Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction-capable data compression algorithms for improving transmission efficiency on distributed systems

Network bandwidth is a limited and precious resource in distributed computing environments. Insufficient bandwidth will severely degrade the performance of a distributed computing task in exchanging massive amounts of data among the networked hosts. A feasible solution to save bandwidth is to incorporate data compression during transmission. However blind, or unconditional, compression may only result in waste of CPU power and even slow down the overall network transfer rate, if the data to be transmitted are hard to compress. We present a prediction-capable lossless data compression algorithm to address this problem. By adapting to the compression speed of a host CPU, current system load, and network speed, our algorithm can accurately estimate the compression time of each data block given, and decide whether it should be compressed or not. Experimental results indicate that our prediction mechanism is both efficient and effective, achieving 93% of prediction accuracy at the cost of only 3.2% of the execution time of unconditional compression.
The Effect of Redundancy on Video Broadcasting in Vehicular Networks

Advancements in both on-board and wireless communication technologies provide the necessary backbone for the deployment of a network between vehicles. Most of the envisioned applications for these networks would be greatly favored by multimedia support provisioning. However, there are several issues to be handled in order to provide multimedia services with reasonable quality. In this work, we analyze a specific multimedia service that can be used by several interesting applications: video broadcasting. Particularly, we throughly discuss and evaluate the role of redundancy in improving this service. We have verified that although redundancy does increase the effectiveness in video broadcasting, coding techniques do not improve redundancy's efficiency.
European Semantic Web Conference 2009-Matthias Klusch, Patrick Kapahnke and Ingo Zinnikus. Hybrid Adaptive Web Service Selection with SAWSDL-MX and WSDL Analyzer

: Building on our initial work on SAWSLD-MX, in this paper we extended the hybrid matchmaker by a structural approach utilizing the WSDL Analyzer tool, which performs structural, string-based and lexical comparison of WSDL 1.1 service descriptions and aims at supporting semi-automated Web service integration. A variant for aggregating results of different matching approaches using Support Vector Machines is presented. We report on the preliminary results of our performance evaluation of proposed aggregated SAWSDL matching vairants over the test collection SAWSDL-TC 1.
Utilizing Massive Spatiotemporal Samples for Efficient and Accurate Trajectory Prediction

Trajectory prediction is widespread in mobile computing, and helps support wireless network operation, location-based services, and applications in pervasive computing. However, most prediction methods are based on very coarse geometric information such as visited base transceiver stations, which cover tens of kilometers. These approaches undermine the prediction accuracy, and thus restrict the variety of application. Recently, due to the advance and dissemination of mobile positioning technology, accurate location tracking has become prevalent. The prediction methods based on precise spatiotemporal information are then possible. Although the prediction accuracy can be raised, a massive amount of data gets involved, which is undoubtedly a huge impact on network bandwidth usage. Therefore, employing fine spatiotemporal information in an accurate prediction must be efficient. However, this problem is not addressed in many prediction methods. Consequently, this paper proposes a novel prediction framework that utilizes massive spatiotemporal samples efficiently. This is achieved by identifying and extracting the information that is beneficial to accurate prediction from the samples. The proposed prediction framework circumvents high bandwidth consumption while maintaining high accuracy and being feasible. The experiments in this study examine the performance of the proposed prediction framework. The results show that it outperforms other popular approaches.
Photoperiodic regulation of insect and molluscan hormones.

The universal occurrence of photoperiodism in insects is due to convergent evolution. The neuroendocrine system, playing the central role in the photoperiodic response, gives diversified terminal expressions of adaptive significance. The response curve reflects the distributions of individual threshold photoperiods in three different ranges corresponding to the minimum number of dark and light hours that are presumably required for the time-measuring function and the genetic time-scale (critical photoperiod). Variations of the photoperiodic response curve can be derived from graded shifts of these distributions. There are, however, some unusual response curves that do not fit this model. Resonance tests suggest two different types for the photoperiodic clock mechanism: the hour-glass and the circadian oscillator. For the latter, the possibility cannot be ruled out that the observed periodicity is due to the interference by the circadian organization and not to the oscillatory nature of the timer itself. In any case, the long-night effect is similarly reversed by light breaks, and various modes of the response to night interruption may be ascribed to variations in the duration and light sensitivity of the two separate stages in the dark reaction. The possible existence of pre-adaptation to seasonal photoperiodism is suggested by the response of hybrids between the temperate and tropical forms of ground crickets. 1984 Photoperiodic regulation of insect and molluscan hormones. Pitman, London (Ciba Foundation symposium I04), p 7-25 This review is a cursory glance at unity and diversity in insect photoperiodism, an approach that is a feasible, though perhaps biased, way of drawing a quick sketch of the many diversified photoperiodic phenomena in insects. The large body of published work on insect photoperiodism, accumulated since the first experiments over 50 years ago, has been thoroughly reviewed recently (Beck 1980, Saunders 1982, Tyshchenko 1977), providing ample material for discussion on any special problem in this field. I have relied on these excellent reviews for background information, and have omitted detailed references for rather general statements.
Discussion on the Results of the Detection of Design Defects

