-nm could detect chilling injured skins with a success rate of over 90%. Furthermore, the dual-band algorithm was applied to the analysis of images of cucumbers at different conditions, and the resultant images showed more correct identification of chilling injured spots than PCA method. The results also suggested that chilling injury was relatively difficult to detect at the stage of the first 0 to 2 days of post-chilling RT storage, due to insignificant manifestation of chilling induced symptoms.