Miracle-dz/UniFAR-FTUs huggingface dataset:
A new perspective on the dynamics of information technology‐enabled strategic change

This paper explores the dynamic nature of information technology (IT)‐enabled strategic change by using empirical evidence from a variety of organizational contexts. The pilot research described here uses the MIT 1990s framework (Scott‐Morton, 1991) as the underpinning theory to challenge the dominant approach to the management of strategic change. The preliminary findings provide new insights into how strategic change is pursued and offer a new perspective from which the propositions suggested by Scott‐Morton (1991) and Yetton et al. (1994) can be reformulated. Four change patterns (archetypes) are identified that describe planned or emergent modes of change, driven by different change elements under various circumstances. These findings lead to a redesign of the M/T 1990s framework and suggest new ways of understanding how strategic change can be sustained. Further research is required to explore the link between the archetypes identified and the various methods of change.
Noise Measurements in Timber Industries

The intensifi cation of industrial production and the concurrent increase in machine operation speeds has led to the rise in the intensity of noise generated in most workplaces. The purpose of the present paper is to investigate occupational noise exposure in timber processing units. For this reason, a number of measurements were carried out in various parts of three sawmills. Maximum allowable exposure limits (permissible levels) were set at 85 dB(A) for the production area and warehouses, and 55 dB(A) for offi ces. According to the results of the study, the production area in a sawmill gives rise to higher levels of noise compared to warehouses and offi ces, which signifi cantly exceed maximum permissible levels. More specifi cally, the highest noise levels recorded were found to be produced by cutting machinery. In such cases it is imperative that employees be protected either with the use of personal hearing protectors or with the implementation of a regular break scheme. In addition, substantial reduction in noise levels can be achieved with the use of other protective measures such as control of noise at its source or in its path.
Shape Distortion of Carbon/Epoxy Composite Parts During Fabrication

Summary The accurate modelling of residual stress in thermoset composites requires comprehensive consideration of all the phenomena contributing to its formation. These include: chemical shrinkage of matrix due to cross linking of molecules, mismatch of thermal expansion or contraction among fibres and matrix and viscoelastic relaxation during the fabrication. Thermo-physical and thermo-mechanical properties are altered during the curing process due to an exothermic chemical reaction. Since all the phenomena causing residual stress are strongly coupled, the solution procedure is a complex task. In this study, spring-in angle of carbon/epoxy woven composite bracket with three different thicknesses are calculated by simultaneously solving the thermo-kinetics and thermo-mechanics coupling by a finite element code COMSOL Multiphysics. Properties of composites required for numerical simulation are obtained using an analytical method. The spring-in angle values obtained by numerical simulation are also compared with the results of the analytical model.
Competitor phenology as a social cue in breeding site selection

Predicting habitat quality is a major challenge for animals selecting a breeding patch, because it affects reproductive success. Breeding site selection may be based on previous experience, or on social information from the density and success of competitors with an earlier phenology. Variation in animal breeding phenology is often correlated with variation in habitat quality. Generally, animals breed earlier in high-quality habitats that allow them to reach a nutritional threshold required for breeding earlier or avoid nest predation. In addition, habitat quality may affect phenological overlap between species and thereby interspecific competition. Therefore, we hypothesized that competitor breeding phenology can be used as social cue by settling migrants to locate high-quality breeding sites. To test this hypothesis, we experimentally advanced and delayed hatching phenology of two resident tit species on the level of study plots and studied male and female settlement patterns of migratory pied flycatchers Ficedula hypoleuca. The manipulations were assigned at random in two consecutive years, and treatments were swapped between years in sites that were used in both years. In both years, males settled in equal numbers across treatments, but later arriving females avoided pairing with males in delayed phenology plots. Moreover, male pairing probability declined strongly with arrival date on the breeding grounds. Our results demonstrate that competitor phenology may be used to assess habitat quality by settling migrants, but we cannot pinpoint the exact mechanism (e.g. resource quality, predation pressure or competition) that has given rise to this pattern. In addition, we show that opposing selection pressures for arrival timing may give rise to different social information availabilities between sexes. We discuss our findings in the context of climate warming, social information use and the evolution of protandry in migratory animals.
Contents 1 R 7 RS Considered Unifier of Previous Standards 1 2 State Exploration Choices in a Small -