Software engineers often need to identify in their systems “poor” design choices—design defects—that hinder the development and maintenance, as opportunities of improvements and as a measure of the quality of their systems. However, the detection of design defects is difficult because of the lack of specifications and tools. We propose DECOR, a method to specify design defects systematically and to generate automatically detection algorithms. With this method, software engineers analyse and specify design defects at a high-level of abstraction using a unified vocabulary and a dedicated language for generating detection algorithms. To illustrate our method, in this paper, we specify 4 well-known design defects, the antipatterns Blob, Functional Decomposition, Spaghetti Code, and Swiss Army Knife and their 15 underlying code smells and we generate automatically their detection algorithms. We apply and validate the detection algorithms in terms of precision and recall and discuss the precision of these algorithms on 11 open-source object-oriented systems.
Parents’ experiences of professionals’ involvement for children with extreme demand avoidance

Objectives: Pathological demand avoidance (PDA) is characterized by an extreme difficulty in tolerating everyday demands, with a range of ‘socially manipulative’ strategies used to avoid demands. Children also display poor social identity, impulsivity, and obsessive behavior. While research and knowledge about PDA is slowly growing, families continue to encounter considerable difficulties in ensuring their children’s needs are understood and met by health and education services. This study explored what parents of children with extreme demand avoidance valued about professionals’ input. Methods: Parents of 42 children who scored above the cut-off on a standardized measure with validated sensitivity and specificity for PDA responded to questionnaire items asking them to identify professionals’ involvement and helpfulness. They were also asked what had characterized the most helpful professional involvement. Thematic analysis was used to identify the key themes in parents’ responses. Results: Families tended to have high levels of professional involvement. Most professionals were experienced as helpful by the majority of parents. Three main themes were identified as characterizing helpful involvement: enabling access to services and resources, concrete outcomes such assessments and management strategies, and professional-level factors such as skills and qualities. Conclusions: Parents felt positive about practitioners who had listened to their experiences, made efforts to understand the child, and provided or arranged for help. Parents found involvement most helpful when it resulted in comprehensive assessment, appropriate intervention, practical advice and management strategies, and a focus on the well-being of all family members. The overall ratings of helpfulness are encouraging, and the specific feedback about what is most helpful could be of value in shaping services.
Surviving mass extinction by bridging the benthic/planktic divide

Evolution of planktic organisms from benthic ancestors is commonly thought to represent unidirectional expansion into new ecological domains, possibly only once per clade. For foraminifera, this evolutionary expansion occurred in the Early–Middle Jurassic, and all living and extinct planktic foraminifera have been placed within 1 clade, the Suborder Globigerinina. The subsequent radiation of planktic foraminifera in the Jurassic and Cretaceous resulted in highly diverse assemblages, which suffered mass extinction at the end of the Cretaceous, leaving an impoverished assemblage dominated by microperforate triserial and biserial forms. The few survivor species radiated to form diverse assemblages once again in the Cenozoic. There have, however, long been doubts regarding the monophyletic origin of planktic foraminifera. We present surprising but conclusive genetic evidence that the Recent biserial planktic Streptochilus globigerus belongs to the same biological species as the benthic Bolivina variabilis, and geochemical evidence that this ecologically flexible species actively grows within the open-ocean surface waters, thus occupying both planktic and benthic domains. Such a lifestyle (tychopelagic) had not been recognized as adapted by foraminifera. Tychopelagic are endowed with great ecological advantage, enabling rapid recolonization of the extinction-susceptible pelagic domain from the benthos. We argue that the existence of such forms must be considered in resolving foraminiferal phylogeny.
Child - Parent Violence (CPV): an exploratory exercise

CPV as an adolescent concern tends to be the focus of research published and guidelines indicated to date (for example see The Home Office, 2015 Guidance for working with Adolescents ) a notion indicated in the findings of Cottrell and Monk (2004) whose qualitative overview of common themes (ranging from mid 1970’s) identified that most studies focused on CPV involving those aged 10-18 years of age. In contrast the results of the exploratory survey suggest that more focus needs to be given to families of younger children, particularly those within the primary age phase, as argued by Bonnick (2016) in her podcast discussion interview. What appears evident, from the initial exploratory exercise, is the need for early intervention and wider professional understanding regarding CPV, insomuch as a continued focus around adolescent CPV may lead to many families being overlooked. Following this suggestion these families could then find they are passed over for the support they require, at a time when it is needed.
An Error-Correction Model of the Determinants of Private Saving in Nigeria

This article examines the determinants of private saving in Nigeria. It compares the estimation results of the Error-Correction Model with those of three conventional models: Partial-Adjustment, Growth Rate and Static Models. The conclusion is that the ECM performs much better than the other models. Its results reveal that the saving rate rises with the level of disposable income but falls with the rate of growth of disposable income. The real interest rate on bank deposits has a significant negative impact while public saving seems not to crowd out private saving. Furthermore, external terms of trade, inflation rate and external debt service ratio have a positive impact on private saving.
A top-down manner-based DCNN architecture for semantic image segmentation

Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability. In the study, superpixels containing visual attention information are introduced in a top-down manner, and an extensible architecture is proposed to improve the segmentation results of current DCNN-based methods. We employ the current state-of-the-art fully convolutional network (FCN) and FCN with conditional random field (DeepLab-CRF) as baselines to validate our architecture. Experimental results of the PASCAL VOC segmentation task qualitatively show that coarse edges and error segmentation results are well improved. We also quantitatively obtain about 2%-3% intersection over union (IOU) accuracy improvement on the PASCAL VOC 2011 and 2012 test sets.
A survey of indoor localization systems

Nowadays, indoor and outdoor location-aware applications has been become increasingly widespread. Location information for outdoor applications is obtained from well-known systems such as GPS and GLONASS. Although there are several systems for indoor localization, there is not a consensus on them like the outdoor. In this study, several aspects of indoor localization systems such as system topologies, mesaurement techniques, location calculation methods, performance metrics, signal types are overviewed. Some foresights are also made about the future of indoor localization.
MILS virtualization for Integrated Modular Avionics

Integrated modular avionics (IMA) is driving computer systems to manage and protect increasingly high value and complex information and applications across the aircraft. Virtualization is a promising emerging technology in avionics systems as a mechanism for consolidating disparate operating environments and functions onto a single computer. However, from a security perspective, commercial virtualization solutions add to the already fertile attack surface of general-purpose operating systems. This paper will provide an overview of embedded hypervisor technology and propose a MILS- and IMA-compliant virtualization architecture that assures the safety and security of critical applications while incorporating highly functional general purpose virtualized environments. A practical avionics application of this architecture will be presented.
Mission-Specific Processing Techniques Enable Low-Power, High-Performance Surveillance Sensors

lance sensors are continually pushing the need for ever-increasing electronics performance. Full-time, large-area ground surveillance capability, for instance, has been proven to be of great importance in recent conflicts; it drives the requirement for the signal processing of growing numbers of radar channels with increasing bandwidth. At the same time, the surveillance platforms carrying these sensors, typified by unmanned aerial vehicles (UAVs) or other small aircraft or spacecraft, are being pushed to be smaller, lighter, cheaper, and to have longer mission durations. On the Defense Advanced Research Projects Agency (DARPA)-sponsored Mission-Specific Processing (MSP) project, the team of Northrop Grumman Electronic Systems, Purdue University, University of Illinois at Urbana-Champaign, Morpho Technologies, and Atmel Corporation developed and demonstrated low-power, high-performance applicationspecific integrated circuit (ASIC) cells and computation techniques to provide 10× throughput performance per watt improvements to meet next-generation DoD sensor-processing applications. Northrop Grumman has defined a set of cells that form the core of the digital signal processing (DSP) functions needed for emerging military radio-frequency (RF) applications. The University of Illinois has developed a fluid intellectual property (IP) core generator that produces fully laid out optimized DSP function cells using transistor and voltage scaling to maximize ASIC performance per watt. Purdue University developed low-computation complexity algorithms and advanced-circuit techniques to achieve additional improvements in operational performance and input/output (I/O) throughput per watt. Morpho Technologies analyzed application of these MSP techniques to their reconfigurable core cell, which provides on-the-fly reconfiguration with reduced power and improved performance. MSP cells were integrated into the design process of a commercial ASIC foundry (Atmel Corporation) standard cell library, allowing designers to achieve ASIC performance levels normally associated with full custom designs, yet using a standard-cell design flow to reduce the design cost by a factor of ten. Northrop Grumman defined a wideband adaptive processing ASIC chip set incorporating these cells and projected their performance in a multichannel real time surveillance radar processor, exceeding the 10× improvement goals. An overview of the project is shown in Figure 1.
RPCA-Based Tumor Classification Using Gene Expression Data