The R7RS (small) language standard can be implemented while preserving near-perfect backward compatibility with the R6RS standard and substantial compatibility with the R5RS and IEEE/ANSI standards for the Scheme programming language. When this is done, as in Larceny, R6RS Scheme becomes a proper subset of R7RS Scheme.
Hubble: An Advanced Dynamic Folder Technology for XML

A significant amount of information is stored in computer systems today, but people are struggling to manage their documents such that the information is easily found. XML is a de-facto standard for content publishing and data exchange. The proliferation of XML documents has created new challenges and opportunities for managing document collections. Existing technologies for automatically organizing document collections are either imprecise or based on only simple criteria. Since XML documents are self describing, it is now possible to automatically categorize XML documents precisely, according to their content. With the availability of the standard XML query languages, e.g. XQuery, much more powerful folder technologies are now feasible. To address this new challenge and exploit this new opportunity, this paper proposes a new and powerful dynamic folder mechanism, called Hubble. Hubble fully exploits the rich data model and semantic information embedded in the XML documents to build folder hierarchies dynamically and to categorize XML collections precisely. Besides supporting basic folder operations, Hubble also provides advanced features such as multi-path navigation and folder traversal across multiple document collections. Our performance study shows that Hubble is both efficient and scalable. Thus, it is an ideal technology for automating the process of organizing and categorizing XML documents.
Foundations of garbled circuits

Garbled circuits, a classical idea rooted in the work of Yao, have long been understood as a cryptographic technique, not a cryptographic goal. Here we cull out a primitive corresponding to this technique. We call it a garbling scheme. We provide a provable-security treatment for garbling schemes, endowing them with a versatile syntax and multiple security definitions. The most basic of these, privacy, suffices for two-party secure function evaluation (SFE) and private function evaluation (PFE). Starting from a PRF, we provide an efficient garbling scheme achieving privacy and we analyze its concrete security. We next consider obliviousness and authenticity, properties needed for private and verifiable outsourcing of computation. We extend our scheme to achieve these ends. We provide highly efficient blockcipher-based instantiations of both schemes. Our treatment of garbling schemes presages more efficient garbling, more rigorous analyses, and more modularly designed higher-level protocols.
Active Design Method for the Static Characteristics of a Piezoelectric Six-Axis Force/Torque Sensor

To address the bottleneck issues of an elastic-style six-axis force/torque sensor (six-axis force sensor), this work proposes a no-elastic piezoelectric six-axis force sensor. The operating principle of the piezoelectric six-axis force sensor is analyzed, and a structural model is constructed. The static-active design theory of the piezoelectric six-axis force sensor is established, including a static analytical/mathematical model and numerical simulation model (finite element model). A piezoelectric six-axis force sensor experimental prototype is developed according to the analytical mathematical model and numerical simulation model, and selected static characteristic parameters (including sensitivity, isotropic degree and cross-coupling) are tested using this model with three approaches. The measured results are in agreement with the analytical results from the static-active design method. Therefore, this study has successfully established a foundation for further research into the piezoelectric multi-axis force sensor and an overall design approach based on static characteristics.
Variance-based Gradient Compression for Efficient Distributed Deep Learning

Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation workers. However, by nature, workers need to frequently communicate gradients, causing severe bottlenecks, especially on lower bandwidth connections. A few methods have been proposed to compress gradient for efficient communication, but they either suffer a low compression ratio or significantly harm the resulting model accuracy, particularly when applied to convolutional neural networks. To address these issues, we propose a method to reduce the communication overhead of distributed deep learning. Our key observation is that gradient updates can be delayed until an unambiguous (high amplitude, low variance) gradient has been calculated. We also present an efficient algorithm to compute the variance with negligible additional cost. We experimentally show that our method can achieve very high compression ratio while maintaining the result model accuracy. We also analyze the efficiency using computation and communication cost models and provide the evidence that this method enables distributed deep learning for many scenarios with commodity environments.
Radiation-induced evolution of microstructure and mechanical properties of stainless steels

Radiation-induced changes in microstructures often lead to significant changes in mechanical properties of alloys used in the construction of nuclear reactors. It is desirable to test small specimens to make efficient use of the small volumes available in test and commercial reactor cores and also because small specimens are less affected by the sometimes steep flux gradients experienced in reactor cores and the sometimes large temperature gradients developed in the specimens from gamma heating. (Continues.).
Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks

Recent work has shown that optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology. In this paper, we make the network estimate its local uncertainty about the correctness of its prediction, which is vital information when building decisions on top of the estimated optical flow. For the first time we compare several strategies and techniques to estimate uncertainty in a large-scale computer vision task like optical flow estimation. Moreover, we introduce a new network architecture that enforces complementary hypotheses and provides uncertainty estimates efficiently within a single forward pass without the need for sampling or ensembles. We demonstrate high-quality uncertainty estimates that clearly improve over previous confidence measures on optical flow and allow for interactive frame rates.
Do chimpanzees know what conspecifics know?

We conducted three experiments on social problem solving by chimpanzees, Pan troglodytes. In each experiment a subordinate and a dominant individual competed for food, which was placed in various ways on the subordinate's side of two opaque barriers. In some conditions dominants had not seen the food hidden, or food they had seen hidden was moved elsewhere when they were not watching (whereas in control conditions they saw the food being hidden or moved). At the same time, subordinates always saw the entire baiting procedure and could monitor the visual access of their dominant competitor as well. If subordinates were sensitive to what dominants did or did not see during baiting, they should have preferentially approached and retrieved the food that dominants had not seen hidden or moved. This is what they did in experiment 1 when dominants were either uninformed or misinformed about the food's location. In experiment 2 subordinates recognized, and adjusted their behaviour accordingly, when the dominant individual who witnessed the hiding was replaced with another dominant individual who had not witnessed it, thus demonstrating their ability to keep track of precisely who has witnessed what. In experiment 3 subordinates did not choose consistently between two pieces of hidden food, one of which dominants had seen hidden and one of which they had not seen hidden. However, their failure in this experiment was likely to be due to the changed nature of the competition under these circumstances and not to a failure of social-cognitive skills. These findings suggest that at least in some situations (i.e. competition with conspecifics) chimpanzees know what conspecifics have and have not seen (do and do not know), and that they use this information to devise effective social-cognitive strategies. Copyright 2001 The Association for the Study of Animal Behaviour.
Terrestrial ecosystems from space: a review of earth observation products for macroecology applications

Aim Earth observation (EO) products are a valuable alternative to spectral vegetation indices. We discuss the availability of EO products for analysing patterns in macroecology, particularly related to vegetation, on a range of spatial and temporal scales. Location Global. Methods We discuss four groups of EO products: land cover/cover change, vegetation structure and ecosystem productivity, fire detection, and digital elevation models. We address important practical issues arising from their use, such as assumptions underlying product generation, product accuracy and product transferability between spatial scales. We investigate the potential of EO products for analysing terrestrial ecosystems. Results Land cover, productivity and fire products are generated from long-term data using standardized algorithms to improve reliability in detecting change of land surfaces. Their global coverage renders them useful for macroecology. Their spatial resolution (e.g. GLOBCOVER vegetation, 300 m; MODIS vegetation and fire, 500 m; ASTER digital elevation, 30 m) can be a limiting factor. Canopy structure and productivity products are based on physical approaches and thus are independent of biome-specific calibrations. Active fire locations are provided in near-real time, while burnt area products show actual area burnt by fire. EO products can be assimilated into ecosystem models, and their validation information can be employed to calculate uncertainties during subsequent modelling. Main conclusions Owing to their global coverage and long-term continuity, EO end products can significantly advance the field of macroecology. EO products allow analyses of spatial biodiversity, seasonal dynamics of biomass and productivity, and consequences of disturbances on regional to global scales. Remaining drawbacks include inter-operability between products from different sensors and accuracy issues due to differences between assumptions and models underlying the generation of different EO products. Our review explains the nature of EO products and how they relate to particular ecological variables across scales to encourage their wider use in ecological applications.
Association between Knowledge-Attitude-Practices and Control of Blood Glucose, Blood Pressure, and Blood Lipids in Patients with Type 2 Diabetes in Shanghai, China: A Cross-Sectional Study