Microarray techniques have been used to delineate cancer groups or to identify candidate genes for cancer prognosis. As such problems can be viewed as classification ones, various classification methods have been applied to analyze or interpret gene expression data. In this paper, we propose a novel method based on robust principal component analysis (RPCA) to classify tumor samples of gene expression data. Firstly, RPCA is utilized to highlight the characteristic genes associated with a special biological process. Then, RPCA and RPCA+LDA (robust principal component analysis and linear discriminant analysis) are used to identify the features. Finally, support vector machine (SVM) is applied to classify the tumor samples of gene expression data based on the identified features. Experiments on seven data sets demonstrate that our methods are effective and feasible for tumor classification.
How Land Registration Affects Financial Development and Economic Growth in Thailand

Using an economy-wide conceptual framework, the author analyzes how land registration affects financial development and economic growth in Thailand. He uses contemporary techniques, such as error correction and co-integration, to deal with such problems as time-series data not being stationary. He also uses the auto-regressive distributed lag model to analyze long lags in output response to changes in land registration. His key findings: 1) Land titling has significant positive long-run effects on financial development. 2) Economic growth responds to land titling following a J curve, by first registering a fall and recovering gradually, thereafter to post a long, strong rally. 3) The quality of land registration services, as measured by public spending on land registration, has strongly positive and significant long-run effects on economic growth.
Visual support for the understanding of simulation processes

Current visualization systems are typically based on the concept of interactive post-processing. This decoupling of data visualization from the process of data generation offers a flexible application of visualization tools. It can also lead, however, to information loss in the visualization. Therefore, a combination of the visualization of the data generating process with the visualization of the produced data offers significant support for the understanding of the abstract data sets as well as the underlying process. Due to the application-specific characteristics of data generating processes, the task requires tailored visualization concepts. In this work, we focus on the application field of simulating biochemical reaction networks as discrete-event systems. These stochastic processes generate multi-run and multivariate time-series, which are analyzed and compared on three different process levels: model, experiment, and the level of multi-run simulation data, each associated with a broad range of analysis goals. To meet these challenging characteristics, we present visualization concepts specifically tailored to all three process levels. The fundament of all three visualization concepts is a compact view that relates the multi-run simulation data to the characteristics of the model structure and the experiment. The view provides the visualization at the experiment level. The visualization at the model level coordinates multiple instances of this view for the comparison of experiments. At the level of multi-run simulation data, the views gives an overview on the data, which can be analyzed in detail in time-series views suited for the analysis goals. Although we derive our visualization concepts for one concrete simulation process, our general concept of tailoring the visualization concepts to process levels is generally applicable for the visualization of simulation processes.
Real-time hand tracking using Flocks of features

There is a growing demand to interact with computers in a more natural way. For example using hand gestures to interact with certain type of applications would be more efficient than old-fashioned keyboard and mouse. Hand tracking is one of the keen problems in computer vision. We have analyzed many different approaches used for hand tracking. Flocks of features introduced by Mathias Kolsch and Matthew Turk can track human hand continuously during various movements and pose variations. It uses the Kanade Lucas Tomasi (KLT) tracker for features located on a human hand to track them in a frame sequence. It can handle fast tracking of non-rigid highly articulated objects such as hands. We propose an improvement to this algorithm by processing the frame using histogram back projection of the skin color prior to applying flocks of features (FoF). This modification provides better results with lower false positive error.
Using a finite element eddy-current formulation to simulate the performance of a single-phase line-start permanent magnet machine

Line-start permanent magnet (LSPM) motors have been shown to be capable of very desirable performance characteristics. They may operate with very high efficiencies and near unity power factor. As Natural Resources Canada have increased energy efficiency regulations for both electric motors and appliances which use electric motors, there is a need for practical high efficiency motors. However, investigation into their design, particularly single phase motors, has been limited. Two apparent reasons for this are increased initial motor cost and the difficulty of simulating motor performance accurately within an acceptable time frame. This paper presents the results of using a finite element eddy-current formulation to simulate motor performance. It is shown that it is possible to obtain accurate simulation results within an acceptable time frame.