Knowledge-attitude-practices (KAP) significantly impact the outcome of self-management in patients with diabetes, yet the association between KAP and the combined control of the levels of blood glucose, blood pressure, and blood lipids in these patients remains uncertain. This community-based cross-sectional study was conducted from December 2014 to December 2016 on 3977 patients with type 2 diabetes in Shanghai. KAP were evaluated using the modified Chinese version of the Diabetes, Hypertension and Hyperlipidemia (DHL) Knowledge Instrument, Diabetes Empowerment Scale–Short Form (DES-SF), and Summary of Diabetes Self-Care Activities (SDSCA). Clinical and biochemical measurements were performed at each sampling site. The association between KAP scores and achieving the combined target goal was assessed by multiple logistic regression. Patients having a higher score of knowledge were more likely to achieve the combined target goal. Furthermore, a turning point of knowledge score was found that the possibility of achieving the combined target goal presented a sharp increase when the knowledge score was more than 70. However, the scores of attitude and practices had no significant relations with achieving the combined target goal. Health intervention strategies, especially increasing integrated diabetes knowledge, should be targeted to patients with type 2 diabetes in communities.
Structured Triplet Learning with POS-Tag Guided Attention for Visual Question Answering

Visual question answering (VQA) is of significant interest due to its potential to be a strong test of image understanding systems and to probe the connection between language and vision. Despite much recent progress, general VQA is far from a solved problem. In this paper, we focus on the VQA multiple-choice task, and provide some good practices for designing an effective VQA model that can capture language-vision interactions and perform joint reasoning. We explore mechanisms of incorporating part-ofspeech (POS) tag guided attention, convolutional n-grams, triplet attention interactions between the image, question and candidate answer, and structured learning for triplets based on image-question pairs 1. We evaluate our models on two popular datasets: Visual7W and VQA Real Multiple Choice. Our final model achieves the state-of-the-art performance of 68.2% on Visual7W, and a very competitive performance of 69.6% on the test-standard split of VQA Real Multiple Choice.
DNN-Based Score Calibration With Multitask Learning for Noise Robust Speaker Verification

This paper proposes and investigates several deep neural network (DNN) based score compensation, transformation, and calibration algorithms for enhancing the noise robustness of i-vector speaker verification systems. Unlike conventional calibration methods where the required score shift is a linear function of SNR or log-duration, the DNN approach learns the complex relationship between the score shifts and the combination of i-vector pairs and uncalibrated scores. Furthermore, with the flexibility of DNNs, it is possible to explicitly train a DNN to recover the clean scores without having to estimate the score shifts. To alleviate the overfitting problem, multitask learning is applied to incorporate auxiliary information such as SNRs and speaker ID of training utterances into the DNN. Experiments on NIST 2012 SRE show that score calibration derived from multitask DNNs can improve the performance of the conventional score-shift approch significantly, especially under noisy conditions.
Nonparallel Support Vector Ordinal Regression

Ordinal regression is a supervised learning problem where training samples are labeled by an ordinal scale. The ordering relation and nonmetric property of the label set distinguish it from the multiclass classification and metric regression. To better exploit the inherent structure in the label and benefit from the hidden information in data distribution, we propose a novel ordinal regression model, which is named as nonparallel support vector ordinal regression (NPSVOR) to emphasis the utilization of nonparallel proximal hyperplanes. The new model constructs a hyperplane for each rank such that the patterns of this rank lie in the close proximity while maintaining clear separation with the other ranks. Since the learning of hyperplanes can be carried out independently, NPSVOR can be trained in parallel. Furthermore, we design an efficient solver at the same time for training the hyperplanes in NPSVOR based on the alternating direction method of multipliers. Extensive experimentation demonstrates that NPSVOR yields a large and statistically significant improvement in terms of generalization performance and training speed against nine baselines.
Utilization of felled oil palm trunk: Trunk sections storage on oil palm sap production

Oil palm trunk (OPT) is one of the biomass produce during replanting of palm tree and this is generated every twenty five years in a given plantation. The core of the OPT when mechanically squeezed produce sap, which can be used to produce bio-ethanol. In this study, four OPT was used and each OPT was chopped into four sections which were upper, middle 1, middle 2 and bottom. The sections were stored for 1, 15, 30 and 45 days before being mechanically pretreated to produce palm sap. The mass balance during the production was recorded which resulted in the decrease of the sap volume during trunk storage time from 304.98L to 85.74L.
Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM) and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD) and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR) algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.